Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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13 pages, 2828 KiB  
Article
Two Fe-Zr-B-Cu Nanocrystalline Magnetic Alloys Produced by Mechanical Alloying Technique
by Jason Daza, Wael Ben Mbarek, Lluisa Escoda, Joan Saurina and Joan-Josep Suñol
Technologies 2023, 11(3), 78; https://doi.org/10.3390/technologies11030078 - 16 Jun 2023
Viewed by 2422
Abstract
Fe-rich soft magnetic alloys are candidates for applications as magnetic sensors and actuators. Spring magnets can be obtained when these alloys are added to hard magnetic compounds. In this work, two nanocrystalline Fe-Zr-B-Cu alloys are produced by mechanical alloying, MA. The increase in [...] Read more.
Fe-rich soft magnetic alloys are candidates for applications as magnetic sensors and actuators. Spring magnets can be obtained when these alloys are added to hard magnetic compounds. In this work, two nanocrystalline Fe-Zr-B-Cu alloys are produced by mechanical alloying, MA. The increase in boron content favours the reduction of the crystalline size. Thermal analysis (by differential scanning calorimetry) shows that, in the temperature range compressed between 450 and 650 K, wide exothermic processes take place, which are associated with the relaxation of the tensions of the alloys produced by MA. At high temperatures, a main crystallisation peak is found. A Kissinger and an isoconversional method were used to determine the apparent activation of the exothermic processes. The values are compared with those found in the scientific literature. Likewise, adapted thermogravimetry allowed for the determination of the Curie temperature. The functional response has been analysed by hysteresis loop cycles. According to the composition, the decrease of the Fe/B ratio diminishes the soft magnetic behaviour. Full article
(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)
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17 pages, 6271 KiB  
Article
Injectable Hydrated Calcium Phosphate Bone-like Paste: Synthesis, In Vitro, and In Vivo Biocompatibility Assessment
by Anastasia Yu. Teterina, Vladislav V. Minaychev, Polina V. Smirnova, Margarita I. Kobiakova, Igor V. Smirnov, Roman S. Fadeev, Alexey A. Egorov, Artem A. Ashmarin, Kira V. Pyatina, Anatoliy S. Senotov, Irina S. Fadeeva and Vladimir S. Komlev
Technologies 2023, 11(3), 77; https://doi.org/10.3390/technologies11030077 - 15 Jun 2023
Cited by 3 | Viewed by 1827
Abstract
The injectable hydrated calcium phosphate bone-like paste (hCPP) was developed with suitable rheological characteristics, enabling unhindered injection through standard 23G needles. In vitro assays showed the cytocompatibility of hCPP with mesenchymal embryonic C3H10T1/2 cell cultures. The hCPP was composed of aggregated micro-sized particles [...] Read more.
The injectable hydrated calcium phosphate bone-like paste (hCPP) was developed with suitable rheological characteristics, enabling unhindered injection through standard 23G needles. In vitro assays showed the cytocompatibility of hCPP with mesenchymal embryonic C3H10T1/2 cell cultures. The hCPP was composed of aggregated micro-sized particles with sphere-like shapes and low crystallinity. The ability of hCPP particles to adsorb serum proteins (FBS) was investigated. The hCPP demonstrated high protein adsorption capacity, indicating its potential in various biomedical applications. The results of the in vivo assay upon subcutaneous injection in Wistar rats indicated nontoxicity and biocompatibility of experimental hCPP, as well as gradual resorption of hCPP, comparable to the period of bone regeneration. The data obtained are of great interest for the development of commercial highly effective osteoplastic materials for bone tissue regeneration and augmentation. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2022))
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13 pages, 1635 KiB  
Article
Cross-Tier Interference Mitigation for RIS-Assisted Heterogeneous Networks
by Abdel Nasser Soumana Hamadou, Ciira wa Maina and Moussa Moindze Soidridine
Technologies 2023, 11(3), 73; https://doi.org/10.3390/technologies11030073 - 09 Jun 2023
Viewed by 2611
Abstract
With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is [...] Read more.
With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts. Full article
(This article belongs to the Special Issue Intelligent Reflecting Surfaces for 5G and Beyond)
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14 pages, 1840 KiB  
Article
Utilization of Artificial Neural Networks for Precise Electrical Load Prediction
by Christos Pavlatos, Evangelos Makris, Georgios Fotis, Vasiliki Vita and Valeri Mladenov
Technologies 2023, 11(3), 70; https://doi.org/10.3390/technologies11030070 - 26 May 2023
Cited by 11 | Viewed by 1620
Abstract
In the energy-planning sector, the precise prediction of electrical load is a critical matter for the functional operation of power systems and the efficient management of markets. Numerous forecasting platforms have been proposed in the literature to tackle this issue. This paper introduces [...] Read more.
In the energy-planning sector, the precise prediction of electrical load is a critical matter for the functional operation of power systems and the efficient management of markets. Numerous forecasting platforms have been proposed in the literature to tackle this issue. This paper introduces an effective framework, coded in Python, that can forecast future electrical load based on hourly or daily load inputs. The framework utilizes a recurrent neural network model, consisting of two simpleRNN layers and a dense layer, and adopts the Adam optimizer and tanh loss function during the training process. Depending on the size of the input dataset, the proposed system can handle both short-term and medium-term load-forecasting categories. The network was extensively tested using multiple datasets, and the results were found to be highly promising. All variations of the network were able to capture the underlying patterns and achieved a small test error in terms of root mean square error and mean absolute error. Notably, the proposed framework outperformed more complex neural networks, with a root mean square error of 0.033, indicating a high degree of accuracy in predicting future load, due to its ability to capture data patterns and trends. Full article
(This article belongs to the Collection Electrical Technologies)
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17 pages, 807 KiB  
Article
Preprocessing Selection for Deep Learning Classification of Arrhythmia Using ECG Time-Frequency Representations
by Rafael Holanda, Rodrigo Monteiro and Carmelo Bastos-Filho
Technologies 2023, 11(3), 68; https://doi.org/10.3390/technologies11030068 - 11 May 2023
Cited by 1 | Viewed by 3075
Abstract
The trend of using deep learning techniques to classify arbitrary tasks has grown significantly in the last decade. Such techniques in the background provide a stack of non-linear functions to solve tasks that cannot be solved in a linear manner. Naturally, deep learning [...] Read more.
The trend of using deep learning techniques to classify arbitrary tasks has grown significantly in the last decade. Such techniques in the background provide a stack of non-linear functions to solve tasks that cannot be solved in a linear manner. Naturally, deep learning models can always solve almost any problem with the right amount of functional parameters. However, with the right set of preprocessing techniques, these models might become much more accessible by negating the need for a large set of model parameters and the concomitant computational costs that accompany the need for many parameters. This paper studies the effects of such preprocessing techniques, and is focused, more specifically, on the resulting learning representations, so as to classify the arrhythmia task provided by the ECG MIT-BIH signal dataset. The types of noise we filter out from such signals are the Baseline Wander (BW) and the Powerline Interference (PLI). The learning representations we use as input to a Convolutional Neural Network (CNN) model are the spectrograms extracted by the Short-time Fourier Transform (STFT) and the scalograms extracted by the Continuous Wavelet Transform (CWT). These features are extracted using different parameter values, such as the window size of the Fourier Transform and the number of scales from the mother wavelet. We highlight that the noise with the most significant influence on a CNN’s classification performance is the BW noise. The most accurate classification performance was achieved using the 64 wavelet scales scalogram with the Mexican Hat and with only the BW noise suppressed. The deployed CNN has less than 90k parameters and achieved an average F1-Score of 90.11%. Full article
(This article belongs to the Section Assistive Technologies)
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11 pages, 2334 KiB  
Communication
Identifying Growth Patterns in Arid-Zone Onion Crops (Allium Cepa) Using Digital Image Processing
by David Duarte-Correa, Juvenal Rodríguez-Reséndiz, Germán Díaz-Flórez, Carlos Alberto Olvera-Olvera and José M. Álvarez-Alvarado
Technologies 2023, 11(3), 67; https://doi.org/10.3390/technologies11030067 - 10 May 2023
Viewed by 1473
Abstract
The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on onion crops [...] Read more.
The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on onion crops and the challenges presented throughout its phenological cycle. Unmanned aerial vehicles (UAVs) and digital image processing were used to monitor the crop and identify patterns such as humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 tons/ha. However, only 25.00 tons/ha were reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being medium-sized. Approximately 20% of the production was lost due to blight and unfavorable weather conditions. Full article
(This article belongs to the Special Issue Image and Signal Processing)
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7 pages, 460 KiB  
Brief Report
A Deeper Look into Exercise Intensity Tracking through Mobile Applications: A Brief Report
by Alexie Elder, Gabriel Guillen, Rebecca Isip, Ruben Zepeda and Zakkoyya H. Lewis
Technologies 2023, 11(3), 66; https://doi.org/10.3390/technologies11030066 - 01 May 2023
Cited by 1 | Viewed by 2647
Abstract
Mobile fitness applications (apps) allow for time-efficient opportunities for physical activity. Current research suggests that fitness apps do not accurately comply with the frequency, intensity, time, and type (FITT) principle. FITT is an important principle in exercise prescription as it applies scientific evidence [...] Read more.
Mobile fitness applications (apps) allow for time-efficient opportunities for physical activity. Current research suggests that fitness apps do not accurately comply with the frequency, intensity, time, and type (FITT) principle. FITT is an important principle in exercise prescription as it applies scientific evidence to improve the quality of exercise. Based on app assessment using the Fitness Apps Scoring Instrument, most fitness apps adequately address FITT in their exercise plans. In particular, fitness apps do not adequately adhere to the FITT intensity guidelines. Many apps allow the users to track their heart rate as a method of assessing their exercise intensity, but few use that information to provide real-time feedback on the intensity of the workout. For app users, awareness and education of intensity standards should be put forth in coordination with exercise professionals, rather than relying on apps alone. Full article
(This article belongs to the Special Issue Wearable Technologies III)
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10 pages, 4236 KiB  
Communication
Towards Safe Visual Navigation of a Wheelchair Using Landmark Detection
by Christos Sevastopoulos, Mohammad Zaki Zadeh, Michail Theofanidis, Sneh Acharya, Nishi Patel and Fillia Makedon
Technologies 2023, 11(3), 64; https://doi.org/10.3390/technologies11030064 - 25 Apr 2023
Viewed by 1616
Abstract
This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be [...] Read more.
This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding and paving the path towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair to gather image instances from four indoor academic environments consisting of multiple labels. Afterwards, the fine-tuning of a pretrained vision transformer (ViT) is conducted, and the performance is evaluated through an ablation study versus well-established state-of-the-art deep architectures for image classification such as ResNet. Results show that the fine-tuned ViT outperforms all other deep convolutional architectures while achieving satisfactory levels of generalization. Full article
(This article belongs to the Collection Selected Papers from the PETRA Conference Series)
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11 pages, 1896 KiB  
Communication
Research on Outdoor Mobile Music Speaker Battery Management Algorithm Based on Dynamic Redundancy
by Xiaofei Yu, Yanke Li, Xiaonan Li, Licheng Wang and Kai Wang
Technologies 2023, 11(2), 60; https://doi.org/10.3390/technologies11020060 - 18 Apr 2023
Cited by 18 | Viewed by 1564
Abstract
In terms of the battery management system of a mobile music speaker, reliability optimization has always been an important topic. This paper proposes a new dynamic redundant battery management algorithm based on the existing fault-tolerant structure of a lithium battery pack. The internal [...] Read more.
In terms of the battery management system of a mobile music speaker, reliability optimization has always been an important topic. This paper proposes a new dynamic redundant battery management algorithm based on the existing fault-tolerant structure of a lithium battery pack. The internal configuration is adjusted according to the SOC of each battery, and the power supply battery is dynamically allocated. This paper selects four batteries to experiment on with two different algorithms. The simulation results show that compared with the traditional battery management algorithm, the dynamic redundant battery management algorithm extends the battery pack working time by 18.75%, and the energy utilization rate of B1 and B4 increases by 96.0% and 99.8%, respectively. This proves that the dynamic redundant battery management algorithm can effectively extend battery working time and improve energy utilization. Full article
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14 pages, 2433 KiB  
Article
Photovoltaic Inverter Reliability Study through SiC Switches Redundant Structures
by Ignacio Villanueva, Nimrod Vázquez, Joaquín Vaquero, Claudia Hernández, Héctor López-Tapia and Rene Osorio-Sánchez
Technologies 2023, 11(2), 59; https://doi.org/10.3390/technologies11020059 - 14 Apr 2023
Cited by 1 | Viewed by 1591
Abstract
Reliability is a very important issue in power electronics; however, sometimes it is not considered, studied, or analyzed. At present, renewables have become more popular, and more complex setups are required to drive this type of system. In the specific case of inverters [...] Read more.
Reliability is a very important issue in power electronics; however, sometimes it is not considered, studied, or analyzed. At present, renewables have become more popular, and more complex setups are required to drive this type of system. In the specific case of inverters in photovoltaic systems, the user’s safety, quality, reliability, and the system’s useful life must be guaranteed. In this paper, the reliability of a full bridge inverter is predicted by calculating metrics such as failure rates and Mean Time Between Failures. Reliability is obtained using different types of structures for SiC MOSFETs: serial systems, active parallel redundant systems, and passive parallel redundant systems. Finally, the reliability study shows that a system with a passive parallel redundant structure is more reliable and has a higher useful life compared to the other structures. Full article
(This article belongs to the Collection Electrical Technologies)
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16 pages, 3584 KiB  
Article
Computational Investigation of a Tibial Implant Using Topology Optimization and Finite Element Analysis
by Nikolaos Kladovasilakis, Theologos Bountourelis, Konstantinos Tsongas and Dimitrios Tzetzis
Technologies 2023, 11(2), 58; https://doi.org/10.3390/technologies11020058 - 13 Apr 2023
Cited by 1 | Viewed by 1721
Abstract
Additive manufacturing methods enable the rapid fabrication of fully functional customized objects with complex geometry and lift the limitations of traditional manufacturing techniques, such as machining. Therefore, the structural optimization of parts has concentrated increased scientific interest and more especially for topology optimization [...] Read more.
Additive manufacturing methods enable the rapid fabrication of fully functional customized objects with complex geometry and lift the limitations of traditional manufacturing techniques, such as machining. Therefore, the structural optimization of parts has concentrated increased scientific interest and more especially for topology optimization (TO) processes. In this paper, the working principles and the two approaches of the TO procedures were analyzed along with an investigation and a comparative study of a novel case study for the TO processes of a tibial implant designed for additive manufacturing (DfAM). In detail, the case study focused on the TO of a tibial implant for knee replacement surgery in order to improve the overall design and enhance its efficiency and the rehabilitation process. An initial design of a customized tibial implant was developed utilizing reserve engineering procedures with DICOM files from a CT scan machine. The mechanical performance of the designed implant was examined via finite element analyses (FEA) under realistic static loads. The TO was conducted with two distinct approaches, namely density-based and discrete-based, to compare them and lead to the best approach for biomechanical applications. The overall performance of each approach was evaluated through FEA, and its contribution to the final mass reduction was measured. Through this study, the maximum reduction in the implant’s mass was achieved by maintaining the mechanical performance at the desired levels and the best approach was pointed out. To conclude, with the discrete-based approach, a mass reduction of around 45% was achieved, almost double of the density-based approach, offering on the part physical properties which provide comprehensive advantages for biomechanical application. Full article
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17 pages, 1314 KiB  
Article
A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
by Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh, Raed Abu Zitar, Ahmad Yacoub Nasereddin and Laith Abualigah
Technologies 2023, 11(2), 55; https://doi.org/10.3390/technologies11020055 - 11 Apr 2023
Cited by 8 | Viewed by 2855
Abstract
Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the [...] Read more.
Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach. Full article
(This article belongs to the Special Issue Wearable Technologies III)
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15 pages, 1994 KiB  
Article
Visual Performance and Perceptual–Motor Skills of Late Preterm Children and Healthy Controls Using the TVPS-3rd and VMI-6th Editions
by Danjela Ibrahimi, Jorge D. Mendiola Santibañez and Juvenal Rodríguez-Reséndiz
Technologies 2023, 11(2), 53; https://doi.org/10.3390/technologies11020053 - 04 Apr 2023
Cited by 1 | Viewed by 2021
Abstract
Background: The visual system is key to the learning process, preterm births are commonly followed by visual dysfunctions and other neurological conditions. Objective: to measure, analyze and compare the visual efficacy, visual–perceptual, and visual–motor skills of 20 late preterm children (34–36 weeks) born [...] Read more.
Background: The visual system is key to the learning process, preterm births are commonly followed by visual dysfunctions and other neurological conditions. Objective: to measure, analyze and compare the visual efficacy, visual–perceptual, and visual–motor skills of 20 late preterm children (34–36 weeks) born by caesarean section and appropriate weight for gestational age with 20 healthy controls born at full term by natural birth, age 5 to 12 years, from Querétaro, México. Methods: This was an observational, transverse, and prospective study. Parametric and non-parametric tests were performed using the SPSS 25.0. The visual acuity at distance and near, the phoria state, and the degree of stereopsis were analyzed. The Test of Visual-Perceptual Skills, Third Edition, was used to assess the overall performance, basic, sequencing, and complex processes. Fine motor skills were evaluated using the Visual–Motor Integration Test of Beery, Sixth Edition. Results: Visual acuity at distance and near (p<0.001), stereopsis (p<0.001), and the amount of exophoria at distance (p=0.01) showed statistically significant differences between the groups. The overall performance (p=0.006), basic processes (p=0.001), sequencing processes (p=0.02), and General and Motor VMI (p<0.001 and 0.002, respectively) presented lower values in children born preterm. Conclusion: This research showed that even late preterm children present visual deficiencies and are at risk of delays on perceptual–motor skills. Early evaluation of their visual and motor abilities should be considered in order to help improve their cognitive functioning. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2022))
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11 pages, 4140 KiB  
Communication
HAIS: Highways Automated-Inspection System
by Hossam A. Gabbar, Abderrazak Chahid, Manir U. Isham, Shashwat Grover, Karan Pal Singh, Khalid Elgazzar, Ahmad Mousa and Hossameldin Ouda
Technologies 2023, 11(2), 51; https://doi.org/10.3390/technologies11020051 - 01 Apr 2023
Cited by 2 | Viewed by 2003
Abstract
A smart city is a trending concept describing a new generation of cities operated intelligently with minimal human intervention. It promotes energy sustainability, minimal environmental impact, and better governance. In transportation, the remote highway infrastructure monitoring will enhance the driver’s safety, continuously report [...] Read more.
A smart city is a trending concept describing a new generation of cities operated intelligently with minimal human intervention. It promotes energy sustainability, minimal environmental impact, and better governance. In transportation, the remote highway infrastructure monitoring will enhance the driver’s safety, continuously report road conditions, and identify potential hazardous incidents such as accidents, floods, or snow storms. In addition, it facilitates the integration of future cuttingedge technologies such as self-driving vehicles. This paper presents a general introduction to a smart monitoring system for automated real-time road condition inspection. The proposed solution includes hardware devices/nodes and software applications for data processing, road condition inspection using hybrid algorithms based on digital signal processing, and artificial intelligence technologies. The proposed system has an interactive web interface for real-time data sharing, infrastructure monitoring, visualization, and management of inspection reports which can improve the maintenance process. Full article
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18 pages, 1984 KiB  
Article
Forecasting by Combining Chaotic PSO and Automated LSSVR
by Wei-Chang Yeh and Wenbo Zhu
Technologies 2023, 11(2), 50; https://doi.org/10.3390/technologies11020050 - 30 Mar 2023
Cited by 1 | Viewed by 1142
Abstract
An automatic least square support vector regression (LSSVR) optimization method that uses mixed kernel chaotic particle swarm optimization (CPSO) to handle regression issues has been provided. The LSSVR model is composed of three components. The position of the particles (solution) in a chaotic [...] Read more.
An automatic least square support vector regression (LSSVR) optimization method that uses mixed kernel chaotic particle swarm optimization (CPSO) to handle regression issues has been provided. The LSSVR model is composed of three components. The position of the particles (solution) in a chaotic sequence with good randomness and ergodicity of the initial characteristics is taken into consideration in the first section. The binary particle swarm optimization (PSO) used to choose potential input characteristic combinations makes up the second section. The final step involves using a chaotic search to narrow down the set of potential input characteristics before combining the PSO-optimized parameters to create CP-LSSVR. The CP-LSSVR is used to forecast the impressive datasets testing targets obtained from the UCI dataset for purposes of illustration and evaluation. The results suggest CP-LSSVR has a good predictive capability discussed in this paper and can build a projected model utilizing a limited number of characteristics. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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19 pages, 11909 KiB  
Article
Image-Based Quantification of Color and Its Machine Vision and Offline Applications
by Woo Sik Yoo, Kitaek Kang, Jung Gon Kim and Yeongsik Yoo
Technologies 2023, 11(2), 49; https://doi.org/10.3390/technologies11020049 - 29 Mar 2023
Cited by 2 | Viewed by 2494
Abstract
Image-based colorimetry has been gaining relevance due to the wide availability of smart phones with image sensors and increasing computational power. The low cost and portable designs with user-friendly interfaces, and their compatibility with data acquisition and processing, are very attractive for interdisciplinary [...] Read more.
Image-based colorimetry has been gaining relevance due to the wide availability of smart phones with image sensors and increasing computational power. The low cost and portable designs with user-friendly interfaces, and their compatibility with data acquisition and processing, are very attractive for interdisciplinary applications from art, the fashion industry, food science, medical science, oriental medicine, agriculture, geology, chemistry, biology, material science, environmental engineering, and many other applications. This work describes the image-based quantification of color and its machine vision and offline applications in interdisciplinary fields using specifically developed image analysis software. Examples of color information extraction from a single pixel to predetermined sizes/shapes of areas, including customized regions of interest (ROIs) from various digital images of dyed T-shirts, tongues, and assays, are demonstrated. Corresponding RGB, HSV, CIELAB, Munsell color, and hexadecimal color codes, from a single pixel to ROIs, are extracted for machine vision and offline applications in various fields. Histograms and statistical analyses of colors from a single pixel to ROIs are successfully demonstrated. Reliable image-based quantification of color, in a wide range of potential applications, is proposed and the validity is verified using color quantification examples in various fields of applications. The objectivity of color-based diagnosis, judgment and control can be significantly improved by the image-based quantification of color proposed in this study. Full article
(This article belongs to the Special Issue Image and Signal Processing)
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19 pages, 10511 KiB  
Article
Mobilenetv2_CA Lightweight Object Detection Network in Autonomous Driving
by Peicheng Shi, Long Li, Heng Qi and Aixi Yang
Technologies 2023, 11(2), 47; https://doi.org/10.3390/technologies11020047 - 23 Mar 2023
Cited by 1 | Viewed by 1388
Abstract
A lightweight network target detection algorithm was proposed, based on MobileNetv2_CA, focusing on the problem of high complexity, a large number of parameters, and the missed detection of small targets in the target detection network based on candidate regions and regression methods in [...] Read more.
A lightweight network target detection algorithm was proposed, based on MobileNetv2_CA, focusing on the problem of high complexity, a large number of parameters, and the missed detection of small targets in the target detection network based on candidate regions and regression methods in autonomous driving scenarios. First, Mosaic image enhancement technology is used in the data pre-processing stage to enhance the feature extraction of small target scenes and complex scenes; second, the Coordinate Attention (CA) mechanism is embedded into the Mobilenetv2 backbone feature extraction network, combined with the PANet and Yolo detection heads for multi-scale feature fusion; finally, a Lightweight Object Detection Network is built. The experimental test results show that the designed network obtained the highest average detection accuracy of 81.43% on the Voc2007 + 2012 dataset, and obtained the highest average detection accuracy of 85.07% and a detection speed of 31.84 FPS on the KITTI dataset. The total amount of network parameters is only 39.5 M. This is beneficial to the engineering application of MobileNetv2 network in automatic driving. Full article
(This article belongs to the Special Issue Image and Signal Processing)
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17 pages, 2022 KiB  
Review
How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life
by Subhra Mondal, Subhankar Das and Vasiliki G. Vrana
Technologies 2023, 11(2), 44; https://doi.org/10.3390/technologies11020044 - 17 Mar 2023
Cited by 29 | Viewed by 11113
Abstract
Generative Artificial Intelligence (GAI) has brought revolutionary changes to the world, enabling businesses to create new experiences by combining virtual and physical worlds. As the use of GAI grows along with the Metaverse, it is explored by academics, researchers, and industry communities for [...] Read more.
Generative Artificial Intelligence (GAI) has brought revolutionary changes to the world, enabling businesses to create new experiences by combining virtual and physical worlds. As the use of GAI grows along with the Metaverse, it is explored by academics, researchers, and industry communities for its endless possibilities. From ChatGPT by OpenAI to Bard AI by Google, GAI is a leading technology in physical and virtual business platforms. This paper focuses on GAI’s economic and societal impact and the challenges it poses. Businesses must rethink their operations and strategies to create hybrid physical and virtual experiences using GAI. This study proposes a framework that can help business managers develop effective strategies to enhance their operations. It analyzes the initial applications of GAI in multiple sectors to promote the development of future customer solutions and explores how GAI can help businesses create new value propositions and experiences for their customers, and the possibilities of digital communication and information technology. A research agenda is proposed for developing GAI for business management to enhance organizational efficiency. The results highlight a healthy conversation on the potential of GAI in various business sectors to improve customer experience. Full article
(This article belongs to the Section Information and Communication Technologies)
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10 pages, 753 KiB  
Perspective
Developments and Applications of Artificial Intelligence in Music Education
by Xiaofei Yu, Ning Ma, Lei Zheng, Licheng Wang and Kai Wang
Technologies 2023, 11(2), 42; https://doi.org/10.3390/technologies11020042 - 16 Mar 2023
Cited by 36 | Viewed by 9935
Abstract
With the continuous developments of information technology, advanced computer technology and information technology have been promoted and used in the field of music. As one of the products of the rapid development of information technology, Artificial Intelligence (AI) involves many interdisciplinary subjects, adding [...] Read more.
With the continuous developments of information technology, advanced computer technology and information technology have been promoted and used in the field of music. As one of the products of the rapid development of information technology, Artificial Intelligence (AI) involves many interdisciplinary subjects, adding new elements to music education. By analyzing the advantages of AI in music education, this paper systematically summarizes the application of AI in music education and discusses the development prospects of AI in music education. With the aid of AI, the combination of intelligent technology and on-site teaching solves the lack of individuation in the traditional mode and enhances students’ interest in learning. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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14 pages, 277 KiB  
Review
A Review of Deep Transfer Learning and Recent Advancements
by Mohammadreza Iman, Hamid Reza Arabnia and Khaled Rasheed
Technologies 2023, 11(2), 40; https://doi.org/10.3390/technologies11020040 - 14 Mar 2023
Cited by 71 | Viewed by 12189
Abstract
Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two significant constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts [...] Read more.
Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two significant constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such reliance and costs by reusing obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training costs. Moreover, the training cost reduction makes DTL viable on edge devices with limited resources. Like any new advancement, DTL methods have their own limitations, and a successful transfer depends on specific adjustments and strategies for different scenarios. This paper reviews the concept, definition, and taxonomy of deep transfer learning and well-known methods. It investigates the DTL approaches by reviewing applied DTL techniques in the past five years and a couple of experimental analyses of DTLs to discover the best practice for using DTL in different scenarios. Moreover, the limitations of DTLs (catastrophic forgetting dilemma and overly biased pre-trained models) are discussed, along with possible solutions and research trends. Full article
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23 pages, 5488 KiB  
Article
Non-Contact In-Vehicle Occupant Monitoring System Based on Point Clouds from FMCW Radar
by Yixuan Chen, Yunlong Luo, Jianhua Ma, Alex Qi, Runhe Huang, Francesco De Paulis and Yihong Qi
Technologies 2023, 11(2), 39; https://doi.org/10.3390/technologies11020039 - 13 Mar 2023
Cited by 2 | Viewed by 2412
Abstract
In order to reduce the probability of automobile safety incidents, the in-vehicle occupant monitoring is indispensable. However, occupant monitoring using frequency-modulated continuous wave (FMCW) radar can be challenging due to the interference from passengers’ posture, movement, and the presence of multiple people. This [...] Read more.
In order to reduce the probability of automobile safety incidents, the in-vehicle occupant monitoring is indispensable. However, occupant monitoring using frequency-modulated continuous wave (FMCW) radar can be challenging due to the interference from passengers’ posture, movement, and the presence of multiple people. This paper proposes an improved method for generating point clouds using FMCW radar. The approach involves point cloud clustering, post-processing operations such as segmentation, merging, and filtering of the clustered point cloud to match the actual in-vehicle environment, and a state machine combination step. Experimental results show that the proposed method can achieve high recognition accuracy in scenarios with multiple passengers who are moving and sitting in a relaxed manner. Full article
(This article belongs to the Section Information and Communication Technologies)
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15 pages, 3882 KiB  
Review
Aging Mechanism and Models of Supercapacitors: A Review
by Ning Ma, Dongfang Yang, Saleem Riaz, Licheng Wang and Kai Wang
Technologies 2023, 11(2), 38; https://doi.org/10.3390/technologies11020038 - 03 Mar 2023
Cited by 31 | Viewed by 3656
Abstract
Electrochemical supercapacitors are a promising type of energy storage device with broad application prospects. Developing an accurate model to reflect their actual working characteristics is of great research significance for rational utilization, performance optimization, and system simulation of supercapacitors. This paper presents the [...] Read more.
Electrochemical supercapacitors are a promising type of energy storage device with broad application prospects. Developing an accurate model to reflect their actual working characteristics is of great research significance for rational utilization, performance optimization, and system simulation of supercapacitors. This paper presents the fundamental working principle and applications of supercapacitors, analyzes their aging mechanism, summarizes existing supercapacitor models, and evaluates the characteristics and application scope of each model. By examining the current state and limitations of supercapacitor modeling research, this paper identifies future development trends and research focuses in this area. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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29 pages, 2313 KiB  
Article
Reconstruction of Industrial and Historical Heritage for Cultural Enrichment Using Virtual and Augmented Reality
by Lukas Paulauskas, Andrius Paulauskas, Tomas Blažauskas, Robertas Damaševičius and Rytis Maskeliūnas
Technologies 2023, 11(2), 36; https://doi.org/10.3390/technologies11020036 - 25 Feb 2023
Cited by 11 | Viewed by 3032
Abstract
Because of its benefits in providing an engaging and mobile environment, virtual reality (VR) has recently been rapidly adopted and integrated in education and professional training. Augmented reality (AR) is the integration of VR with the real world, where the real world provides [...] Read more.
Because of its benefits in providing an engaging and mobile environment, virtual reality (VR) has recently been rapidly adopted and integrated in education and professional training. Augmented reality (AR) is the integration of VR with the real world, where the real world provides context and the virtual world provides or reconstructs missing information. Mixed reality (MR) is the blending of virtual and physical reality environments allowing users to interact with both digital and physical objects at the same time. In recent years, technology for creating reality-based 3D models has advanced and spread across a diverse range of applications and research fields. The purpose of this paper is to design, develop, and test VR for kinaesthetic distance learning in a museum setting. A VR training program has been developed in which learners can select and perform pre-made scenarios in a virtual environment. The interaction in the program is based on kinaesthetic learning characteristics. Scenarios with VR controls simulate physical interaction with objects in a virtual environment for learners. Learners can grasp and lift objects to complete scenario tasks. There are also simulated devices in the virtual environment that learners can use to perform various actions. The study’s goal was to compare the effectiveness of the developed VR educational program to that of other types of educational material. Our innovation is the development of a system for combining their 3D visuals with rendering capable of providing a mobile VR experience for effective heritage enhancement. Full article
(This article belongs to the Special Issue Immersive Technologies and Applications on Arts, Culture and Tourism)
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15 pages, 14241 KiB  
Article
Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
by Ioannis D. Bougas, Maria S. Papadopoulou, Achilles D. Boursianis, Spyridon Nikolaidis and Sotirios K. Goudos
Technologies 2023, 11(2), 34; https://doi.org/10.3390/technologies11020034 - 24 Feb 2023
Cited by 2 | Viewed by 2034
Abstract
Radio-frequency (RF) energy harvesting (EH) is emerging as a reliable and constantly available free energy source. The primary factor determining whether this energy can be utilized is how efficiently it can be collected. In this work, an RF EH system is presented. More [...] Read more.
Radio-frequency (RF) energy harvesting (EH) is emerging as a reliable and constantly available free energy source. The primary factor determining whether this energy can be utilized is how efficiently it can be collected. In this work, an RF EH system is presented. More particularly, we designed a dual-band RF to DC rectifier circuit at sub-6 GHz in the 5G bands, able to supply low-power sensors and microcontrollers used in agriculture, the military, or health services. The system operates at 3.5 GHz and 5 GHz in the 5G cellular network’s frequency band FR1. Numerical results reveal that the system provides maximum power conversion efficiency (PCE) equal to 53% when the output load (sensor or microcontroller) is 1.74 kΩ and the input power is 12 dBm. Full article
(This article belongs to the Special Issue Intelligent Reflecting Surfaces for 5G and Beyond)
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28 pages, 1321 KiB  
Article
A Layer-Wise Coupled Thermo-Elastic Shell Model for Three-Dimensional Stress Analysis of Functionally Graded Material Structures
by Salvatore Brischetto, Domenico Cesare and Roberto Torre
Technologies 2023, 11(2), 35; https://doi.org/10.3390/technologies11020035 - 24 Feb 2023
Cited by 3 | Viewed by 1423
Abstract
In this work, a coupled 3D thermo-elastic shell model is presented. The primary variables are the scalar sovra-temperature and the displacement vector. This model allows for the thermal stress analysis of one-layered and sandwich plates and shells embedding Functionally Graded Material (FGM) layers. [...] Read more.
In this work, a coupled 3D thermo-elastic shell model is presented. The primary variables are the scalar sovra-temperature and the displacement vector. This model allows for the thermal stress analysis of one-layered and sandwich plates and shells embedding Functionally Graded Material (FGM) layers. The 3D equilibrium equations and the 3D Fourier heat conduction equation for spherical shells are put together into a set of four coupled equations. They automatically degenerate in those for simpler geometries thanks to proper considerations about the radii of curvature and the use of orthogonal mixed curvilinear coordinates α, β, and z. The obtained partial differential governing the equations along the thickness direction are solved using the exponential matrix method. The closed form solution is possible assuming simply supported boundary conditions and proper harmonic forms for all the unknowns. The sovra-temperature amplitudes are directly imposed at the outer surfaces for each geometry in steady-state conditions. The effects of the thermal environment are related to the sovra-temperature profiles through the thickness. The static responses are evaluated in terms of displacements and stresses. After a proper and global preliminary validation, new cases are presented for different thickness ratios, geometries, and temperature values at the external surfaces. The considered FGM is metallic at the bottom and ceramic at the top. This FGM layer can be embedded in a sandwich configuration or in a one-layered configuration. This new fully coupled thermo-elastic model provides results that are coincident with the results proposed by the uncoupled thermo-elastic model that separately solves the 3D Fourier heat conduction equation. The differences are always less than 0.5% for each investigated displacement, temperature, and stress component. The differences between the present 3D full coupled model and the the advantages of this new model are clearly shown. Both the thickness layer and material layer effects are directly included in all the conducted coupled thermal stress analyses. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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14 pages, 955 KiB  
Article
On the Sliding Mode Control Applied to a DC-DC Buck Converter
by Sandra Huerta-Moro, Oscar Martínez-Fuentes, Victor Rodolfo Gonzalez-Diaz and Esteban Tlelo-Cuautle
Technologies 2023, 11(2), 33; https://doi.org/10.3390/technologies11020033 - 23 Feb 2023
Viewed by 2147
Abstract
This work shows the voltage regulation of a DC–DC buck converter by applying sliding mode control using three different cases of sliding surfaces. The DC–DC buck converter is modeled by ordinary differential equations (ODEs) that are solved by applying numerical methods. The ODEs [...] Read more.
This work shows the voltage regulation of a DC–DC buck converter by applying sliding mode control using three different cases of sliding surfaces. The DC–DC buck converter is modeled by ordinary differential equations (ODEs) that are solved by applying numerical methods. The ODEs describe two state variables that are associated to the capacitor voltage and the inductor current. The state variable associated to voltage is regulated by applying two well-known sliding surfaces and a third one that is introduced herein to improve the response of the sliding mode control. The stability of the proposed sliding surface is verified by using a Lyapunov theorem to guarantee closed-loop stability. Finally, simulation results show the improvement of voltage regulation when applying the proposed sliding surface compared to already reported approaches. Full article
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33 pages, 11421 KiB  
Article
Identifying Historic Buildings over Time through Image Matching
by Kyriaki A. Tychola, Stamatis Chatzistamatis, Eleni Vrochidou, George E. Tsekouras and George A. Papakostas
Technologies 2023, 11(1), 32; https://doi.org/10.3390/technologies11010032 - 17 Feb 2023
Viewed by 2088
Abstract
The buildings in a city are of great importance. Certain historic buildings are landmarks and indicate the city’s architecture and culture. The buildings over time undergo changes because of various factors, such as structural changes, natural disaster damages, and aesthetic interventions. The form [...] Read more.
The buildings in a city are of great importance. Certain historic buildings are landmarks and indicate the city’s architecture and culture. The buildings over time undergo changes because of various factors, such as structural changes, natural disaster damages, and aesthetic interventions. The form of buildings in each period is perceived and understood by people of each generation, through photography. Nevertheless, each photograph has its own characteristics depending on the camera (analog or digital) used for capturing it. Any photo, even depicting the same object, is impossible to capture in the same way in terms of illumination, viewing angle, and scale. Hence, to study two or more photographs depicting the same object, first they should be identified and then properly matched. Nowadays, computer vision contributes to this process by providing useful tools. In particular, for this purpose, several feature detection and description algorithms of homologous points have been developed. In this study, the identification of historic buildings over time through feature correspondence techniques and methods is investigated. Especially, photographs from landmarks of Drama city, in Greece, on different dates and conditions (weather, light, rotation, scale, etc.), were gathered and experiments on 2D pairs of images, implementing traditional feature detectors and descriptors algorithms, such as SIFT, ORB, and BRISK, were carried out. This study aims to evaluate the feature matching procedure focusing on both the algorithms’ performance (accuracy, efficiency, and robustness) and the identification of the buildings. SIFT and BRISK are the most accurate algorithms while ORB and BRISK are the most efficient. Full article
(This article belongs to the Special Issue Image and Signal Processing)
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15 pages, 1059 KiB  
Article
FogTrust: Fog-Integrated Multi-Leveled Trust Management Mechanism for Internet of Things
by Abdul Rehman, Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren and Mohammed Alabdulkareem
Technologies 2023, 11(1), 27; https://doi.org/10.3390/technologies11010027 - 07 Feb 2023
Cited by 5 | Viewed by 1826
Abstract
The Internet of Things (IoT) is widely used to reduce human dependence. It is a network of interconnected smart devices with internet connectivity that can send and receive data. However, the rapid growth of IoT devices has raised security and privacy concerns, with [...] Read more.
The Internet of Things (IoT) is widely used to reduce human dependence. It is a network of interconnected smart devices with internet connectivity that can send and receive data. However, the rapid growth of IoT devices has raised security and privacy concerns, with the identification and removal of compromised and malicious nodes being a major challenge. To overcome this, a lightweight trust management mechanism called FogTrust is proposed. It has a multi-layer architecture that includes edge nodes, a trusted agent, and a fog layer. The trust agent acts as an intermediary authority, communicating with both IoT nodes and the fog layer for computation. This reduces the burden on nodes and ensures a trustworthy environment. The trust agent calculates the trust degree and transmits it to the fog layer, which uses encryption to maintain integrity. The encrypted value is shared with the trust agent for aggregation to improve the trust degree’s accuracy. The performance of the FogTrust approach was evaluated against various potential attacks, including On-off, Good-mouthing, and Bad-mouthing. The simulation results demonstrate that it effectively assigns low trust degrees to malicious nodes in different scenarios, even with varying percentages of malicious nodes in the network. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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25 pages, 4600 KiB  
Article
A Comprehensive Methodology for the Development of an Open Source Experimental Platform for Control Courses
by Marcos Aviles, Juvenal Rodríguez-Reséndiz, Juan Pérez-Ospina and Oscar Lara-Mendoza
Technologies 2023, 11(1), 25; https://doi.org/10.3390/technologies11010025 - 03 Feb 2023
Cited by 1 | Viewed by 1607
Abstract
This article presents the methodology for developing a control laboratory project that provides practical experience based on the ABET criteria. The project is structured around a portable and cheap ball and beam whose integrated system is made using printed circuit boards as the [...] Read more.
This article presents the methodology for developing a control laboratory project that provides practical experience based on the ABET criteria. The project is structured around a portable and cheap ball and beam whose integrated system is made using printed circuit boards as the first task. For the expression of the plant, students are guided to execute the essential stages of the control system design, from system modeling, through the design of the basic or advanced control strategy in the MATLAB and Arduino environment, to the implementation and validation of the closed loop. The proposed methods are clear and direct, greatly fostering the understanding of feedback control techniques and enabling students to gain extensive knowledge in practical implementations of control systems. The methodology is easy to interpret and modify in order to adopt it to any computer, allowing for the implementation of new practical tasks in control courses. Additionally, application examples and student-focused comments are included. This paper describes, in detail, the implementation and development of six laboratory practices for control courses, which have been developed based on ESP32 and other existing equipment. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2022))
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24 pages, 2335 KiB  
Article
An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification
by Mohammad Arifuzzaman, Md. Rakibul Hasan, Tasnia Jahan Toma, Samia Binta Hassan and Anup Kumar Paul
Technologies 2023, 11(1), 24; https://doi.org/10.3390/technologies11010024 - 01 Feb 2023
Cited by 2 | Viewed by 3734
Abstract
Deep neural networks (DNNs), the integration of neural networks (NNs) and deep learning (DL), have proven highly efficient in executing numerous complex tasks, such as data and image classification. Because the multilayer in a nonlinearly separable data structure is not transparent, it is [...] Read more.
Deep neural networks (DNNs), the integration of neural networks (NNs) and deep learning (DL), have proven highly efficient in executing numerous complex tasks, such as data and image classification. Because the multilayer in a nonlinearly separable data structure is not transparent, it is critical to develop a specific data classification model from a new and unexpected dataset. In this paper, we propose a novel approach using the concepts of DNN and decision tree (DT) for classifying nonlinear data. We first developed a decision tree-based neural network (DTBNN) model. Next, we extend our model to a decision tree-based deep neural network (DTBDNN), in which the multiple hidden layers in DNN are utilized. Using DNN, the DTBDNN model achieved higher accuracy compared to the related and relevant approaches. Our proposal achieves the optimal trainable weights and bias to build an efficient model for nonlinear data classification by combining the benefits of DT and NN. By conducting in-depth performance evaluations, we demonstrate the effectiveness and feasibility of the proposal by achieving good accuracy over different datasets. Full article
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20 pages, 7629 KiB  
Article
Application and Analysis of Modified Metal-Oxide Memristor Models in Electronic Devices
by Valeri Mladenov
Technologies 2023, 11(1), 20; https://doi.org/10.3390/technologies11010020 - 28 Jan 2023
Cited by 3 | Viewed by 2952
Abstract
The design of memristor-based electronic circuits and devices gives researchers opportunities for the engineering of CMOS-memristor-based electronic integrated chips with ultra-high density and various applications. Metal-oxide memristors have good compatibility with the present CMOS integrated circuits technologies. The analysis of new electronic circuits [...] Read more.
The design of memristor-based electronic circuits and devices gives researchers opportunities for the engineering of CMOS-memristor-based electronic integrated chips with ultra-high density and various applications. Metal-oxide memristors have good compatibility with the present CMOS integrated circuits technologies. The analysis of new electronic circuits requires suitable software and fast-functioning models. The main purpose of this paper is to propose the application of several modified, simplified, and improved metal-oxide memristor models in electronic devices and provide a comparison of their behavior, basic characteristics, and properties. According to this, LTSPICE is utilized in this paper because it is a free software product with good convergence. Several memristor-based electronic circuits, such as non-volatile passive and hybrid memory crossbars, a neural network, and different reconfigurable devices–filters, an amplifier, and a generator are analyzed in the LTSPICE environment, applying several standards and modified metal-oxide memristor models. After a comparison of the operation of the considered schemes, the main advantages of the modified metal-oxide memristor models, according to their standard analogs, are expressed, including fast operation, good accuracy, respectable convergence, switching properties, and successful applicability in complex electronic circuits. Full article
(This article belongs to the Special Issue MOCAST 2022)
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16 pages, 5064 KiB  
Article
Floating Interleaved Boost Converter with Zero-Ripple Input Current Using Variable Inductor
by Hector Hidalgo, Nimrod Vázquez, Rodolfo Orosco, Hector Huerta-Ávila, Sergio Pinto and Leonel Estrada
Technologies 2023, 11(1), 21; https://doi.org/10.3390/technologies11010021 - 28 Jan 2023
Cited by 4 | Viewed by 1722
Abstract
A zero-ripple input current is known to improve the lifetime of battery sets and fuel cells and to assure maximum power point tracking in PV panels. To perform current ripple elimination in a floating interleaved boost converter (FIBC), one of the typical linear [...] Read more.
A zero-ripple input current is known to improve the lifetime of battery sets and fuel cells and to assure maximum power point tracking in PV panels. To perform current ripple elimination in a floating interleaved boost converter (FIBC), one of the typical linear inductors is substituted by a variable inductor, and phases of the converter have complementary duty cycles. This variable inductor is controlled using a switched current-source converter, which adjusts the input current ripple. An equivalent model for the variable inductor is presented, including uncertainties in the component description. To achieve current stabilization, a variable inductor controller was designed using the sliding modes approach via fixed frequency. An experimental prototype is implemented and tested with an output voltage controller to compare with the conventional FIBC. The results demonstrate that the input current ripple of the proposed converter is eliminated without significantly decreasing the efficiency. Full article
(This article belongs to the Section Environmental Technology)
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19 pages, 4927 KiB  
Article
Evaluation of a Remote-Controlled Drone System for Bedridden Patients Using Their Eyes Based on Clinical Experiment
by Yoshihiro Kai, Yuuki Seki, Riku Suzuki, Atsunori Kogawa, Ryuichi Tanioka, Kyoko Osaka, Yueren Zhao and Tetsuya Tanioka
Technologies 2023, 11(1), 15; https://doi.org/10.3390/technologies11010015 - 17 Jan 2023
Viewed by 2331
Abstract
With the aging of the population in Japan, the number of bedridden patients who need long-term care is increasing. The Japanese government has been promoting the creation of an environment that enables everyone, including bedridden patients, to enjoy travel, based on the principle [...] Read more.
With the aging of the population in Japan, the number of bedridden patients who need long-term care is increasing. The Japanese government has been promoting the creation of an environment that enables everyone, including bedridden patients, to enjoy travel, based on the principle of normalization. However, it is difficult for bedridden patients to enjoy the scenery of distant places and to talk with the local people because they need support from helpers to travel to distant places using travel agencies. Therefore, to enhance their quality of life (QOL), we developed a remote-controlled drone system, which involves using only the eyes. We believe that bedridden patients are able to operate the system’s drone in a distant place, to easily view the scenery of the distant place with a camera installed on the drone, and to talk with the local people. However, we have never evaluated whether actual bedridden patients can operate the drone in a distant place, to see the scenery, and to talk with the local people. In this paper, we presented clinical experimental results to verify the effectiveness of this drone system. Findings showed that, not only subjects with relatively high levels of independence in activities of daily living, but also bedridden subjects, could operate the drone at a distant place with only their eyes and communicate with others. Full article
(This article belongs to the Section Assistive Technologies)
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22 pages, 6361 KiB  
Article
Investigation of Surface Layer Condition of SiAlON Ceramic Inserts and Its Influence on Tool Durability When Turning Nickel-Based Superalloy
by Sergey N. Grigoriev, Marina A. Volosova and Anna A. Okunkova
Technologies 2023, 11(1), 11; https://doi.org/10.3390/technologies11010011 - 12 Jan 2023
Cited by 3 | Viewed by 1950
Abstract
SiAlON is one of the problematic and least previously studied but prospective cutting ceramics suitable for most responsible machining tasks, such as cutting sophisticated shapes of aircraft gas turbine engine parts made of chrome–nickel alloys (Inconel 718 type) with increased mechanical and thermal [...] Read more.
SiAlON is one of the problematic and least previously studied but prospective cutting ceramics suitable for most responsible machining tasks, such as cutting sophisticated shapes of aircraft gas turbine engine parts made of chrome–nickel alloys (Inconel 718 type) with increased mechanical and thermal loads (semi-finishing). Industrially produced SiAlON cutting inserts are replete with numerous defects (stress concentrators). When external loads are applied, the wear pattern is difficult to predict. The destruction of the cutting edge, such as the tearing out of entire conglomerates, can occur at any time. The complex approach of additional diamond grinding, lapping, and polishing combined with an advanced double-layer (CrAlSi)N/DLC coating was proposed here for the first time to minimize it. The criterion of failure was chosen to be 0.4 mm. The developed tri-nitride coating sub-layer plays a role of improving the main DLC coating adhesion. The microhardness of the DLC coating was 28 ± 2 GPa, and the average coefficient of friction during high-temperature heating (up to 800 °C) was ~0.4. The average durability of the insert after additional diamond grinding, lapping, polishing, and coating was 12.5 min. That is superior to industrial cutting inserts and those subjected to (CrAlSi)N/DLC coating by 1.8 and 1.25 times, respectively. Full article
(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)
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25 pages, 2599 KiB  
Article
Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems
by Luis H. Manjarrez, Julio C. Ramos-Fernández, Eduardo S. Espinoza and Rogelio Lozano
Technologies 2023, 11(1), 12; https://doi.org/10.3390/technologies11010012 - 12 Jan 2023
Cited by 3 | Viewed by 2011
Abstract
An essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the [...] Read more.
An essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the available energy and the energy required by the UAV to complete the mission. This paper addresses the problem of estimating the energy required to perform a mission, for which a fuzzy Takagi–Sugeno system was implemented, whose premises were developed using fuzzy C-means to estimate the power required in the different stages of the mission. The parameters used in the fuzzy C-means algorithm were optimized using particle swarm optimization. On the other hand, an equivalent circuit model of a battery was used, for which fuzzy modeling was employed to determine the relationship between the open-circuit voltage and the state of charge of the battery, which in conjunction with an extended Kalman filter allows determining the battery charge. In addition, we developed a methodology to determine the minimum allowable battery charge level. From this, it is possible to determine the available flight time at the end of a mission defined as the flight time margin. In order to evaluate the developed methodology, a physical experiment was performed using an hexarotor UAV obtaining a maximum prediction error equivalent to the energy required to operate for 7 s, which corresponds to 2% of the total mission time. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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17 pages, 3857 KiB  
Article
An Efficient Hybrid CNN Classification Model for Tomato Crop Disease
by Maria Vasiliki Sanida, Theodora Sanida, Argyrios Sideris and Minas Dasygenis
Technologies 2023, 11(1), 10; https://doi.org/10.3390/technologies11010010 - 04 Jan 2023
Cited by 7 | Viewed by 3247
Abstract
Tomato plants are vulnerable to a broad number of diseases, each of which has the potential to cause significant damage. Diseases that affect crops substantially negatively impact the quantity and quality of agricultural products. Regarding quality crop maintenance, the importance of a timely [...] Read more.
Tomato plants are vulnerable to a broad number of diseases, each of which has the potential to cause significant damage. Diseases that affect crops substantially negatively impact the quantity and quality of agricultural products. Regarding quality crop maintenance, the importance of a timely and accurate diagnosis cannot be overstated. Deep learning (DL) strategies are now a critical research field for crop disease diagnoses. One independent system that can diagnose plant illnesses based on their outward manifestations is an example of an intelligent agriculture solution that could address these problems. This work proposes a robust hybrid convolutional neural network (CNN) diagnostic tool for various disorders that may affect tomato leaf tissue. A CNN and an inception module are the two components that make up this hybrid technique. The dataset employed for this study consists of nine distinct categories of tomato diseases and one healthy category sourced from PlantVillage. The findings are promising on the test set, with 99.17% accuracy, 99.23% recall, 99.13% precision, 99.56% AUC, and 99.17% F1-score, respectively. The proposed methodology offers a solution that boasts high performance for the diagnostics of tomato crops in the actual agricultural setting. Full article
(This article belongs to the Section Assistive Technologies)
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21 pages, 7053 KiB  
Article
Electrical Discharge Machining of Alumina Using Cu-Ag and Cu Mono- and Multi-Layer Coatings and ZnO Powder-Mixed Water Medium
by Anna A. Okunkova, Marina A. Volosova, Khaled Hamdy and Khasan I. Gkhashim
Technologies 2023, 11(1), 6; https://doi.org/10.3390/technologies11010006 - 27 Dec 2022
Cited by 2 | Viewed by 1811
Abstract
The paper aims to extend the current knowledge on electrical discharge machining of insulating materials, such as cutting ceramics used to produce cutting inserts to machine nickel-based alloys in the aviation and aerospace industries. Aluminum-based ceramics such as Al2O3, [...] Read more.
The paper aims to extend the current knowledge on electrical discharge machining of insulating materials, such as cutting ceramics used to produce cutting inserts to machine nickel-based alloys in the aviation and aerospace industries. Aluminum-based ceramics such as Al2O3, AlN, and SiAlON are in the most demand in the industry but present a scientific and technical problem in obtaining sophisticated shapes. One of the existing solutions is electrical discharge machining using assisting techniques. Using assisting Cu-Ag and Cu mono- and multi-layer coatings of 40–120 µm and ZnO powder-mixed deionized water-based medium was proposed for the first time. The developed coatings were subjected to tempering and testing. It was noticed that Ag-adhesive reduced the performance when tempering had a slight effect. The unveiled relationship between the material removal rate, powder concentration, and pulse frequency showed that performance was significantly improved by adding assisting powder up to 0.0032–0.0053 mm3/s for a concentration of 14 g/L and pulse frequency of 2–7 kHz. Further increase in concentration leads to the opposite trend. The most remarkable results corresponded to the pulse duration of 1 µs. The obtained data enlarged the knowledge of texturing insulating cutting ceramics using various powder-mixed deionized water-based mediums. Full article
(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)
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23 pages, 5762 KiB  
Article
A Conceptual Framework for Data Sensemaking in Product Development—A Case Study
by Tommy Langen, Haytham B. Ali and Kristin Falk
Technologies 2023, 11(1), 4; https://doi.org/10.3390/technologies11010004 - 22 Dec 2022
Viewed by 2180
Abstract
The industry acknowledges the value of using data and digitalization approaches to improve their systems. However, companies struggle to use data effectively in product development. This paper presents a conceptual framework for Data Sensemaking in Product Development, exemplified through a case study of [...] Read more.
The industry acknowledges the value of using data and digitalization approaches to improve their systems. However, companies struggle to use data effectively in product development. This paper presents a conceptual framework for Data Sensemaking in Product Development, exemplified through a case study of an Automated Parking System. The work is grounded in systems engineering, human centered-design, and data science theory. The resulting framework applies to practitioners and researchers in the early phase of product development. The framework combines conceptual models and data analytics, facilitating the range from human judgment and decision-making to verifications. The case study and feedback from several industrial actors suggest that the framework is valuable, usable, and feasible for more effective use of data in product development. Full article
(This article belongs to the Special Issue Human-Centered Cyber-Physical Systems)
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18 pages, 10341 KiB  
Article
Design and Analysis of Guidance Function of Permanent Magnet Electrodynamic Suspension
by Yuqing Xiang, Zigang Deng, Hongfu Shi, Kaiwen Li, Ting Cao, Bin Deng, Le Liang and Jun Zheng
Technologies 2023, 11(1), 3; https://doi.org/10.3390/technologies11010003 - 21 Dec 2022
Cited by 2 | Viewed by 1595
Abstract
Inspired by the guidance principle in the electromagnetic levitation system, a new permanent magnet electrodynamic suspension (PM EDS) structure with ferromagnetic guidance track is proposed and analyzed in this paper. Considering the lack of effective guidance ability for the PM EDS system, we [...] Read more.
Inspired by the guidance principle in the electromagnetic levitation system, a new permanent magnet electrodynamic suspension (PM EDS) structure with ferromagnetic guidance track is proposed and analyzed in this paper. Considering the lack of effective guidance ability for the PM EDS system, we adopted the ferromagnetic guidance track as being mounted under the conductor plate. The guidance principle is studied and the implementation of the guidance function is also introduced, and the finite element method (FEM) is employed and its accuracy is confirmed via the PM EDS high-speed rotating experimental platform fabricated in our laboratory. The influence of longitudinal speed on the guidance force is taken into account, which shows that the guidance performance is enhanced more obviously at low speeds. Moreover, the influence of the guidance track parameters on the guidance performance is also analyzed, including the geometric parameters, section shape, installation position and material. The equivalent small-scale PM EDS system experimental prototype is carried out to validate the effectiveness of the ferromagnetic guidance. The proposed ferromagnetic guidance structure is demonstrated to improve the guidance performance of the PM EDS system effectively, which will offer a technical reference for the practical engineering application of the PM EDS system. Full article
(This article belongs to the Section Assistive Technologies)
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10 pages, 1007 KiB  
Case Report
Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning
by Constantin Waubert de Puiseau, Dimitri Tegomo Nanfack, Hasan Tercan, Johannes Löbbert-Plattfaut and Tobias Meisen
Technologies 2022, 10(6), 129; https://doi.org/10.3390/technologies10060129 - 11 Dec 2022
Cited by 2 | Viewed by 2977
Abstract
The warehousing industry is faced with increasing customer demands and growing global competition. A major factor in the efficient operation of warehouses is the strategic storage location assignment of arriving goods, termed the dynamic storage location assignment problem (DSLAP). This paper presents a [...] Read more.
The warehousing industry is faced with increasing customer demands and growing global competition. A major factor in the efficient operation of warehouses is the strategic storage location assignment of arriving goods, termed the dynamic storage location assignment problem (DSLAP). This paper presents a real-world use case of the DSLAP, in which deep reinforcement learning (DRL) is used to derive a suitable storage location assignment strategy to decrease transportation costs within the warehouse. The DRL agent is trained on historic data of storage and retrieval operations gathered over one year of operation. The evaluation of the agent on new data of two months shows a 6.3% decrease in incurring costs compared to the currently utilized storage location assignment strategy which is based on manual ABC-classifications. Hence, DRL proves to be a competitive solution alternative for the DSLAP and related problems in the warehousing industry. Full article
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18 pages, 631 KiB  
Article
HADD: High-Accuracy Detection of Depressed Mood
by Yu Liu, Kyoung-Don Kang and Mi Jin Doe
Technologies 2022, 10(6), 123; https://doi.org/10.3390/technologies10060123 - 29 Nov 2022
Cited by 3 | Viewed by 1634
Abstract
Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of [...] Read more.
Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection. Full article
(This article belongs to the Section Assistive Technologies)
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20 pages, 2143 KiB  
Article
Simulation Analysis of Signal Conditioning Circuits for Plants’ Electrical Signals
by Mirella Carneiro, Victor Oliveira, Fernanda Oliveira, Marco Teixeira and Milena Pinto
Technologies 2022, 10(6), 121; https://doi.org/10.3390/technologies10060121 - 25 Nov 2022
Viewed by 1633
Abstract
Electrical signals are generated and transmitted through plants in response to stimuli caused by external environment factors, such as touching, luminosity, and leaf burning. By analyzing a specific plant’s electrical responses, it is possible to interpret the impact of external aspects in the [...] Read more.
Electrical signals are generated and transmitted through plants in response to stimuli caused by external environment factors, such as touching, luminosity, and leaf burning. By analyzing a specific plant’s electrical responses, it is possible to interpret the impact of external aspects in the plasma membrane potential and, thus, determine the cause of the electrical signal. Moreover, these signals permit the whole plant structure to be informed almost instantaneously. This work presents a brief discussion of plants electrophysiology theory and low-cost signal conditioning circuits, which are necessary for the acquisition of plants’ electrical signals. Two signal conditioning circuits, which must be chosen depending on the signal to be measured, are explained in detail and electrical simulation results, performed in OrCAD Capture Software are presented. Furthermore, Monte Carlo simulations were performed to evaluate the impact of components variations on the accuracy and efficiency of the signal conditioning circuits. Those simulations showed that, even after possible component variations, the filters’ cut-off frequencies had at most 4% variation from the mean. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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18 pages, 2498 KiB  
Article
Infrared Thermal Imaging and Artificial Neural Networks to Screen for Wrist Fractures in Pediatrics
by Olamilekan Shobayo, Reza Saatchi and Shammi Ramlakhan
Technologies 2022, 10(6), 119; https://doi.org/10.3390/technologies10060119 - 22 Nov 2022
Cited by 2 | Viewed by 1594
Abstract
Paediatric wrist fractures are commonly seen injuries at emergency departments. Around 50% of the X-rays taken to identify these injuries indicate no fracture. The aim of this study was to develop a model using infrared thermal imaging (IRTI) data and multilayer perceptron (MLP) [...] Read more.
Paediatric wrist fractures are commonly seen injuries at emergency departments. Around 50% of the X-rays taken to identify these injuries indicate no fracture. The aim of this study was to develop a model using infrared thermal imaging (IRTI) data and multilayer perceptron (MLP) neural networks as a screening tool to assist clinicians in deciding which patients require X-ray imaging to diagnose a fracture. Forty participants with wrist injury (19 with a fracture, 21 without, X-ray confirmed), mean age 10.50 years, were included. IRTI of both wrists was performed with the contralateral as reference. The injured wrist region of interest (ROI) was segmented and represented by the means of cells of 10 × 10 pixels. The fifty largest means were selected, the mean temperature of the contralateral ROI was subtracted, and they were expressed by their standard deviation, kurtosis, and interquartile range for MLP processing. Training and test files were created, consisting of randomly split 2/3 and 1/3 of the participants, respectively. To avoid bias of participant inclusion in the two files, the experiments were repeated 100 times, and the MLP outputs were averaged. The model’s sensitivity and specificity were 84.2% and 71.4%, respectively. Further work involves a larger sample size, adults, and other bone fractures. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing III)
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15 pages, 9713 KiB  
Article
Friction Stir Welding of Ti-6Al-4V Using a Liquid-Cooled Nickel Superalloy Tool
by Sergei Tarasov, Alihan Amirov, Andrey Chumaevskiy, Nikolay Savchenko, Valery E. Rubtsov, Aleksey Ivanov, Evgeniy Moskvichev and Evgeny Kolubaev
Technologies 2022, 10(6), 118; https://doi.org/10.3390/technologies10060118 - 18 Nov 2022
Cited by 5 | Viewed by 1729
Abstract
Friction stir welding (FSW) of titanium alloy was carried out using liquid cooling of the FSW tool made of heat-resistant nickel superalloy. Cooling of the nickel superalloy tool was performed by means of circulating water inside the tool. The FSW joints were characterized [...] Read more.
Friction stir welding (FSW) of titanium alloy was carried out using liquid cooling of the FSW tool made of heat-resistant nickel superalloy. Cooling of the nickel superalloy tool was performed by means of circulating water inside the tool. The FSW joints were characterized by microstructures and mechanical strength. The mechanical strength of the joints was higher than that of the base metal. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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26 pages, 2625 KiB  
Article
Towards a Modern Learning Organization: Human-Centered Digitalization of Lessons Learned Management for Complex Systems Development Projects
by YangYang Zhao and Henrik Jensen
Technologies 2022, 10(6), 117; https://doi.org/10.3390/technologies10060117 - 16 Nov 2022
Viewed by 2068
Abstract
The importance of learning from experience is incontrovertible; however, little is studied regarding the digitalization of in- and inter-project lessons learned in modern organizational practices. As a critical part of organizational knowledge, lessons learned are known to help organizations adapt to the ever-changing [...] Read more.
The importance of learning from experience is incontrovertible; however, little is studied regarding the digitalization of in- and inter-project lessons learned in modern organizational practices. As a critical part of organizational knowledge, lessons learned are known to help organizations adapt to the ever-changing world via the complex systems development projects they use to capitalize on and to develop their competitive advantage. In this paper, we introduce the concept of human-centered digitalization for this unique type of organizational knowledge and explain why this approach to managing lessons learned for complex systems development projects is necessary. Drawing from design thinking and systems thinking theories, we further outline the design principles for guiding actions and provide a case study of their implementation in automated systems projects for maritime industries. Full article
(This article belongs to the Special Issue Human-Centered Cyber-Physical Systems)
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20 pages, 8521 KiB  
Article
Electrical Discharge Machining of Al2O3 Using Copper Tape and TiO2 Powder-Mixed Water Medium
by Sergey N. Grigoriev, Anna A. Okunkova, Marina A. Volosova, Khaled Hamdy and Alexander S. Metel
Technologies 2022, 10(6), 116; https://doi.org/10.3390/technologies10060116 - 11 Nov 2022
Cited by 5 | Viewed by 2485
Abstract
Aluminum-based ceramics are used in industry to produce cutting tools that resist extreme mechanical and thermal load conditions during the machining of Ni-based and high-entropy alloys. There is wide field of application also in the aerospace industry. Microtexturing of cutting ceramics reduces contact [...] Read more.
Aluminum-based ceramics are used in industry to produce cutting tools that resist extreme mechanical and thermal load conditions during the machining of Ni-based and high-entropy alloys. There is wide field of application also in the aerospace industry. Microtexturing of cutting ceramics reduces contact loads and wear of cutting tools. However, most of the published works are related to the electrical discharge machining of alumina in hydrocarbons, which creates risks for the personnel and equipment due to the formation of chemically unstable dielectric carbides (methanide Al3C4 and acetylenide Al2(C2)3). An alternative approach for wire electrical discharge machining Al2O3 in the water-based dielectric medium using copper tape of 40 µm thickness and TiO2 powder suspension was proposed for the first time. The performance was evaluated by calculating the material removal rate for various combinations of pulse frequency and TiO2 powder concentration. The obtained kerf of 54.16 ± 0.05 µm in depth demonstrated an increasing efficiency of more than 1.5 times with the closest analogs for the workpiece thickness up to 5 mm in height. The comparison of the performance (0.0083–0.0084 mm3/s) with the closest analogs shows that the results may correlate with the electrical properties of the assisting materials. Full article
(This article belongs to the Section Innovations in Materials Processing)
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21 pages, 15427 KiB  
Article
Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems
by Chin-Teng Lin, Hsiu-Yu Fan, Yu-Cheng Chang, Liang Ou, Jia Liu, Yu-Kai Wang and Tzyy-Ping Jung
Technologies 2022, 10(6), 115; https://doi.org/10.3390/technologies10060115 - 08 Nov 2022
Viewed by 1950
Abstract
The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust [...] Read more.
The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over 50% compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better. Full article
(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)
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34 pages, 11931 KiB  
Article
Open-Source Photovoltaic—Electrical Vehicle Carport Designs
by Nicholas Vandewetering, Koami Soulemane Hayibo and Joshua M. Pearce
Technologies 2022, 10(6), 114; https://doi.org/10.3390/technologies10060114 - 07 Nov 2022
Cited by 6 | Viewed by 5046
Abstract
Solar powering the increasing fleet of electrical vehicles (EV) demands more surface area than may be available for photovoltaic (PV)-powered buildings. Parking lot solar canopies can provide the needed area to charge EVs but are substantially costlier than roof- or ground-mounted PV systems. [...] Read more.
Solar powering the increasing fleet of electrical vehicles (EV) demands more surface area than may be available for photovoltaic (PV)-powered buildings. Parking lot solar canopies can provide the needed area to charge EVs but are substantially costlier than roof- or ground-mounted PV systems. To provide a low-cost PV parking lot canopy to supply EV charging, in this study, we provide a full mechanical and economic analysis of three novel PV canopy systems: (1) an exclusively wood, single-parking-spot spanning system, (2) a wood and aluminum double-parking-spot spanning system, and (3) a wood and aluminum cantilevered system for curbside parking. All three systems can be scaled to any amount of EV parking spots. The complete designs and bill of materials (BOM) of the canopies are provided, along with basic instructions, and are released with an open-source license that will enable anyone to fabricate them. Analysis results indicate that single-span systems provide cost savings of 82–85%, double-span systems save 43–50%, and cantilevered systems save 31–40%. In the first year of operation, PV canopies can provide 157% of the energy needed to charge the least efficient EV currently on the market if it is driven the average driving distance in London, ON, Canada. Full article
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24 pages, 10290 KiB  
Article
Modular Multi-Input DC/DC Converter for EV Fast Charging
by Hossam A. Gabbar and Abdalrahman Elshora
Technologies 2022, 10(6), 113; https://doi.org/10.3390/technologies10060113 - 07 Nov 2022
Viewed by 2065
Abstract
In this paper, a modular multi-input, single output DC/DC converter is proposed to enhance the energy management of a fast-charging station for electric vehicles (EVs). The proposed bidirectional converter can work in different modes of operation with fewer components and a modular design [...] Read more.
In this paper, a modular multi-input, single output DC/DC converter is proposed to enhance the energy management of a fast-charging station for electric vehicles (EVs). The proposed bidirectional converter can work in different modes of operation with fewer components and a modular design to extend the input power sources and increase the charging power rate. The converter has several merits compared to the conventional converters, such as centralizing the control, reducing power devices, and reducing power conversion stages. By using MATLAB/Simulink, the converter was tested in many operation modes and was used to charge a Nissan Leaf EV’s battery (350 V, 60 Ah) from hybrid sources simultaneously and individually in power up to (17 kW). In addition, it was tested on a hardware scale at a low power rate (100 W) for the validation of the simulation work and the topology concept. In addition, its different losses and efficiency were calculated during the different operation modes. Full article
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8 pages, 362 KiB  
Communication
Variance-Based Sensitivity Analysis of Fitting Parameters to Impact on Cycling Durability of Polymer Electrolyte Fuel Cells
by Victor A. Kovtunenko
Technologies 2022, 10(6), 111; https://doi.org/10.3390/technologies10060111 - 28 Oct 2022
Cited by 2 | Viewed by 1348
Abstract
Degradation of a catalyst layer in polymer electrolyte membrane fuel cells is considered, which is caused by electrochemical reactions of the platinum ion dissolution and oxide coverage. An accelerated stress test is applied, where the electric potential cycling is given by a non-symmetric [...] Read more.
Degradation of a catalyst layer in polymer electrolyte membrane fuel cells is considered, which is caused by electrochemical reactions of the platinum ion dissolution and oxide coverage. An accelerated stress test is applied, where the electric potential cycling is given by a non-symmetric square profile. Computer simulations of the underlying one-dimensional Holby–Morgan model predict durability of the fuel cell operating. A sensitivity analysis based on the variance quantifies how loss of the platinum mass subjected to the degradation is impacted by the variation of fitting parameters in the model. Full article
(This article belongs to the Section Environmental Technology)
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