Electronics

19 pages, 1270 KiB

Open AccessArticle

Space Discretization-Based Optimal Trajectory Planning for Automated Vehicles in Narrow Corridor Scenes

by Biao Xu, Shijie Yuan, Xuerong Lin, Manjiang Hu, Yougang Bian and Zhaobo Qin

Electronics 2022, 11(24), 4239; https://doi.org/10.3390/electronics11244239 - 19 Dec 2022

Cited by 1 | Viewed by 1737

The narrow corridor is a common working scene for automated vehicles, where it is pretty challenging to plan a safe, feasible, and smooth trajectory due to the narrow passable area constraints. This paper presents a space discretization-based optimal trajectory planning method for automated [...] Read more.

The narrow corridor is a common working scene for automated vehicles, where it is pretty challenging to plan a safe, feasible, and smooth trajectory due to the narrow passable area constraints. This paper presents a space discretization-based optimal trajectory planning method for automated vehicles in a narrow corridor scene with the consideration of travel time minimization and boundary collision avoidance. In this method, we first design a mathematically-described driving corridor model. Then, we build a space discretization-based trajectory optimization model in which the objective function is travel efficiency, and the vehicle-kinematics constraints, collision avoidance constraints, and several other constraints are proposed to ensure the feasibility and comfortability of the planned trajectory. Finally, the proposed method is verified with both simulations and field tests. The experimental results demonstrate the trajectory planned by the proposed method is smoother and more computationally efficient compared with the baseline methods while significantly reducing the tracking error indicating the proposed method has huge application potential in trajectory planning in the narrow corridor scenario for automated vehicles. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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12 pages, 2875 KiB

Open AccessArticle

Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm

by Jian Yang and Xuejun Huang

Electronics 2022, 11(24), 4238; https://doi.org/10.3390/electronics11244238 - 19 Dec 2022

Cited by 1 | Viewed by 1565

Abstract

As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial [...] Read more.

As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objective optimization algorithm has a good performance in solving optimization problems. In this paper, the intelligent planning model of UAV cluster was established based on multi-objective optimization algorithm, and its path is optimized. In the aspect of modeling, this paper studied and analyzed online task planning, search rules and cluster formation control using an agent-based intelligent modeling method. For mission planning and optimization, it combined multi-objective optimization algorithm to build the model from three aspects of mission allocation, route planning and planning evaluation. The final simulation results showed that the UAV cluster intelligent planning modeling method and path optimization method based on multi-objective optimization algorithm met the requirements of route design and improved the path search efficiency with 2.26% task completion satisfaction. Full article

(This article belongs to the Section Artificial Intelligence)

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21 pages, 1452 KiB

Open AccessArticle

Spectral Efficiency of Precoded 5G-NR in Single and Multi-User Scenarios under Imperfect Channel Knowledge: A Comprehensive Guide for Implementation

by David Alejandro Urquiza Villalonga, Hatem OdetAlla, M. Julia Fernández-Getino García and Adam Flizikowski

Electronics 2022, 11(24), 4237; https://doi.org/10.3390/electronics11244237 - 19 Dec 2022

Cited by 5 | Viewed by 2156

Abstract

Digital precoding techniques have been widely applied in multiple-input multiple-output (MIMO) systems to enhance spectral efficiency (SE) which is crucial in 5G New Radio (NR). Therefore, the 3rd Generation Partnership Project (3GPP) has developed codebook-based MIMO precoding strategies to achieve a good trade-off [...] Read more.

Digital precoding techniques have been widely applied in multiple-input multiple-output (MIMO) systems to enhance spectral efficiency (SE) which is crucial in 5G New Radio (NR). Therefore, the 3rd Generation Partnership Project (3GPP) has developed codebook-based MIMO precoding strategies to achieve a good trade-off between performance, complexity, and signal overhead. This paper aims to evaluate the performance bounds in SE achieved by the 5G-NR precoding matrices in single-user (SU) and multi-user (MU) MIMO systems, namely Type I and Type II, respectively. The implementation of these codebooks is covered providing a comprehensive guide with a detailed analysis. The performance of the 5G-NR precoder is compared with theoretical precoding techniques such as singular value decomposition (SVD) and block-diagonalization to quantify the margin of improvement of the standardized methods. Several configurations of antenna arrays, number of antenna ports, and parallel data streams are considered for simulations. Moreover, the effect of channel estimation errors on the system performance is analyzed in both SU and MU-MIMO cases. For a realistic framework, the SE values are obtained for a practical deployment based on a clustered delay line (CDL) channel model. These results provide valuable insights for system designers about the implementation and performance of the 5G-NR precoding matrices. Full article

(This article belongs to the Special Issue Transmit and Receive Techniques for Next Generation Massive MIMO Systems)

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13 pages, 3384 KiB

Open AccessArticle

Zhongshan HF Radar Elevation Calibration Based on Ground Backscatter Echoes

by Weijie Jiang, Erxiao Liu, Xing Kong, Shengsheng Shi and Jianjun Liu

Electronics 2022, 11(24), 4236; https://doi.org/10.3390/electronics11244236 - 19 Dec 2022

Viewed by 1268

Abstract

The super dual auroral radar network (SuperDARN) is an important tool in the remote sensing of ionospheric potential convection in middle and high latitudes, and also a major source of elevation data detection. A reliable elevation angle helps estimate the propagation paths of [...] Read more.

The super dual auroral radar network (SuperDARN) is an important tool in the remote sensing of ionospheric potential convection in middle and high latitudes, and also a major source of elevation data detection. A reliable elevation angle helps estimate the propagation paths of high-frequency radio signals between scattering spots and radars, which is crucial for determining high-frequency radar target geolocation. The SuperDARN radar uses interferometry to estimate the elevation of the returned signal. However, elevation data are still underutilized owing to the difficulties of phase difference calibration induced by the propagation time delay between two arrays. This paper statistically analyzes the distribution features of the group range-elevation angle and group range-virtual height before and after calibration using elevation data from the ground backscatter echoes of the Zhongshan SuperDARN radar, calculating the root mean square error (RMSE) of the virtual height; the results show that the RMSE after calibration is mostly reduced to within 54% of that before calibration. Furthermore, we validate the calibration factor based on the primary phase data. The data from 2013 to 2015 indicate that this technique can be efficiently used to estimate the daily calibration factor. Finally, we present the statistical distribution of the calibration factor, which provides technical support for the calibration of elevation data in the future. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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Graphical abstract

10 pages, 489 KiB

Open AccessReview

A Systematic Review on Machine Learning Techniques for Early Detection of Mental, Neurological and Laryngeal Disorders Using Patient’s Speech

by Mohammadjavad Sayadi, Vijayakumar Varadarajan, Mostafa Langarizadeh, Gholamreza Bayazian and Farhad Torabinezhad

Electronics 2022, 11(24), 4235; https://doi.org/10.3390/electronics11244235 - 19 Dec 2022

Cited by 2 | Viewed by 1889

Abstract

There is a substantial unmet need to diagnose speech-related disorders effectively. Machine learning (ML), as an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve these issues. The purpose of this study was to categorize and compare machine learning methods [...] Read more.

There is a substantial unmet need to diagnose speech-related disorders effectively. Machine learning (ML), as an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve these issues. The purpose of this study was to categorize and compare machine learning methods in the diagnosis of speech-based diseases. In this systematic review, a comprehensive search for publications was conducted on the Scopus, Web of Science, PubMed, IEEE and Cochrane databases from 2002–2022. From 533 search results, 48 articles were selected based on the eligibility criteria. Our findings suggest that the diagnosing of speech-based diseases using speech signals depends on culture, language and content of speech, gender, age, accent and many other factors. The use of machine-learning models on speech sounds is a promising pathway towards improving speech-based disease diagnosis and treatments in line with preventive and personalized medicine. Full article

(This article belongs to the Special Issue Machine Learning in Electronic and Biomedical Engineering, Volume II)

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17 pages, 5844 KiB

Open AccessArticle

Computer Vision-Based Kidney’s (HK-2) Damaged Cells Classification with Reconfigurable Hardware Accelerator (FPGA)

by Arfan Ghani, Rawad Hodeify, Chan H. See, Simeon Keates, Dah-Jye Lee and Ahmed Bouridane

Electronics 2022, 11(24), 4234; https://doi.org/10.3390/electronics11244234 - 19 Dec 2022

Cited by 3 | Viewed by 2723

Abstract

In medical and health sciences, the detection of cell injury plays an important role in diagnosis, personal treatment and disease prevention. Despite recent advancements in tools and methods for image classification, it is challenging to classify cell images with higher precision and accuracy. [...] Read more.

In medical and health sciences, the detection of cell injury plays an important role in diagnosis, personal treatment and disease prevention. Despite recent advancements in tools and methods for image classification, it is challenging to classify cell images with higher precision and accuracy. Cell classification based on computer vision offers significant benefits in biomedicine and healthcare. There have been studies reported where cell classification techniques have been complemented by Artificial Intelligence-based classifiers such as Convolutional Neural Networks. These classifiers suffer from the drawback of the scale of computational resources required for training and hence do not offer real-time classification capabilities for an embedded system platform. Field Programmable Gate Arrays (FPGAs) offer the flexibility of hardware reconfiguration and have emerged as a viable platform for algorithm acceleration. Given that the logic resources and on-chip memory available on a single device are still limited, hardware/software co-design is proposed where image pre-processing and network training were performed in software, and trained architectures were mapped onto an FPGA device (Nexys4DDR) for real-time cell classification. This paper demonstrates that the embedded hardware-based cell classifier performs with almost 100% accuracy in detecting different types of damaged kidney cells. Full article

(This article belongs to the Special Issue Convolutional Neural Networks and Vision Applications, Volume II)

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19 pages, 3324 KiB

Open AccessReview

Radiofrequency Coils for Low-Field (0.18–0.55 T) Magnetic Resonance Scanners: Experience from a Research Lab–Manufacturing Companies Cooperation

by Giulio Giovannetti, Francesca Frijia and Alessandra Flori

Electronics 2022, 11(24), 4233; https://doi.org/10.3390/electronics11244233 - 19 Dec 2022

Cited by 2 | Viewed by 2654

Abstract

Low-field magnetic resonance imaging (MRI) has become increasingly popular due to cost reduction, artifact minimization, use for interventional radiology, and a better safety profile. The different applications of low-field systems are particularly wide (muscle–skeletal, cardiac, neuro, small animals, food science, as a hybrid [...] Read more.

Low-field magnetic resonance imaging (MRI) has become increasingly popular due to cost reduction, artifact minimization, use for interventional radiology, and a better safety profile. The different applications of low-field systems are particularly wide (muscle–skeletal, cardiac, neuro, small animals, food science, as a hybrid scanner for hyperthermia, in interventional radiology and in radiotherapy). The low-field scanners produce lower signal-to-noise ratio (SNR) images with respect to medium- and high-field scanners. Thus, particular attention must be paid in the minimization of the radiofrequency (RF) coil losses compared to the sample noise. Following a short description of the coil design and simulation methods (magnetostatic and full-wave), in this paper we will describe how the choice of electrical parameters (such as conductor geometry and capacitor quality) affects the coil’s overall performance in terms of the quality factor Q, ratio between unloaded and loaded Q, and coil sensitivity. Subsequently, we will summarize the work carried out at our electromagnetic laboratory in collaboration with MR-manufacturing companies in the field of RF coil design, building, and testing for 0.18–0.55 T magnetic resonance (MR) clinical scanners by classifying them between surface-, volume-, and phased-array coils. Full article

(This article belongs to the Section Bioelectronics)

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15 pages, 5543 KiB

Open AccessArticle

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

by Guoxing Bai, Yu Meng, Li Liu, Qing Gu, Jianxiu Huang, Guodong Liang, Guodong Wang, Li Liu, Xinrui Chang and Xin Gan

Electronics 2022, 11(24), 4232; https://doi.org/10.3390/electronics11244232 - 19 Dec 2022

Cited by 3 | Viewed by 1892

Abstract

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. [...] Read more.

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions. Full article

(This article belongs to the Collection Advance Technologies of Navigation for Intelligent Vehicles)

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14 pages, 693 KiB

Open AccessArticle

A Novel Turbo Detector Design for a High-Speed SSVEP-Based Brain Speller

by Changkai Tong, Huali Wang and Jun Cai

Electronics 2022, 11(24), 4231; https://doi.org/10.3390/electronics11244231 - 19 Dec 2022

Viewed by 1109

Abstract

The past decade has witnessed the rapid development of brain-computer interfaces (BCIs). The contradiction between communication rates and tedious training processes has become one of the major barriers restricting the application of steady-state visual-evoked potential (SSVEP)-based BCIs. A turbo detector was proposed in [...] Read more.

The past decade has witnessed the rapid development of brain-computer interfaces (BCIs). The contradiction between communication rates and tedious training processes has become one of the major barriers restricting the application of steady-state visual-evoked potential (SSVEP)-based BCIs. A turbo detector was proposed in this study to resolve this issue. The turbo detector uses the filter bank canonical correlation analysis (FBCCA) as the first-stage detector and then utilizes the soft information generated by the first-stage detector and the pool of identified data generated during use to complete the second-stage detection. This strategy allows for rapid performance improvements as the data pool size increases. A standard benchmark dataset was used to evaluate the performance of the proposed method. The results show that the turbo detector can achieve an average ITR of 130 bits/min, which is about 8% higher than FBCCA. As the size of the data pool increases, the ITR of the turbo detector could be further improved. Full article

(This article belongs to the Special Issue Recent Advances in Brain–Computer Interfaces and Human–Computer Interaction)

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23 pages, 1373 KiB

Open AccessArticle

A Novel Geo-Social-Aware Video Edge Delivery Strategy Based on Modeling of Social-Geographical Dynamic in an Urban Area

by Shijie Jia, Yan Cui and Ruiling Zhang

Electronics 2022, 11(24), 4230; https://doi.org/10.3390/electronics11244230 - 19 Dec 2022

Viewed by 940

Abstract

Social networks change the way and approaches of video spread and promote range and speed of video spread, which results in frequent traffic blowout and a heavy load on the networks. The social and geographical communication efficiency determines the efficiency of video sharing, [...] Read more.

Social networks change the way and approaches of video spread and promote range and speed of video spread, which results in frequent traffic blowout and a heavy load on the networks. The social and geographical communication efficiency determines the efficiency of video sharing, which enables the eruptible traffic to be offloaded in underlaying networks to relieve the load of networks and ensure the user quality of the experience. In this paper, we propose a novel geo-social-aware video edge delivery strategy based on the modeling of the social-geographical dynamic in urban area (GSVD). By investigating the frequency of sharing behaviors, social communication efficiency, and efficiency of social sub-network consisting of one-hop social neighbors of users, GSVD estimates the interactive and basic social relationship to calculate the closeness of the social relationship between mobile users. GSVD makes use of grid partition and coding subarea to express the geographical location of mobile users and designs a calculation method of coding-based geographical distance. GSVD considers the dynamic update of social distance and geographical location and designs a measurement of video delivery quality in terms of delivery delay and playback continuity. A strategy of video delivery with the consideration of adapting to social-geographical dynamic is designed, which effectively promotes the efficiency of video sharing. Extensive tests show how GSVD achieves much better performance results in comparison with other state of the art solutions. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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10 pages, 6288 KiB

Open AccessArticle

Design and Implementation of Sigma-Delta ADC Filter

by Renzhuo Wan, Yuandong Li, Chengde Tian, Fan Yang, Wendi Deng, Siyu Tang, Jun Wang and Wei Zhang

Electronics 2022, 11(24), 4229; https://doi.org/10.3390/electronics11244229 - 19 Dec 2022

Cited by 3 | Viewed by 4586

Abstract

This paper presents a digital decimation filter based on a third-order four-bit Sigma-Delta modulator. The digital decimation filter is an important part of the Sigma-Delta ADC and is designed to make the Sigma-Delta ADC (Analog-to-Digital Converter) meets the requirements of Signal-to-Noise Ratio (SNR) [...] Read more.

This paper presents a digital decimation filter based on a third-order four-bit Sigma-Delta modulator. The digital decimation filter is an important part of the Sigma-Delta ADC and is designed to make the Sigma-Delta ADC (Analog-to-Digital Converter) meets the requirements of Signal-to-Noise Ratio (SNR) not less than 120 dB and Equivalent Number of Bits (ENOB) not less than 20 bits. It adopts a three-stages cascaded structure including a Cascaded Integrator Comb (CIC) decimation filter, a Finite Impulse Response (FIR) compensation filter, and a half-band (HB) filter. This structure effectively reduces about 13% multiplier cells and memory cells. The coefficient symmetry technique and CSD (Canonic Signed Digit) coding technique are used to optimize the parameters of the filter, which further reduces the computational complexity. After optimization, the circuit area is reduced by about 15%, and the logic resources are decreased by about 23%. The Verilog hardware description language is used to describe the behavior of the digital decimation filter, and the simulation is carried out based on the VCS (Verilog Compile Simulator) platform. At the same time, the prototype verification is implemented on the Xilinx Artix-7 series FPGA, and the ADC achieves 113 dB SNR and 18.5 bits ENOB. Finally, the Sigma-Delta ADC is fabricated on SMIC 0.18

μ

m CMOS process with the layout area of 714.8

μ

m × 628.4

μ

m and the power consumption of 11.2 mW. The more tests for the fabricated prototypes will be performed in the future to verify that the Sigma-Delta ADC complies with the design specifications. Full article

(This article belongs to the Special Issue Recent Advances in Microelectronics Devices and Integrated Circuit)

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10 pages, 2910 KiB

Open AccessArticle

Comparative Estimation of Electrical Characteristics of a Photovoltaic Module Using Regression and Artificial Neural Network Models

by Jonghwan Lee and Yongwoo Kim

Electronics 2022, 11(24), 4228; https://doi.org/10.3390/electronics11244228 - 19 Dec 2022

Cited by 3 | Viewed by 1163

Abstract

Accurate modeling of photovoltaic (PV) modules under outdoor conditions is essential to facilitate the optimal design and assessment of PV systems. As an alternative model to the translation equations based on regression methods, various data-driven models have been adopted to estimate the current–voltage [...] Read more.

Accurate modeling of photovoltaic (PV) modules under outdoor conditions is essential to facilitate the optimal design and assessment of PV systems. As an alternative model to the translation equations based on regression methods, various data-driven models have been adopted to estimate the current–voltage (I–V) characteristics of a photovoltaic module under varying operation conditions. In this paper, artificial neural network (ANN) models are compared with the regression models for five parameters of a single diode solar cell. In the configuration of the proposed PV models, the five parameters are predicted by regression and neural network models, and these parameters are put into an explicit expression such as the Lambert W function. The multivariate regression parameters are determined by using the least square method (LSM). The ANN model is constructed by using a four-layer, feed-forward neural network, in which the inputs are temperature and solar irradiance, and the outputs are the five parameters. By training an experimental dataset, the ANN model is built and utilized to predict the five parameters by reading the temperature and solar irradiance. The performance of the regression and ANN models is evaluated by using root mean squared error (RMSE) and mean absolute percentage error (MAPE). A comparative study of the regression and ANN models shows that the performance of the ANN models is better than the regression models. Full article

(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)

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22 pages, 2440 KiB

Open AccessArticle

Ability-Restricted Indoor Reconnaissance Task Planning for Multiple UAVs

by Ruowei Zhang, Lihua Dou, Qing Wang, Bin Xin and Yulong Ding

Electronics 2022, 11(24), 4227; https://doi.org/10.3390/electronics11244227 - 19 Dec 2022

Cited by 3 | Viewed by 1118

Abstract

For indoor multi-task planning problems of small unmanned aerial vehicles (UAVs) with different abilities, task assignment and path planning play a crucial role. The multi-dimensional requirements of reconnaissance tasks bring great difficulties to the task execution of multi-UAV cooperation. Meanwhile, the complex internal [...] Read more.

For indoor multi-task planning problems of small unmanned aerial vehicles (UAVs) with different abilities, task assignment and path planning play a crucial role. The multi-dimensional requirements of reconnaissance tasks bring great difficulties to the task execution of multi-UAV cooperation. Meanwhile, the complex internal environment of buildings has a great impact on the path planning of UAVs. In this paper, the ability-restricted indoor reconnaissance task-planning (ARIRTP) problem is solved by a bi-level problem-solving framework. In the upper level, an iterative search algorithm is used to solve the task assignment problem. According to the characteristics of the problem, a solution-space compression mechanism (SSCM) is proposed to exclude solutions that do not satisfy the task requirements. In the lower level, based on a topological map, the nearest neighbor (NN) algorithm is used to quickly construct the path sequence of a UAV. Finally, the genetic algorithm (GA) and simulated annealing (SA) algorithm are applied to the upper level of the framework as iterative search algorithms, which produces two hybrid algorithms named the GA-NN and SA-NN, respectively. ARIRTP instances of different scales are designed to verify the effectiveness of the SSCM and the performance of the GA-NN and SA-NN methods. It is demonstrated that the SSCM can significantly compress the solution space and effectively improve the performance of the algorithms. The proposed bi-level problem-solving framework provides a methodology for the cooperation of multi-UAV to perform reconnaissance tasks in indoor environments. The experimental results show that the GA-NN and SA-NN methods can quickly and efficiently solve the ARIRTP problem. The performance of the GA-NN method is similar to that of the SA-NN method. The GA-NN method runs slightly faster. In large-scale instances, the performance of the SA-NN method is slightly better than that of the GA-NN method. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Unmanned Systems)

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19 pages, 671 KiB

Open AccessArticle

Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model

by Hira Mariam, Irfan Ahmed, Sundus Ali, Muhammad Imran Aslam and Ikram Ur Rehman

Electronics 2022, 11(24), 4226; https://doi.org/10.3390/electronics11244226 - 19 Dec 2022

Cited by 2 | Viewed by 1473

Abstract

Future wireless networks are expected to be dense and employ a higher frequency spectrum such as millimeter wave (mmwave) to support higher data rates. In a dense urban environment, the presence of obstructions causes the transmissions between the user equipment and base stations [...] Read more.

Future wireless networks are expected to be dense and employ a higher frequency spectrum such as millimeter wave (mmwave) to support higher data rates. In a dense urban environment, the presence of obstructions causes the transmissions between the user equipment and base stations to transit from line-of-sight (LOS) to non-LOS (NLOS). This transit hence emphasizes the significance of NLOS links for reliable mmwave communication. The work presented in this paper investigates the downlink performance of a mmwave cellular system by modeling the NLOS channel using stretched exponential path loss model (SEPLM) and employing a 3GPP distance-dependent LOS probability function. This path loss model has the inherent ability to define short ranges as well as obstructions in the environment as a function of its parameter resulting in a more realistic performance analysis. The path loss model is first validated for NLOS link using a data set from an open-source mmwave channel simulator. Then, a mathematical model incorporating LOS and NLOS transmissions is developed to study the impact of path loss on signal-to-interference-plus-noise (SINR) coverage probability and area spectral efficiency (ASE). The proposed framework can provide coverage performance indication over various blockage environments. Our results demonstrate that SINR coverage probability decreases exponentially with increasing base station density. Moreover, ASE initially increases with increasing BS density and is maximized for a particular density value, after which it converges to zero for higher densities. The results are also benchmarked with the existing path loss model of mmwave cellular system with different exponents for LOS and NLOS paths. It was observed that as the base station density increases, the SINR degrades more rapidly when using SEPLM as compared to the existing mmwave path loss model. Full article

(This article belongs to the Special Issue Smart Applications of 5G Network)

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12 pages, 3687 KiB

Open AccessArticle

Analysis of the Influencing Factors on S-Band Sea Spikes

by Peng Zhao, Zhensen Wu, Yushi Zhang and Dong Zhu

Electronics 2022, 11(24), 4225; https://doi.org/10.3390/electronics11244225 - 19 Dec 2022

Cited by 1 | Viewed by 1106

Abstract

While detecting targets on the sea by radar that looks downward, sea spikes similar to the target echoes cause false alarms, especially at low grazing angles. To suppress the interference of sea spikes on target detection, it is important to study the factors [...] Read more.

While detecting targets on the sea by radar that looks downward, sea spikes similar to the target echoes cause false alarms, especially at low grazing angles. To suppress the interference of sea spikes on target detection, it is important to study the factors influencing the occurrence probability of sea spikes during radar inspections. Based on measured sea clutter data, this study uses the amplitude threshold, duration time, and interval time to identify sea spikes from sea clutter and uses the pulse number per unit time to characterize their occurrence probability. The influences of the grazing angle, wave height, wave direction, and wind speed on the occurrence probability of sea spikes were analyzed. The results indicate that the occurrence probability of sea spikes increases exponentially with decreasing grazing angle in the range from 0.7° to 7.1°, and linearly with increasing wind speed. Wave direction has little or no influence on the probability of sea spikes. At the smaller grazing angles, from 0.7° to 1.7°, the influence of wave height on the probability of sea spikes’ occurrence is obvious, showing a linear trend, but in the range of 2.6° to 7.1°, the influence is not obvious. In addition, the occurrence probability of sea spikes is found greater at wave heights from 0.9 m to 1.1 m relative to other wave heights, which is worthy of further study. Full article

(This article belongs to the Section Microwave and Wireless Communications)

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35 pages, 3682 KiB

Open AccessArticle

An Improved Whale Optimizer with Multiple Strategies for Intelligent Prediction of Talent Stability

by Hong Li, Sicheng Ke, Xili Rao, Caisi Li, Danyan Chen, Fangjun Kuang, Huiling Chen, Guoxi Liang and Lei Liu

Electronics 2022, 11(24), 4224; https://doi.org/10.3390/electronics11244224 - 18 Dec 2022

Cited by 2 | Viewed by 1769

Abstract

Talent resources are a primary resource and an important driving force for economic and social development. At present, researchers have conducted studies on talent introduction, but there is a paucity of research work on the stability of talent introduction. This paper presents the [...] Read more.

Talent resources are a primary resource and an important driving force for economic and social development. At present, researchers have conducted studies on talent introduction, but there is a paucity of research work on the stability of talent introduction. This paper presents the first study on talent stability in higher education, aiming to design an intelligent prediction model for talent stability in higher education using a kernel extreme learning machine (KELM) and proposing a differential evolution crisscross whale optimization algorithm (DECCWOA) for optimizing the model parameters. By introducing the crossover operator, the exchange of information regarding individuals is facilitated and the problem of dimensional lag is improved. Differential evolution operation is performed in a certain period of time to perturb the population by using the differences in individuals to ensure the diversity of the population. Furthermore, 35 benchmark functions of 23 baseline functions and CEC2014 were selected for comparison experiments in order to demonstrate the optimization performance of the DECCWOA. It is shown that the DECCWOA can achieve high accuracy and fast convergence in solving both unimodal and multimodal functions. In addition, the DECCWOA is combined with KELM and feature selection (DECCWOA-KELM-FS) to achieve efficient talent stability intelligence prediction for universities or colleges in Wenzhou. The results show that the performance of the proposed model outperforms other comparative algorithms. This study proposes a DECCWOA optimizer and constructs an intelligent prediction of talent stability system. The designed system can be used as a reliable method of predicting talent mobility in higher education. Full article

(This article belongs to the Special Issue Advanced Machine Learning Applications in Big Data Analytics)

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20 pages, 7532 KiB

Open AccessArticle

Design of an Adaptive Distributed Drive Control Strategy for a Wheel-Side Rear-Drive Electric Bus

by Huipeng Chen, Weiyang Wang, Shaopeng Zhu, Sen Chen, Jian Gao, Rougang Zhou and Wei Wei

Electronics 2022, 11(24), 4223; https://doi.org/10.3390/electronics11244223 - 18 Dec 2022

Viewed by 1148

Abstract

A wheel motor simplifies the chassis structure of an electric bus, greatly improving its response speed and controllability. How to improve the lateral stability of the vehicle under complex and changeable driving conditions is a major problem in the motion control of electric [...] Read more.

A wheel motor simplifies the chassis structure of an electric bus, greatly improving its response speed and controllability. How to improve the lateral stability of the vehicle under complex and changeable driving conditions is a major problem in the motion control of electric buses. This study proposed an adaptive distributed drive control strategy for a rear-wheel drive electric bus. An adaptive fuzzy controller was designed to obtain the additional yaw moment of the vehicle and then combined with a rule distribution method to modify the steering characteristics of the vehicle to obtain the optimal driving torque distribution. Hardware-in-the-loop test results showed that under adaptive fuzzy control, the yaw rate deviations under low- and high-speed conditions were reduced from 18% and 42% without control to 10% and 23% with control, respectively. Under sine wave conditions, the deviation of the yaw rate and the vehicle’s sideslip angle were reduced from 83% and 852% without control to 12% and 15% with control, respectively. It was verified that the electric bus with adaptive fuzzy control could maintain good vehicle stability at full speed. Full article

(This article belongs to the Special Issue Fault Diagnosis and Control Technology of Electric Vehicle)

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11 pages, 2516 KiB

Open AccessArticle

A Substation Fire Early Warning Scheme Based on Multi-Information Fusion

by Junjie Miao, Bingyu Li, Xuhao Du and Haobin Wang

Electronics 2022, 11(24), 4222; https://doi.org/10.3390/electronics11244222 - 18 Dec 2022

Cited by 4 | Viewed by 1769

Abstract

In view of the substation fire early warning using a single information sensor monitoring, it is easy to make mistakes and omissions. Taking the cable in substation as the research object, a multi-information fusion fire prediction model based on back propagation neural network [...] Read more.

In view of the substation fire early warning using a single information sensor monitoring, it is easy to make mistakes and omissions. Taking the cable in substation as the research object, a multi-information fusion fire prediction model based on back propagation neural network (BPNN) and fuzzy set theory is proposed. Firstly, the BPNN model is trained by using the existing data. Secondly, the artificial fish swarm algorithm (AFSA) is used to optimize the BPNN, which speeds up convergence speed of the model and improves the accuracy of prediction. The fuzzy set theory is applied to fuse the predicted fire probability to obtain the optimal fire prevention and control decision. Finally, the fire protection measures are taken according to the fire decision. The experimental show that the average absolute errors of no fire, smoldering and open fire decreased by 26.06%, 38.5% and 43.13% respectively. The model has higher prediction accuracy, can reasonably output different levels of fire alarm signals, establish substation fire warning and prevention and control system, and provide reference for future substation fire and other disasters warning and prevention and control. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications)

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11 pages, 1414 KiB

Open AccessArticle

Hybrid Beamforming for Multi-User Millimeter-Wave Heterogeneous Networks

by Zhannan Li and Tao Chen

Electronics 2022, 11(24), 4221; https://doi.org/10.3390/electronics11244221 - 18 Dec 2022

Cited by 1 | Viewed by 1144

Abstract

Millimeter-wave (mmWave) communications are a critical technique with next-generation network characteristics such as ultra-dense small cells can meet the skyrocketing demand for mobile data. Hybrid precoding, which combines analog and digital processing to provide both spatial diversity gains and beamforming, is commonly studied [...] Read more.

Millimeter-wave (mmWave) communications are a critical technique with next-generation network characteristics such as ultra-dense small cells can meet the skyrocketing demand for mobile data. Hybrid precoding, which combines analog and digital processing to provide both spatial diversity gains and beamforming, is commonly studied for mmWave communications to lower the power and cost consumption of radio frequency (RF) networks. However, the combination of ultra-dense small cells and ever-increasing data traffic results in massive interference. In this paper, we propose a minimum mean square error (MMSE)-based hybrid beamforming strategy for downlink mmWave MIMO two-tier heterogeneous networks (HetNets). The analog beamforming is generalized by an orthogonal matching pursuit technique. The analog beamforming problem is formulated as a sparse signal recovery problem. An MMSE-based digital beamforming algorithm is proposed to minimize the sum MSE of the user-intended data streams so that the inter- and intra-tier interferences are mitigated iteratively. The simulation results demonstrate the advantageous performance of the proposed hybrid beamforming schemes under different cellular cooperation and data transmission scenarios when hardware constraints are taken into account. Full article

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24 pages, 8742 KiB

Open AccessArticle

Integral Windup Resetting Enhancement for Sliding Mode Control of Chemical Processes with Longtime Delay

by Alvaro Javier Prado, Marco Herrera, Xavier Dominguez, Jose Torres and Oscar Camacho

Electronics 2022, 11(24), 4220; https://doi.org/10.3390/electronics11244220 - 18 Dec 2022

Cited by 4 | Viewed by 1924

Abstract

The effects of the windup phenomenon impact the performance of integral controllers commonly found in industrial processes. In particular, windup issues are critical for controlling variable and longtime delayed systems, as they may not be timely corrected by the tracking error accumulation and [...] Read more.

The effects of the windup phenomenon impact the performance of integral controllers commonly found in industrial processes. In particular, windup issues are critical for controlling variable and longtime delayed systems, as they may not be timely corrected by the tracking error accumulation and saturation of the actuators. This work introduces two anti-windup control algorithms for a sliding mode control (SMC) framework to promptly reset the integral control action in the discontinuous mode without inhibiting the robustness of the overall control system against disturbances. The proposed algorithms are intended to anticipate and steer the tracking error toward the origin region of the sliding surface based on an anti-saturation logistic function and a robust compensation action fed by system output variations. Experimental results show the effectiveness of the proposed algorithms when they are applied to two chemical processes, i.e., (i) a Variable Height Mixing Tank (VHMT) and (ii) Continuous Stirred Tank Reactor (CSTR) with a variable longtime delay. The control performance of the proposed anti-windup approaches has been assessed under different reference and disturbance changes, exhibiting that the tracking control performance in the presence of disturbances is enhanced up to 24.35% in terms of the Integral Square Error (ISE) and up to 88.7% regarding the Integral Time Square Error (ITSE). Finally, the results of the proposed methodology demonstrated that the excess of cumulative energy by the actuator saturation could reduce the process resources and also extend the actuator’s lifetime span. Full article

(This article belongs to the Special Issue Sliding Mode Control in Dynamic Systems)

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13 pages, 5824 KiB

Open AccessArticle

Convolutional Network Research for Defect Identification of Productor Appearance Surface

by Xu Xie and Xizhong Shen

Electronics 2022, 11(24), 4218; https://doi.org/10.3390/electronics11244218 - 18 Dec 2022

Cited by 2 | Viewed by 1355

Abstract

The accurate and rapid identification of surface defects is an important element of product appearance quality evaluation, and the application of deep learning for surface defect recognition is an ongoing hot topic. In this paper, a lightweight KD-EG-RepVGG network based on structural reparameterization [...] Read more.

The accurate and rapid identification of surface defects is an important element of product appearance quality evaluation, and the application of deep learning for surface defect recognition is an ongoing hot topic. In this paper, a lightweight KD-EG-RepVGG network based on structural reparameterization is designed for the identification of surface defects on strip steel as an example. In order to improve the stability and accuracy in the recognition of strip steel surface defects, an efficient attention network was introduced into the network, and then a Gaussian error linear activation function was applied in order to prevent the neurons from being set to zero during neural network training, leaving neuron parameters without being updated. Finally, knowledge distillation is used to transfer the knowledge of the RepVGG-A0 network to give the lightweight model better accuracy and generalization capability. The outcomes of the experiments indicate that the model has a computational and parametric volume of 22.3 M and 0.14 M, respectively, in the inference phase, a defect recognition accuracy of 99.44% on the test set, and a single image detection speed of 2.4 ms, making it more suitable for deployment in real engineering environments. Full article

(This article belongs to the Special Issue Advances in Human-Machine Interaction, Artificial Intelligence, and Robotics)

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21 pages, 3820 KiB

Open AccessArticle

Design of a Wheel-Side Rear-Drive Distributed Electric Bus Control Strategy Based on Self-Correcting Fuzzy Control

by Wenhua Luo, Huipeng Chen, Shaopeng Zhu, Sen Chen, Jian Gao, Weiyang Wang and Rougang Zhou

Electronics 2022, 11(24), 4219; https://doi.org/10.3390/electronics11244219 - 17 Dec 2022

Cited by 2 | Viewed by 1353

Abstract

A suitable and effective control strategy is a prerequisite for achieving the stable driving of a distributed drive electric bus. In order to effectively utilize the advantage of the independent controllability of each rear wheel, this paper designs and compares two direct transverse [...] Read more.

A suitable and effective control strategy is a prerequisite for achieving the stable driving of a distributed drive electric bus. In order to effectively utilize the advantage of the independent controllability of each rear wheel, this paper designs and compares two direct transverse moment control strategies of sliding mode control and self-correcting fuzzy control and distributes the drive torque in combination with the vehicle steering torque constraint. Moreover, based on the established seven-degrees-of-freedom vehicle model, the simulation was verified in the MATLAB/Simulink and TruckSim co-simulation platforms. The simulation results show that, compared with the sliding mode control, the self-correcting fuzzy control strategy can reduce the maximum sideslip angle deviation by 19%, 6% and 9.7%, respectively, under the double shift line condition, the high-speed small steering angle step condition and the sinusoidal line shift condition and can more effectively reduce the vehicle lateral acceleration and improve the vehicle yaw rate tracking ability, significantly improving the lateral stability of the vehicle. Full article

(This article belongs to the Special Issue Fault Diagnosis and Control Technology of Electric Vehicle)

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Graphical abstract

23 pages, 3934 KiB

Open AccessArticle

Broadband Modeling and Simulation Strategy for Conducted Emissions of Power Electronic Systems Up to 400 MHz

by Christian Riener, Herbert Hackl, Jan Hansen, Andreas Barchanski, Thomas Bauernfeind, Amin Pak and Bernhard Auinger

Electronics 2022, 11(24), 4217; https://doi.org/10.3390/electronics11244217 - 17 Dec 2022

Cited by 5 | Viewed by 2615

Abstract

Energy efficiency is becoming one of the most important topics in electronics. Among others, wide band-gap semiconductors can raise efficiency and lead to shrinking volumes in power conversion systems. As different markets have regulations that require different designs, it is necessary to cope [...] Read more.

Energy efficiency is becoming one of the most important topics in electronics. Among others, wide band-gap semiconductors can raise efficiency and lead to shrinking volumes in power conversion systems. As different markets have regulations that require different designs, it is necessary to cope with a large variety of similar designs. By using effective modeling and simulation strategies, the efforts of building these variants can be diminished, and re-designs can be avoided. In this paper, we present a universally valid way to come to reasonable simulation results for conducted emissions of a power electronic system in the frequency range from 150 kHz up to 400 MHz. After giving an overview of the state-of-the-art, the authors show how to implement and set up a simulation environment for a gallium-nitride (GaN) power converter. It shows how to differentiate between important and not that important components for Electromagnetic Compatibility (EMC), how to model these components, the printed circuit board, the load, and the setup, including the Line Impedance Stabilization Networks (LISNs), etc. Multiport S-parameter strategies as well as vector fitting methods are employed. Computational costs are kept low, and all simulations are verified with measurements; thus, this model is valid up to 400 MHz. Full article

(This article belongs to the Special Issue Electromagnetic Interference, Compatibility and Applications)

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14 pages, 639 KiB

Open AccessArticle

A Gate-Level Information Leakage Detection Framework of Sequential Circuit Using Z3

by Qizhi Zhang, Liang Liu, Yidong Yuan, Zhe Zhang, Jiaji He, Ya Gao, Yao Li, Xiaolong Guo and Yiqiang Zhao

Electronics 2022, 11(24), 4216; https://doi.org/10.3390/electronics11244216 - 16 Dec 2022

Viewed by 1580

Abstract

Hardware intellectual property (IP) cores from untrusted vendors are widely used, raising security concerns for system designers. Although formal methods provide powerful solutions for detecting malicious behaviors in hardware, the participation of manual work prevents the methods from reaching practical applications. For example, [...] Read more.

Hardware intellectual property (IP) cores from untrusted vendors are widely used, raising security concerns for system designers. Although formal methods provide powerful solutions for detecting malicious behaviors in hardware, the participation of manual work prevents the methods from reaching practical applications. For example, Information Flow Tracking (IFT) represents a powerful approach to preventing leakage of sensitive information. However, existing IFT solutions either introduce hardware overheads or lack practical automatic working procedures, especially for hardware sequential logic. To alleviate these challenges, we propose a framework that fully automates information leakage detection at the gate level of hardware. This framework introduces Z3, an SMT solver, to automatically check the violation of confidentiality. On the other hand, an automatic tool is developed to remove the manual workload further. In this tool, the gate level hardware is converted to the formal model firstly, and the integrity of the model is assessed. Along with the model converting step, the property for leakage detection is generated as well. The proposed solution is tested on 25 gate-level netlist benchmarks, where sequential designs are included to validate the effectiveness. As a result, Trojans leaking information from circuit outputs can be automatically detected. The measured time consumption of the entire working procedure validates the efficiency of the proposed approach. Full article

(This article belongs to the Section Microelectronics)

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23 pages, 5779 KiB

Open AccessArticle

Simulation Study on Variable Pressure Margin Energy Recovery of Electric Loader Actuator

by Hongyun Mu, Yanlei Luo, Yu Luo and Lunjun Chen

Electronics 2022, 11(24), 4215; https://doi.org/10.3390/electronics11244215 - 16 Dec 2022

Cited by 2 | Viewed by 1431

Abstract

The conventional electric loader uses a motor instead of an engine, which aligns with the current energy-saving and emission-reduction trend. However, the motor’s speed control performance and overload capacity are under-utilized, and the actuator suffers from the potential energy waste problem of the [...] Read more.

The conventional electric loader uses a motor instead of an engine, which aligns with the current energy-saving and emission-reduction trend. However, the motor’s speed control performance and overload capacity are under-utilized, and the actuator suffers from the potential energy waste problem of the boom arm. This study proposes a variable pressure margin energy recovery system for the electric loader actuator. It uses a combination of a permanent magnet synchronous motor (PMSM) and a quantitative pump. It can achieve variable pressure margin control and energy recovery through the pressure feedback closed-loop control. AMESim is used to build the simulation model based on the system control strategy, actuator, supercapacitor, and PMSM mathematical mode. Under typical working conditions, the simulation study is conducted on a 50-type wheel loader to obtain cylinder displacement, system energy recovery, and energy-saving performance. The simulation results show that the system is feasible and can effectively reduce energy consumption. Its energy recovery efficiency is 65.4%. The PMSM energy supply is reduced by 28.6% with the variable pressure margin control. It has high energy-saving performance, and the energy-saving efficiency is 38.5%. It provides a reference for research on energy-saving systems for electric construction machinery. Full article

(This article belongs to the Special Issue Fault Diagnosis and Intelligent Control Applications in Fluid Power System)

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10 pages, 4043 KiB

Open AccessArticle

A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings

by Xiangliang Li, Yijie Tang, Peng Zhao, Shichang Chen, Kuiwen Xu and Gaofeng Wang

Electronics 2022, 11(24), 4214; https://doi.org/10.3390/electronics11244214 - 16 Dec 2022

Viewed by 1207

Abstract

A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dummy metal fillings (DMFs) is proposed. By comparing different activation functions and loss functions, a deep neural network for DMF modeling is built using a smooth maximum unit activation [...] Read more.

A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dummy metal fillings (DMFs) is proposed. By comparing different activation functions and loss functions, a deep neural network for DMF modeling is built using a smooth maximum unit activation function and log-cosh loss function. The parasitic capacitive effect of DMFs is quickly and accurately extracted though this model, and the effective permittivity can be obtained subsequently. An on-chip inductor containing DMFs with different filling densities is analyzed using this proposed method and compared with the electromagnetic simulation of entire structures. The results validate the accuracy and efficiency of this proposed method. Full article

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28 pages, 11463 KiB

Open AccessArticle

A Direct Single-Phase to Three-Phase AC/AC Power Converter

by Shuvra Prokash Biswas, Md. Shihab Uddin, Md. Rabiul Islam, Sudipto Mondal and Joysree Nath

Electronics 2022, 11(24), 4213; https://doi.org/10.3390/electronics11244213 - 16 Dec 2022

Cited by 1 | Viewed by 4696

Abstract

The traditional DC-link indirect AC/AC power converters (AC/DC/AC converters) employ two-stage power conversion, which increases the circuit complexity along with gate driving challenges, placing an excessive burden on the processor while implementing complex switching modulation techniques and leads to power conversion losses due [...] Read more.

The traditional DC-link indirect AC/AC power converters (AC/DC/AC converters) employ two-stage power conversion, which increases the circuit complexity along with gate driving challenges, placing an excessive burden on the processor while implementing complex switching modulation techniques and leads to power conversion losses due to the use of a large amount of controlled power semiconductor switches. On the contrary, the traditional direct AC/AC voltage controllers, as well as frequency changers, suffer from high total harmonic distortion (THD) problems. In this paper, a new single-phase to three-phase AC/AC step-down power converter is proposed, which utilizes a multi-linking transformer and bilateral triode thyristors (TRIACs) as power semiconductor switches. The proposed direct AC/AC power converter employs single-stage power conversion, which mitigates the complexity of two-stage DC-link indirect AC/AC converters and traditional single-stage AC/AC frequency changers. Instead of using high-frequency pulse width modulated gate driving signals, line frequency gate pulses are used to trigger the TRIACs of the proposed AC/AC converter, which not only aids in reducing the power loss of the converter but also mitigates the cost and complexity of gate driver circuits. The proposed AC/AC converter reduces the THD of the output voltage significantly as compared to traditional direct AC/AC frequency changers. The performance of the proposed AC/AC converter is validated against RL and induction motor load in terms of overall THD and individual harmonic components through MATLAB/Simulink environment. A reduced-scale laboratory prototype is built and tested to evaluate the performance of the proposed AC/AC power converter. The experimental and simulation outcomes reveal the feasibility and excellent features of the proposed single-phase to three-phase AC/AC converter topology. Full article

(This article belongs to the Special Issue Single-Stage DC-AC Power Conversion Systems)

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19 pages, 4852 KiB

Open AccessArticle

A Cooperative Control Strategy for a Hydraulic Regenerative Braking System Based on Chassis Domain Control

by Ning Li, Junping Jiang, Fulu Sun, Mingrui Ye, Xiaobin Ning and Pengzhan Chen

Electronics 2022, 11(24), 4212; https://doi.org/10.3390/electronics11244212 - 16 Dec 2022

Cited by 1 | Viewed by 1423

Abstract

In order to solve the problems of wheel locking and loss of vehicle control due to understeering or oversteering during the braking energy-recovery process of the hydraulic regenerative braking system (HRBS), aiming at the characteristics of chassis domain control that can realize coordinated [...] Read more.

In order to solve the problems of wheel locking and loss of vehicle control due to understeering or oversteering during the braking energy-recovery process of the hydraulic regenerative braking system (HRBS), aiming at the characteristics of chassis domain control that can realize coordinated work among various chassis systems, a cooperative control strategy of HRBS based on chassis domain control was proposed. Firstly, a HRBS test bench was built, and the accuracy of the simulation model was verified by comparing it with the test. Next, the proposed cooperative control strategy was designed, which coordinates the wheel anti-lock actuation system (WAAS) to adjust the wheel cylinder pressure to solve the wheel locking problem of HRBS in the process of braking energy recovery and coordinate the vehicle anti-loss control actuation system (VACAS) to generate a yaw compensation moment to solve the vehicle loss of the control problem of HRBS in the process of braking energy recovery by detecting the wheel slip ratio, yaw rate and sideslip angle. Finally, the established control strategy was verified through the co-simulation of Carsim and Matlab software, and the results showed that the control strategy proposed in this paper could not only avoid wheel locking and loss of vehicle control during turning braking on low-adhesion roads, but also improve the energy-recovery efficiency by 29.64% compared with a vehicle that only controls the slip ratio. Full article

(This article belongs to the Special Issue Advances in Optimization and Control of Electronic Devices for Renewable and Clean Energy Systems and Applications)

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20 pages, 4606 KiB

Open AccessArticle

YOLO-RFF: An Industrial Defect Detection Method Based on Expanded Field of Feeling and Feature Fusion

by Gang Li, Shilong Zhao, Mingle Zhou, Min Li, Rui Shao, Zekai Zhang and Delong Han

Electronics 2022, 11(24), 4211; https://doi.org/10.3390/electronics11244211 - 16 Dec 2022

Cited by 7 | Viewed by 2052

Abstract

Aiming at the problems of low efficiency, high false detection rate, and poor real-time performance of current industrial defect detection methods, this paper proposes an industrial defect detection method based on an expanded perceptual field and feature fusion for practical industrial applications. First, [...] Read more.

Aiming at the problems of low efficiency, high false detection rate, and poor real-time performance of current industrial defect detection methods, this paper proposes an industrial defect detection method based on an expanded perceptual field and feature fusion for practical industrial applications. First, to improve the real-time performance of the network, the original network structure is enhanced by using depth-separable convolution to reduce the computation while ensuring the detection accuracy, and the critical information extraction from the feature map is enhanced by using MECA (More Efficient Channel Attention) attention to the detection network. To reduce the loss of small target detail information caused by the pooling operation, the ASPF (Atrous Spatial Pyramid Fast) module is constructed using dilate convolution with different void rates to extract more contextual information. Secondly, a new feature fusion method is proposed to fuse more detailed information by introducing a shallower feature map and using a dense multiscale weighting method to improve detection accuracy. Finally, in the model optimization process, the K-means++ algorithm is used to reconstruct the prediction frame to speed up the model’s convergence and verify the effectiveness of the combination of the Mish activation function and the SIoU loss function. The NEU-DET steel dataset and PCB dataset is used to test the effectiveness of the proposed model, and compared to the original YOLOv5s, our method in terms of mAP metrics by 6.5% and 1.4%, and in F1 by 5.74% and 1.33%, enabling fast detection of industrial surface defects to meet the needs of real industry. Full article

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20 pages, 3088 KiB

Open AccessArticle

Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video

by Esakky Selvi, Malaiyalathan Adimoolam, Govindharaju Karthi, Kandasamy Thinakaran, Nagaiah Mohanan Balamurugan, Raju Kannadasan, Chitapong Wechtaisong and Arfat Ahmad Khan

Electronics 2022, 11(24), 4210; https://doi.org/10.3390/electronics11244210 - 16 Dec 2022

Cited by 5 | Viewed by 4615

Abstract

Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious [...] Read more.

Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations. Full article

(This article belongs to the Section Computer Science & Engineering)

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