Research

25 pages, 3400 KiB

Open AccessArticle

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

by Małgorzata Kuźnar and Augustyn Lorenc

Appl. Sci. 2023, 13(22), 12439; https://doi.org/10.3390/app132212439 - 17 Nov 2023

Viewed by 686

In the field of transport, and more precisely in supply chains, if any of the vehicle components are damaged, it may cause delays in the delivery of goods. Eliminating undesirable damage to the means of transport through the possibility of predicting technical conditions [...] Read more.

In the field of transport, and more precisely in supply chains, if any of the vehicle components are damaged, it may cause delays in the delivery of goods. Eliminating undesirable damage to the means of transport through the possibility of predicting technical conditions and a state of failure may increase the reliability of the entire supply chain. From the aspect of sustainability, the issue of reducing the number of failures also makes it possible to reduce supply chain disturbances, to reduce costs associated with delays, and to reduce the materials needed for the repair of the means of transport, since, in this case, the costs only relate to the replaced elements before their damage. Thus, it is impossible for more serious damage to occur. Often, failure of one item causes damage to others, which generates unnecessary costs and increases the amount of waste due to the number of damaged items. This article provides an author’s method of technical condition prediction; by applying the method, it would be possible to develop recommended maintenance activities for key elements related to the safety and reliability of transport. The combination of at least two artificial intelligence methods allows us to achieve very good prediction results thanks to the possibility of individual adjustments of weights between the methods used. Such predictive maintenance methods can be successfully used to ensure sustainable development in supply chains. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

20 pages, 17489 KiB

Open AccessArticle

ODIN112–AI-Assisted Emergency Services in Romania

by Dan Ungureanu, Stefan-Adrian Toma, Ion-Dorinel Filip, Bogdan-Costel Mocanu, Iulian Aciobăniței, Bogdan Marghescu, Titus Balan, Mihai Dascalu, Ion Bica and Florin Pop

Appl. Sci. 2023, 13(1), 639; https://doi.org/10.3390/app13010639 - 03 Jan 2023

Cited by 3 | Viewed by 1761

Abstract

The evolution of Natural Language Processing technologies transformed them into viable choices for various accessibility features and for facilitating interactions between humans and computers. A subset of them consists of speech processing systems, such as Automatic Speech Recognition, which became more accurate and [...] Read more.

The evolution of Natural Language Processing technologies transformed them into viable choices for various accessibility features and for facilitating interactions between humans and computers. A subset of them consists of speech processing systems, such as Automatic Speech Recognition, which became more accurate and more popular as a result. In this article, we introduce an architecture built around various speech processing systems to enhance Romanian emergency services. Our system is designed to help the operator evaluate various situations with the end goal of reducing the response times of emergency services. We also release the largest high-quality speech dataset of more than 150 h for Romanian. Our architecture includes an Automatic Speech Recognition model to transcribe calls automatically and augment the operator’s notes, as well as a Speech Recognition model to classify the caller’s emotions. We achieve state-of-the-art results on both tasks, while our demonstrator is designed to be integrated with the Romanian emergency system. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

13 pages, 1379 KiB

Open AccessArticle

An Assistant System for Translation Flipped Classroom

by Jingxian Chen, Feng Li, Xuejun Zhang and Bin Li

Appl. Sci. 2023, 13(1), 327; https://doi.org/10.3390/app13010327 - 27 Dec 2022

Viewed by 1401

Abstract

To achieve the goal of training translators that meet the current social needs, the innovation of translation teaching methods is necessary. Studies have proven that students in flipped classrooms (FCs) have greater performance than students in traditional classrooms. However, the preparation time for [...] Read more.

To achieve the goal of training translators that meet the current social needs, the innovation of translation teaching methods is necessary. Studies have proven that students in flipped classrooms (FCs) have greater performance than students in traditional classrooms. However, the preparation time for FCs could be three times higher than that of traditional classrooms, which leads to the reluctance of teachers to conduct FCs. Machine translation (MT) is believed to be a useful tool to improve the translation efficiency of human translators. However, in practice, teachers found that many students cannot work with MT effectively. To solve the above problems, this paper designs a Translation Flipped Classroom Assistance System (TFCAS) based on cloud computing and MT. A parameter is proposed to measure students’ ability to translate evaluation. TFCAS has reduced the burden of teachers in the FC mode and helped students become accustomed to working with MT. Application data stored in the MySQL database, such as sentence pairs, will be used to optimize the neural machine translation model we developed for the system. The system makes MT and the training of translators support each other’s sustainable development and conforms to the trend of deepening teaching reform. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

10 pages, 414 KiB

Open AccessArticle

A Study on Customized Prediction of Daily Illness Risk Using Medical and Meteorological Data

by Minji Kim, Jiwon Jang, Seungjin Jeon and Sekyoung Youm

Appl. Sci. 2022, 12(12), 6060; https://doi.org/10.3390/app12126060 - 15 Jun 2022

Cited by 1 | Viewed by 1250

Abstract

This study selected the most common illnesses in children and older adults and aimed to provide a customized degree of daily risk for each illness based on patient data for specific regions and illnesses. Sample medical data of one million people provided by [...] Read more.

This study selected the most common illnesses in children and older adults and aimed to provide a customized degree of daily risk for each illness based on patient data for specific regions and illnesses. Sample medical data of one million people provided by the National Health Insurance Corporation and information regarding the meteorological environment and atmosphere from the Korea Meteorological Administration and a public data portal using application programing interface were collected. Learning and predictions were carried out with machine learning. Models with high R² were selected and tuned to determine the optimal hyperparameter for predicting the degree of daily risk of an illness. Illnesses with an R² value greater than 0.65 were considered significant. For children, these consisted of acute bronchitis, the common cold, rhinitis and tonsillitis, and middle ear inflammation. For older adults, they consisted of high blood pressure and heart disease, the common cold, esophageal inflammation and gastritis, acute bronchitis, eczema and dermatitis, and chronic bronchitis. This study provides the degree of daily risk for the most common illnesses in each age group. Furthermore, the results of this study are expected to raise awareness of illnesses that occur in certain climates and to help prevent them. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

15 pages, 6899 KiB

Open AccessArticle

Dementia Prediction Support Model Using Regression Analysis and Image Style Transfer

by Ji-Won Baek and Kyungyong Chung

Appl. Sci. 2022, 12(7), 3536; https://doi.org/10.3390/app12073536 - 30 Mar 2022

Viewed by 1730

Abstract

It is difficult to provide information to patients because the cause of Alzheimer’s disease is not accurately identified. Therefore, there are difficulties in management and prevention. However, if one can manage the basic influencing factors, one can maintain a healthy brain. Therefore, this [...] Read more.

It is difficult to provide information to patients because the cause of Alzheimer’s disease is not accurately identified. Therefore, there are difficulties in management and prevention. However, if one can manage the basic influencing factors, one can maintain a healthy brain. Therefore, this study proposes a prediction support model for dementia based on regression analysis using an image style transfer. The proposed method collects images of factors extracted from text information about Alzheimer’s disease, images of a normal brain, and images of a brain with Alzheimer’s disease to provide precautions for the factors affecting Alzheimer’s disease. Accordingly, it transforms the brain’s style by transferring image features of the factors affecting it onto the normal brain image. The transformed results allow for discovery of the factors that affect Alzheimer’s disease, compared to the brain with Alzheimer’s disease, and allow the medical team or the patients themselves to prevent and manage it. In addition, performance evaluation compares the similarities in style transmission results for factors affecting it according to each stage of the dementia condition. A comparison of similarities shows that a brain with cerebral hemorrhage and the brain of an alcoholic have the highest similarities to all stages of dementia. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

15 pages, 1196 KiB

Open AccessArticle

Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices

by Jisu Kwon and Daejin Park

Appl. Sci. 2021, 11(22), 11073; https://doi.org/10.3390/app112211073 - 22 Nov 2021

Cited by 13 | Viewed by 3865

Abstract

On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are important when running TinyML applications. In this paper, we [...] Read more.

On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are important when running TinyML applications. In this paper, we propose a structure in which the part that preprocesses externally input data in the TinyML application is distributed to the hardware. These processes are performed using software in the microcontroller unit of an edge device. Furthermore, resistor–transistor logic, which perform not only windowing using the Hann function, but also acquire audio raw data, is added to the inter-integrated circuit sound module that collects audio data in the voice-recognition application. As a result of the experiment, the windowing function was excluded from the TinyML application of the embedded board. When the length of the hardware-implemented Hann window is 80 and the quantization degree is

2^{- 5}

, the exclusion causes a decrease in the execution time of the front-end function and energy consumption by 8.06% and 3.27%, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

14 pages, 1035 KiB

Open AccessFeature PaperArticle

Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices

by Semyoung Oh and Daejin Park

Appl. Sci. 2021, 11(4), 1608; https://doi.org/10.3390/app11041608 - 10 Feb 2021

Cited by 2 | Viewed by 1610

Abstract

Collaborative beamforming (CB) enables uplink transmission in a wireless sensor network (WSN) composed of sensors (nodes) and far-away access points (APs). It can also be applied to the case where the sensors are equipped with beam-switching structures (BSSs). However, as the antenna arrays [...] Read more.

Collaborative beamforming (CB) enables uplink transmission in a wireless sensor network (WSN) composed of sensors (nodes) and far-away access points (APs). It can also be applied to the case where the sensors are equipped with beam-switching structures (BSSs). However, as the antenna arrays of the BSSs are randomly headed due to the irregular mounting surface, some sensors form beams that do not illuminate a desired AP and waste their limited energy. Therefore, to resolve this problem, it is required to switch the beams toward the desired AP. While an exhaustive search can provide the globally optimal combination, a greedy search (GS) is utilized to solve this optimization problem efficiently. Simulation and experimental results verify that under certain conditions the proposed algorithm can drive the sensors to switch their beams properly and increase the received signal-to-noise ratio (SNR) significantly with low computational complexity and energy consumption. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

15 pages, 5671 KiB

Open AccessArticle

A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

by SeonWoo Lee, HyeonTak Yu, HoJun Yang, InSeo Song, JungMu Choi, JaeHeung Yang, GangMin Lim, Kyu-Sung Kim, ByeongKeun Choi and JangWoo Kwon

Appl. Sci. 2021, 11(4), 1564; https://doi.org/10.3390/app11041564 - 09 Feb 2021

Cited by 13 | Viewed by 3496

Abstract

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures [...] Read more.

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D. Full article

(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Artificial Intelligence for Sustainable Services, Applications and Education

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI