Pub Date : 2024-09-15DOI: 10.3390/electronics13183667
Ying Ma, Hongjie Lin, Wei Chen, Weijie Chen, Qianting Wang
With the significant annual increase in market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising. To achieve efficient and low-energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processes hourly time series data on temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict the historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time series features. After augmentation, the root mean square error (RMSE) of the temperature prediction decreased by 27.3%, and the RMSE of the humidity prediction decreased by 32.8%, significantly improving the accuracy of the multistep predictions and providing substantial industrial application value.
{"title":"Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering","authors":"Ying Ma, Hongjie Lin, Wei Chen, Weijie Chen, Qianting Wang","doi":"10.3390/electronics13183667","DOIUrl":"https://doi.org/10.3390/electronics13183667","url":null,"abstract":"With the significant annual increase in market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising. To achieve efficient and low-energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processes hourly time series data on temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict the historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time series features. After augmentation, the root mean square error (RMSE) of the temperature prediction decreased by 27.3%, and the RMSE of the humidity prediction decreased by 32.8%, significantly improving the accuracy of the multistep predictions and providing substantial industrial application value.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-15DOI: 10.3390/electronics13183668
Padmanabhan Balasubramanian, Douglas L. Maskell
We introduce a new carry look-ahead adder (NCLA) architecture that employs non-uniform-size carry look-ahead adder (CLA) modules, in contrast to the conventional CLA (CCLA) architecture, which utilizes uniform-size CLA modules. We adopted two strategies for the implementation of the NCLA. Our novel approach enables improved speed and energy efficiency for the NCLA architecture compared to the CCLA architecture without incurring significant area and power penalties. Various adders were implemented to demonstrate the advantages of NCLA, ranging from the slower ripple carry adder to the widely regarded fastest parallel-prefix adder viz. the Kogge–Stone adder, and their performance metrics were compared. The 32-bit addition was used as an example, with the adders implemented using a semi-custom design method and a 28 nm CMOS standard cell library. Synthesis results show that the NCLA architecture offers substantial improvements in design metrics compared to its high-speed counterparts. Specifically, an NCLA achieved (i) a 14.7% reduction in delay and a 13.4% reduction in energy compared to an optimized CCLA, while occupying slightly more area; (ii) a 42.1% reduction in delay and a 58.3% reduction in energy compared to a conditional sum adder, with an 8% increase in the area; (iii) a 14.7% reduction in delay and a 37.7% reduction in energy compared to an optimized carry select adder, while requiring 37% less area; and (iv) a 20.2% reduction in energy and a 55.4% reduction in area compared to the Kogge–Stone adder.
{"title":"A New Carry Look-Ahead Adder Architecture Optimized for Speed and Energy","authors":"Padmanabhan Balasubramanian, Douglas L. Maskell","doi":"10.3390/electronics13183668","DOIUrl":"https://doi.org/10.3390/electronics13183668","url":null,"abstract":"We introduce a new carry look-ahead adder (NCLA) architecture that employs non-uniform-size carry look-ahead adder (CLA) modules, in contrast to the conventional CLA (CCLA) architecture, which utilizes uniform-size CLA modules. We adopted two strategies for the implementation of the NCLA. Our novel approach enables improved speed and energy efficiency for the NCLA architecture compared to the CCLA architecture without incurring significant area and power penalties. Various adders were implemented to demonstrate the advantages of NCLA, ranging from the slower ripple carry adder to the widely regarded fastest parallel-prefix adder viz. the Kogge–Stone adder, and their performance metrics were compared. The 32-bit addition was used as an example, with the adders implemented using a semi-custom design method and a 28 nm CMOS standard cell library. Synthesis results show that the NCLA architecture offers substantial improvements in design metrics compared to its high-speed counterparts. Specifically, an NCLA achieved (i) a 14.7% reduction in delay and a 13.4% reduction in energy compared to an optimized CCLA, while occupying slightly more area; (ii) a 42.1% reduction in delay and a 58.3% reduction in energy compared to a conditional sum adder, with an 8% increase in the area; (iii) a 14.7% reduction in delay and a 37.7% reduction in energy compared to an optimized carry select adder, while requiring 37% less area; and (iv) a 20.2% reduction in energy and a 55.4% reduction in area compared to the Kogge–Stone adder.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183658
Ruru Liu, Liping Xu, Tao Zeng, Tao Luo, Mengfei Wang, Yuming Zhou, Chunpeng Chen, Shuo Zhao
PM2.5 pollution poses an important threat to the atmospheric environment and human health. To precisely forecast PM2.5 concentration, this study presents an innovative combined model: EMD-SE-GWO-VMD-ZCR-CNN-LSTM. First, empirical mode decomposition (EMD) is used to decompose PM2.5, and sample entropy (SE) is used to assess the subsequence complexity. Secondly, the hyperparameters of variational mode decomposition (VMD) are optimized by Gray Wolf Optimization (GWO) algorithm, and the complex subsequences are decomposed twice. Next, the sequences are divided into high-frequency and low-frequency parts by using the zero crossing rate (ZCR); the high-frequency sequences are predicted by a convolutional neural network (CNN), and the low-frequency sequences are predicted by a long short-term memory network (LSTM). Finally, the predicted values of the high-frequency and low-frequency sequences are reconstructed to obtain the final results. The experiment was conducted based on the data of 1009A, 1010A, and 1011A from three air quality monitoring stations in the Beijing area. The results indicate that the R2 value of the designed model increased by 2.63%, 0.59%, and 1.88% on average in the three air quality monitoring stations, respectively, compared with the other single model and the mixed model, which verified the significant advantages of the proposed model.
{"title":"A Novel Short-Term PM2.5 Forecasting Approach Using Secondary Decomposition and a Hybrid Deep Learning Model","authors":"Ruru Liu, Liping Xu, Tao Zeng, Tao Luo, Mengfei Wang, Yuming Zhou, Chunpeng Chen, Shuo Zhao","doi":"10.3390/electronics13183658","DOIUrl":"https://doi.org/10.3390/electronics13183658","url":null,"abstract":"PM2.5 pollution poses an important threat to the atmospheric environment and human health. To precisely forecast PM2.5 concentration, this study presents an innovative combined model: EMD-SE-GWO-VMD-ZCR-CNN-LSTM. First, empirical mode decomposition (EMD) is used to decompose PM2.5, and sample entropy (SE) is used to assess the subsequence complexity. Secondly, the hyperparameters of variational mode decomposition (VMD) are optimized by Gray Wolf Optimization (GWO) algorithm, and the complex subsequences are decomposed twice. Next, the sequences are divided into high-frequency and low-frequency parts by using the zero crossing rate (ZCR); the high-frequency sequences are predicted by a convolutional neural network (CNN), and the low-frequency sequences are predicted by a long short-term memory network (LSTM). Finally, the predicted values of the high-frequency and low-frequency sequences are reconstructed to obtain the final results. The experiment was conducted based on the data of 1009A, 1010A, and 1011A from three air quality monitoring stations in the Beijing area. The results indicate that the R2 value of the designed model increased by 2.63%, 0.59%, and 1.88% on average in the three air quality monitoring stations, respectively, compared with the other single model and the mixed model, which verified the significant advantages of the proposed model.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183661
Ahmad Esmaeil Abbasi, Agostino Marcello Mangini, Maria Pia Fanti
Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss.
{"title":"Object and Pedestrian Detection on Road in Foggy Weather Conditions by Hyperparameterized YOLOv8 Model","authors":"Ahmad Esmaeil Abbasi, Agostino Marcello Mangini, Maria Pia Fanti","doi":"10.3390/electronics13183661","DOIUrl":"https://doi.org/10.3390/electronics13183661","url":null,"abstract":"Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183659
Ivan Vajs, Srđan Brkić, Predrag Ivaniš, Dejan Drajic
The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.
{"title":"Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks","authors":"Ivan Vajs, Srđan Brkić, Predrag Ivaniš, Dejan Drajic","doi":"10.3390/electronics13183659","DOIUrl":"https://doi.org/10.3390/electronics13183659","url":null,"abstract":"The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183662
Krzysztof Górecki, Paweł Górecki
This article proposes an electrothermal averaged model of a half-bridge DC–DC converter containing a power module. This kind of model enables the computation of characteristics of DC–DC converters using DC analysis. The form of the elaborated model is presented. Both the electrical and thermal properties of the analyzed DC–DC converter are included in this model. This is the first averaged electrothermal model of a DC–DC converter which makes it possible to compute the junction temperature of all the semiconductor devices and magnetic components. The accuracy of the model was experimentally verified in a wide range of switching frequencies and output currents. Particularly, the influence of mutual thermal couplings between the transistors contained in the considered module on the characteristics of the converter and the junction temperature of the transistors is analyzed.
{"title":"Electrothermal Averaged Model of a Half-Bridge DC–DC Converter Containing a Power Module","authors":"Krzysztof Górecki, Paweł Górecki","doi":"10.3390/electronics13183662","DOIUrl":"https://doi.org/10.3390/electronics13183662","url":null,"abstract":"This article proposes an electrothermal averaged model of a half-bridge DC–DC converter containing a power module. This kind of model enables the computation of characteristics of DC–DC converters using DC analysis. The form of the elaborated model is presented. Both the electrical and thermal properties of the analyzed DC–DC converter are included in this model. This is the first averaged electrothermal model of a DC–DC converter which makes it possible to compute the junction temperature of all the semiconductor devices and magnetic components. The accuracy of the model was experimentally verified in a wide range of switching frequencies and output currents. Particularly, the influence of mutual thermal couplings between the transistors contained in the considered module on the characteristics of the converter and the junction temperature of the transistors is analyzed.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183657
Li Liu, Jinhui Wang, Shijuan Chen, Zongmei Li
Speech-driven lip synchronization is a crucial technology for generating realistic facial animations, with broad application prospects in virtual reality, education, training, and other fields. However, existing methods still face challenges in generating high-fidelity facial animations, particularly in addressing lip jitter and facial motion instability issues in continuous frame sequences. This study presents VividWav2Lip, an improved speech-driven lip synchronization model. Our model incorporates three key innovations: a cross-attention mechanism for enhanced audio-visual feature fusion, an optimized network structure with Squeeze-and-Excitation (SE) residual blocks, and the integration of the CodeFormer facial restoration network for post-processing. Extensive experiments were conducted on a diverse dataset comprising multiple languages and facial types. Quantitative evaluations demonstrate that VividWav2Lip outperforms the baseline Wav2Lip model by 5% in lip sync accuracy and image generation quality, with even more significant improvements over other mainstream methods. In subjective assessments, 85% of participants perceived VividWav2Lip-generated animations as more realistic compared to those produced by existing techniques. Additional experiments reveal our model’s robust cross-lingual performance, maintaining consistent quality even for languages not included in the training set. This study not only advances the theoretical foundations of audio-driven lip synchronization but also offers a practical solution for high-fidelity, multilingual dynamic face generation, with potential applications spanning virtual assistants, video dubbing, and personalized content creation.
{"title":"VividWav2Lip: High-Fidelity Facial Animation Generation Based on Speech-Driven Lip Synchronization","authors":"Li Liu, Jinhui Wang, Shijuan Chen, Zongmei Li","doi":"10.3390/electronics13183657","DOIUrl":"https://doi.org/10.3390/electronics13183657","url":null,"abstract":"Speech-driven lip synchronization is a crucial technology for generating realistic facial animations, with broad application prospects in virtual reality, education, training, and other fields. However, existing methods still face challenges in generating high-fidelity facial animations, particularly in addressing lip jitter and facial motion instability issues in continuous frame sequences. This study presents VividWav2Lip, an improved speech-driven lip synchronization model. Our model incorporates three key innovations: a cross-attention mechanism for enhanced audio-visual feature fusion, an optimized network structure with Squeeze-and-Excitation (SE) residual blocks, and the integration of the CodeFormer facial restoration network for post-processing. Extensive experiments were conducted on a diverse dataset comprising multiple languages and facial types. Quantitative evaluations demonstrate that VividWav2Lip outperforms the baseline Wav2Lip model by 5% in lip sync accuracy and image generation quality, with even more significant improvements over other mainstream methods. In subjective assessments, 85% of participants perceived VividWav2Lip-generated animations as more realistic compared to those produced by existing techniques. Additional experiments reveal our model’s robust cross-lingual performance, maintaining consistent quality even for languages not included in the training set. This study not only advances the theoretical foundations of audio-driven lip synchronization but also offers a practical solution for high-fidelity, multilingual dynamic face generation, with potential applications spanning virtual assistants, video dubbing, and personalized content creation.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183666
Congyu Zhang Sprenger, Juan Antonio Corrales Ramón, Norman Urs Baier
Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level.
{"title":"ORPP—An Ontology for Skill-Based Robotic Process Planning in Agile Manufacturing","authors":"Congyu Zhang Sprenger, Juan Antonio Corrales Ramón, Norman Urs Baier","doi":"10.3390/electronics13183666","DOIUrl":"https://doi.org/10.3390/electronics13183666","url":null,"abstract":"Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183660
Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka, Piotr Borkowski, Adrianna Łobodzińska
Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex environments with minimal human intervention. This review critically examines the potential dangers associated with the increasing reliance on AI in AV navigation. It explores the current state of AI technologies, highlighting key techniques such as machine learning and neural networks, and identifies significant challenges including technical limitations, safety risks, and ethical and legal concerns. Real-world incidents, such as Uber’s fatal accident and Tesla’s crash, underscore the potential risks and the need for robust safety measures. Future threats, such as sophisticated cyber-attacks, are also considered. The review emphasizes the importance of improving AI systems, implementing comprehensive regulatory frameworks, and enhancing public awareness to mitigate these risks. By addressing these challenges, we can pave the way for the safe and reliable deployment of autonomous vehicles, ensuring their benefits can be fully realized.
{"title":"A Critical AI View on Autonomous Vehicle Navigation: The Growing Danger","authors":"Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka, Piotr Borkowski, Adrianna Łobodzińska","doi":"10.3390/electronics13183660","DOIUrl":"https://doi.org/10.3390/electronics13183660","url":null,"abstract":"Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex environments with minimal human intervention. This review critically examines the potential dangers associated with the increasing reliance on AI in AV navigation. It explores the current state of AI technologies, highlighting key techniques such as machine learning and neural networks, and identifies significant challenges including technical limitations, safety risks, and ethical and legal concerns. Real-world incidents, such as Uber’s fatal accident and Tesla’s crash, underscore the potential risks and the need for robust safety measures. Future threats, such as sophisticated cyber-attacks, are also considered. The review emphasizes the importance of improving AI systems, implementing comprehensive regulatory frameworks, and enhancing public awareness to mitigate these risks. By addressing these challenges, we can pave the way for the safe and reliable deployment of autonomous vehicles, ensuring their benefits can be fully realized.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.
{"title":"Multi-Feature Extraction and Selection Method to Diagnose Burn Depth from Burn Images","authors":"Xizhe Zhang, Qi Zhang, Peixian Li, Jie You, Jingzhang Sun, Jianhang Zhou","doi":"10.3390/electronics13183665","DOIUrl":"https://doi.org/10.3390/electronics13183665","url":null,"abstract":"Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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