When high-impedance faults(HIFs)occur in resonant grounded distribution networks,the current that flows is extremely weak,and the noise interference caused by the distribution network operation and the sampling error ...When high-impedance faults(HIFs)occur in resonant grounded distribution networks,the current that flows is extremely weak,and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics.Consequently,locating a fault section with high sensitivity is difficult.Unlike existing technologies,this study presents a novel fault feature identification framework that addresses this issue.The framework includes three key steps:(1)utilizing the variable mode decomposition(VMD)method to denoise the fault transient zero-sequence current(TZSC);(2)employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding(t-SNE)to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space;and(3)classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location.Numerical simulations and field testing confirm that the proposed method accurately detects the fault location,even under the influence of strong noise interference.展开更多
Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers,providers and the workers. Requisition for Edge Computing based item...Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers,providers and the workers. Requisition for Edge Computing based items havebeen increasing tremendously. Apart from the advantages it holds, there remainlots of objections and restrictions, which hinders it from accomplishing the needof consumers all around the world. Some of the limitations are constraints oncomputing and hardware, functions and accessibility, remote administration andconnectivity. There is also a backlog in security due to its inability to create a trustbetween devices involved in encryption and decryption. This is because securityof data greatly depends upon faster encryption and decryption in order to transferit. In addition, its devices are considerably exposed to side channel attacks,including Power Analysis attacks that are capable of overturning the process.Constrained space and the ability of it is one of the most challenging tasks. Toprevail over from this issue we are proposing a Cryptographic LightweightEncryption Algorithm with Dimensionality Reduction in Edge Computing. Thet-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. Thethree dimensional image data obtained from the system, which are connected withit, are dimensionally reduced, and then lightweight encryption algorithm isemployed. Hence, the security backlog can be solved effectively using thismethod.展开更多
The gantry crane system is a crucial equipment for loading and unloading containers at the shore site.The existing trend of crane technology is in the transition from in-site operators to remote operators outside the ...The gantry crane system is a crucial equipment for loading and unloading containers at the shore site.The existing trend of crane technology is in the transition from in-site operators to remote operators outside the cargo handling site,which will comply with all safety regulations,including conditional crane monitoring.However,remote control introduces certain drawbacks in machine maintenance,as no on-site operators can provide real-time feedback on abnormalities.Therefore,this study proposes a failure detection system that uses vibratory sensors installed on machines to monitor and provide early warnings for various failures.For faulty event identification,the Fast Fourier Transform is carried out for the raw vibratory signals,and several frequency bands are classified by using t-SNE to evaluate the significance among clusters.The adjustment of hyperparameters of the t-SNE will alter the quality of the classification of different events,and this process is conventionally operated in accordance with users’experience.In this study,we propose a novel rating approach to automatically tune the hyperparameters of t-SNE to evaluate the separation and cluster compactness of the t-SNE results.Then,the results of clusters served as input features for training the faulty event detection model,and the detection model shows more than 95%accuracy in identifying different abnormal conditions of the main hosting motor.展开更多
Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsens...Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.展开更多
Accurate traffic state estimations(TSEs)within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies.Traditional TSE methods often assume hom...Accurate traffic state estimations(TSEs)within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies.Traditional TSE methods often assume homogeneous traffic,where all vehicles are considered identical,which does not accurately reflect the complexities of real traffic conditions that often exhibit heterogeneous characteristics.In this study,we address the limitations of conventional models by introducing a novel TSE model designed for precise estimations of heterogeneous traffic flows.We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements:basic traffic parameters,heterogeneous vehicle speeds,heterogeneous vehicle flows,and mixed flow rates.This system aids in capturing the unique traffic characteristics of different vehicle types.Our proposed high-dimensional fuzzy TSE model,termed HiF-TSE,integrates three main processes:feature selection,which eliminates redundant traffic features using Spearman correlation coefficients;dimension reduction,which utilizes the T-distributed stochastic neighbor embedding machine learning algorithm to reduce high-dimensional traffic feature data;and FCM clustering,which applies the fuzzy C-means algorithm to classify the simplified data into distinct clusters.The HiF-TSE model significantly reduces computational demands and enhances efficiency in TSE processing.We validate our model through a real-world case study,demonstrating its ability to adapt to variations in vehicle type compositions within heterogeneous traffic and accurately represent the actual traffic state.展开更多
In order to improve the light welfare of Nile tilapia in aquaculture,the influence of hunger level on light spectrum preference of Nile tilapia was explored in this study.The whole experiment was based on the emptying...In order to improve the light welfare of Nile tilapia in aquaculture,the influence of hunger level on light spectrum preference of Nile tilapia was explored in this study.The whole experiment was based on the emptying of the gastrointestinal contents,and carried out under the controlled laboratory conditions.The light spectrum preference was assessed by counting the head location of fish in each experimental tank,which containing seven compartments(i.e.,red,blue,white,yellow,black,green and public area).t-Distributed Stochastic Neighbor Embedding(t-SNE)was adopted to visualize the hunger level-based dynamic preference on light spectrum in two-dimensional space.According to the clustering results,significant differences in light spectrum preferences of Nile tilapia,under the different hunger levels,were indicated.In addition,the average visit frequency in green compartment was significantly lower than that in other color compartments throughout the whole experiment,and the total visit frequency in red compartment was relatively higher during the whole experiment.展开更多
基金supported in part by the Science and Technology Program of State Grid Corporation of China(No.5108-202218280A-2-75-XG)the Fundamental Research Funds for the Central Universities(No.B200203129)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(No.KYCX20_0432)。
文摘When high-impedance faults(HIFs)occur in resonant grounded distribution networks,the current that flows is extremely weak,and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics.Consequently,locating a fault section with high sensitivity is difficult.Unlike existing technologies,this study presents a novel fault feature identification framework that addresses this issue.The framework includes three key steps:(1)utilizing the variable mode decomposition(VMD)method to denoise the fault transient zero-sequence current(TZSC);(2)employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding(t-SNE)to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space;and(3)classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location.Numerical simulations and field testing confirm that the proposed method accurately detects the fault location,even under the influence of strong noise interference.
文摘Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers,providers and the workers. Requisition for Edge Computing based items havebeen increasing tremendously. Apart from the advantages it holds, there remainlots of objections and restrictions, which hinders it from accomplishing the needof consumers all around the world. Some of the limitations are constraints oncomputing and hardware, functions and accessibility, remote administration andconnectivity. There is also a backlog in security due to its inability to create a trustbetween devices involved in encryption and decryption. This is because securityof data greatly depends upon faster encryption and decryption in order to transferit. In addition, its devices are considerably exposed to side channel attacks,including Power Analysis attacks that are capable of overturning the process.Constrained space and the ability of it is one of the most challenging tasks. Toprevail over from this issue we are proposing a Cryptographic LightweightEncryption Algorithm with Dimensionality Reduction in Edge Computing. Thet-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. Thethree dimensional image data obtained from the system, which are connected withit, are dimensionally reduced, and then lightweight encryption algorithm isemployed. Hence, the security backlog can be solved effectively using thismethod.
基金funded by National Science and Technology Council,Taiwan,grant numbers 112-2622-E-992-022 and 112-2221-E-992-086.
文摘The gantry crane system is a crucial equipment for loading and unloading containers at the shore site.The existing trend of crane technology is in the transition from in-site operators to remote operators outside the cargo handling site,which will comply with all safety regulations,including conditional crane monitoring.However,remote control introduces certain drawbacks in machine maintenance,as no on-site operators can provide real-time feedback on abnormalities.Therefore,this study proposes a failure detection system that uses vibratory sensors installed on machines to monitor and provide early warnings for various failures.For faulty event identification,the Fast Fourier Transform is carried out for the raw vibratory signals,and several frequency bands are classified by using t-SNE to evaluate the significance among clusters.The adjustment of hyperparameters of the t-SNE will alter the quality of the classification of different events,and this process is conventionally operated in accordance with users’experience.In this study,we propose a novel rating approach to automatically tune the hyperparameters of t-SNE to evaluate the separation and cluster compactness of the t-SNE results.Then,the results of clusters served as input features for training the faulty event detection model,and the detection model shows more than 95%accuracy in identifying different abnormal conditions of the main hosting motor.
基金supported by a grant (2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation (NRF)funded by the Ministry of Education,Republic of Korea.
文摘Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.
基金supported by the National Key R&D Program of China(Grant No.2023YFB4302702)the Fundamental Research Funds for the Central Universities(Grant No.2023JKF02ZK08).
文摘Accurate traffic state estimations(TSEs)within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies.Traditional TSE methods often assume homogeneous traffic,where all vehicles are considered identical,which does not accurately reflect the complexities of real traffic conditions that often exhibit heterogeneous characteristics.In this study,we address the limitations of conventional models by introducing a novel TSE model designed for precise estimations of heterogeneous traffic flows.We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements:basic traffic parameters,heterogeneous vehicle speeds,heterogeneous vehicle flows,and mixed flow rates.This system aids in capturing the unique traffic characteristics of different vehicle types.Our proposed high-dimensional fuzzy TSE model,termed HiF-TSE,integrates three main processes:feature selection,which eliminates redundant traffic features using Spearman correlation coefficients;dimension reduction,which utilizes the T-distributed stochastic neighbor embedding machine learning algorithm to reduce high-dimensional traffic feature data;and FCM clustering,which applies the fuzzy C-means algorithm to classify the simplified data into distinct clusters.The HiF-TSE model significantly reduces computational demands and enhances efficiency in TSE processing.We validate our model through a real-world case study,demonstrating its ability to adapt to variations in vehicle type compositions within heterogeneous traffic and accurately represent the actual traffic state.
基金supported by the National Key R&D Program of China(Grant No.2017YFB0404000)the Key R&D Program of Ningxia Hui Autonomous Region(Grant No.2018BBF02009)Open Fund of Yunnan Province Key Laboratory of Food Processing and Safety Control(Grant No.K16-507106-007)。
文摘In order to improve the light welfare of Nile tilapia in aquaculture,the influence of hunger level on light spectrum preference of Nile tilapia was explored in this study.The whole experiment was based on the emptying of the gastrointestinal contents,and carried out under the controlled laboratory conditions.The light spectrum preference was assessed by counting the head location of fish in each experimental tank,which containing seven compartments(i.e.,red,blue,white,yellow,black,green and public area).t-Distributed Stochastic Neighbor Embedding(t-SNE)was adopted to visualize the hunger level-based dynamic preference on light spectrum in two-dimensional space.According to the clustering results,significant differences in light spectrum preferences of Nile tilapia,under the different hunger levels,were indicated.In addition,the average visit frequency in green compartment was significantly lower than that in other color compartments throughout the whole experiment,and the total visit frequency in red compartment was relatively higher during the whole experiment.
