Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227215
Mayur Hulke, A. Jafari, Appolinaire C. Etoundi
It has been estimated that approximately 7000 people undergo limb amputation in the UK every year [1]. This issue is even more significant in the US, where over 150,000 people undergo lower limb extremity amputations, and this number is predicted to increase by 47% in 2050 [2]. This traumatic and risky procedure leads to lifelong disability that has a direct impacts a patients mobility [4]. As a result, this creates a economic burden on the healthcare system and the economy as a whole [4]. Despite the ever-increasing number of amputees, the fitting of prosthetic sockets remains artisan in nature and often fails to satisfactorily address the stresses experienced between the socket and the RL (RL). This leads to patient discomfort and an average of 25% of users abandoning their prosthesis (Fully Equipped). In this paper, we present a process for monitoring the internal area of a prosthetic socket for above-knee amputees through the use of an electronic circuit incorporating pressure and temperature sensors. This experiment is an extension of the previous experiment where Finite Element Analysis (FEA) has been applied to the same case study and compared with patient experience to analyze the internal socket conditions in the context of discomfort areas. This experiment also demonstrates how commercially available sensors could be integrated within a socket to determine the stresses experienced and hence validate further the FEA studies. Ultimately, the objective of this experiment is to identify the correlation between the collected sensor data from the socket, the discomfort areas, and the verbal feedback on the pain experienced by the amputee. As far as the authors are concerned, this is the first time this type of experiment is being conducted in both outdoor and indoor conditions where real-time sensor data is being collected while an amputee is performing six different activities from high impact level to low impact level.
{"title":"Investigation into the Customization of a Transfemoral Prosthetic Socket to Minimize Discomfort for Residual Limb (RL) Volume Change","authors":"Mayur Hulke, A. Jafari, Appolinaire C. Etoundi","doi":"10.1109/ICSSE58758.2023.10227215","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227215","url":null,"abstract":"It has been estimated that approximately 7000 people undergo limb amputation in the UK every year [1]. This issue is even more significant in the US, where over 150,000 people undergo lower limb extremity amputations, and this number is predicted to increase by 47% in 2050 [2]. This traumatic and risky procedure leads to lifelong disability that has a direct impacts a patients mobility [4]. As a result, this creates a economic burden on the healthcare system and the economy as a whole [4]. Despite the ever-increasing number of amputees, the fitting of prosthetic sockets remains artisan in nature and often fails to satisfactorily address the stresses experienced between the socket and the RL (RL). This leads to patient discomfort and an average of 25% of users abandoning their prosthesis (Fully Equipped). In this paper, we present a process for monitoring the internal area of a prosthetic socket for above-knee amputees through the use of an electronic circuit incorporating pressure and temperature sensors. This experiment is an extension of the previous experiment where Finite Element Analysis (FEA) has been applied to the same case study and compared with patient experience to analyze the internal socket conditions in the context of discomfort areas. This experiment also demonstrates how commercially available sensors could be integrated within a socket to determine the stresses experienced and hence validate further the FEA studies. Ultimately, the objective of this experiment is to identify the correlation between the collected sensor data from the socket, the discomfort areas, and the verbal feedback on the pain experienced by the amputee. As far as the authors are concerned, this is the first time this type of experiment is being conducted in both outdoor and indoor conditions where real-time sensor data is being collected while an amputee is performing six different activities from high impact level to low impact level.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227192
Van-Dung Hoang, Thanh-an Michel Pham
Over a decade, deep learning methods using convolutional neural network (CNN) architecture have achieved breakthroughs in the precision criterion, which compared to the traditional machine learning methods. However, those approaches still faced some limitations of processing time and precision when they are applied to large samples and hard datasets. Recently, some new methods based on the transformer learning approach have been applied to image processing. This direction approach has illustrated the promising results in the terms of accuracy and computational time. This paper presents a new approach, which combines a pre-processing technique of image filtering and vision transformer (ViT) learning for the problem of plant insect pests and diseases recognition. The proposed solution involves some stages: neural network-based image filtering, then passes results through a ViT module to extract feature map, and then fed to multiple head network for classification. The proposed method applies image filtering pre-processing to highlight features before passing results to the ViT processing stage instead of using ViT from raw input images. Furthermore, element-wise multiplication in the frequency domain reduces processing time instead of using convolutional processing in the spatial domain. Experimental results demonstrate that applying filtering preprocessing does not significantly increase the number of learning parameters and training time compared to using ViT directly and it leverages to improve accuracy to compare to well-known models based on deep CNN. The research results also illustrated that the ViT solution and the proposed method are reached more accurate than CNN-based deep learning methods.
{"title":"Fusion of ViT Technique and Image Filtering in Deep Learning for Plant Pests and Diseases Recognition","authors":"Van-Dung Hoang, Thanh-an Michel Pham","doi":"10.1109/ICSSE58758.2023.10227192","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227192","url":null,"abstract":"Over a decade, deep learning methods using convolutional neural network (CNN) architecture have achieved breakthroughs in the precision criterion, which compared to the traditional machine learning methods. However, those approaches still faced some limitations of processing time and precision when they are applied to large samples and hard datasets. Recently, some new methods based on the transformer learning approach have been applied to image processing. This direction approach has illustrated the promising results in the terms of accuracy and computational time. This paper presents a new approach, which combines a pre-processing technique of image filtering and vision transformer (ViT) learning for the problem of plant insect pests and diseases recognition. The proposed solution involves some stages: neural network-based image filtering, then passes results through a ViT module to extract feature map, and then fed to multiple head network for classification. The proposed method applies image filtering pre-processing to highlight features before passing results to the ViT processing stage instead of using ViT from raw input images. Furthermore, element-wise multiplication in the frequency domain reduces processing time instead of using convolutional processing in the spatial domain. Experimental results demonstrate that applying filtering preprocessing does not significantly increase the number of learning parameters and training time compared to using ViT directly and it leverages to improve accuracy to compare to well-known models based on deep CNN. The research results also illustrated that the ViT solution and the proposed method are reached more accurate than CNN-based deep learning methods.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116427736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227229
Thanh Hoang Le Hai, Luan Le Dinh, Dat Ngo Tien, Dat Bui Huu Tien, N. Thoai
Exploiting current High Performance Computing (HPC) systems is a critical task for resolving urgent worldwide problems. However, existing scheduling heuristics such as First Come First Served (FCFS) have limitations in dealing with the increasing complexity of computing systems and the dynamic nature of application workloads. Reinforcement learning (RL) has emerged as a promising approach to designing HPC schedulers that can learn to adapt to dynamic system configurations and workload conditions. However, existing RL-based schedulers often lack the ability to incorporate important identity features of jobs and do not consider user behavior.To address these limitations, we propose an improvement to the latest Deep Reinforcement Learning Agent for Scheduling (DRAS) model, called Improved Reinforcement Learning Scheduler (IRLS). The IRLS model incorporates additional identity features in the state definition to recognize similarities between tasks from the same source and utilizes an empirical approach to perform job runtime prediction. Our experiments demonstrate that by using the IRLS model, we can significantly improve the performance of real-life HPC workloads, with improvements of up to 15.4% compared to the original DRAS model and 35.7% compared to FCFS.
{"title":"IRLS: An Improved Reinforcement Learning Scheduler for High Performance Computing Systems","authors":"Thanh Hoang Le Hai, Luan Le Dinh, Dat Ngo Tien, Dat Bui Huu Tien, N. Thoai","doi":"10.1109/ICSSE58758.2023.10227229","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227229","url":null,"abstract":"Exploiting current High Performance Computing (HPC) systems is a critical task for resolving urgent worldwide problems. However, existing scheduling heuristics such as First Come First Served (FCFS) have limitations in dealing with the increasing complexity of computing systems and the dynamic nature of application workloads. Reinforcement learning (RL) has emerged as a promising approach to designing HPC schedulers that can learn to adapt to dynamic system configurations and workload conditions. However, existing RL-based schedulers often lack the ability to incorporate important identity features of jobs and do not consider user behavior.To address these limitations, we propose an improvement to the latest Deep Reinforcement Learning Agent for Scheduling (DRAS) model, called Improved Reinforcement Learning Scheduler (IRLS). The IRLS model incorporates additional identity features in the state definition to recognize similarities between tasks from the same source and utilizes an empirical approach to perform job runtime prediction. Our experiments demonstrate that by using the IRLS model, we can significantly improve the performance of real-life HPC workloads, with improvements of up to 15.4% compared to the original DRAS model and 35.7% compared to FCFS.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116661452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227217
Hai-Binh Le, Thai Dinh Kim, Manh-Hung Ha, Anh Long Quang Tran, Duy-Thuc Nguyen, X. Dinh
Surgica1 tool detection involves identifying the position and type of instruments in an image. This is one of the significant issues in automatic video analysis that can aid in evaluating the surgical skills of doctors or automating the process of controlling the viewing angle of the endoscopic camera. This paper presents a robust method for detecting surgical tools using the YOLOv8 model. We trained four different versions of YOLOv8, evaluated their effectiveness, and compared them with previous models. The experimental results indicate that the YOLOv8 models have an average mAP50 greater than 95.6% across all classes, and are significantly better than some previous research findings.
{"title":"Robust Surgical Tool Detection in Laparoscopic Surgery using YOLOv8 Model","authors":"Hai-Binh Le, Thai Dinh Kim, Manh-Hung Ha, Anh Long Quang Tran, Duy-Thuc Nguyen, X. Dinh","doi":"10.1109/ICSSE58758.2023.10227217","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227217","url":null,"abstract":"Surgica1 tool detection involves identifying the position and type of instruments in an image. This is one of the significant issues in automatic video analysis that can aid in evaluating the surgical skills of doctors or automating the process of controlling the viewing angle of the endoscopic camera. This paper presents a robust method for detecting surgical tools using the YOLOv8 model. We trained four different versions of YOLOv8, evaluated their effectiveness, and compared them with previous models. The experimental results indicate that the YOLOv8 models have an average mAP50 greater than 95.6% across all classes, and are significantly better than some previous research findings.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227078
Nghe-Nhan Truong, M. Le, Truong-Dong Do, Le-Anh Tran, T. Nguyen, Hoang-Hon Trinh
Fire is considered one of the most serious threats to human lives which results in a high probability of fatalities. Those severe consequences stem from the heavy smoke emitted from a fire that mostly restricts the visibility of escaping victims and rescuing squad. In such hazardous circumstances, the use of a vision-based human detection system is able to improve the ability to save more lives. To this end, a thermal and infrared imaging fusion strategy based on multiple cameras for human detection in low-visibility scenarios caused by smoke is proposed in this paper. By processing with multiple cameras, vital information can be gathered to generate more useful features for human detection. Firstly, the cameras are calibrated using a Light Heating Chessboard. Afterward, the features extracted from the input images are merged prior to being passed through a lightweight deep neural network to perform the human detection task. The experiments conducted on an NVIDIA Jetson Nano computer demonstrated that the proposed method can process with reasonable speed and can achieve favorable performance with a mAP@0.5 of 95%.
{"title":"Efficient Infrared and Thermal Imaging Fusion Approach for Real-time Human Detection in Heavy Smoke Scenarios","authors":"Nghe-Nhan Truong, M. Le, Truong-Dong Do, Le-Anh Tran, T. Nguyen, Hoang-Hon Trinh","doi":"10.1109/ICSSE58758.2023.10227078","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227078","url":null,"abstract":"Fire is considered one of the most serious threats to human lives which results in a high probability of fatalities. Those severe consequences stem from the heavy smoke emitted from a fire that mostly restricts the visibility of escaping victims and rescuing squad. In such hazardous circumstances, the use of a vision-based human detection system is able to improve the ability to save more lives. To this end, a thermal and infrared imaging fusion strategy based on multiple cameras for human detection in low-visibility scenarios caused by smoke is proposed in this paper. By processing with multiple cameras, vital information can be gathered to generate more useful features for human detection. Firstly, the cameras are calibrated using a Light Heating Chessboard. Afterward, the features extracted from the input images are merged prior to being passed through a lightweight deep neural network to perform the human detection task. The experiments conducted on an NVIDIA Jetson Nano computer demonstrated that the proposed method can process with reasonable speed and can achieve favorable performance with a mAP@0.5 of 95%.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129707261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227150
Chi-Thang Phan-Tan, Thuong Ngo-Phi, N. Nguyen-Quang
Induction heating (IH) is applied to convert electricity into thermal energy with a high frequency (HF) current flowing through an inductor. Litz wires help mitigate the power loss at the inductor winding in HF applications by reducing eddy currents. This work presents a detailed approach to designing a power inductor using Litz wires for IH using an inductor-inductor-capacitor (LLC) resonant tank. In comparison to a single solid or stranded wire with the same requirements, the developed formulas in this paper show that the size of the Litz wire inductor is approximately 15% smaller. The step-by-step design procedure is presented with all required formulas and associated information. The feasibility of the proposed design process is illustrated and verified through an experiment on a 2 kW, 100 kHz LLC IH system.
{"title":"Design Procedure and Implementation of Inductor Using Litz Wires for Induction Heating","authors":"Chi-Thang Phan-Tan, Thuong Ngo-Phi, N. Nguyen-Quang","doi":"10.1109/ICSSE58758.2023.10227150","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227150","url":null,"abstract":"Induction heating (IH) is applied to convert electricity into thermal energy with a high frequency (HF) current flowing through an inductor. Litz wires help mitigate the power loss at the inductor winding in HF applications by reducing eddy currents. This work presents a detailed approach to designing a power inductor using Litz wires for IH using an inductor-inductor-capacitor (LLC) resonant tank. In comparison to a single solid or stranded wire with the same requirements, the developed formulas in this paper show that the size of the Litz wire inductor is approximately 15% smaller. The step-by-step design procedure is presented with all required formulas and associated information. The feasibility of the proposed design process is illustrated and verified through an experiment on a 2 kW, 100 kHz LLC IH system.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130133056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227145
Van Thinh Pham, V. Pham, M. Nguyen, Hai-Chau Le
The rise in heart-related diseases has led to a need for proper automatic diagnosis methods to identify irregular heart problems. It has proven to be challenging to promptly and accurately diagnose many complicated and interferential symptom diseases including arrhythmia. Recently, thanks to the evolution of artificial intelligence (AI) and the advance in signal processing, automated arrhythmia detection has become easier and widely applied for physicians and practitioners with machine learning (ML) techniques and the only use of electrocardiograms (ECG). In this paper, we propose an ECG-based machine learning arrhythmia detection approach that exploits R-peak detection and machine learning. Our proposed solution targeting a binary classification of heartbeats employs an efficient R-peak detection that uses a Butterworth bypass filter, Ensemble Empirical Mode Decomposition (EEMD), and Hilbert Transforms (HT) for processing ECG signals, and applies the most effective machine learning algorithm among typical ML algorithms to improve the performance of the arrhythmia diagnosis. In order to select the most suitable one with the highest achievable performance, typical ML algorithms such as BG, BS, KNN, and RF were investigated. A popular public dataset, MIT-BIH Arrhythmia, is used for the numerical experiments. The attained results prove that our developed solution outperforms the notable traditional algorithms and it offers the best performance with an accuracy of 93.4%, a sensitivity of 95.4%, and an F1-score of 96.3%. The high obtained F1-score implies that our solution can overcome the data imbalance to detect arrhythmia correctly and be effective in practical clinical environments.
{"title":"Efficient Electrocardiogram-based Arrhythmia Detection Utilizing R-peaks and Machine Learning","authors":"Van Thinh Pham, V. Pham, M. Nguyen, Hai-Chau Le","doi":"10.1109/ICSSE58758.2023.10227145","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227145","url":null,"abstract":"The rise in heart-related diseases has led to a need for proper automatic diagnosis methods to identify irregular heart problems. It has proven to be challenging to promptly and accurately diagnose many complicated and interferential symptom diseases including arrhythmia. Recently, thanks to the evolution of artificial intelligence (AI) and the advance in signal processing, automated arrhythmia detection has become easier and widely applied for physicians and practitioners with machine learning (ML) techniques and the only use of electrocardiograms (ECG). In this paper, we propose an ECG-based machine learning arrhythmia detection approach that exploits R-peak detection and machine learning. Our proposed solution targeting a binary classification of heartbeats employs an efficient R-peak detection that uses a Butterworth bypass filter, Ensemble Empirical Mode Decomposition (EEMD), and Hilbert Transforms (HT) for processing ECG signals, and applies the most effective machine learning algorithm among typical ML algorithms to improve the performance of the arrhythmia diagnosis. In order to select the most suitable one with the highest achievable performance, typical ML algorithms such as BG, BS, KNN, and RF were investigated. A popular public dataset, MIT-BIH Arrhythmia, is used for the numerical experiments. The attained results prove that our developed solution outperforms the notable traditional algorithms and it offers the best performance with an accuracy of 93.4%, a sensitivity of 95.4%, and an F1-score of 96.3%. The high obtained F1-score implies that our solution can overcome the data imbalance to detect arrhythmia correctly and be effective in practical clinical environments.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"579 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123133316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227193
Thinh Huynh, Cao-Tri Dinh, Young-Bok Kim
This paper investigates the motion control problems of an aerial system powered by water jet propulsion in which the water is conveyed through a flexible hose attached underneath. In this system, the thrust is generated by jetting water out of four nozzles, whose cross-sectional area is much smaller than the inlet, while the necessary torques for fight maneuvers are achieved by rotating these nozzles to direct the respective thrust. The system can be thought of as a tethered drone and its dynamics are described by coupled ordinary–partial differential equations showing the motion interaction of the hose and the system. Based on Lyapunov’s direct method, an observer-based boundary control is designed to achieve the desired flight maneuver of the system while still preserving the stabilization of both the system and the hose. As a result, the uniform ultimate boundedness of the entire control system is achieved, and its performance is verified by simulations.
{"title":"Observer-based Boundary Control of a Water-powered Aerial System","authors":"Thinh Huynh, Cao-Tri Dinh, Young-Bok Kim","doi":"10.1109/ICSSE58758.2023.10227193","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227193","url":null,"abstract":"This paper investigates the motion control problems of an aerial system powered by water jet propulsion in which the water is conveyed through a flexible hose attached underneath. In this system, the thrust is generated by jetting water out of four nozzles, whose cross-sectional area is much smaller than the inlet, while the necessary torques for fight maneuvers are achieved by rotating these nozzles to direct the respective thrust. The system can be thought of as a tethered drone and its dynamics are described by coupled ordinary–partial differential equations showing the motion interaction of the hose and the system. Based on Lyapunov’s direct method, an observer-based boundary control is designed to achieve the desired flight maneuver of the system while still preserving the stabilization of both the system and the hose. As a result, the uniform ultimate boundedness of the entire control system is achieved, and its performance is verified by simulations.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227252
Thanh-Hoan Nguyen, V. Trương, H. Nguyen, D. Truong, Quang-Thai-Dan Nguyen, Thanh-Nhan Nguyen
In the near future, Photovoltaic (PV) network and Electric Vehicle Charging station (EVC) will be deployed in Ho Chi Minh City (HCMC), the use of Cross-phase characteristic will help to reduce the influence of these distributed sources and will improve the imbalance. phase of the current low voltage distribution network. The optimization aims to reduce the loss caused by phase unbalance. Convex optimization model is considered to solve the optimization problem with quadratic constraint and voltage balance equation system (VUF) and phase constraints. Algorithms run according to the above model including OPF, Cross-phase and using unbalanced 3-phase IEEE 33 bus and IEEE 192 bus systems. The results show that using the Cross-phase characteristic significantly reduces phase imbalance.
{"title":"Reduce Phase Unbalance with Cross-phase of PV and EV Chargers, using Convex Optimization on Quadratic Constraint in Distribution Network","authors":"Thanh-Hoan Nguyen, V. Trương, H. Nguyen, D. Truong, Quang-Thai-Dan Nguyen, Thanh-Nhan Nguyen","doi":"10.1109/ICSSE58758.2023.10227252","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227252","url":null,"abstract":"In the near future, Photovoltaic (PV) network and Electric Vehicle Charging station (EVC) will be deployed in Ho Chi Minh City (HCMC), the use of Cross-phase characteristic will help to reduce the influence of these distributed sources and will improve the imbalance. phase of the current low voltage distribution network. The optimization aims to reduce the loss caused by phase unbalance. Convex optimization model is considered to solve the optimization problem with quadratic constraint and voltage balance equation system (VUF) and phase constraints. Algorithms run according to the above model including OPF, Cross-phase and using unbalanced 3-phase IEEE 33 bus and IEEE 192 bus systems. The results show that using the Cross-phase characteristic significantly reduces phase imbalance.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127995665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227163
Mahmood Khalsan, Mu Mu, E. Al-Shamery, Lee Machado, Michael Opoku Agyeman, S. Ajit
Machine learning (ML) methods have a plaid an important role in classification and prediction in most fields. However, analyzing gene expression is remain complex in cancer classification because of the high dimensionality of the provided dataset in gene expression. Consequentially, intersection-based three feature selection methods (ITFS) was developed to select optimal features (genes) that would be used as identifiers for classification and reduce the dimensionality of the available data in gene expression. ITFS has employed three feature selection methods (Mutual Information (MI), F-ClassIf, and Minimum Redundancy Maximum Relevance (mRMR)). Therefore, employing intersection concept that leads to select only the genes that have been selected by the three feature selection techniques. These selected genes would be used as identifiers for the training classifier model. Our study applied the proposed ITFS to six gene expression datasets downloaded from (Microarray and RNAseq tools) for validating the effectiveness of ITFS on classifier methods. The highest average accuracy improvement in the six datasets was when Multilayer Perceptron (MLP) and ITFS employed together compared to employing MLP individually. The proposed ITFS-MLP model has produced classification accuracy between (92% to 100%) for the six datasets and the average accuracy is 96%.
{"title":"Intersection Three Feature Selection and Machine Learning Approaches for Cancer Classification","authors":"Mahmood Khalsan, Mu Mu, E. Al-Shamery, Lee Machado, Michael Opoku Agyeman, S. Ajit","doi":"10.1109/ICSSE58758.2023.10227163","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227163","url":null,"abstract":"Machine learning (ML) methods have a plaid an important role in classification and prediction in most fields. However, analyzing gene expression is remain complex in cancer classification because of the high dimensionality of the provided dataset in gene expression. Consequentially, intersection-based three feature selection methods (ITFS) was developed to select optimal features (genes) that would be used as identifiers for classification and reduce the dimensionality of the available data in gene expression. ITFS has employed three feature selection methods (Mutual Information (MI), F-ClassIf, and Minimum Redundancy Maximum Relevance (mRMR)). Therefore, employing intersection concept that leads to select only the genes that have been selected by the three feature selection techniques. These selected genes would be used as identifiers for the training classifier model. Our study applied the proposed ITFS to six gene expression datasets downloaded from (Microarray and RNAseq tools) for validating the effectiveness of ITFS on classifier methods. The highest average accuracy improvement in the six datasets was when Multilayer Perceptron (MLP) and ITFS employed together compared to employing MLP individually. The proposed ITFS-MLP model has produced classification accuracy between (92% to 100%) for the six datasets and the average accuracy is 96%.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133148071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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