Pub Date : 2024-12-18DOI: 10.1109/ICJECE.2024.3498395
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering","authors":"","doi":"10.1109/ICJECE.2024.3498395","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3498395","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"C2-C2"},"PeriodicalIF":2.1,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/ICJECE.2024.3487893
M. Jajini;N. Shanmuga Vadivoo;Sivasankar Gangatharan
The usage of electric vehicles (EVs) has increased and it leads to additional demand along with existing residential demand and managing it becomes challenging. Further EV charging systems that function during the daytime in multistorey buildings expedite the peak loading. The main objective of this work is to minimize the operating cost of the system and conversion losses. In this work, the microgrid incorporated with a bidirectional converter plays a major role in dc-ac and ac-dc conversion. The photo voltaic (PV) sources support the system with sufficient dc power generation and batteries store the dc power and supply the load in case of insufficiency. By utilizing a genetic algorithm (GA) and appropriate energy management (EM) to charge EVs according to time-of-use tariff patterns, the impact of growing demand on the grid is greatly mitigated. To ease the burden on the grid during peak hours, the interruptible loads are shifted to off-peak times. Other challenges of EV charging such as energy saving, maximum peak demand, voltage instability, and high current drawing issues are rectified and well presented with existing topology. When compared to the standard scheme, the energy savings in the proposed topology are much increased, reaching 33.04%, while the cost reduction is 57.27%.
{"title":"Intelligent Energy Management for Multistorey Building With Photovoltaic-Based Electric Vehicle Charging Infrastructure","authors":"M. Jajini;N. Shanmuga Vadivoo;Sivasankar Gangatharan","doi":"10.1109/ICJECE.2024.3487893","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3487893","url":null,"abstract":"The usage of electric vehicles (EVs) has increased and it leads to additional demand along with existing residential demand and managing it becomes challenging. Further EV charging systems that function during the daytime in multistorey buildings expedite the peak loading. The main objective of this work is to minimize the operating cost of the system and conversion losses. In this work, the microgrid incorporated with a bidirectional converter plays a major role in dc-ac and ac-dc conversion. The photo voltaic (PV) sources support the system with sufficient dc power generation and batteries store the dc power and supply the load in case of insufficiency. By utilizing a genetic algorithm (GA) and appropriate energy management (EM) to charge EVs according to time-of-use tariff patterns, the impact of growing demand on the grid is greatly mitigated. To ease the burden on the grid during peak hours, the interruptible loads are shifted to off-peak times. Other challenges of EV charging such as energy saving, maximum peak demand, voltage instability, and high current drawing issues are rectified and well presented with existing topology. When compared to the standard scheme, the energy savings in the proposed topology are much increased, reaching 33.04%, while the cost reduction is 57.27%.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"250-259"},"PeriodicalIF":2.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810685","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}
The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the healthcare industry has significantly boosted the importance of real-time remote patient monitoring. The Internet of Medical Things (IoMT) systems facilitate the seamless transfer of health records to data centers, allowing medical professionals and caregivers to analyze, process, and access them. This data is often stored in cloud-based systems. Nevertheless, the transmission of data and execution of computations in a cloud environment may lead to delays and affect the efficiency of real-time healthcare services. In addition, the use of edge computing (EC) layers has become prevalent in performing local data processing and storage to reduce service response times for IoMT applications. The main objective of this article is to develop an adaptive EC infrastructure for IoMT systems, with a specific emphasis on maintaining optimal performance for real-time health services. It also designs a model to predict the server resources required to meet service level agreements (SLAs) regarding response time. Simulation results demonstrate that EC significantly improves service response time for real-time IoMT applications. The proposed model can accurately and efficiently predict the computing resources required for medical data services to achieve SLAs under varying workload conditions.
{"title":"An Adaptive Edge Computing Infrastructure for Internet of Medical Things Applications","authors":"Dang Van Anh;Abdellah Chehri;Chu Thi Minh Hue;Tran Duc Tan;Nguyen Minh Quy","doi":"10.1109/ICJECE.2024.3471652","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3471652","url":null,"abstract":"The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the healthcare industry has significantly boosted the importance of real-time remote patient monitoring. The Internet of Medical Things (IoMT) systems facilitate the seamless transfer of health records to data centers, allowing medical professionals and caregivers to analyze, process, and access them. This data is often stored in cloud-based systems. Nevertheless, the transmission of data and execution of computations in a cloud environment may lead to delays and affect the efficiency of real-time healthcare services. In addition, the use of edge computing (EC) layers has become prevalent in performing local data processing and storage to reduce service response times for IoMT applications. The main objective of this article is to develop an adaptive EC infrastructure for IoMT systems, with a specific emphasis on maintaining optimal performance for real-time health services. It also designs a model to predict the server resources required to meet service level agreements (SLAs) regarding response time. Simulation results demonstrate that EC significantly improves service response time for real-time IoMT applications. The proposed model can accurately and efficiently predict the computing resources required for medical data services to achieve SLAs under varying workload conditions.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"242-249"},"PeriodicalIF":2.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810464","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 : 2024-10-31DOI: 10.1109/ICJECE.2024.3475878
Ayaz Ahmad;Shanu Kumar;Jayanta Mukherjee
In this work, the effect of a two-section stub for minimizing gain variation near broadside frequency in a comb-line leaky-wave antenna (LWA) is investigated. Mathematical design conditions for stubs are derived based on the matched input impedance requirement of the unit cell. Based on the derived conditions, two different stubs (Case-I and Case-II) are designed and placed in the middle of the host transmission line (50 Ω delay line) to make comb-line unit cells (UC#1, and UC#2). The open stopband (OSB) suppression is investigated by analyzing the input impedance and the leakage constant of the unit cells. Next, the variation in the gain of the LWAs (LWA#1, and LWA#2) with the proposed stubs is compared with a comb-line LWA with the conventional radiating stub (CRS). The gain variation in LWA#1 is only 0.7 dB as compared to the 2.5 dB gain variation for LWA using CRSs. Moreover, the gain is improved by 2 dB near the broadside for LWA#2 as compared to LWA using CRS. In the entire analysis, the broadside frequency is chosen near 10 GHz.
{"title":"Design of Stubs in a Comb-Line Leaky-Wave Antenna for Minimizing Gain Variation at Broadside","authors":"Ayaz Ahmad;Shanu Kumar;Jayanta Mukherjee","doi":"10.1109/ICJECE.2024.3475878","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3475878","url":null,"abstract":"In this work, the effect of a two-section stub for minimizing gain variation near broadside frequency in a comb-line leaky-wave antenna (LWA) is investigated. Mathematical design conditions for stubs are derived based on the matched input impedance requirement of the unit cell. Based on the derived conditions, two different stubs (Case-I and Case-II) are designed and placed in the middle of the host transmission line (50 Ω delay line) to make comb-line unit cells (UC#1, and UC#2). The open stopband (OSB) suppression is investigated by analyzing the input impedance and the leakage constant of the unit cells. Next, the variation in the gain of the LWAs (LWA#1, and LWA#2) with the proposed stubs is compared with a comb-line LWA with the conventional radiating stub (CRS). The gain variation in LWA#1 is only 0.7 dB as compared to the 2.5 dB gain variation for LWA using CRSs. Moreover, the gain is improved by 2 dB near the broadside for LWA#2 as compared to LWA using CRS. In the entire analysis, the broadside frequency is chosen near 10 GHz.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"233-241"},"PeriodicalIF":2.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810463","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 : 2024-10-31DOI: 10.1109/ICJECE.2024.3469390
N. Sivakumar;S. Charles Raja;Chelladurai Balasundar;M. Geethanjali
Coil alignment plays a vital role in wireless charging systems which affects the transmission power and resonance coupling efficiency in electric vehicle (EV) charging. Also, the cutting-edge controlling model is used to improve the converter operations in the wireless inductive power transfer (IPT) system for EV charging. This work proposes a deer hunting optimized converter control (DHOCC) algorithm for buck dc–dc converter to effectively step down the desired voltage and reduce the system complexity such as misalignments and air gap. The coil’s misalignment and air gaps are changed through the buck dc–dc converter output. This algorithm aligns the coil by changing the ranges of misalignment and air gap to improve coupling efficiency. The EV is placed on its surface to charge the battery. The proposed work is designed in the MATLAB/Simulink platform and the experimental setup validation has been carried out through the laboratory test setup. The simulation output shows the high effective coupling between two coils for an 8 cm air gap with 89.7% power transfer efficiency (PTE) and the experimental output shows an 8 cm air gap with 84.77% of PTE. The obtained result demonstrates the performance of the DHOCC based on a wireless IPT system under less complexity.
{"title":"A Cutting-Edge Deer Hunting Optimized Converter Control (DHOCC) Based Dynamic Wireless IPT System for EV Charging Applications","authors":"N. Sivakumar;S. Charles Raja;Chelladurai Balasundar;M. Geethanjali","doi":"10.1109/ICJECE.2024.3469390","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3469390","url":null,"abstract":"Coil alignment plays a vital role in wireless charging systems which affects the transmission power and resonance coupling efficiency in electric vehicle (EV) charging. Also, the cutting-edge controlling model is used to improve the converter operations in the wireless inductive power transfer (IPT) system for EV charging. This work proposes a deer hunting optimized converter control (DHOCC) algorithm for buck dc–dc converter to effectively step down the desired voltage and reduce the system complexity such as misalignments and air gap. The coil’s misalignment and air gaps are changed through the buck dc–dc converter output. This algorithm aligns the coil by changing the ranges of misalignment and air gap to improve coupling efficiency. The EV is placed on its surface to charge the battery. The proposed work is designed in the MATLAB/Simulink platform and the experimental setup validation has been carried out through the laboratory test setup. The simulation output shows the high effective coupling between two coils for an 8 cm air gap with 89.7% power transfer efficiency (PTE) and the experimental output shows an 8 cm air gap with 84.77% of PTE. The obtained result demonstrates the performance of the DHOCC based on a wireless IPT system under less complexity.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"218-225"},"PeriodicalIF":2.1,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810523","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 : 2024-10-29DOI: 10.1109/ICJECE.2024.3472657
Quan Tian;Ruiyan Cai;Yang Luo
As a key technology for radio monitoring and positioning, direction-of-arrival (DOA) estimation has garnered significant attention and has undergone in-depth research. This article proposes a new subspace-based DOA estimation algorithm based on an adversarial learning network. Considering the impact of the number of antennas in the signal-receiving array on the resulting DOA estimation accuracy, the proposed algorithm takes a covariance matrix corresponding to a small antenna array as the input of the adversarial learning network and reconstructs an extended covariance matrix corresponding to a virtual large antenna array. By introducing subspace technology, the multiple signal classification (MUSIC) algorithm can achieve high-resolution DOA estimation. Therefore, the extended covariance matrix corresponding to the virtual large antenna array is combined with the MUSIC to achieve DOA estimation. Simulated and real-world experimental results demonstrate that compared with conventional subspace-based DOA estimation algorithms, the proposed algorithm achieves significantly improved DOA estimation performance.
{"title":"DOA Estimation Based on an Adversarial Learning Network via Small Antenna Arrays","authors":"Quan Tian;Ruiyan Cai;Yang Luo","doi":"10.1109/ICJECE.2024.3472657","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3472657","url":null,"abstract":"As a key technology for radio monitoring and positioning, direction-of-arrival (DOA) estimation has garnered significant attention and has undergone in-depth research. This article proposes a new subspace-based DOA estimation algorithm based on an adversarial learning network. Considering the impact of the number of antennas in the signal-receiving array on the resulting DOA estimation accuracy, the proposed algorithm takes a covariance matrix corresponding to a small antenna array as the input of the adversarial learning network and reconstructs an extended covariance matrix corresponding to a virtual large antenna array. By introducing subspace technology, the multiple signal classification (MUSIC) algorithm can achieve high-resolution DOA estimation. Therefore, the extended covariance matrix corresponding to the virtual large antenna array is combined with the MUSIC to achieve DOA estimation. Simulated and real-world experimental results demonstrate that compared with conventional subspace-based DOA estimation algorithms, the proposed algorithm achieves significantly improved DOA estimation performance.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"226-232"},"PeriodicalIF":2.1,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810524","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 : 2024-10-24DOI: 10.1109/ICJECE.2024.3429273
Alaor Cervati Neto;Alexandre L. M. Levada;Michel Ferreira Cardia Haddad
The t-distributed stochastic neighbor embedding (t-SNE) consists of a powerful algorithm for visualizing high-dimensional data in a lower dimensional space. It is extensively employed in machine learning (ML) and data analysis, including unsupervised metric learning. In this article, we propose improvements concerning two main aspects of the t-SNE. First, the incorporation of class labels is adopted to increase its suitability for supervised classification. Second, stochastic and geodesic distances are used as dissimilarity measures to avoid the dependence of the standard Euclidean distance, which is particularly sensitive to outliers. Computational experiments with several real-world datasets indicate that the proposed methodological approach is capable of improving classification accuracy compared with established methods. The results indicate a superior performance compared with the regular t-SNE and linear discriminant analysis (LDA), and a dependence on fewer parameters in comparison with the state-of-the-art supervised uniform manifold approximation and projection (UMAP) algorithm.
{"title":"Supervised t-SNE for Metric Learning With Stochastic and Geodesic Distances","authors":"Alaor Cervati Neto;Alexandre L. M. Levada;Michel Ferreira Cardia Haddad","doi":"10.1109/ICJECE.2024.3429273","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3429273","url":null,"abstract":"The t-distributed stochastic neighbor embedding (t-SNE) consists of a powerful algorithm for visualizing high-dimensional data in a lower dimensional space. It is extensively employed in machine learning (ML) and data analysis, including unsupervised metric learning. In this article, we propose improvements concerning two main aspects of the t-SNE. First, the incorporation of class labels is adopted to increase its suitability for supervised classification. Second, stochastic and geodesic distances are used as dissimilarity measures to avoid the dependence of the standard Euclidean distance, which is particularly sensitive to outliers. Computational experiments with several real-world datasets indicate that the proposed methodological approach is capable of improving classification accuracy compared with established methods. The results indicate a superior performance compared with the regular t-SNE and linear discriminant analysis (LDA), and a dependence on fewer parameters in comparison with the state-of-the-art supervised uniform manifold approximation and projection (UMAP) algorithm.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"199-205"},"PeriodicalIF":2.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1109/ICJECE.2024.3409156
Wafaa Anani;Abdelkader Ouda
Abstract-The escalating demand for secure communication in the Internet of Things (IoT), particularly in energy-sensitive devices such as smart meters, highlights a critical challenge: achieving robust security without excessive energy consumption. While various solutions have been proposed to minimize energy use, many fail to address the unique constraints of the IoT devices effectively. This article introduces an innovative approach by proposing a secure, lightweight wireless meter-bus (wM-Bus) protocol, specifically designed for the stringent resource constraints of the IoT environments. By incorporating the noise protocol framework (NPF), our protocol significantly reduces computational and power requirements without compromising security integrity. Through a methodical implementation that spanned five distinct phases, including a comparative analysis with the conventional transport layer security (TLS), our findings are compelling. The NPF, particularly with its NX and XX patterns, dramatically surpasses TLS in performance, extending operational lifetimes to approximately 9 and 7.88 years, respectively, in contrast to the 3.81 years offered by TLS. These results not only demonstrate the superior efficiency of the NPF in the IoT settings but also highlight its potential in striking an optimal balance between security and operational longevity.
{"title":"A Secure Lightweight Wireless M-Bus Protocol for IoT: Leveraging the Noise Protocol Framework","authors":"Wafaa Anani;Abdelkader Ouda","doi":"10.1109/ICJECE.2024.3409156","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3409156","url":null,"abstract":"Abstract-The escalating demand for secure communication in the Internet of Things (IoT), particularly in energy-sensitive devices such as smart meters, highlights a critical challenge: achieving robust security without excessive energy consumption. While various solutions have been proposed to minimize energy use, many fail to address the unique constraints of the IoT devices effectively. This article introduces an innovative approach by proposing a secure, lightweight wireless meter-bus (wM-Bus) protocol, specifically designed for the stringent resource constraints of the IoT environments. By incorporating the noise protocol framework (NPF), our protocol significantly reduces computational and power requirements without compromising security integrity. Through a methodical implementation that spanned five distinct phases, including a comparative analysis with the conventional transport layer security (TLS), our findings are compelling. The NPF, particularly with its NX and XX patterns, dramatically surpasses TLS in performance, extending operational lifetimes to approximately 9 and 7.88 years, respectively, in contrast to the 3.81 years offered by TLS. These results not only demonstrate the superior efficiency of the NPF in the IoT settings but also highlight its potential in striking an optimal balance between security and operational longevity.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"175-186"},"PeriodicalIF":2.1,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810467","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}
The diffusion of electric vehicles (EVs) is recently gaining great attention in the road transport and automotive sectors as an attempt to bring in an emission-free world. EVs are considered a key to future clean transportation systems. However, these vehicles still suffer from limited battery capacity and range anxiety. Therefore, EVs manufacturers are focusing on reducing energy consumption and CO2 emissions. In addition, research in the context of intelligent transportation systems embedding information and communication technologies are focusing on the optimization of the energy consumption as a valuable solution to foster the wide diffusion of EVs. In this article, we propose a simulation platform for eco-routing services based on estimating EV energy consumption to provide the most energy-efficient routes for the EV while traveling. We provide an energy map that can be used for eco-routing through a real-time data collection of the EV energy consumption. The energy map was established in the traffic simulator Simulation of Urban MObility (SUMO) to show the efficiency of the proposed eco-routing strategy compared to the other strategies based on establishing the fastest routes. This map will be exploited as good support, in the future, for advanced research on the EV concept.
{"title":"Energy-Efficient Route Navigation (Eco-Routing) for Electric Vehicles in SUMO","authors":"Insaf Sagaama;Amine Kchiche;Wassim Trojet;Farouk Kamoun","doi":"10.1109/ICJECE.2024.3425515","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3425515","url":null,"abstract":"The diffusion of electric vehicles (EVs) is recently gaining great attention in the road transport and automotive sectors as an attempt to bring in an emission-free world. EVs are considered a key to future clean transportation systems. However, these vehicles still suffer from limited battery capacity and range anxiety. Therefore, EVs manufacturers are focusing on reducing energy consumption and CO2 emissions. In addition, research in the context of intelligent transportation systems embedding information and communication technologies are focusing on the optimization of the energy consumption as a valuable solution to foster the wide diffusion of EVs. In this article, we propose a simulation platform for eco-routing services based on estimating EV energy consumption to provide the most energy-efficient routes for the EV while traveling. We provide an energy map that can be used for eco-routing through a real-time data collection of the EV energy consumption. The energy map was established in the traffic simulator Simulation of Urban MObility (SUMO) to show the efficiency of the proposed eco-routing strategy compared to the other strategies based on establishing the fastest routes. This map will be exploited as good support, in the future, for advanced research on the EV concept.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"187-198"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810466","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 : 2024-09-23DOI: 10.1109/ICJECE.2024.3451965
Haojie Peng;Weihua Li;Sifan Dai;Ruihai Chen
With recent advances in airborne weapons, modern air combats tend to be accomplished in the beyond-visual-range (BVR) phase. Multiaircraft cooperation is also required to adapt to the complexities of modern air combats. The scale of the traditional rule-based expert system will become incredible in this case. In view of this, a mixed-reward multiagent proximal policy optimization (MRMAPPO) method is proposed in this article that is used to help train cooperative BVR air combat tactics via adversarial self-play. First, a two-on-two BVR air combat simulation platform is established, and the combat game is modeled as a Markov game. Second, centralized training with decentralized execution architecture is established. Multiple actors are involved in the architecture, each corresponding to a policy that generates a specified kind of command, e.g., the maneuvering and firing command. Moreover, in order to accelerate training as well as enhance the stability of the training process, four optimization mechanisms are introduced. The experimental section discusses how the effectiveness of the MRMAPPO is verified with comparative and ablation experiments, along with several air combat tactics that emerge in the training process.
{"title":"Mixed-Reward Multiagent Proximal Policy Optimization Method for Two-on-Two Beyond-Visual-Range Air Combat","authors":"Haojie Peng;Weihua Li;Sifan Dai;Ruihai Chen","doi":"10.1109/ICJECE.2024.3451965","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3451965","url":null,"abstract":"With recent advances in airborne weapons, modern air combats tend to be accomplished in the beyond-visual-range (BVR) phase. Multiaircraft cooperation is also required to adapt to the complexities of modern air combats. The scale of the traditional rule-based expert system will become incredible in this case. In view of this, a mixed-reward multiagent proximal policy optimization (MRMAPPO) method is proposed in this article that is used to help train cooperative BVR air combat tactics via adversarial self-play. First, a two-on-two BVR air combat simulation platform is established, and the combat game is modeled as a Markov game. Second, centralized training with decentralized execution architecture is established. Multiple actors are involved in the architecture, each corresponding to a policy that generates a specified kind of command, e.g., the maneuvering and firing command. Moreover, in order to accelerate training as well as enhance the stability of the training process, four optimization mechanisms are introduced. The experimental section discusses how the effectiveness of the MRMAPPO is verified with comparative and ablation experiments, along with several air combat tactics that emerge in the training process.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"206-217"},"PeriodicalIF":2.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810469","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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