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SmartRail - Optimized Train Management System for Sri Lanka Railways 9

SmartRail - Optimized Train Management System for Sri Lanka ...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Silva, K. R. Rasanga Dilshan, A.J.Y.J. Herath, Achini M. Senadeera, N.A.J.N. Wijendra, Dinuka R. De Silva, D.I. Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350394474

The SmartRail system is a comprehensive solution designed to address the challenges faced by the Sri Lanka Railway Department. Through its four primary subsystems, namely the Self Ticketing System and Chatbot, Timetable Management and Passenger Demand Forecasting System, Driver Management System and Fleet Management System, SmartRail aims to enhance the efficiency, convenience and security of railway operations. The Self Ticketing System and Chatbot enable passengers to book tickets and obtain real-time information through a user-friendly web application, reducing queues and improving customer satisfaction. The Timetable Management and Passenger Demand Forecasting System optimize train schedules and resource allocation based on predictive modelling, ensuring efficient utilization of resources and enhancing passenger convenience. The Driver Management System focuses on allocating drivers for train journeys while considering health conditions, previous journey information, and driver grades to improve safety and operational efficiency. The Fleet Management System utilizes blockchain technology to facilitate secure data sharing among sub-departments, ensuring data integrity and confidentiality for effective fleet management. By leveraging advanced technologies, SmartRail offers a transformative solution that has the potential to modernize and improve railway operations, resulting in enhanced efficiency, customer satisfaction, income and overall performance of the railway network. © 2024 IEEE.

关键词： Customer satisfaction

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Advanced Bandwidth Management Techniques Using Wireless Mesh Networks for High-Speed Optical Fiber Communications 3

Advanced Bandwidth Management Techniques Using Wireless Mesh...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Yadav, Rakesh Kumar Sharma, Sunil Seth, Kanika Jeyalaxmi, M. Ingle, Yashwant S. Vivek, V. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This abstract encompasses superior bandwidth management techniques the usage of Wi-Fi mesh networks to improve the performance of high-velocity optical fiber communications. the point of interest lies at the access-, relay-and center-mesh architectures with their diverse, discrete techniques consisting of dynamic frequency allocation, load balancing and node optimization, as well as on highlights from latest research. Within the experiments, the effects exhibit giant enhancement in signal first-rate and effectiveness of bandwidth resources when the use of the proposed version, based totally at the convergence speed of the network fee. Furthermore, it's also evident that the optimized wireless mesh configuration brings about enormous fee financial savings. All the findings obtained at some stage in this research have eminently greater the talents of the proposed structure for growing green communications networks. © 2023 IEEE.

关键词： Communications convergence Experiments Networks Superior

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Vehicle Target Detection with an Integrated Attention Mechanism 3

Vehicle Target Detection with an Integrated Attention Mechan...

引用

3rd international conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2024

作者： Wang, Yuzhuo Wang, Haihui Hu, Po Hu, Yuanzhong School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331506582

To address the problems of traditional vehicle detection models regarding equipment requirements, detection accuracy, and overlapping target leakage rate, this study proposes an improved vehicle target detection algorithm for YOLOv8. Firstly, we introduce EMA, an attention mechanism for cross-space learning, to improve the attention to feature information. Secondly, it combines EMA with the C2f module in the Neck network to fuse more important feature information, obtaining more accurate vehicle feature information. Finally, it uses the MPDloU loss function to speed up the model's convergence and improve detection accuracy. Experiments conducted on Pascal VOC and MS COCO datasets show that the performance of the improved algorithm proposed in this paper exceeds that of the original model. The enhanced model's mAP reaches 85.71%, 3.27% higher than the original model, improving vehicle target detection accuracy and being highly practical. © 2024 IEEE.

关键词： attention mechanism MPDIoU vehicle detection YOLOv8

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Topsis-Based Genetic Algorithm for Non-Dominated Sorting of Local Search Im-NSGA-II 6

Topsis-Based Genetic Algorithm for Non-Dominated Sorting of ...

引用

6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Xue, Tonglai Zhang, Zheng Gao, Tianyu Han, Fei North China University of Technology Beijing China China National General Mechanical Engineering Corporation Beijing China

ISBN: (纸本)9798350374315

Aiming at the problem of large computation of non-dominated sorting genetic algorithm of local search class, this paper proposes an improved local search NSGA-II algorithm (Im-NSGA-II) based on Topsis algorithm by adjusting adaptive crossover and genetic operators. By applying the Topsis algorithm to the population individuals, the solutions with low fitness are taken out, and the local search algorithm is applied to the set of solutions with low fitness in each genetic process. Simulation experiments on the ZDT standard test function are conducted and compared with the traditional NSGA-II algorithm, the multi-objective particle swarm algorithm MOPSO, and the multi-objective differential evolutionary algorithm MOEA/D. The results show that Im-NSGA-II is able to effectively balance the ability of global search and local exploration and accelerate the convergence of the populations compared with the other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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A Wideband Half Mode SIW-Based RDRA MIMO Antenna for Next-Generation 5G Networks 3

A Wideband Half Mode SIW-Based RDRA MIMO Antenna for Next-Ge...

引用

3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Girjashankar, Pandey Rajat Upadhyaya, Trushit Chandubhai S. Patel Institute of Technology Department of Electronics and Communication Engineering Changa India

ISBN: (纸本)9798350343922

This paper introduces a novel design featuring Half Mode SIW fed four element RDRA MIMO. The proposed MIMO RDRA configuration offers a combination of polarization and spatial diversity to effectively counteract signal interference from multiple paths. The suggested MIMO DRA exhibits a simulated impedance bandwidth of 17.95% (36.3 GHz to 43.46 GHz) while maintaining inter-port isolation more than 21 dB over the entire frequency range. The proposed design has a peak gain of 5.37 to 10.90 dBi over the desired frequency band. Extensive analysis reveals a channel capacity loss (CCL) below 0.6 bits/sec/Hz and an envelope correlation coefficient (ECC) lower than 0.0025 across the frequency range of operation. The findings demonstrate the potential of this antenna design for future 5G systems. © 2023 IEEE.

关键词： 5G mobile communication systems

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AI-Powered Energy Management System for Industry 8

AI-Powered Energy Management System for Industry

引用

8th IEEE international conference on computing, Communication, Control and Automation, ICCUBEA 2024

作者： Gamne, Piyush Gaikwad, Prathmesh Jadhav, Prajakta Gore, Shridhar Vhatkar, Kapil N. Dr. D. Y. Patil Institute of Technology Department of Computer Engineering Pimpri Pune18 India

ISBN: (纸本)9798350391770

The industrial sector, serving as a linchpin of global economic growth, continually shapes innovation, job creation, and wealth accumulation. However, its indispensable role is juxtaposed with substantial energy consumption, contributing significantly to the world's energy usage and environmental footprint. In their pursuit of heightened competitiveness and profitability, industries confront a pressing challenge: the judicious management of energy resources. Striking a harmonious equilibrium between cost-efficiency, operational excellence, and environmental sustainability presents a formidable complexity. Compounding this complexity is the reluctance of traditional energy management methodologies to adapt to the dynamic and data-rich landscapes characterizing modern industrial domains. This research paper ventures into the heart of these challenges, exploring the convergence of the industrial sector, energy management, and technological advancements. Within this multifaceted landscape, it scrutinizes the intricacies of contemporary industrial environments, exposing the inherent complexities of responsible energy management. With data-driven technologies and advanced analytics as guiding lights, this study illuminates a transformative path forward - transcending conventional paradigms to forge a new era of efficiency, sustainability, and profitability. © 2024 IEEE.

关键词： Energy economics

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Optimization of Cloud computing Workload Prediction Model with Domain-based Feature Selection Method 5

Optimization of Cloud Computing Workload Prediction Model wi...

引用

5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lee, ChangHoon Song, MinJae Min, KyungHa Ha, EunGyeom Lee, Junha Kim, Wooju OKESTRO Artificial Intelligence Lab Seoul South Korea Yonsei Univ Dept Ind Engn Seoul South Korea Yonsei Univ Dept Digital Analyt Seoul South Korea

ISBN: (纸本)9781665456456

Recently, the demand for monitoring large-scale cloud environments has grown rapidly. In addition, anomaly detection or optimization is becoming increasingly challenging as the environment becomes large and complex. Therefore, accurately predicting the metrics that each virtual machine will represent in the future is important. Real-time prediction of workloads in the cloud computing environment requires reducing the inference time of the deep learning model. It can be used in multivariate models with various variables to increase prediction accuracy. However, applying them to real-time prediction services is difficult because inference time is overlooked. We solve these problems through an effective feature selection method. We propose selecting features through statistical techniques based on domain knowledge and verifying them through quantitative indicators. Five environments, such as an actual cloud computing environment, were constructed for the experiment. According to the proposed domain feature selection method, we selected 12 features from the 87 features identified. It was confirmed that the model trained with these features provided a higher predictive success rate and lower average inference time than the model trained with 87 features. The model can proactively detect possible anomalies that occur or recommend policies that optimize the cloud data center.

关键词： Cloud computing Workload Prediction Deep Learning Long Short-Term Memory

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Cascade AOA Estimation Technique Based on The Combined Flexible Massive Array Antenna 12

Cascade AOA Estimation Technique Based on The Combined Flexi...

引用

Joint 12th international conference on Soft computing and Intelligent Systems / 23rd international Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Kim, Tae-yun Park, Heui-seon Jeon, Seul-Bi Jo, Tae-Ho Hwang, Suk-seung Chosun Univ Inst AI Convergence Gwangju South Korea Chosun Univ Dept Elect Engn Interdisciplinary Program IT Bio Convergence Syst Gwangju South Korea Chosun Univ Sch Elect Engn Interdisciplinary Program IT Bio Convergence Syst Gwangju South Korea

ISBN: (纸本)9781665499248

In the next-generation communication system, various communication services employs beamforming and massive array antenna techniques for reliable and efficient wireless communications. Based on the massive array antenna with a huge number of elements, the beamforming technique such as the estimation of Angle of Arrival (AOA) may not operate in real time, because the computational complexity for this situation should be extremely high. In order to enhance this problem, in this paper, we propose the cascade AOA estimation algorithm based on the Flexible Massive Combined Array (FMCA) antenna. The proposed algorithm finds AOA groups including multiple signal AOAs employing Capon based on some elements of the FMCA antenna, and it estimates the detail signal AOAs in the found AOA groups employing Beamspace Multiple Signal Classification (MUSIC) based on entire elements of the FMCA antenna. In addition, we provide the received signal model for applying the FMCA antenna and the proposed cascade AOA estimation algorithm.

关键词： flexible massive combined array antenna AOA estimation cascade estimation Capon Beamspace MUSIC

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A Wireless Power Transfer (WPT) System for Series - Series Compensation topology in Electric Vehicle Battery Charging Applications 8

A Wireless Power Transfer (WPT) System for Series - Series C...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Sakthi, B Brindha Deiva Sundari, P. KCG College of Technology Department of Electrical & Electronics Engineering TamilNadu India

ISBN: (纸本)9798350334012

Electric vehicles (EVs) are increasingly common and attractive, as they minimize dependency on fossil fuels, increase the secure for vehicles and convenient because energy transfer does not require any cables to begin once the sender is synchronized with the receiver. There are still many challenges, however complicate the adoption of EVs, EVs have short driving range and long charging period. This method of charging is an emerging as a potential approach to address these challenges. The aim of this paper is to investigate the parameters to design and model an inductive coupling based Wireless Power Transfer (WPT) system for EVs. Transmitter coils are buried in the road in EV charging applications and recipient coils are mounted in the car. The WPT voltage and current are simulated using in MATLAB, coil design using ANSYS MAXWELL and small laboratory based experimental setup is verified. For enhancing the performance soft computing techniques is also can be considerable. © 2023 IEEE.

关键词： Energy transfer

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Research on production scheduling method considering multidimensional testing ability of employees

Research on production scheduling method considering multidi...

引用

2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Wang, Renhao Industrial Internet Research Institute Nanjing University of Science & Technology Jiangsu Wuxi214400 China

ISBN: (纸本)9781510685840

In complex inspection procedures, there is a significant variation among different personnel. However, the existing personnel scheduling methods often treat individuals as similar or identical entities, which can result in low overall yield and substantial losses for the company. In this context, a study was conducted to improve the yield from the perspective of personnel scheduling in inspections. Based on the interception capabilities of different personnel for various types of defects, and in combination with the concentrations of different defect types for different models, a hybrid algorithm combining genetic algorithms and particle swarm optimization algorithms with elite decision-making was proposed. This algorithm allows for the selection of suitable personnel from the personnel pool, followed by targeted personnel scheduling based on the defect concentrations specific to each personnel and model. This ensures that the appropriate personnel are assigned to the corresponding models, thereby maximizing the yield for all models. Through experiments and result analysis on different scenarios, as well as comparisons with other algorithms, the test results demonstrate the superiority of this method in terms of convergence and optimization capability. This approach offers a new perspective on scheduling based on the multidimensional testing capability of employees, providing valuable insights for optimizing production and scheduling in enterprises. © 2024 SPIE.

关键词： Genetic algorithms

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