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IEEE International Conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Babul Salam Ibrahim Dur Muhammad Soomro Sharman Sundarajoo Muhammad Noor Akhir Tahrir Electrical and Computer Engineering Department Gulf University for Science & Technology Kuwait Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia

Lightning is a very significant occurrence that exists on earth. One stroke of lightning on a distribution network can be enough to be the root of a power outage. Therefore, this study examines and investigates the consequences of lightning and surge arresters in the power distribution system. A lightning surge is designed to examine consequences on a network. The metal oxide surge arrester (MOSA) performance illustration is pointedly diverse during significant impulse transients are considered. The MOSA is selected after comparing the shielding stages, momentary overvoltage, and power captivation competency. Picking a greater valued surge arrester raises its momentary overvoltage scope. Hence, its capability to endure against network voltage pressures develops but decreases the arrester's safety level for a specified shielding range. However, for the fast front surge arrester, its vital features are important for overvoltage related investigations relating quick front surges. Lightning surge and surge arrester simulation were carried out in the PSCAD software environment to investigate the effects of lightning and surge arresters in the distribution system. This study's simulation results indicate the significance and requirement of lightning defense systems installation in distribution networks.

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Dynamic Hierarchical Reinforcement Learning Framework for Energy-Efficient 5G Base Stations in Urban Environments

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Dianlei Su, Xiang Premsankar, Gopika Wang, Huandong Tarkoma, Sasu Hui, Pan University of Helsinki Department of Computer Science Helsinki00014 Finland Department of Electronic Engineering Beijing100084 China University of Helsinki Department of Agricultural Sciences Helsinki00014 Finland Aalto University Department of Information and Communications Engineering Finland University of Oulu Faculty of Information Technology and Electrical Engineering Oulu90014 Finland The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

The energy consumption of 5G base stations (BSs) is significantly higher than that of 4G BSs, creating challenges for operators due to increased costs and carbon emissions. Existing solutions address this issue by switching off BSs during specific periods or forming cooperation coalitions where some BSs deactivate while others serve users. However, these approaches often rely on fixed geographic configurations, making them unsuitable for urban areas with numerous BSs and mobile users. To tackle these challenges, we propose a hierarchical reinforcement learning (RL) framework for energy conservation in large-scale 5G networks. In the upper-layer, we propose a deep Q-network integrated with a graph convolutional network that dynamically groups BSs into coalitions from a macro perspective. This layer focuses on high-level coalition formation to optimize system-wide energy efficiency by considering the global state of the network. In the lower-layer, we combine attention mechanism with multi-agent RL and graph convolutional networks to design a scalable algorithm that maximizes local energy efficiency through optimizing the cooperation within each coalition. These two layers align global coalition dynamics with local intra-coalition cooperation to achieve system-wide energy optimization. Moreover, we accurately model large-scale urban 5G scenarios leveraging a high-fidelity network simulator, which enables our RL framework to learn from real-world feedback. Extensive experiments conducted with the simulator demonstrate that our proposed framework achieves remarkable energy savings of up to 75.6%, significantly outperforming baseline approaches. These findings highlight the effectiveness and superiority of our hierarchical RL optimization framework in addressing the energy consumption challenges faced by large-scale 5G networks. © 2002-2012 IEEE.

关键词： 5G mobile communication systems

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Model-Driven Sensing-Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks

arXiv

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arXiv 2023年

作者： Xie, Lei He, Hengtao Song, Shenghui Eldar, Yonina C. Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Maneuvering target tracking will be an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. For example, the dense network and high-speed targets make the selection of the sensing nodes (SNs) and the associated power allocation very challenging. Existing methods demonstrated engaging performance, but with high computational complexity. In this paper, we propose a model-driven deep learning (DL)-based approach for SN selection. To this end, we first propose an iterative SN selection method by jointly exploiting the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the iterative algorithm as a deep neural network and prove its convergence. The proposed method achieves lower computational complexity, because the number of layers is less than the number of iterations required by the original algorithm, and each layer only involves simple matrix-vector additions/multiplications. Finally, we propose an efficient power allocation method based on fixed point (FP) water filling and solve the joint SN selection and power allocation problem under the alternative optimization framework. Simulation results show that the proposed method achieves better performance than the conventional optimization-based methods with much lower computational complexity. © 2023, CC BY.

关键词： Wireless networks

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Natural Lighting System Using Fiber Optics for Energy Efficiency

Natural Lighting System Using Fiber Optics for Energy Effici...

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IEEE International Conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Babul Salam Ibrahim Dur Muhammad Soomro Sharman Sundarajoo Zulhelmi Nordin Electrical and Computer Engineering Department Gulf University for Science & Technology Kuwait Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia

The sunlight's utilization as an illumination supply for lighting inside the building has expanded significantly since the 1900s. Lighting holds an integral part of the total electricity usage in a building. It is also one of the scopes with numerous ways to increase energy effectiveness by lowering energy usage. Hence, a natural lighting system is recommended to curtail electrical energy consumption for illumination throughout the daytime. This work aims to create a natural lighting system model to direct and address natural illuminance into challenging and complicated buildings. In this work, solar energy from the sun is conducted towards the fiber optics package's edge by utilizing a convex lens. This system aims to lower electrical-based lighting consumption during daytime illumination by transmitting the solar light over fiber optics. According to the sun's position, the system is sustained by an optic directional controller to guarantee the utmost connected illuminance. The control system would assure the highest illuminance accumulation also automatically turn on the electricity-based illumination if the acquired natural light did not satisfy the sufficient lux. The PIC16F877A microcontroller was used as a processor to power the tracking device engine. The percentage of the reduction in energy usage for daytime lighting was obtained. The obtained results show that this system will save energy during the day by 57%. Therefore, this paper represents a notable improvement in optical fiber natural lighting systems' commercialization in energy-saving and cost reduction.

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Mining Composite Spatio-Temporal Lifestyle Patterns from Geotagged Social Data 15

Mining Composite Spatio-Temporal Lifestyle Patterns from Geo...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： De, Suparna Jassat, Usamah Grace, Alex Wang, Wei Moessner, Klaus Dept. of Computer Science University of Surrey Guildford United Kingdom Dept. of Electronic Engineering University of Surrey Guildford United Kingdom School of Advanced Technology Xian Jiaotong Liverpool University Suzhou China Faculty of Electrical Engineering Chemnitz University of Technology Chemnitz Germany

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As social networks become increasingly integrated with their users' daily lives, and users are willing to publicly share data about their offline activities on these networks, the resultant data offers a powerful tool to non-intrusively understand city dynamics as it captures human behaviour and interactions. In this paper, we derive lifestyle patterns from the Foursquare social network data, using matrix factorization and tensor decomposition as unsupervised methods to extract latent spatio-temporal behavior patterns. The extracted patterns offer precise definition of activity levels associated with specific lifestyles and showcase that users' behaviors are a combination of several lifestyles, in contrast to traditional circadian topology theory which classifies individuals to a specific temporal pattern. The obtained patterns can provide deeper insights into city dynamics, the people within them and how society functions. © 2022 IEEE.

关键词： Tensors

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Svd-Kd: Svd-Based Hidden Layer Feature Extraction for Knowledge Distillation

SSRN

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SSRN 2024年

作者： Zhang, Jianhua Gao, Yi Zhou, Mian Liu, Ruyu Cheng, Xu Nikolić, Saša V. Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Department of Electronics Faculty of Electronic Engineering University of Nis Nis Serbia

Recent advancement of Knowledge distillation (KD) is to extract and transfer middle layer knowledge of teacher models to student models, which is better than original KDs which only transfer the last layer of knowledge. However, the middle layer knowledge commonly appears as a complex tensor, which is more difficult to transfer than the one-dimensional knowledge in the last layer. Moreover, when there are large differences between the teachers and students in terms of model parameter capabilities and model structures, the middle layers of teacher and student models will not be aligned, which will further increase the learning difficulty for student models. To solve these problems, we propose a novel knowledge extraction module to transform the complex tensor-based knowledge in middle layers to one-dimensional knowledge based on Singular Value Decomposition. Thus, the knowledge at the middle layers of teacher models can be effectively extracted and simplified, and extremely facilitate the learning of student models, even though the structure and parameter capacity between the teacher network and the student network are very different. To help the students learn the knowledge from the middle layers of teachers as accurately as possible, we also propose a novel loss function that can constrain values of the one-dimension knowledge learned by the student model to be as close as possible to that extracted from the teacher model, thus improving the learning efficiency of the student model. We have conducted extensive experiments on three major datasets (CIFAR-10, CIFAR-100, and ImageNet), and the results of our method demonstrate superior performance by comparing with the state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

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Journal of computer and Communications 2023年第6期11卷 73-93页

作者： Eric Michel Deussom Djomadji Ivan Basile Kabiena Valery Nkemeni Ayrton Garcia Belinga À Njere Michael Ekonde Sone Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Division of ICT National Advanced School of Post Telecommunication and ICT University of Yaounde I Yaounde Cameroon Department of Computer Engineering and Telecommunications National Advanced School of Engineering University of Douala Douala Cameroon Department of Computer Engineering Faculty of Engineering and Technology University of Buea Buea Cameroon

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4

关键词： RRC Resources 4G Network Linear Regression Convolutional Neural Networks Long Short-Term Memory Precision

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How do Final Year Projects of engineering Students Relate to Myer-Briggs Type Indicator Assessment? 11

How do Final Year Projects of Engineering Students Relate to...

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11th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2022

作者： Toosy, Fesal Arif, Arslan Ahmad, Ali Herodotou, Christothea Poslad, Stefan Hamid, Khurram University of Central Faculty of Engineering Punjab Lahore Pakistan Open University Milton Institute of Educational Technology Keynes United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom University of Central Faculty of Management Sciences Punjab Lahore Pakistan

ISBN: (纸本)9781665491174

This work-in-progress paper describes the analysis of an MBTI based personality study conducted on twenty-one final year project groups of electrical engineering undergraduate students at a private university in Pakistan. The potential influence of each of the 16 possible MBTI profiles of members of each group was studied in relation to the final performance and success of the project. The results showed that the presence of certain personality types in each group, in particular ENTJ and ESTP along with combinations of different types had an influence on the overall outcome and deliverables achieved in the project. © 2022 IEEE.

关键词： Students

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

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computers, Materials & Continua 2021年第11期69卷 2313-2337页

作者： Mahmoud Alawi Raed Alsaqour Abdi Abdalla Maha Abdelhaq Mueen Uddin Department of Computer Engineering and IT Faculty of EngineeringZanzibar UniversityZanzibarTanzania Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Electronics and Telecommunications Engineering College of ICTUniversity of Dar es SalaamTanzania Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Science Universiti Brunei DarussalamNegara Brunei Darussalam

The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular *** scenario affects the quality of service(QoS)of vehicle and non-vehicle ***,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly ***-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A ***,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult *** condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of *** studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading ***,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is *** present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading *** proposed mechanism is decentralized,where each vehicle makes the prediction by *** characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several *** MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading rat

关键词： Vehicular network markov predictor 4G LTE-A Wi-Fi offloading prediction model heterogeneous network

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