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IEEE Conference on Global Communications (GLOBECOM)

作者： Anwar Asad Mostafa M. Fouda Zubair Md Fadlullah Mohamed I. Ibrahem Nidal Nasser Department of Computer Science Lakehead University Thunder Bay Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science University of Western Ontario London ON Canada School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt College of Engineering Alfaisal University Saudi Arabia

Federated learning is a promising approach for training models on distributed data, driven by increasing demand in various industries. However, federated learning framework faces several key challenges, including communication bottlenecks and client data heterogeneity. Personalized asynchronous federated learning addresses these challenges by customizing the model for individual users based on their local data while trading model updates asynchronously. In this paper, we propose the Personal-ized Moreau Envelopes-based Asynchronous Federated Learning (APFedMe). Our approach combines the strengths of Moreau En-velopes to handle optimization problems and asynchronous weight updates to improve communication efficiency while mitigating heterogeneity data challenges through a personalized learning environment. We evaluate our approach on several datasets and compare it with the baseline PFedMe method. Our experiments demonstrate that the proposed APFedMe outperforms other meth-ods in terms of convergence speed and communication efficiency. Overall, our work contributes to developing more effective and efficient federated learning methods that can be applied in various real-world scenarios.

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Proactive Network Optimization Using Deep Learning in Predicting IoT Traffic Dynamics

Proactive Network Optimization Using Deep Learning in Predic...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Anu Priya Niyati Bhat B. Rajesh Kanna S. Rajalakshmi R. Beaulah Jeyavathana Srimathi S Department of Computer Applications Vels Institute of Science Technology & Advanced Studies ((VISTAS) Chennai B.E Computer Engineering University of Mumbai Mumbai Department of Electronics and Communication Engineering Alagappa Chettiyar College of Engineering and Technology Karaikudi Department of Electronics and Communication Engineering RMK College of Engineering and Technology Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era.

关键词： Deep learning Analytical models Recurrent neural networks Ecosystems Predictive models Traffic control Internet of Things

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Unlocking the Global Synergies in Low-Rank Adapters

arXiv

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arXiv 2024年

作者： Zhang, Zixi Zhang, Cheng Gao, Xitong Mullins, Robert D. Constantinides, George A. Zhao, Yiren Department of Computer Science University of Cambridge Cambridge United Kingdom Department of Electrical and Eletronic Engineering Imperial College London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China

Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the limited LoRA trainable parameters across the model for better fine-tuned performance. In addition to the allocation for the standard LoRA-adapted models, we also demonstrate the efficacy of HeteroLoRA by performing the allocation in a more challenging search space that includes LoRA modules and LoRA-adapted shortcut connections. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge. For example, on MRPC, we see an improvement of 1.6% in accuracy with similar training parameter budget. We will open-source our algorithm once the paper is accepted. © 2024, CC BY.

关键词： Budget control

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An Artificial Intelligence Solution for Energy Management in Smart Grid

An Artificial Intelligence Solution for Energy Management in...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Naceur, Ferdaws Ben Toumi, Sana Salah, Chokri Ben Mahjoub, Mohamed Ali National School of Engineers of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse4011 Tunisia Department of Electrical Engineering Monastir Tunisia Brest29238 France Issat Sousse Sousse Tunisia

ISBN: (纸本)9798350325539

This paper deals with an intelligent decision-making strategy of energy management for a smart grid within an element storage in order to reduce the system cost and improve its reliability. Indeed, the integration of distributed energy storage devices allows the customers to store energy and reuse at a later time or delayed some appliances operation according to a time-varying price signal. The adopted strategy is based on an artificial intelligence control algorithm 'fuzzy logic'. The main objective is to determine the appropriate source (grid or battery) used for meeting the consumer. The decision solution shows the system reliability where the switches are synchronized. Switches draw electricity from the grid for both load and battery when price is low and in contrary when electricity price is high, we find a variation's price correspondance with the switch drawing electricity from the battery. The obtained results are verified carrying out Matlab simulation using real data such as the electricity price (Price (t)) and the battery state of charge (SOC (t)). © 2023 IEEE.

关键词： Decision making

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Nanocrystalline Core Loss in High Power IPT Systems for EV Charging Applications

Nanocrystalline Core Loss in High Power IPT Systems for EV C...

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Wireless Power Week (WPW)

作者： Wenting Zhang Seho Kim Duleepa J. Thrimawithana Grant A. Covic Lei Zhao Zongzhen Li Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand School of Automation Chongqing University Chongqing China Jiangsu JITRI Advanced Energy Materials Research Institute Changzhou China

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

Mn-Zn ferrite is commonly used in inductive power transfer (IPT) systems to enhance magnetic coupling and reduce flux leakage. However, ferrite is not ideal for high power transfer due to the large volume of material needed for its low saturation flux density, brittleness, and high core loss leading to thermal issues. Nanocrystalline alloys are potential alternatives to replace ferrite in IPT pads to improve magnetic performance. This paper analyses the core loss and equivalent series resistance (ESR) of ferrite and nanocrystalline cores in high-power IPT pads based on the measurement of magnetic properties using the partial cancellation method. The experimental findings show that the ESR of IPT pads using nanocrystalline cores decreases with higher excitation current in the pads, contrary to the typical behaviour of IPT pads using ferrite.

关键词： Ferrites Wireless communication Magnetic flux leakage Magnetic cores Current measurement Core loss Metals

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Agent-based Modeling and Simulation for the Electricity Market with Residential Demand Response

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 368-380页

作者： Shuyang Xu Xingying Chen Jun Xie Saifur Rahman Jixiang Wang Hongxun Hui Tao Chen College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China Department of Electrical and Computer Engineering Viginia TechAlington VA 22203 USAand also with the Advanced Research InstituteViginia TechArlingtonVA 22203 USA State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 519031China Collegue of Electrical Engineering Southeast UniversityNanjing 210009China.DOI:10.17775/CSEEJPES.2019.01750

Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)*** typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market *** those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive *** this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)*** simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)*** bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR *** and GenCos optimize their bidding strategies via the RL *** modified optimization approach in this procedure can prevent the training results from falling into local optimum *** ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)*** on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential *** show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand *** models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions

关键词： Agent-based modeling and simulation(ABMS) electricity market residential demand response(RDR) reinforcement learning(RL)

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz School of Automation China University of Geosciences Wuhan China School of Automation the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data

arXiv

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arXiv 2024年

作者： Lichtlé, Nathan Jang, Kathy Shah, Adit Vinitsky, Eugene Lee, Jonathan W. Bayen, Alexandre M. Department of Electrical Engineering and Computer Sciences UC Berkeley United States CERMICS École des Ponts ParisTech France Department of Mechanical Engineering UC Berkeley United States Institute for Transportation Studies UC Berkeley United States

Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass the common issue of having to carefully fine-tune a large traffic micro-simulator by leveraging real-world trajectory data from the I-24 highway in Tennessee, replayed in a one-lane simulation. Using standard deep reinforcement learning methods, we train energy-reducing wave-smoothing policies. As an input to the agent, we observe the speed and distance of only the vehicle in front, which are local states readily available on most recent vehicles, as well as non-local observations about the downstream state of the traffic. We show that at a low 4% autonomous vehicle penetration rate, we achieve significant fuel savings of over 15% on trajectories exhibiting many stop-and-go waves. Finally, we analyze the smoothing effect of the controllers and demonstrate robustness to adding lane-changing into the simulation as well as the removal of downstream information. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Intelligent Energy Management Strategies for Hybrid Electric Transportation

Intelligent Energy Management Strategies for Hybrid Electric...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Pankaj Yadav Vikas Vikash Kumar Saini Ameena S. Al-Sumaiti Rajesh Kumar Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Center for Energy and Environment Malaviya National Institute of Technology Jaipur India Electrical Engineering and Computer Science Advanced Power and Energy Center Khalifa University UAE

Shortage of fossil fuels around the world, oil prices, and environmental concerns like climate change and air pollution are big challenges for the transport sector. Hybrid electric vehicles (HEVs) are an alternative solution to overcome the above challenge. HEVs require an efficient energy management strategy (EMS) to promote consumption efficiency in the different test cycles. The learning-based algorithms are frequently used to control how efficiently HEVs use energy. The tremendous processing intensity, the extensive data training, and the stringent necessity of accurately predicting the future state of operation all work against the maximum use of these systems. In this study, the model-free reinforcement control mechanism is used for the energy management of HEVs. A deep $\mathbf{Q}$ network (DQN) and deep deterministic policy gradient (DDPG) are utilized to the enhance the learning process and reliability of the EMS framework. Since it is a memoryless random process, the Markovian decision process has been used to formulate the issue. Both learning algorithms are compared with different test driving cycles. The simulation results present that the deep DDPG performance is more reliable and fast converges than the deep $\mathbf{Q}$ learning algorithm.

关键词： Training Renewable energy sources Simulation Transportation Reinforcement learning Robustness Real-time systems

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