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science and Technology for Energy Transition (STET) 2025年 80卷

作者： Raman, Arasu Sayed, Biju Theruvil Alkhayyat, Ahmad Ved, Amit Chetan, M. Singh, Ashish Kumar, Raj Prasad, K.D.V. Sapaev, I.B. Mohammedi, M. Department of Faculty of Business and Communications Inti International University Putra Nilai 71800 Malaysia Department of Computer Science Dhofar University PO Box 2509 Salalah211 Oman Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Centre Department of Electrical Engineering Faculty of Engineering and Technology Marwadi University Gujarat Rajkot360003 India Karnataka Bangalore India Nims School of Electrical and Electronics Engineering Nims University Rajasthan Jaipur India Chandigarh College of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali140307 India Pune India Department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan Western Caspian University Baku Azerbaijan Department of Electrical Engineering Islamic Azad University Branch of Central Tehran Tehran Iran

This research is dedicated to exploring and identifying the most effective design for an energy source tailored specifically to meet the electricity demands of a residential community. In an era where energy efficiency and sustainability are paramount, this study emphasizes the importance of technical and economic considerations in energy sourcing. It posits that any viable solution must not only be efficient in its energy production and consumption but also reliable in its delivery and financially feasible for the residents who will depend on it. To address this multifaceted challenge, the study proposes the innovative use of a rotation-invariant coordinate convolutional neural network in conjunction with binary battle royale optimization techniques. These advanced methodologies are selected for their potential to enhance the modelling and optimization processes involved in energy source design. The primary goal of employing these methods is to minimize two critical factors: the net present cost of the energy system and the overall energy cost incurred by the residents. By focusing on these objectives, the research aims to ensure that the proposed energy solutions are not only cost-effective but also sustainable over the long term. To rigorously test the proposed model and evaluate its performance, the research is conducted using the MATLAB platform. The study employs established methodologies and performance metrics to assess the outcomes of the model, ensuring that the findings are both credible and applicable to real-world scenarios. Through comprehensive testing and detailed analysis, this research aims to provide significant insights and actionable recommendations for the optimal design of energy sources in residential areas. By contributing to the ongoing discourse on sustainable energy solutions, the study seeks to inform policymakers, energy planners, and community stakeholders about effective strategies for meeting residential energy demands while promotin

关键词： Clean energy

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Retraction Note: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

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Neural Computing and Applications 2024年第16期36卷 1-2页

作者： Ghani, Mohd Khanapi Abd Mohammed, Mazin Abed Arunkumar, N. Mostafa, Salama A. Ibrahim, Dheyaa Ahmed Abdullah, Mohamad Khir Jaber, Mustafa Musa Abdulhay, Enas Ramirez-Gonzalez, Gustavo Burhanuddin, M. A. Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia Planning and Follow Up Department University Headquarter University of Anbar Anbar Iraq School of EEE Sastra University Thanjavur India Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department of Otorinolaringologi Hospital Pakar Sultanah Fatimah Muar Muar Malaysia Dijlah University College Baghdad Iraq Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Departamento de Telemática Universidad del Cauca Popayán Colombia

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Solving the Traveling Salesperson Problem using Frequency Fitness Assignment

Solving the Traveling Salesperson Problem using Frequency Fi...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tianyu Liang Zhize Wu Jörg Lässig Daan van den Berg Thomas Weise Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui China Enterprise Application Development Group Faculty of Electrical Engineering and Computer Science Höchschule Zittau/Gorlitz Görlitz Germany Department of Computer Science Vrije Universiteit Amsterdam Amsterdam The Netherlands Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei Anhui China

ISBN: (纸本)9781665487696

Metaheuristic optimization is based on the idea that better solutions are preferable to worse ones. Frequency Fitness Assignment (FFA) is a module that can be plugged into most optimization algorithms. It replaces the objective values in the selection step with their encounter frequency during the search so far. The search effort is therefore distributed evenly over the whole range of the objective values. Recently, it was shown that using FFA can significantly improve the performance of algorithms on hard problems such as Trap and Jump functions and the NP-hard MaxSat problem. However, the objective functions of all of these problems have relatively small ranges. This work is the first to explore the impact of FFA on metaheuristics for solving Traveling Salesperson Problem (TSP) instances, whose objective values tend to cover a wider range. We plug FFA into the (1+1) EA to obtain the (1+1) FEA. We perform extensive experiments on 18 instances from TSPLIB using two different unary search operators. We find that the (1+1) FEA does not get stuck in local optima and can solve many more instances to optimality than the (1+1) EA. However, it tends to be slower in reaching good intermediate solutions. Its performance decreases with the problem scale and the number of different possible tour lengths.

关键词： Metaheuristics Search problems Linear programming Computational intelligence Plugs

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Sustainability-Driven Exploration of Topological Material

arXiv

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arXiv 2023年

作者： Boonkird, Artittaya Drucker, Nathan C. Mandal, Manasi Nguyen, Thanh Yeo, Jingjie Belosevich, Vsevolod Spero, Ellan Ortiz, Christine Ma, Qiong Fu, Liang Palacios, Tomas Li, Mingda Quantum Measurement Group MIT CambridgeMA02139 United States Department of Nuclear Science and Engineering MIT CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY14850 United States Department of Physics Boston College Chestnut HillMA02467 United States Department of Materials Science and Engineering MIT CambridgeMA02139 United States Department of Physics MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

Topological materials are at the forefront of quantum materials research, offering tremendous potential for next-generation energy and information devices. However, current investigation of these materials remains largely focused on performance and often neglects the crucial aspect of sustainability. Recognizing the pivotal role of sustainability in addressing global pollution, carbon emissions, resource conservation, and ethical labor practices, we present a comprehensive evaluation of topological materials based on their sustainability and environmental impact. Our approach involves a hierarchical analysis encompassing cost, toxicity, energy demands, environmental impact, social implications, and resilience to imports. By applying this framework to over 16,000 topological materials, we establish a sustainable topological materials database. Our endeavor unveils environmental-friendly topological materials candidates which have been previously overlooked, providing insights into their environmental ramifications and feasibility for industrial scalability. The work represents a critical step toward industrial adoption of topological materials, offering the potential for significant technological advancements and broader societal benefits. © 2023, CC BY.

关键词： Environmental impact

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Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment

arXiv

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arXiv 2024年

作者： Wu, Jiaqi Zhang, Shihao Chen, Simin Wang, Lixu Wang, Zehua Chen, Wei He, Fangyuan Tian, Zijian Yu, F. Richard Leung, Victor C.M. Beijing100083 China Department of Electrical and Computer Engineering University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada University of Texas at dallas 800 W Campbell Rd RichardsonTX75080 United States Northwestern University 633 Clark St EvanstonIL60208 United States School of Computer Science and Technology China University of Mining and Technology Jiangsu province Xuzhou221116 China Department of Electrical and Computer Engineering The University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China College of Computer Science and Software Engineering Shenzhen University Shenzhen528060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada College of Applied Science and Technology Beijing Union University Beijing100012 China

Recently, edge computing has emerged as a prevailing paradigm in applying deep learning-based object detection models, offering a promising solution for time-sensitive tasks. However, existing edge object detection faces several challenges: 1) These methods struggle to balance detection precision and model lightweightness. 2) Existing generalized edge-deployment designs offer limited adaptability for object detection. 3) Current works lack real-world evaluation and validation. To address these challenges, we propose the Edge Detection Toolbox (ED-TOOLBOX), which leverages generalizable plug-and-play components to enable edge-site adaptation of object detection models. Specifically, we propose a lightweight Reparameterized Dynamic Convolutional Network (Rep-DConvNet) that employs a weighted multi-shape convolutional branch structure to enhance detection performance. Furthermore, ED-TOOLBOX includes a Sparse Cross-Attention (SC-A) network that adopts a localized-mapping-assisted self-attention mechanism to facilitate a well-crafted Joint Module in adaptively transferring features for further performance improvement. In real-world implementation and evaluation, we incorporate an Efficient Head into the popular You-Only-Look-Once (YOLO) approach to achieve faster edge model optimization. Moreover, we identify that helmet detection—one of the most representative edge object detection tasks—overlooks band fastening, which introduces potential safety hazards. To address this, we build a Helmet Band Detection Dataset (HBDD) and apply an edge object detection model optimized by the ED-TOOLBOX to tackle this real-world task. Extensive experiments validate the effectiveness of components in ED-TOOLBOX. In visual surveillance simulations, ED-TOOLBOX-assisted edge detection models outperform six state-of-the-art methods, enabling real-time and accurate detection. These results demonstrate that our approach offers a superior solution for edge object detection. Copyright © 2024, T

关键词： Deep learning

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On Motion Artifacts Arising when Integrating Inertial Sensors into Loose Clothing Such as a Working Jacket

On Motion Artifacts Arising when Integrating Inertial Sensor...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Michael Lorenz Rebecca Keilhauer Takayuki Akiyama Takehiro Niikura Didier Stricker Bertram Taetz Gabriele Bleser-Taetz Department of Computer Science Augmented Vision Group German Research Center for Artificial Intelligence (DFKI) Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) Kaiserslautern Germany Department of Mathematics Augmented Vision Group German Research Center for Artificial Intelligence (DFKI) Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) Kaiserslautern Germany Research and Development Group Hitachi Ltd. Tokyo Japan IT & Engineering International University of Applied Sciences (IU) Erfurt Germany

Inertial human motion capture (IHMC) has become a robust tool to estimate human kinematics in the wild such as industrial facilities. In contrast to optical motion capture, where occlusions might take place, the kinematics of a worker can be continuously provided. This is for instance a prerequisite for an ergonomic assessments of the workers. State-of-the-art IHMC solutions require inertial sensors to be tightly attached to body segments. This requires an additional setup time and lowers the practicability and ease of use when it comes to an industrial application. In contrast, sensors integrated into loose clothing such as a working jacket, may yield corrupted kinematics estimates due to the additional motion of loose clothing. In this work we present a study of orientations deviations obtained from kinematics estimates using tightly attached inertial sensors and into a working jacket integrated ones. We performed a quantitative analysis using data from the two hardware setups worn by 19 subjects performing different industry related tasks and measures of their body shapes. Using this data we approximated probability distributions of the deviation angles for each person and body segment. Applying different statistical measures we could gain insights to questions like, how severe orientation deviations are, if there is an influence of body shapes on the distribution and how probability distributions of the deviation angles can indicate physical motion limitations of a sensor attached to a segment.

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Safe Bottom-Up Flexibility Provision from Distributed Energy Resources

arXiv

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arXiv 2025年

作者： Mylonas, Costas Varvarigos, Emmanouel Tsaousoglou, Georgios Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark Energy Digitalization Group UBITECH Athens Greece

Modern renewables-based power systems need to tap on the flexibility of Distributed Energy Resources (DERs) connected to distribution networks. It is important, however, that DER owners/users remain in control of their assets, decisions, and objectives. At the same time, the dynamic landscape of DER-penetrated distribution networks calls for agile, data-driven flexibility management frameworks. In the face of these developments, the Multi-Agent Reinforcement Learning (MARL) paradigm is gaining significant attention, as a distributed and data-driven decision-making policy. This paper addresses the need for bottom-up DER management decisions to account for the distribution network’s safety-related constraints. While the related literature on safe MARL typically assumes that network characteristics are available and incorporated into the policy’s safety layer, which implies active DSO engagement, this paper ensures that self-organized DER communities are enabled to provide distribution-network-safe flexibility services without relying on the aspirational and problematic requirement of bringing the DSO in the decision-making loop. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk perception

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Self-Healing Secure Blockchain Framework in Microgrids

arXiv

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arXiv 2022年

作者： Rath, Suman Nguyen, Lam Duc Sahoo, Subham Popovski, Petar The Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Software Systems Research Group CSIRO's Data61 Sydney2015 Australia The Department of Energy Aalborg University Aalborg9220 Denmark The Department of Electronic Systems Aalborg University Aalborg9220 Denmark

Blockchain has recently been depicted as a secure protocol for information exchange in cyber-physical microgrids. However, it is still found vulnerable to consensus manipulation attacks. These stealth attacks are often difficult to detect as they use kernel-level access to mask their actions. In this paper, we firstly build a trusted and secured peer-to-peer network mechanism for physical DC microgrids' validation of transactions over Distributed Ledger. Secondly, we leverage from a physics-informed approach for detecting malware-infected nodes and then recovering from stealth attacks using a self-healing recovery scheme augmented into the microgrid Blockchain network. This scheme allows compromised nodes to adapt to a reconstructed trustworthy signal in a multi-hop manner using corresponding measurements from the reliable nodes in the network. Additionally, recognizing the possible threat of denial-of-service attacks and random time delays (where information sharing via communication channels is blocked), we also integrate a model-free predictive controller with the proposed system that can locally reconstruct an expected version of the attacked/delayed signals. This supplements the capabilities of Blockchain, enabling it to detect and mitigate consensus manipulation attempts, and network latencies. © 2022, CC BY.

关键词： Blockchain

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Simulation of hybrid edge computing architectures

arXiv

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arXiv 2021年

作者： Serena, Luca Zichichi, Mirko D'Angelo, Gabriele Ferretti, Stefano Department of Computer Science and Engineering University of Bologna Bologna Italy Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Department of Computer Science and Engineering University of Bologna Cesena Italy Department of Pure and Applied Sciences University of Urbino "Carlo Bo" Urbino Italy

Dealing with a growing amount of data is a crucial challenge for the future of information and communication technologies. More and more devices are expected to transfer data through the Internet, therefore new solutions have to be designed in order to guarantee low latency and efficient traffic management. In this paper, we propose a solution that combines the edge computing paradigm with a decentralized communication approach based on Peer-to-Peer (P2P). According to the proposed scheme, participants to the system are employed to relay messages of other devices, so as to reach a destination (usually a server at the edge of the network) even in absence of an Internet connection. This approach can be useful in dynamic and crowded environments, allowing the system to outsource part of the traffic management from the Cloud servers to end-devices. To evaluate our proposal, we carry out some experiments with the help of LUNES, an open source discrete events simulator specifically designed for distributed environments. In our simulations, we tested several system configurations in order to understand the impact of the algorithms involved in the data dissemination and some possible network arrangements. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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Microscopic Disruption Management: Energy Consumption and Passenger Compensation Optimisation

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Transportation Research Procedia 2022年 62卷 43-50页

作者： Luis Cadarso Esteve Codina Ricardo García-Ródenas Aerospace Systems and Transport Research Group European Institute for Aviation Training and Accreditation (EIATA) Rey Juan Carlos University Fuenlabrada 28942 Madrid Spain Department of Statistics and Operations Research Universitat Politècnica de Catalunya (UPC) Spain Department of Mathematics School of Computer Science Institute of Applied Mathematics in Science and Engineering (IMACI) University of Castilla-La Mancha Ciudad Real Spain

Rail operations are often disrupted by accidents that cause traffic to diverge from the scheduled operations, rendering it difficult to run the schedule as planned. In such a case, the operator must change the schedule to return to the original schedule. If passengers are delayed, a train operator may have a policy of economically compensating them (e.g., refunding ticket fare). Compensation amounts are usually determined by the length of the delay. As a result, it is critical to have a smart way of determining whether to accelerate trains to absorb delays, thus increasing energy usage, or to compensate passengers. This paper presents a mathematical model for determining the speed profile while taking passenger usage into account. The model determines the best sequence of operating regimes and switching points between them for a variety of different situations and train types, all while accounting for delays and passenger compensation policies. The aim is to reduce both the amount of energy consumed and the amount of compensation paid to passengers. There are constraints on traction and braking forces, train velocity, forces induced by vertical and horizontal track profile, and passenger compensation policy. The results of computational tests performed on practical problem instances of the Spanish rail operator RENFE are showed. The suggested approach is capable of producing strategies that strike an excellent balance between different managerial objectives.

关键词： Train re-scheduling Passenger compensation Delays Optimal control

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