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1st International Conference on engineering and technology, ICoEngTech 2021

作者： Ramli, Nuraminah Othman, Rozmie Razif Hendradi, Rimuljo Fauzi, Shukor Sanim Mohd Ismail, Iszaidy Ahmad, Mohd Zamri Zahir Nasrudin, Mohd Wafi Universiti Malaysia Perlis Pauh Putra Perlis Arau02600 Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Pauh Putra Perlis Arau02600 Malaysia Faculty of Sciences and Technology Universitas Airlangga Jawa Timur Surabaya60115 Indonesia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Perlis Arau02600 Malaysia

Combinatorial testing or t-way testing (t represents strength) is useful to detect faults due to interactions. Pairwise testing is one type of t-way testing. The technique is effective in reducing the number of test cases without decreasing the level of coverage. Besides, its purpose is to overcome the problem of exhaustive testing that produces a great number of test cases and is impossible to be implemented due to time and cost constraints. Pairwise T-way Test Suite Generation Strategy based on Ant Colony Optimization (pTTSGA) is introduced to generate a near-optimum test suite size. Experiments have been conducted to evaluate the ability of this strategy for pairwise testing. The results are compared to benchmark results. Overall, pTTSGA produces a comparable test suite size. © Published under licence by IOP Publishing Ltd.

关键词： Ant colony optimization

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Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10143-10155页

作者： Malu Zhang Xiaoling Luo Jibin Wu Ammar Belatreche Siqi Cai Yang Yang Haizhou Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things Sichuan University of Science and Engineering Yibin China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne U.K. Department of Electrical and Computer Engineering National University of Singapore Lower Kent Ridge Rd Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong (CUHK) Shenzhen China

The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processes within the brain remains limited. However, no existing artificial intelligence models have yet matched human-level capabilities in terms of memory storage and retrieval. This study introduces a brain-inspired spiking neural model that integrates the learning and forgetting processes of sequential memory. The proposed model closely mimics the distributed and sparse temporal coding observed in the biological neural system. It employs one-shot online learning for memory formation and uses biologically plausible mechanisms of neural oscillation and phase precession to retrieve memorized sequences reliably. In addition, an active forgetting mechanism is integrated into the spiking neural model, enabling memory removal, flexibility, and updating. The proposed memory model not only enhances our understanding of human memory processes but also provides a robust framework for addressing temporal modeling tasks.

关键词： Neurons Biological system modeling Computational modeling Brain modeling Dendrites (neurons) Oscillators Neural activity Membrane potentials Encoding Adaptation models

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Development of Mutal Recognition of Education Approach in Advanced computer-Oriented engineering Technologies in the Baltic Region

Development of Mutal Recognition of Education Approach in Ad...

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International Scientific Conference on Power and Electrical engineering of Riga Technical University (RTUCON)

作者： Nadezhda Kunicina Anton Rassõlkin Rasa Bruzgiene Darius Plonis Jelena Caiko Institute of Electrical Engineering and Electronics FEEE Riga Technical University Riga Latvia Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Computer Sciences Faculty of Informatics Kaunas University of Technology Kaunas Lithuania Department of Electronic Systems Vilnius Gediminas Technical University Vilnius Lithuania

The continued improvement and development of study programmes content for mutual recognition of study programmes among the largest technical universities in the Baltic region provides a very good opportunity for regional development as it brings a good advancement in academics and research at the involved universities, allows for more flexible and reliable collaboration with industrial partners, and provides an opportunity to develop new technologies and implement them for regional support. The goal of the development is to promote the involvement of students in the design of adaptive systems by providing them with the necessary set of skills that will allow them to immediately engage in the design of innovative adaptive systems and successfully solve technological challenges that have been identified through the cooperation of educational and research institutions. The adoption of an innovative educational strategy will aid in the creation of new technology and creative solutions even before students begin their studies. Collaboration between academic personnel and industry will help students build their abilities.

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A multiplayer and activities based approach to Chulalongkorn Virtual Interactive Memorial Hall

A multiplayer and activities based approach to Chulalongkorn...

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International Conference on engineering, Applied Sciences, and technology (ICEAST)

作者： Suebphong Noisri Rizwan Ullah Avishi Waidyaratne SK Wijayasekara Pisit Vanichchanunt Nittinun Sinpan Imran Saeed Mirza Tanakorn Tanmalaporn Pruk Sasithong Aisha Munir Siwanart Jearavongtakul Lunchakorn Wuttisittikulkij Department of Electrical Engineering Wireless Communication Ecosystem Research Unit Chulalongkorn University Bangkok Thailand Department of Electrical Electronic and Telecommunication Engineering Faculty of Engineering General Sir John Kotelawala Defence University Ratmalana Sri Lanka Department of Electrical and Computer Engineering Facalty of Engineering King Monkut's University of Technology North Bangkok Bangkok Thailand

Chulalongkorn University is the premier university in Thailand, and its memorial hall is a historical landmark that showcases the prestigious history and accomplishments of the university. As a result of coronavirus-related closures, the number of visitors who were able to visit this memorial was drastically lower over the last two years, and some relics were prohibited from being visited and appreciated as well. As a solution to these problems, a virtual tour using metaverse technology is described in this paper; specifically, the memorial hall is modeled digitally using three-dimensional (3D) models, and users can observe the virtual museum environment and perform various activities virtually. A mechanism called the multiplayer approach is adopted, where several visitors can enjoy the museum's tour simultaneously by using our web-based metaverse. An activity-based environment is created where the visitors can play the piano, find ancient items hidden inside the museum, and get a reward upon successful completion of the task. It is believed that this virtual tour will meet a visitor's needs while at the same time safeguarding priceless cultural and historical artifacts and other sources of knowledge.

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An Exploratory Investigation into Sustaining Cybersecurity Protection Through the Implementation of SETA

An Exploratory Investigation into Sustaining Cybersecurity P...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Abdullah Mahfuzul Huda Mohammed Brayyich Noor Hanoon Haroon Abdul Azeem Khan Rajan Verma University Institute of Computing Chandigarh University Punjab India College of Computing and Informatics Saudi Electronic University Riyadh KSA College of Technical Engineering National University of Science & Technology Dhi Qar Iraq Department of Computer Technical Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Faculty of Islamic Technology Negara Brunei Darussalam Division of Research and Development Lovely Professional University Phagwara KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Most modern businesses run their competitive operations online. Cybersecurity is crucial to commercial operations. No cybersecurity defensive solution is universal. Standard security strategy involves using hardware and software to counter known security threats that may become stronger over time. Attackers and security professionals fight constantly. A security approach based primarily on tools is untenable. However, some firms skip employee awareness training, resulting in biased cybersecurity breach decisions. This study objectively analyzes key factors that help enterprises and organizations deploy Security Education, Training, and Awareness (SETA) to find ways to maintain such protection. A dataset of UK corporate cybersecurity breach surveys is used to perform performance testing on eight supervised learning models. The entire research uses the model's built characteristics that are excellent at recognizing SETA-adopting enterprises and organizations. The trial shows that awareness is vital when organizations use SETA. Many firms don't know how to safeguard employees from cybersecurity risks when they use online products and services, such as external web services.

关键词： Training Surveys Radio frequency Analytical models Web services Standards organizations Information security computer security Protection Testing

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Exploring Fairness for FAS-assisted Communication Systems: from NOMA to OMA

arXiv

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arXiv 2024年

作者： Yao, Junteng Zhou, Liaoshi Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Empirical analysis validates that our proposed solutions outperform conventional NOMA and OMA benchmarks in terms of fairness. © 2024, CC BY.

关键词： Approximation algorithms

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Model Predictive Control Method for Ten-Switch Three-Phase Three-Level Inverter

Model Predictive Control Method for Ten-Switch Three-Phase T...

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Annual Conference of Industrial electronics Society

作者： Naki Guler Ugur Fesli Hasan Komurcugil Sertac Bayhan Electrical and Electronic Engineering Technology Faculty Gazi University Ankara Turkey Renewable Energy and Smart Grids Application and Research Center Gazi University Ankara Turkey Electronic and Automation Department Technical Sciences Vocational School Gazi University Ankara Turkey Department of Computer Engineering Eastern Mediterranean University Famagusta Turkey Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper proposes a multi-objective model predictive control (MPC) for a grid-connected ten-switch three-phase three-level inverter. The main objective of the proposed MPC method is to control the grid current. In addition, the voltage balancing of the DC capacitors in the ten-switch three-level inverter is essential to generate voltage levels properly. Therefore, two objectives need to be achieved simultaneously. The multi-objective cost function feature of the MPC method is used to achieve these objectives. The simulation studies are performed using MATLAB/Simulink environments to investigate the effectiveness of the proposed MPC method. The results show that the proposed control method exhibits accurate performance under steady-state conditions and fast dynamic responses under transient conditions. Moreover, the grid current spectrums show that the total harmonic distortion is 2.21% in normal operation, and it is under the desired limits, even with a 50% parameter mismatch in filter inductance.

关键词： Industrial electronics Total harmonic distortion Capacitors Cost function Inverters Steady-state Voltage control Transient analysis MATLAB Predictive control

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Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Xiaolu Huang, Chenghao Zhang, Yanru Wang, Hao University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Monash University Faculty of IT Monash Energy Institute Department of Data Science and AI MelbourneVIC3800 Australia Shenzhen Institute for Advanced Study of UESTC Shenzhen China

The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operations, as unobservable PV generation further complicates the supply-demand balance. Therefore, estimating this generation from net load, known as PV disaggregation, is critical. Given privacy concerns and the need for large training datasets, federated learning becomes a promising approach, but statistical heterogeneity, arising from geographical and behavioral variations among prosumers, poses new challenges to PV disaggregation. To overcome these challenges, a privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL). The proposed method employs a two-level framework that combines local and global modeling. At the local level, a transformer-based PV disaggregation model is designed to generate solar irradiance embeddings for representing local PV conditions. A novel adaptive local aggregation mechanism is adopted to mitigate the impact of statistical heterogeneity on the local model, extracting a portion of global information that benefits the local model. At the global level, a central server aggregates information uploaded from multiple data centers, preserving privacy while enabling cross-center knowledge sharing. Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods. © 1963-2012 IEEE.

关键词： Training Load modeling Data centers Solar irradiance Data models Servers Adaptation models Accuracy Clouds Electricity

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Stereo Image Super Resolution Reinforced by Non-Local Means Denoising (NLMD) Algorithm

Stereo Image Super Resolution Reinforced by Non-Local Means ...

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International Conference on Developments in eSystems engineering (DESE)

作者： Ali Salim Rasheed Marwa Jabberi Tarak M. Hamdani Adel M. Alimi College of Engineering University of Information Technology and Communications Baghdad Iraq REsearch Groups in Intelligent Machines (REGIM Lab) University of Sfax National Engineering School of Sfax (ENIS) BP 1173 Sfax Tunisia Higher Institute of Computer Science Mahdia (ISIMa) University of Monastir Monastir Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

For single-image reconstruction and enhancement techniques, deep convolutional neural networks (CNNs) performed better at producing super-resolution (SR) images from the details of low-resolution (LR) images. As a result, by utilizing intra- and cross-view information, Stereo Super Resolution approaches increased the effectiveness of these convolutional networks in recovering the original texture of individual images and provided further information about their tiny details. This paper presents a model of the Stereo Image Super Resolution Algorithm (SSR nlmd ) reinforced by non-local means denoising (NLMD). Using two weight-sharing networks (NAFNet Blocks) to extract intra-view features of the left and right input image pair, then fuse these features to SR reconstruction, is one method for recovering super-resolution (SR) image details from low-resolution and improving the visual rendering of their surface textures. The resolution levels of the images generated by NAFNet Blocks have been reinforced using the non-local means denoising algorithm to remove noise and distortion and obtain an explicit scene close to the original image (HR Image). Quantitative results of (SSR nlmd ) model demonstrated that our method outperformed state-of-the-art methods in terms of metric (PNSR/ SSIM), It was 31.29dB/0.8722 of 0376 Image on the DIV2K dataset. Quantitative scores on the BSD100 dataset were 38.88dB/0.9739 of 3096 Image. The result on the Set5 dataset was 32.29dB/0.8879 for the baby Image. The scores on the Set14 dataset were 21.46dB/0.5414 for the baboon Image. The visual evaluation of the images reconstructed by our method achieved high accuracy and clarity in the scene with less noise and enhanced quality.

关键词： Measurement Surface reconstruction Pediatrics Superresolution Noise reduction Noise Feature extraction Surface texture Image reconstruction Visual perception

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Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification

arXiv

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arXiv 2023年

作者： Gao, Zan Wei, Shengxun Guan, Weili Zhu, Lei Wang, Meng Chen, Shengyong The Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China The Faculty of Information Technology Monash University Clayton Campus Australia The School of Electronic and Information Engineering Tongji University Shanghai200092 China The school of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Cloth-changing person reidentification (ReID) is a newly emerging research topic aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID remains challenging because pedestrian appearance representations can change at any time. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is effectively utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but are also suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on six public clothing person ReID datasets (LaST, LTCC, PRCC, NKUP, Celeb-reID-light, and VC-Clothes) demonstrate the superiority of the IGCL method. It outperforms existing methods on multiple datasets, and the extracted features have stronger representation and discrimination ability and are weakly corre

关键词： Semantics

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