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Artificial Circulation System Algorithm:A Novel Bio-Inspired Algorithm

computer Modeling in engineering & Sciences 2025年第1期142卷 635-663页

作者： NerminÖzcan Semih Utku Tolga Berber Department of Biomedical Technologies Dokuz Eylül UniversityIzmir35390Turkey Department of Biomedical Engineering Iskenderun Technical UniversityIskenderun31200Turkey Department of Computer Engineering Dokuz Eylül UniversityIzmir35390Turkey Department of Statistics and Computer Sciences Karadeniz Technical UniversityTrabzon61080Turkey

Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering appli*** present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this *** work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark *** method was subsequently examined by application to 12 CEC 2022 benchmark problems of different ***,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and ***,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing *** results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and *** outperformed 42 algorithms statistically,according to post-hoc *** also outperformed 9 algorithms *** study concludes that ACSA offers competitive solutions in comparison to popüler methods.

关键词： Bio-inspired evolutionary heuristic metaheuristic optimization

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Distributionally Robust Chance-Constrained Optimization for Soft Open Points Operation in Active Distribution Networks

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CSEE Journal of Power and Energy Systems 2025年第2期11卷 637-648页

作者： Qilin Hou Ge Chen Ningyi Dai Hongcai Zhang Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart CityUniversity of MacaoMacao 999078China

The escalating installation of distributed generation (DG) within active distribution networks (ADNs) diminishes the reliance on fossil fuels, yet it intensifies the disparity between demand and generation across various regions. Moreover, due to the intermittent and stochastic characteristics, DG also introduces uncertain forecasting errors, which further increase difficulties for power dispatch. To overcome these challenges, an emerging flexible interconnection device, soft open point (SOP), is introduced. A distributionally robust chance-constrained optimization (DRCCO) model is also proposed to effectively exploit the benefits of SOPs in ADNs under uncertainties. Compared with conventional robust, stochastic and chance-constrained models, the DRCCO model can better balance reliability and economic profits without the exact distribution of uncertainties. More-over, unlike most published works that employ two individual chance constraints to approximate the upper and lower bound constraints (e.g, bus voltage and branch current limitations), joint two-sided chance constraints are introduced and exactly reformulated into conic forms to avoid redundant conservativeness. Based on numerical experiments, we validate that SOPs' employment can significantly enhance the energy efficiency of ADNs by alleviating DG curtailment and load shedding problems. Simulation results also confirm that the proposed joint two-sided DRCCO method can achieve good balance between economic efficiency and reliability while reducing the conservativeness of conventional DRCCO methods.

关键词： Active distribution networks distributionally robust chance-constrained optimization soft open points

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Noisy Computing of the Threshold Function 36

Noisy Computing of the Threshold Function

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Wang, Ziao Ghaddar, Nadim Zhu, Banghua Wang, Lele Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical and Computer Engineering University of Washington United States Department of Electrical and Computer Engineering University of British Columbia Canada

Coping with noise in computing is an important problem to consider in large systems. With applications in fault tolerance (Hastad et al., 1987;Pease et al., 1980;Pippenger et al., 1991), noisy sorting (Shah and Wainwright, 2018;Agarwal et al., 2017;Falahatgar et al., 2017;Heckel et al., 2019;Wang et al., 2024a;Gu and Xu, 2023;Kunisky et al., 2024), noisy searching (Berlekamp, 1964;Horstein, 1963;Burnashev and Zigangirov, 1974;Pelc, 1989;Karp and Kleinberg, 2007), among many others, the goal is to devise algorithms with the minimum number of queries that are robust enough to detect and correct the errors that can happen during the computation. In this work, we consider the noisy computing of the threshold-k function. For n Boolean variables x = (x1, ..., xn) ∈ {0, 1}n, the threshold-k function THk(·) computes whether the number of 1's in x is at least k or not, i.e., (Equation presented) The noisy queries correspond to noisy readings of the bits, where at each time step, the agent queries one of the bits, and with probability p, the wrong value of the bit is returned. It is assumed that the constant p ∈ (0, 1/2) is known to the agent. Our goal is to characterize the optimal query complexity for computing the THk function with error probability at most δ. This model for noisy computation of the THk function has been studied by Feige et al. (1994), where the order of the optimal query complexity is established;however, the exact tight characterization of the optimal number of queries is still open. In this paper, our main contribution is tightening this gap by providing new upper and lower bounds for the computation of the THk function, which simultaneously improve the existing upper and lower bounds. The main result of this paper can be stated as follows: for any 1 ≤ k ≤ n, there exists an algorithm that computes the THk function with an error probability at most δ = o(1), and the algorithm uses at most (Equation presented) queries in expectation. Here we define m (Eq

关键词： Active learning

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Implementation of 16-QAM OFDM for TV white space spectrum efficiency gain in AWGN channel

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International Journal of Wireless and Mobile Computing 2025年第3期28卷 245-256页

作者： Azhar, Khola Omar, Muhammad Usman, Muhammad Khan, Saad Saleem Larkin, Stephen Electrical Engineering Department University of Engineering and Technology Punjab Taxila Pakistan Department of Electrical Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Electrical Engineering National University of Computer and Emerging Sciences Islamabad Pakistan Africa New Energies London United Kingdom

Wireless communication has grown tremendously in recent years, impacting nearly every feature of our lives. The increased exigency for wireless broadband services leads to a huge demand for dynamic spectrum access, such as TV White Spaces (TVWS). The possibility of interference increases with the high density of users, reducing the Spectrum Efficiency (SE). SE improvement techniques have been utilised to resolve network congestion issues. This paper presents the Orthogonal Frequency Division Multiplexing (OFDM) wireless communication model with the Quadrature Amplitude Modulation (16-QAM) technique for TVWS SE gain. The performance analysis of the proposed model is based on parameters such as Packet Loss, Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR). The constellation diagrams and signal trajectories are used to evaluate the efficacy of the modulation technique. Zero packet loss is achieved by implementing the 16-QAM OFDM, demonstrating efficient usage of TVWS with extended coverage and almost homogeneous data distributions. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Signal to noise ratio

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Mathematical Model for Delay Locked Loop Discriminator for 5G Demodulation Reference Signal

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IEEE Access 2025年 13卷 69513-69519页

作者： Amjad, Freeha M. Yusof, Kamaludin Mohamad Bouallegue, Belgacem Gasouma, Amir A. A. Universiti Teknologi Malaysia Faculty of Electrical Engineering School of Engineering Skudai81310 Malaysia King Khalid University College of Computer Science Department of Computer Engineering Abha61421 Saudi Arabia

The vulnerable nature of satellite-based positioning systems under certain conditions such as urban canyons and indoors has given rise to the need for alternative positioning system. Cellular signal based positioning is gaining popularity and development of an accurate cellular signal-based positioning receiver is in progress. Among the cellular signals, 5G is the most preferable due to its high carrier frequency, high bandwidth, high power, ease of access and low infrastructure cost. A review of the literature suggests that 5G based positioning receiver uses a Delay Locked Loop (DLL) to track the phase of the demodulation reference signal (DMRS) of the received 5G signal. An essential part of the DLL is the discriminator, which is used to estimate the phase difference between the received DMRS and the replica of transmitted DMRS. In order to understand the working involved in the discriminator, it is ideal to study its mathematical model. The discriminator's mathematical model includes the mathematical representation of early, prompt and late DMRS symbols, the correlations between received DMRS symbols and early, prompt and late DMRS symbols, and the S-curve. These mathematical representations, available in the literature, are analyzed in this work using the MATLAB plots. It is observed that the plots do not accurately depict the theory. Therefore, the need for an improved mathematical model arises. This work is an effort to obtain improved mathematical model for the DLL discriminator processing which is also analyzed using MATLAB plots. The plots show that the improved mathematical model accords with the theory involved in the discriminator. © 2013 IEEE.

关键词： Demodulation

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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks

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IEEE Access 2025年 13卷 74913-74925页

作者： Abd El Moaty Mohamed Gouda, Ahmed Hamad, Ehab K. I. Hussein, Aziza I. Mabrook, M. Mourad Donkol, A.A. Nahda University Faculty of Engineering Electrical Engineering Department Beni Suef62764 Egypt Aswan University Faculty of Engineering Department of Electrical Engineering Aswan81542 Egypt Effat University Department of Electrical and Computer Engineering Jeddah22332 Saudi Arabia Beni-Suef University Faculty of Navigation Science and Space Technology Space Communication Department Beni Suef62511 Egypt South Valley University Faculty of Engineering Electrical Engineering Communication and Electronics Department Qena83523 Egypt

Millimeter-wave (mmWave) communication systems utilize narrow beamforming to ensure adequate signal power. However, beam alignment requires significant training overhead, especially in high mobility scenarios. Previous research has utilized synthetic data for position-aided beam prediction, which does not fully capture real-world complexities. In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. For 64-beams Scenario 1, the E-CNN model showed an increase in average top-1 accuracy from 55.57% to 63.92%, and in case of 32-beams, the accuracy increased from 71.34 % to 82.06%. For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. In addition, besides showing significant power loss reduction in various scenarios, the proposed E-CNN model has demonstrated robustness regarding real-word conditions and adaptability for various beam setups. The model realized as high as a 50% power loss reduction in arguably the most challenging graphs, which is an exercise in reliability. This research fills the existing gap between the simulated aid beam alignment and real-world position beam aided alignment, which can be useful in improving beamforming in the upcoming wireless networks. © 2013 IEEE.

关键词： Convolutional neural networks

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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New Monolithic Modular DC-DC Converter for Constant Current Offshore Interconnection With VSC HVdc Network

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IEEE Access 2025年 13卷 71806-71821页

作者： Mansour, Mohamed Abdel-Moneim, Mohamed G. Abdel-Khalik, Ayman S. Ahmed, Shehab Ahmed, Khaled H. Tennessee Tech University Department of Electrical and Computer Engineering CookevilleTN38501 United States Alexandria University Department of Electrical Engineering Alexandria21526 Egypt Sultan Qaboos University Department of Electrical and Computer Engineering Muscat123 Oman Division Thuwal23955 Saudi Arabia University of Strathclyde Department of Electronic and Electrical Engineering GlasgowG1 1XQ United Kingdom

The rapid growth in HVDC (High Voltage Direct Current) grids has shown the falls of point-to-point connections. Several challenges such as the requirement of DC (Direct Current) fault blocking capability, interfacing of different grounding schemes, offering multi-vendor interoperability, and difficult to achieve high DC voltage stepping, represent serious issues to deployment of HVDC grids. DC-DC converters are considered the optimum candidate to overcome these challenges in HVDC grids interconnection. In this paper, a novel isolated hybrid monolithic modular DC-DC converter is proposed that interconnects LCC/VSC (Line Commutated Converter/Voltage Source Converter) based HVDC networks. It achieves smaller count of semiconductors, lower conduction losses and DC fault blocking. Detailed mathematical analysis, design, and control of the proposed DC-DC converter are illustrated. Also, both simulation model and experimental test rig are built to validate the proposed DC-DC converter under different normal operational and fault scenarios. © 2013 IEEE.

关键词： HVDC power transmission

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A comprehensive analysis of real-time video anomaly detection methods for human and vehicular movement

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Multimedia Tools and Applications 2025年第10期84卷 7519-7564页

作者： Pathirannahalage, Iroshan Jayasooriya, Vidura Samarabandu, Jagath Subasinghe, Akila Department of Electrical and Electronic Engineering Faculty of Engineering University of Sri Jayewardenepura Gangodawila Nugegoda10250 Sri Lanka Department of Electrical and Computer Engineering Faculty of Engineering Western University LondonONN6A 3K7 Canada

The ubiquitous integration of computer vision technologies has revolutionised various application domains, ranging from agriculture and transportation to healthcare and the military. In this context, real-time anomaly detection in video streams has emerged as a critical facet, particularly concerning the movement of vehicles and humans. Despite the increasing adoption of these technologies in real-life applications, a conspicuous research gap exists, the necessity for robust validation of benchmarked datasets and state-of-the-art methods before deployment in real-time settings. This paper addresses this gap through a holistic approach to extensively review the real-time video anomaly detection literature in Human and Vehicular Movement applications. Specifically, we examine the various architectures used for real-time video anomaly detection and provide a detailed analysis of their performance on benchmarked datasets. Furthermore, we delve into the multifaceted challenges encountered when identifying anomalies across different categories, encompassing issues related to computational resources, noise interference, frame complexity, data sparsity, diversity, ethical considerations and their combinations. We address these challenges by proposing potential future trends in anomaly detection, including the development of more sophisticated datasets, exploration of hybrid architectural models, integration of multi-camera systems, advancement in open-set action recognition algorithms, model development for cutting-edge process automation systems, harnessing the potential of IoT, leveraging edge computing, and incorporating drone technologies into anomaly detection strategies. This paper aims to illuminate the direction of real-time anomaly detection for future research in vehicular and human movement. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anomaly detection

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Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks

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IEEE Transactions on Smart Grid 2025年第3期16卷 2647-2658页

作者： Qiu, Wei Yin, He Dong, Yuqing Wei, Xiang Liu, Yilu Yao, Wenxuan Hunan University College of Electrical and Information Engineering Changsha410082 China University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. Results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification. © 2010-2012 IEEE.

关键词： Multi-task learning

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