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A Power-System Economic Dispatching Based on Enhanced Group Search Optimizer 1

18th International Conference on intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Nguyen, Trong-The Nguyen, Trinh-Dong Nguyen, Vinh-Tiep Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Economic dispatch (ED) is one of the critical tasks in a power system’s optimization issues, but its characteristics with non-convex, high-dimensional, non-linear, and non-differentiable, so that caused solutions are more challenging in reality. This paper suggests an enhanced group search optimizer (EGSO) for solving the ED model with the valve-point effect and the different fuels. The EGSO adopts the original group search optimizer (GSO) by changing polar to Cartesian coordinate’s conversion and adding cyclic translation operators to the search behavior of the finder and walker to avoid falling into local optimal and slow convergence of the GSO. In the experiment section, the testing power systems of the ED model are used to validate the EGSO performance. The experimental results reveal that EGSO outperformed the other algorithms regarding convergence speed, the time required to complete calculations, and impacts with lower fuel cost. Additionally, with excellent outcomes in optimization issues involving multiple local optimal values show the EGSO is practical, quick, and efficient in addressing the challenging ED model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Short-term household load forecasting model based on variational mode decomposition and gated recurrent unit with attention mechanism

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 143-153页

作者： Lin, Fu Liu, Shi-Jian Chao, Han-Chieh Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The household load is an important part of the load. Accurate short-term household load forecasting is of great help to issues such as power price formulation, demand response, or power transmission. This paper proposed a forecasting model based on Variational Mode Decomposition (VMD) and Gated Recurrent Unit (GRU) combined with self-attention mechanism (SAM) and attention mechanism (AM) for short-term household load forecasting. First, we use VMD to decompose household electricity load data, extract sub-sequences with strong periodic information, and reorganize the original data sets with sub-sequences and data pre-processed. The new data sets contain more abundant information. Secondly, GRU combined with SAM and AM forecasting model for learning and training to obtain results. Finally, RMSE and MAPE evaluation functions are used for method evaluation to verify the superiority of the proposed scheme. The RMSE and MAPE of the proposed method are 0.193 kW and 15.567%, respectively, which are more favorable than traditional GRU. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Variational mode decomposition

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

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International Conference on Mechatronics (ICM)

作者： Zhenyu Li Ruixin Wu Jin Wang Chuanqi Hu Zhi Zheng Jun Meng Guodong Lu School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China Polytechnic Institute Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture.

关键词： Point cloud compression Visualization Three-dimensional displays Trajectory planning Heuristic algorithms Collaboration Real-time systems Safety Optimization Quadrotors

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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators

Evolutionary Dynamic Optimization-Based Calibration Framewor...

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Congress on Evolutionary Computation

作者： Zhenhua Yang Muyao Zhong Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration.

关键词： Adaptation models Fluctuations Heuristic algorithms Computational modeling Scalability Manuals data models

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Efficient Biomedical Ontology Meta-matching Based on Interpolation Model Based Hybrid Evolutionary Algorithm

Efficient Biomedical Ontology Meta-matching Based on Interpo...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xue, Xingsi Wu, Qi Ye, Miao Zhu, Hai Huang, Yikun Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Taiyuan University of Technology College of Electrical and Power Engineering Taiyuan China Guilin University of Electronic Technology School of Information and Communication Guilin China Henan University of Engineering School of Computer Zhengzhou China Concord University College Fujian Normal University Fuzhou China

ISBN: (纸本)9781665468190

As an advanced biomedical knowledge modeling technology, biomedical ontology models the biomedical domain. However, since the lack of uniform standards for constructing biomedical ontologies, the biomedical ontologies obtained by different construction methods for the same thing may be different, which is known as the biomedical ontology heterogeneity problem. To solve this problem, we need to execute the biomedical ontology matching process, where it is important to integrate different similarity measures to improve the quality of alignment. Evolutionary Algorithm (EA) is an effective algorithm to address the biomedical ontology meta-matching problem. However, the classical EA-based biomedical ontology meta-matching technique needs to traverse reference alignment to evaluate the individuals, which makes the algorithm have high running time. To overcome this drawback, we propose an Interpolation Model (IM) based Hybrid EA (IM-HEA), which combines EA with a problem-specific IM to evaluate the individual and execute the local search process. In particular, we use lattice design to divide the feasible domain into several uniform sub-regions, and on this basis, approximately evaluate the fitness of newly generated individual. In addition, to avoid the algorithm falling into the local optimum, we further introduce an IM-based local search process into EA's evolving process. In the experiment, we test IM-HEA on OAEI's Benchmark and Anatomy and compared them with classical EA in terms of alignment's quality and running time. The experimental results show that IM-HEA greatly enhances the efficiency of EA with little sacrifice on the alignment's quality. © 2022 IEEE.

关键词： Interpolation

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Efficient survivable mapping algorithm for logical topology in IP-over-WDM optical networks against node failure

引用

Journal of Supercomputing 2023年第5期79卷 5037-5063页

作者： Cheng, Dun-Wei Chang, Jo-Yi Lin, Chen-Yen Lin, Limei Huang, Yanze Thulasiraman, Krishnaiyan Hsieh, Sun-Yuan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan701 Taiwan College of Mathematics and Informatics and the Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Mathematics and Physics Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science University of Oklahoma 660 Parrington Oval NormanOK73019 United States Department of Computer Science and Information Engineering National Chi Nan University No. 1 Daxue Rd Nantou County Puli Township54561 Taiwan

The survivable mapping problem in an IP-over-WDM network with a logical graph and physical graph is the problem of finding a mapping of a logical network in a physical layer so that any failure in physical topology does not break the logical topology’s connection. Determining whether a survivable mapping against failure exists is an NP-complete problem, and therefore many heuristic algorithms have been proposed in the literature. In this paper, a heuristic mapping design strategy is proposed to enable the lightpaths to more efficiently endure a node failure. We individually consider the logical topology by building a Hamiltonian cycle and then deal with the mapping problem for disjoint physical paths. Experimental results demonstrate that the proposed algorithm can provide efficient survivable mapping in IP-over-WDM networks. The simulation shows that the survivability of previous methods (SNF and SMART-Node) is around 60–80%, but our proposed algorithm can build survivable mapping in all kinds of situations. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Heuristic algorithms

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WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning

arXiv

引用

arXiv 2023年

作者： Huo, Dongjian Zhang, Zehong Su, Hanjing Li, Guanbin Fang, Chaowei Wu, Qingyao School of Software and Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Peng Cheng Laboratory China Tencent Wechat Department School of Computer Science and Engineering Sun Yat-sen University China Xidian University China

Watermarking serves as a widely adopted approach to safeguard media copyright. In parallel, the research focus has extended to watermark removal techniques, offering an adversarial means to enhance watermark robustness and foster advancements in the watermarking field. Existing watermark removal methods mainly rely on UNet with task-specific decoder branches——one for watermark localization and the other for background image restoration. However, watermark localization and background restoration are not isolated tasks;precise watermark localization inherently implies regions necessitating restoration, and the background restoration process contributes to more accurate watermark localization. To holistically integrate information from both branches, we introduce an implicit joint learning paradigm. This empowers the network to autonomously navigate the flow of information between implicit branches through a gate mechanism. Furthermore, we employ cross-channel attention to facilitate local detail restoration and holistic structural comprehension, while harnessing nested structures to integrate multiscale information. Extensive experiments are conducted on various challenging benchmarks to validate the effectiveness of our proposed method. The results demonstrate our approach’s remarkable superiority, surpassing existing state-ofthe-art methods by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Restoration

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Improved RUL prediction method of wind turbine gearbox bearings under strong noises

Improved RUL prediction method of wind turbine gearbox beari...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Sheng Xiang Penghua Li Jie Hou Mamadsho Ilolov Jiwei Wang Deqi Huang Lirong Xie School of Automation& Key Laboratory of Intelligent Computing for Big Data Chongqing University of Posts and Telecommunications Chongqing China Center for Innovative Development of Science and New Technologies National Academy of Sciences of Tajikistan Dushanbe Tajikistan School of Electrical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

As essential components within the wind turbine gearbox, bearings are critical, and forecasting their Remaining Useful Life (RUL) is paramount. To enhance the integration of current inputs with historical data in Recurrent Neural Network (RNN) models, the Mogrifier Long Short-Term Memory (LSTM) approach has been devised. It interacts with inputs and recurrent data in a predetermined fashion, marking its initial application in wind turbine gearbox bearing RUL prediction. Furthermore, the Relative Standard Deviation of Envelope Spectrum (RSDES), a reliable health indicator (HI) under high noise conditions, is introduced for precise evaluation of the gearbox bearing health. Consequently, a novel RUL prediction technique is introduced. Initially, RSDES from vibration data serves as HIs. Subsequently, utilizing these HIs, the Mogrifier LSTM predicts future HIs, and the bearing’s RUL is determined once the predicted HI surpasses the failure threshold. Empirical outcomes underscore the proposed method’s superiority over existing techniques in bearing RUL prediction.

关键词： Vibrations Recurrent neural networks Accuracy Noise data models Wind turbines Reliability

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Voice Conversion Augmentation for Speaker Recognition on Defective datasets

arXiv

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arXiv 2024年

作者： Tao, Ruijie Shi, Zhan Jiang, Yidi Liu, Tianchi Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Singapore Institute of Technology Institute for Infocomm Research A∗STAR Singapore138632 Singapore Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China University of Bremen Bremen28359 Germany

Modern speaker recognition system relies on abundant and balanced datasets for classification training. However, diverse defective datasets, such as partially-labelled, small-scale, and imbalanced datasets, are common in real-world applications. Previous works usually studied specific solutions for each scenario from the algorithm perspective. However, the root cause of these problems lies in dataset imperfections. To address these challenges with a unified solution, we propose the Voice Conversion Augmentation (VCA) strategy to obtain pseudo speech from the training set. Furthermore, to guarantee generation quality, we designed the VCA-NN (nearest neighbours) strategy to select source speech from utterances that are close to the target speech in the representation space. Our experimental results on three created datasets demonstrated that VCA-NN effectively mitigates these dataset problems, which provides a new direction for handling the speaker recognition problems from the data aspect. © 2024, CC BY.

关键词： Classification (of information)

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