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Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals

CSEE Journal of Power and Energy Systems 2023年第5期9卷 1666-1675页

作者： Xinran Zhang Chao Lu Ying Wang Qiantu Ruan Hongbo Ye Weihong Wang the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China. the State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua UniversityBeijing 100084China School of Technology Beijing Forestry UniversityBeijing 100083China the State Grid Shanghai Electric Power Company Shanghai 200122China

The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and *** practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of *** this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is *** load model structure and the combined optimization and regression method to identify the parameters are first ***,the definition and analysis method of identifiability are *** CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate ***,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power *** results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed ***,the proposed PII can provide guidance in data length selection in practical load model identification situations.

关键词： Confidential interval Load modeling parameter estimation practical identifiability

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Intended Speech Classification with EEG Signals Based on a Temporal Attention Mechanism: A Study of Mandarin Vowels 46

Intended Speech Classification with EEG Signals Based on a T...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Xinyu Lai, Ying-Hui Chen, Fei Ministry of Natural Resources Key Laboratory of Urban Land Resources Monitoring and Simulation China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China National Yang Ming Chiao Tung University Department of Biomedical Engineering Taipei Taiwan

ISBN: (纸本)9798350371499

Speech brain-machine interfaces (BCIs) offer an effective means for patients with voice disorders to communicate, and research on decoding electroencephalography (EEG) signals related to intended speech can help to understand the mechanisms of language production in the brain. This study classified the intended speech EEG signals of four Chinese vowels, utilizing a dataset collected from 10 participants. A proposed TA-EEGNet model was employed, incorporating a temporal attention module. The model achieved an accuracy of 49.47%, surpassing other prevalent EEG classification models. The average accuracy in the binary classification of vowels was 69.83%. The vowels /u/ and /ü/ were classified with the lowest accuracy, suggesting difficulties in classifying vowels with similar articulatory movements based on intended speech EEG signals. Furthermore, the research analyzed the classification performance using data of different brain regions. The results showed that the auditory cortex, Broca's and Wernicke's areas, prefrontal cortex, and motor cortex outperformed the sensory cortex, indicating their contributions in the intended speech process of Mandarin vowels. Results also showed left hemisphere dominance. These findings contribute to the study of the neural mechanisms underlying speech production and articulatory movements, emphasizing the potential of speech BCIs to improve communication for people with speech disorders. © 2024 IEEE.

关键词： Electroencephalography

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Attribution rollout: a new way to interpret visual transformer

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Journal of Ambient Intelligence and Humanized Computing 2023年第1期14卷 163-173页

作者： Xu, Li Yan, Xin Ding, Weiyue Liu, Zechao College of Computer Science and Technology Harbin Engineering University Nantong Street Heilongjiang Harbin150001 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China Department of Medicine Harvard Medical School Longwood Avenue BostonMA02115 United States

Transformer-based models are dominating the field of natural language processing and are becoming increasingly popular in the field of computer vision. However, the black box characteristics of transformers seriously hamper their application in certain fields. Prior work relies on the raw attention scores or employs heuristic propagation along with the attention graph. In this work, we propose a new way to visualize model. The method computes attention scores based on attribution and then propagates these attention scores through the layers. This propagation involves attention layers and multi-head attention mechanism. Our method extracts salient dependencies in each layer to visualize prediction results. We benchmark our method on recent visual transformer networks and demonstrate its many advantages over the existing interpretability methods. Our code is available at: https://***/yxheartipp/attr-rollout. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Natural language processing systems

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Effects of Initialization Methods on the Performance of Multi-Objective Evolutionary Algorithms

Effects of Initialization Methods on the Performance of Mult...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gong, Cheng Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Population initialization is always needed in evolutionary multi-objective optimization (EMO) algorithms. Intuitively, a well-designed initialization method can help facilitate the evolutionary process and improve the performance of EMO algorithms. However, very few studies have investigated the effects of initialization methods on the performance of EMO algorithms. Many existing EMO algorithms randomly generate an initial population to start the evolutionary process. To fill this research gap and attract more attention from EMO researchers to this important yet under-explored issue, in this paper, we examine the effects of various initialization methods that may become promising alternatives to the commonly-used random initialization method. Each initialization method is evaluated through computational experiments on test problems of various sizes with 5-1000 decision variables. Experimental results clearly demonstrate the advantage of well-designed initialization methods over the random initialization method. This study provides useful insights into EMO algorithm design and motivates further research on population initialization. © 2023 IEEE.

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Superconducting dense lithium-based germanides with electride states

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Physical Review B 2024年第14期110卷 144523页

作者： Qianyi Wang Jiahui Wei Ting Zhong Jiance Sun Bo Gao Li Zhu Hanyu Liu Shoutao Zhang Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education School of Physics College of Materials Science and Engineering Department of Physics Key Laboratory of Material Simulation Methods and Software of Ministry of Education and State Key Laboratory of Superhard Materials College of Physics

Lithium-based electrides with interstitial anionic electrons (IAEs) have recently garnered a great deal of attention, since these materials were found to host intriguing properties such as multiple IAE arrangements and potential superconductivity. In this study, we here conducted comprehensive simulations on the pressure-induced stable Li-abundant germanides leveraging the advanced first-principles structure predictions. As a consequence, our simulations identified several metallic Li-Ge electride phases with robust stability, i.e., Li5Ge, Li6Ge, Li7Ge, Li8Ge, and Li10Ge. Strikingly, the evolution from connected one-dimensional (1D) to zero-dimensional (0D) electride states of Li5Ge was identified concurrent with an orthorhombic Cmcm to triclinic C2/c phase transition on account of compression-induced volume reduction, whereas Li10Ge possesses the unusual coexistent 0D and 1D IAEs. Electron-phonon coupling (EPC) calculations uncover that the superconductivity of Cmcm Li5Ge is superior to that of C2/c Li5Ge at 50 GPa, which is due to the larger EPC strength, higher density of states at the Fermi level, and more pronounced softened phonon modes of Cmcm Li5Ge. Remarkably, Li10Ge was calculated to have the highest superconducting critical temperature (Tc) of 8.5 K below 50 GPa compared to other lithium-carbon family electrides, which is ascribed to the fact that the Li 2p electron states near the Fermi energy are strongly coupled with phonon modes associated with the vibrations of Li atoms. Our findings provide insights into lithium-bearing electride systems with implications for the pursuit of distinguished electride superconductors.

关键词： Electronic structure Pressure effects Structural properties Superconductors Density functional calculations First-principles calculations

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Elevated hydrostatic pressure enhances the potential for microbially mediated carbon sequestration at the sediment–water interface in a deep-water reservoir by modulating functional genes and metabolic pathways

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Carbon Research 2024年第1期3卷 1-16页

作者： Li, Yumei He, Lixin Pan, Ying Chen, Kai Zhuo, Tianyu Yu, Kehong Zhang, Jinnan Zhou, Shilei Lei, Xiaohui Chai, Beibei Collaborative Innovation Center for Intelligent Regulation and Comprehensive Management of Water Resources School of Water Conservancy and Hydroelectric Power Hebei University of Engineering Handan056038 China School of Water Conservancy and Hydroelectric Power Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Intelligent Water Conservancy Hebei University of Engineering Handan056038 China School of Energy and Environmental Engineering Hebei University of Engineering Handan056038 China Tianjin Water Group Binhai Water Co. Ltd. Tianjin300308 China School of Architecture and Civil Engineering Xi’an University of Science and Technology Xi’an710054 China Pollution Prevention Biotechnology Laboratory of Hebei Province School of Environmental Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower Research Beijing100038 China

Microbially mediated carbon cycling is essential for the production of refractory dissolved organic carbon and subsequent formation of stable carbon sinks at the sediment–water interface (SWI) in aquatic ecosystems, such as lakes. It remains unclear how this process is influenced by hydrostatic pressure changes due to water level fluctuation in deep-water reservoirs. Here, a microcosm simulation experiment was carried out to decipher the response of microbially mediated carbon cycling to various hydrostatic pressures (i.e., 0.1 MPa [atmospheric pressure], 0.2 MPa, 0.5 MPa, 0.7 MPa) at the SWI in Jinpen Reservoir, Shaanxi Province, China. The response mechanisms of microbial community structure, functional gene abundance, and metabolic pathway activity associated with carbon cycling were explored by metagenomics and metabolomics. Results showed that the number of microbial species in sediment samples increased with elevating pressure. The relative abundance of archaea also increased from 0.2% to 0.4% as a consequence of pressure elevation, accompanied by 0.17% and 0.03% decrease in bacteria and fungi, respectively. In contrast with low pressures, high pressures allowed the microbial communities to form a more closely connected network, which maintained more complex interspecies interactions and greater system stability. High pressures additionally improved the abundances of specific functional genes (e.g., ALDO, ACO, sdhA, and sdhC) in carbon metabolic pathways, promoted carbon fixation by the reductive pentose phosphate (Calvin) and citrate cycles, and hindered methanogenesis. Piezophilic taxa (e.g., Gammaproteobacteria, Alphaproteobacteria, Bacteroidetes) and genes (e.g., ompH, asd) were identified among carbon cycling-associated microbial communities. The piezophilic genes, which were mainly present in the Proteobacteria phylum, increased first and then decreased in abundance with elevating pressure. The findings indicate that elevated hydrostatic pressure contrib

关键词： Reservoirs (water)

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Fabric4show: real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Lin, Huaizhou Cai, Dan Xu, Zengmin Wu, Jinsong Sun, Lixian Jia, Haibin School of Materials Science and Engineering Guangxi Key Laboratory of Information Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Center for Applied Mathematics of Guangxi (GUET) Guilin China Anview.ai Guilin China School of Artificial Intelligence Guilin University of Electronic Technology Guilin China Department of Electrical Engineering University of Chile Santiago Chile

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry. © The Author(s) 2024.

关键词： Automation Computer vision Defect detection Fabric manufacturing Faster R-CNN

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A Driving Area Detection Algorithm Based on Swin Transformer 5

A Driving Area Detection Algorithm Based on Swin Transformer

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Liu, Shuang Li, Ying Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China

ISBN: (纸本)9798350309034

The detection of drivable areas holds immense significance within the perception system of autonomous vehicles. This capability enables intelligent vehicles to gain a comprehensive understanding of the current road conditions and effectively identify safe areas suitable for safe navigation. Convolutional Neural Networks (CNNs) have achieved significant progress in the application of drivable areas detection, particularly in semantic segmentation. However, most existing CNN-based methods for drivable area detection only consider information within a local neighborhood, thus failing to effectively capture long-range dependencies. To tackle this concern, we propose a novel U-shaped network based on the Swin Transformer. The Swin Transformer block was employed as the fundamental element of both the encoder and decoder. Additionally, an upsampling layer was introduced in the decoder to effectively restore the resolution of the feature map. We evaluate our approach on the Cityscapes dataset, a publicly available benchmark for autonomous driving. Experimental results verify the exceptional performance of our proposed method, achieving an impressive mean Intersection over Union (mIoU) score of 91.61%. Our method outperforms existing state-of-the-art techniques in road segmentation detection, further demonstrating its superiority. © 2023 IEEE.

关键词： CNNS Driving Area Detection Semantic segmentation Swin Transformer

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Effects of Including Optimal Solutions into Initial Population on Evolutionary Multiobjective Optimization 23

Effects of Including Optimal Solutions into Initial Populati...

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2023 Genetic and Evolutionary computation Conference, GECCO 2023

作者： Gong, Cheng Nan, Yang Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798400701191

A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on the performance of EMO algorithms. However, in most existing EMO algorithms, one of the commonly-used initialization methods is to randomly generate a set of solutions as an initial population. One possible approach to improve random initialization is to include one or more Pareto optimal (near Pareto optimal) solution(s) in the initial population, which are expected to provide useful information and knowledge on the optimized problem. In this paper, to investigate the effectiveness of this initialization idea, we examine and quantify the effects of including one or more Pareto optimal solution(s) in the initial population on the performance of EMO algorithms. Experimental results demonstrate that it is worthwhile to first obtain and then include some Pareto optimal solutions in the initial population. Through a number of experiments and algorithm behavior analysis, this study provides supports and insights into EMO algorithm design and motivates further research on population initialization for EMO algorithms. © 2023 ACM.

关键词： Evolutionary algorithms

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An Area Optimization Approach for Large-Scale RM-TB Dual Logic Circuits Based on a Multitasking Optimization Algorithm

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IEEE Transactions on Computers 2025年第07期74卷 2348-2363页

作者： Wu, Xiaoqian Wang, Peng Li, Shaoquan Liu, Huaxiao Liu, Lei Jilin University College of Computer Science and Technology Changchun China Key Laboratory of Symbolic Computation China Knowledge Engineering of Ministry of Education China

Logic synthesis is a crucial step in integrated circuit design, and area optimization is an indispensable part of this process. However, the area optimization problem for large-scale Fixed Polarity Reed-Muller (FPRM) circuits is an NP-hard problem. To address this problem, we divide Boolean circuits into small-scale circuits based on the idea of divide-and-conquer using the proposed grouping decomposition mechanism. Each small-scale Boolean circuit is transformed into an FPRM circuit by a polarity transformation algorithm. To ensure the circuit’s functionality remains unaffected, we integrate FPRM circuits into an FPRM and Boolean (RM-TB) dual logic circuit based on the proposed gate-level integration. However, the area optimization problem of RM-TB dual logic circuits is a multi-task, high-dimensional, and multi-extremal combinatorial optimization problem. Therefore, we propose a Multipopulation Multitasking Optimization Algorithm (MMuOA) that integrates self-evolution with a multitasking equilibrium optimizer and cross-task evolution through knowledge sharing and transfer. This forms a dynamic optimization framework for simultaneously searching for the optimal polarity corresponding to the minimal area of RM-TB dual logic circuits. Moreover, we propose an Area Optimization Approach (AOA) for an RM-TB dual logic circuit with the minimum area using the MMuOA. Experimental results based on the Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the AOA compared to the state-of-the-art area optimization approach. © 1968-2012 IEEE.

关键词： Combinatorial optimization

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