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Data-driven Robust State Estimation Through Off-line Learning and On-line Matching

Journal of Modern Power systems and Clean Energy 2021年第4期9卷 897-909页

作者： Yanbo Chen Hao Chen Yang Jiao Jin Ma Yuzhang Lin State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical&Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China School of Electrical and Information Engineering University of SydneySydney 2006Australia Department of Electrical and Computer Engineering University of MassachusettsLowellMA 01852USA

To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

关键词： Robust state estimation historical snapshot off-line learning on-line matching collinearity

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Status and Outlook of Image-based Malware Detection Technology

Status and Outlook of Image-based Malware Detection Technolo...

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computer Technology and Information Science (ISCTIS), International Symposium on

作者： Qiuyan Lan Yao Du Caixia Gao Haotian Zheng School of Computer Science and Engineering Southwest Minzu University Chengdu China Key Laboratory of Computer System State Ethnic Affairs Commission Southwest Minzu University Chengdu China

In recent years, computer vision technology has achieved rapid development and is widely used in the field of information security. Researchers have carried out a lot of research on the malicious code analysis technology based on image analysis, and many excellent image analysis models have successfully migrated to the malware detection area. This paper systematically collates the malware detection technology based on image analysis technology from different angles. Firstly, the development of computer vision technology in the field of malware, the main representation methods of feature images, malicious code detection models, and other issues are sorted out. After that, the relevant representative literature is collected and sorted out. Finally, the potential research direction of computer vision in the realm of malware detection is deliberated.

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Statistical approach and territorial analysis on the Covid-19 emergency: the case study of the municipality of Tito

Statistical approach and territorial analysis on the Covid-1...

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Metrology for Living Environment (MetroLivEn), IEEE International Workshop on

作者： Fausto Bisaccia Valentina Santarsiero Luigi Santopietro Simone Corrado Antonella Iuliano Beniamino Murgante Department of Mathematics Computer Science and Economics University of Basilicata Potenza Italy School of Engineering Laboratory of Urban and Regional System Engineering (LISUT) University of Basilicata Potenza Italy

This paper analyzes the impact of the Covid-19 pandemic on Tito, a small municipality in Basilicata, Italy. Descriptive and inferential statistical models were used to study the course of the pandemic and predict its future development. The paper examines the possible consequences of proximity to a Site of National Interest on the spread of infection. The linear regression model was used to analyze the above phenomena. The paper also illustrates the multiple linear regression model and how it was constructed, considering the qualitative explanatory variables and ANOVA. The models were applied to data collected in the Municipality of Tito to test the trend of infection within the municipal area, with particular attention to the variables of gender, age, area of residence and vaccination status.

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An Adaptively Distributed Algorithm for Noncooperative Games of High-Order Integrator systems

An Adaptively Distributed Algorithm for Noncooperative Games...

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International Conference on Control and Automation (ICCA)

作者： Xixiang Du Feng Xiao Mei Yu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources The School of Control and Computer Engineering North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In this paper, an adaptively distributed Nash equilibrium (NE) seeking algorithm is designed for heterogeneous high-order integrators in noncooperative games. The adaptively distributed algorithm is proposed that introduces a scaling parameter with adaptive adjustments to the weights of the edges in the communication graph. The main advantage is to ensure the convergence of the algorithm to the NE without requiring the knowledge of graph's algebraic connectivity. The convergence of the designed algorithm to the NE is analyzed by the Lyapunov stability theory and input-to-state stability theory. A numerical example is given to illustrate the proposed method.

关键词： Asymptotic stability Automation Games Nash equilibrium Stability analysis Distributed algorithms Numerical stability

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Graph-Augmented Contrastive Clustering for Time Series Data

SSRN

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SSRN 2024年

作者： Zhang, Qin Liang, Zhuoluo Ngueilbaye, Alladoumbaye Liu, Han Zhou, Hong Huang, Joshua Zhexue College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangzhou University China

The recent emergence of time series contrastive clustering methods can be categorized into two classes. The first class uses contrastive learning for universal representations, which can be effective in various downstream tasks, but overlooks important categorical information and clustering objectives, leading to unsuitable models. The second class incorporates contrastive learning into clustering objectives but fails to fully explore potential connections and structures between time series data. To this end, we propose the "Time Series Graph-augmented Contrastive Clustering (TSGCC) method", a graph-augmented contrastive learning framework aiming to create cluster-friendly features and assignments by iteratively optimizing contrastive loss. The method uses a weighted KNN graph to build positive and negative sample pairs, projecting the instances into a feature space with the dimensionality number of clusters. Experimental results demonstrate that TSGCC outperforms 18 advanced time-series clustering methods on the 36 representatives out of the 128 UCR benchmarks, achieving the best clustering results on 12 datasets, with the highest average rank (3.22) of RI and the second-highest average rank (3.89) of NMI among 18 clustering methods. We further compared our method with recent approaches on the entire set of 128 UCR datasets, demonstrating our method's broad applicability and effectiveness. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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On the Selection of Intermediate Length Representative Periods for Capacity Expansion

arXiv

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arXiv 2024年

作者： Anderson, Osten Yu, Nanpeng Oikonomou, Konstantinos Wu, Di Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States System Evaluation Group Pacific Northwest National Laboratory RichlandWA United States

As the decarbonization of power systems accelerates, there has been increasing interest in capacity expansion models for their role in guiding this transition. Representative period selection is an important component of capacity expansion modeling, enabling computational tractability of optimization while ensuring fidelity between the representative periods and the full year. However, little attention has been devoted to selecting representative periods longer than a single day. This prevents the capacity expansion model from directly simulating interday energy sharing, which is of key importance as energy generation becomes more variable and storage more important. To this end, we propose a novel method for selecting representative periods of any length. The method is validated using a capacity expansion model and production cost model based on California’s decarbonization goals. We demonstrate that the representative period length has a substantial impact in the results of the capacity expansion investment plan. © 2024, CC BY-NC-ND.

关键词： Decarbonization

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Zihan Wang Siyang Song Cheng Luo Yuzhi Zhou Shiling Wu Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Computing and Mathematical Sciences University of Leicester

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5.

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning

Defect Screening on Nuclear Power Plant Concrete Structures:...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wenlian Huang Guanming Zhu Qiang Huang Zhuangzhuang Chen Jie Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results.

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Adil Nawaz Faisal Saeed Victor C.M. Leung Jianqiang Li Zhaoxia Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Gastroenterology Shenzhen Children’s Hospital China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB’s input regions with high mutual information with the network’s output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets—CNMC, ISBI2016, and BreaKHis—demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains.

关键词： Pathology Accuracy Biological system modeling Noise Predictive models Reliability Decision trees Biological cells Cancer Biomedical imaging

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Equity-aware Load Shedding Optimization

arXiv

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arXiv 2024年

作者： Fang, Xin Wang, Wenbo Ding, Fei Department of Electrical & Computer Engineering Mississippi State University StarkvilleMS United States Energy System Integration National Renewable Energy Laboratory GoldenCO United States

Load shedding is usually the last resort to balance generation and demand to maintain stable operation of the electric grid after major disturbances. Current load-shedding optimization practices focus mainly on the physical optimality of the network power flow. This might lead to an uneven allocation of load curtailment, disadvantaging some loads more than others. Addressing this oversight, this paper introduces an innovative equity-aware load-shedding optimization model that emphasizes a fair allocation of load curtailment across the network. By proposing a novel equity indicator for load shedding and integrating it into an ACOPF-based optimization framework, we offer grid operators a more balanced and equitable load shedding strategy. Case studies highlight the importance of equity considerations in determining optimal load curtailment between buses. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric load flow

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