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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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Myocardial Segmentation of 2D Echocardiography Based on the Improved Self-Attention Mechanism

Myocardial Segmentation of 2D Echocardiography Based on the ...

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Machine Vision, Image Processing and Imaging Technology (MVIPIT), International Conference on

作者： Delong Li Yixin Sun Xue Han Suyu Dong College of Computer and Control Engineering Northeast Forestry University Harbin China The Department of Ultrasound Harbin Medical University Cancer Hospital Harbin China

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

The measurement of different clinical indicators, such as the myocardial wall's thickness and the ventricle's volume, depends on accurately segmenting the left ventricle in 2D echocardiography. This segmentation is essential for making various clinical decisions, such as whether surgery is necessary. However, segmenting the myocardium requires clinical expertise and makes accurate segmenting time-consuming due to the poor quality of echocardiograms. It is essential to develop an automatic myocardial segmentation method. Some deep learning methods, especially the U-net structure, were proposed to support the automatic myocardium segmentation in echocardiography. The model must simultaneously capture the local and global dependency relationships in the ultrasound images for myocardial segmentation in echocardiography. Given the inherent attribute of local operation of convolution operations, this poses significant limitations for U-Net based on the traditional CNN as the basic architecture. For example, when dealing with complex myocardial textures and structures, we need to accurately identify the myocardium's global morphological features and overall structural patterns. Moreover, this locality may lead to deviations and errors when segmenting the boundary regions or the myocardial parts closely related to the surrounding tissues. Our primary focus will be redefining the skip connection using the attention mechanism. More integrated attention mechanisms can further enhance the segmentation effect of myocardial in echocardiography. Therefore, we integrated the cross-attention mechanism when concatenating features in the skip connection. Through conducting rigorous ablation studies on multiple echocardiography segmentation datasets, the performance of the proposed mechanism was proven.

关键词： Deep learning Image segmentation Attention mechanisms Accuracy Ultrasonic imaging Echocardiography Volume measurement Myocardium Transformers Thickness measurement

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Configuration Optimization of Hybrid Electrolyzer for Wind-Based Green Hydrogen Production System

Configuration Optimization of Hybrid Electrolyzer for Wind-B...

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China International Electrical and Energy Conference (CIEEC)

作者： Jiahu Lin Wenbo Hao Mingfei Ban Northeast Forestry University College of Computer and Control Engineering Harbin China State Grid Heilongjiang Electric Power Research Institute Harbin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

As the development of renewable energy progresses, the prospects for the application of green hydrogen are gradually expanding, with countries strengthening the construction of industries related to green hydrogen. This paper investigates the hybrid electrolytic hydrogen production system and its configuration optimization problem, considering wind power decomposition and multiple types of electrolyzers. Firstly, a model for wind power redistribution is established based on the characteristics of different types of electrolyzers to determine the power loads of each type. Subsequently, based on the solution results of the redistribution model, a hybrid electrolyzer system configuration optimization model with the objective of maximizing annual revenue is proposed. Finally, case studies show that Hybrid electrolyzer system offers advantages in terms of energy efficiency, reliability and economy. Compared to alkaline electrolyzer system, hybrid system can improve the efficiency and stability of energy utilization and better cope with the uncertainty of renewable energy sources. The hybrid system can significantly reduce the initial capital investment and number of configurations compared to proton exchange membrane electrolyzer system, thereby lowering the operating cost of the overall energy system.

关键词： Production systems Costs Uncertainty Wind energy Wind speed Wind power generation Green hydrogen

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Dynamical Analysis of Discrete Monochamus Alternatus-Dastarcus Helophoroides Model with Artificially Released and Spatial Diffusion

SSRN

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SSRN 2024年

作者： Liu, Xiaoxiao Zhang, Chunrui College of Computer and Control Engineering Northeast Forestry University Harbin 150040 China School of Science Northeast Forestry University Harbin 150040 China

In this paper, we consider a discrete-time Monochamus alternates-Dastarcus helophoroides model with artificially released and spatial diffusion. When model without diffusion, we prove that Neimark-Sacker bifurcation and Flip bifurcation occur. The eradication of Monochamus alternatus can be achieved by incorporating sufficient quantities of artificially released Dastarcus helophoroides. And we obtain the normal form of Neimark-Sacker bifurcation. When considering spatial diffusion, taking the parameter regulating the growth of Dastarcus helophoroides c as a bifurcation parameter, we prove that Neimark-Sacker bifurcation, 1:2 Resonance and Turing instability occur. Monochamus alternatus and Dastarcus helophoroides populations generally exhibit complex periodic oscillatory behaviour in two-dimensional space. © 2024, The Authors. All rights reserved.

关键词： Diffusion

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Fault Detection for Battery Energy Storage Systems in Microgrids Under Event-Triggered Protocols: An Interval Observer Approach

Fault Detection for Battery Energy Storage Systems in Microg...

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China International Electrical and Energy Conference (CIEEC)

作者： Mengmeng Su Yong Zhang Zhibin Liu Xin Li North China Branch Of State Grid Corporation Of China Beijing China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

In this paper, we investigate a method to realize fault detection using interval observer for battery energy storage systems containing actuator faults in microgrids. In order to reduce the data communication burden of the battery energy storage system, an improved event triggering mechanism with time-varying threshold is proposed to control the received signal of the interval observer. In addition, the desired interval observer gain is obtained by solving a matrix inequality. The output of the interval observer is utilized to construct the upper and lower bounds of the output of the system when the system does not contain actuator faults, and the actuator fault detection is achieved by whether the output of the system exceeds the constructed upper and lower bounds. Finally, the correctness and effectiveness of the proposed method are verified by numerical simulation.

关键词： Actuators Protocols Battery energy storage system Fault detection Microgrids Observers Numerical simulation

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CLASS-SPECIFIC DISTRIBUTION ALIGNMENT FOR SEMI-SUPERVISED MEDICAL IMAGE CLASSIFICATION

arXiv

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arXiv 2023年

作者： Huang, Zhongzheng Wu, Jiawei Wang, Tao Li, Zuoyong Ioannou, Anastasia College of Computer and Control Engineering Minjiang University China College of Computer and Data Science Fuzhou University China College of Electrical and Mechanical Engineering Fujian Agriculture and Forestry University China International Digital Economy College Minjiang University China Department of Computer Science and Engineering European University Cyprus Cyprus Cyprus

Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases. To address this problem, we propose Class-Specific Distribution Alignment (CSDA), a semi-supervised learning framework based on self-training that is suitable to learn from highly imbalanced datasets. Specifically, we first provide a new perspective to distribution alignment by considering the process as a change of basis in the vector space spanned by marginal predictions, and then derive CSDA to capture class-dependent marginal predictions on both labeled and unlabeled data, in order to avoid the bias towards majority classes. Furthermore, we propose a Variable Condition Queue (VCQ) module to maintain a proportionately balanced number of unlabeled samples for each class. Experiments on three public datasets HAM10000, CheXpert and Kvasir show that our method provides competitive performance on semi-supervised skin disease, thoracic disease, and endoscopic image classification tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Transferable Physical Adversarial Patch Attack for Remote Sensing Object Detection

Transferable Physical Adversarial Patch Attack for Remote Se...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Di Wang Wenxuan Zhu Ke Li Xiao Gao Pengfei Yang School of Computer Science and Technology Xidian University Xi’an China College of Information and Control Engineering Xi’an University of Architecture and Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Deep neural networks (DNNs) have been widely used in remote sensing but demonstrated to be vulnerable with adversarial examples. By adding elaborately designed perturbations on the clean images, DNNs may output wrong prediction. Research on adversarial attack contributes to the study of model robustness. However, previous methods mainly focus on white-box scenario or digital domain for classification tasks, while the vulnerability of remote sensing detectors has not been fully explored. Aiming at attacking black-box remote sensing detectors in physical domain, we propose to generate a transferable physical adversarial patch (TPAP) as the perturbations. Specifically, the initial patch is optimized by a U-Net and modified by the plane mask and position mask before applied to the clean image. By attacking a surrogate model, TPAP can be transferred to the target model. Abundant experimental results validate the attack ability of TPAP and evaluate the robustness of current one-stage detectors.

关键词： YOLO Perturbation methods Closed box Detectors Robustness Sensors Task analysis

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

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SSRN 2024年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

Incidence of surge within axial compressors profoundly influences efficacy, reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on precise ,efficient forecasting critical characteristics during such occurrences. paper presents novel approach for predicting surge phenomenon in axial compressors. Surge is a major issue in operation axial compressors, causing reduced performance, increased wear, tear, complete machine failure. proposed approach utilizes NN model based on compressor's design data to forecast behavior of compressor's working map under off-design conditions. model is verified using real data collected over five years during compressor operation, including opening angle (IGV), important parameter often ignored or assumed to be fixed in previous studies. results show NN-based approach effectively predicts compressor's behavior under different operating conditions, providing valuable insights into onset of surge , offering real-time surge prediction ,control ,ability to predict compressor's working map enables optimizing its performance , controlling instability. model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As result, predictive model enables safe operation of axial compressors by mitigating instability risks during operation. study's findings highlight model's effectiveness, as evidenced by minimal error observed between predicted and actual data collected across various compressor operating modes. model exhibits strong performance, characterized by high regression factor of 0.92667 , low mean squared error of 3.04465 x 10^-3. numerical values underscore reliability , precision proposed approach in predicting operation of axial compressor under diverse conditions, ultimately contributing to enhanced stability , safety during compressor operations. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Interval Observer-Based Attack Detection in Smart Grid Under FlexRay Communication Protocols

Interval Observer-Based Attack Detection in Smart Grid Under...

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China International Electrical and Energy Conference (CIEEC)

作者： Man Hu Yong Zhang Mengmeng Su Mingfei Ban North China Branch Of State Grid Corporation Of China Beijing China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

In this paper, the detection of false data injection attacks (FDIA) in the smart grid based on interval observer is studied under the FlexRay communication protocol. The FDIA is highly stealthy and can modify the sensor data to affect system performance without being detected by common detection mechanisms. A cyber-attack detection mechanism against FDIA is proposed in this paper. Firstly, the smart grid is divided into several sub-areas for analysis and modeling, and a novel FlexRay communication protocol is designed to schedule the transmission of information in each sub-area. Next, an interval observer considering internal state boundaries and disturbances is designed based on the information after transmission to estimate the upper and lower bounds of the system states. An algorithm for attack detection against FDIA using interval residuals from interval observers is proposed. In addition, using the augmentation technique and the Lyapunov stability analysis method, sufficient conditions to ensure the stability of the estimation error system are given in the form of linear matrix inequality (LMI), and the interval observer gains are obtained by solving the LMI. Finally, the correctness and effectiveness of the proposed method is verified by a simulation example.

关键词： Sufficient conditions Schedules Protocols System performance Observers Power system stability Stability analysis

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Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis

arXiv

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arXiv 2023年

作者： Niu, Youcheng Xu, Jinming Sun, Ying Huang, Yan Chai, Li College of Control Science and Engineering Zhejiang University China School of Electrical Engineering and Computer Science The Pennsylvania State University United States

This paper consider solving a class of nonconvex-strongly-convex distributed stochastic bilevel optimization (DSBO) problems with personalized inner-level objectives. Most existing algorithms require computational loops for hypergradient estimation, leading to computational inefficiency. Moreover, the impact of data heterogeneity on convergence in bilevel problems is not explicitly characterized yet. To address these issues, we propose LoPA, a loopless personalized distributed algorithm that leverages a tracking mechanism for iterative approximation of inner-level solutions and Hessian-inverse matrices without relying on extra computation loops. Our theoretical analysis explicitly characterizes the heterogeneity across nodes denoted by b, and establishes a b23 sublinear rate of O((1−ρ1)2K + (1−ρ) 32 K 23 + √1K(σp + √1mσc)) without the boundedness of local hypergradients, where σp and σc represent the gradient sampling variances associated with the inner- and outer-level variables, respectively. We also develop a variant of LoPA based on gradient tracking to eliminate the impact of data heterogeneity, yielding an improved rate of O((1−ρ1)4K + √1K(σp + √1mσc)). The computational complexity of LoPA is of O(ϵ−2) to an ϵ-stationary point, matching the communication complexity due to the loopless structure, which outperforms existing counterparts for DSBO. Numerical experiments validate the effectiveness of the proposed algorithm. © 2023, CC BY.

关键词： Computational complexity

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