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Worst-Case Integrity Attacks and Resilient state Estimation With Partially Secured Measurements

IEEE Transactions on Control of Network systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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OCET: One-Dimensional Convolution Embedding Transformer for Stock Trend Prediction 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yang, Peng Fu, Lang Zhang, Jian Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Shenzhen Securities Information Co. Ltd. Shenzhen China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to the strong data fitting ability of deep learning, the use of deep learning for quantitative trading has gradually sprung up in recent years. As a classical problem of quantitative trading, Stock Trend Prediction (STP) mainly predicts the movement of stock price in the future through the historical price information to better guide quantitative trading. In recent years, some deep learning work has made great progress in STP by effectively grasping long-term timing information. However, as a kind of real-time series data, short-term timing information is also very important, because stock trading is high-frequency and price fluctuates violently. And with the popularity of Transformer, there is a lack of an effective combination of feature extraction and Transformer in STP tasks. To make better use of short term information, we propose One-dimensional Convolution Embedding (OCE). Simultaneously, we introduce effective feature extraction with Transformer into STP problem to extract feature information and capture long-term timing information. By combining OCE and Transformer organically, we propose a noval STP prediction model, One-dimensional Convolution Embedding Transformer (OCET), to capture long-term and short-term time series information. Finally, OCET achieves a highest accuracy up to 0.927 in public benchmark FI-2010 When reasoning speed is twice that of SOTA models and a highest accuracy of 0.426 in HKGSAS-2020. Empirical results on these two datasets show that our OCET is significantly superior than other algorithms in STP tasks. Code are available at https://***/langgege-cqu/OCET. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Model Predictive Robust Control for A Capacitive-Coupling Grid-Connect Inverter (CGCI) in Power Quality Compensation 50

Model Predictive Robust Control for A Capacitive-Coupling Gr...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Chan, Pak-Ian Sou, Wai-Kit Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781665464543

In this paper, a model predictive robust control (MPRC) is proposed for a three-phase capacitive-coupling grid-connect inverter (CGCI) to improve the power quality compensation performance under deviating system parameters. A detail stability study and parameter design of the proposed MPRC is provided, while the robustness and power quality compensation performance of the proposed MPRC are verified by the simulation in comparison to the conventional model predictive control (MPC). © 2024 IEEE.

关键词： Predictive control systems

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Running and Steering Gait Generation Based on Double-Leg 3D-SLIP Model for Bipedal Robots

Running and Steering Gait Generation Based on Double-Leg 3D-...

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International Conference on Biomimetic Intelligence and Robotics & Medical Robotics Forum, ICBIR+MRF 2024

作者： Luo, Guifu Du, Ruilong Song, Sumian Yuan, Haihui Zhou, Hua Gu, Jason The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Zhejiang Hangzhou310058 China Wuba Intelligent Technology Co. Ltd. Hangzhou China The Department of Electrical Engineering Dalhousie University HalifaxNS Canada

The spring-loaded inverted pendulum (SLIP) provides a bio-inspired perspective to generate stable, fast, and compliant gaits for bipedal robots. In this work, an improved decoupled control strategy was proposed to generate stable, fast, human-like, and practical 3D running and steering gaits for double-leg three-dimensional SLIP (3D-SLIP). In the sagittal plane, an approximate-SLIP- based deadbeat controller was introduced for forward velocity and running height tracking with low computational cost and a fast convergence rate. In the lateral plane, an alternating term was introduced to the footstep controller for imitating lateral swing and avoiding left and right leg collision. Simulations were conducted to verify the stability of the periodic running gait, to explore the tracking performance of the forward velocity with the proposed control strategy, and to generate agile and versatile running and steering gaits for bipedal robots. © 2024 The Authors.

关键词： Inverted pendulum

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Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Soft Safety Analysis of Interconnected systems

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IEEE Transactions on Automatic Control 2024年

作者： Zhang, Weihao Zhao, Di Liang, Shu Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Shanghai201804 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201804 China

This paper introduces the concept of soft safety for nonlinear dynamical systems, examined from an input-output perspective, and establishes criteria for verifying soft safety in interconnected systems. Diverging from traditional safety approaches that enforce rigid/hard constraints on system states, our proposal involves integrating soft barrier functions with respect to system inputs and outputs to characterize soft safety. Particularly, when the soft barrier function takes a quadratic form with a constant QSR multiplier - later extended to a dynamic multiplier - we further define QSR safety for a class of nonlinear systems subject to quadratic constraints on input-output signals. The paper presents methods for analyzing QSR safety in both open-loop systems with state-space representations and interconnected systems, with these methods subsequently employed to design QSR soft safe systems meeting prescribed safety and stability requirements. Numerical and simulation examples are provided to illustrate the rationality of the concept and the efficacy of the results. © 1963-2012 IEEE.

关键词： state space methods

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A Wireless Charging System with Bipolar Partial Power Conversion for 400V/800V EV Battery 50

A Wireless Charging System with Bipolar Partial Power Conver...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Wu, Muxing Iam, Io-Wa Gong, Cheng Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781665464543

Currently, electric vehicle (EV) battery platforms typically vary in standard, commonly rated at either 800V or 400V. Conventional wireless charging system (WCS) are limited to single voltage level charging, necessitating wireless communication or additional complex circuits for achieving multi-voltage level charging, which can compromise operational stability and overall efficiency. This article proposes a WCS based on the concept of bipolar partial power conversion. By incorporating reversing series switches on the secondary of the DC/DC converter, regulation voltage control enables switching between positive and negative polarity to supply power to either 800V or 400V battery for EV. This approach ensures high power transmission efficiency, without relying on wireless communication, thereby ensuring stable charging process. Furthermore, this article also presents various combinations of bipolar topologies that can be selectively employed based on application requirements. Finally, simulation results validate the feasibility of the proposed 3.2KW WCS. © 2024 IEEE.

关键词： state of charge

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Cryptanalysis of BAKSHEESH Block Cipher 20th

Cryptanalysis of BAKSHEESH Block Cipher

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20th International Conference on Information Security and Cryptology, Inscrypt 2024

作者： Xu, Shengyuan Chen, Siwei Feng, Xiutao Xiang, Zejun Zeng, Xiangyong Department of Fundamental Courses Shandong University of Science and Technology Shandong Taian China School of Cyber Science and Technology Hubei University Hubei Wuhan China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Beijing China Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University Hubei Wuhan China Key Laboratory of Intelligent Sensing System and Security Ministry of Education Hubei University Hubei Wuhan China

ISBN: (纸本)9789819647330

BAKSHEESH is a lightweight block cipher following up the well-known cipher GIFT-128, which uses a 4-bit SBox that has a non-trivial Linear Structure (LS). Also, the Sbox requires a low number of AND gates that makes BAKSHEESH stronger to resist the side channel attacks compared to GIFT-128. In this paper, we give the first third-party security analysis of BAKSHEESH from the traditional attacks perspective: integral, differential and linear attacks. Firstly, we propose a framework for integral attacks based on the properties of BAKSHEESH ’s Sbox and its inverse. By this, we achieve the 9- and 10-round practical key-recovery attacks, and give a 15-round theoretical attack. Secondly, we re-evaluate the security bound against differential cryptanalysis, correcting two errors from the original paper and presenting a key-recovery attack for 19 rounds. At last, for linear cryptanalysis, we develop an automated model for key-recovery attacks and then demonstrate a key-recovery attack for 21 rounds. We stress that our attacks cannot threaten the full-round BAKSHEESH, but give a deep understanding on its security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BAKSHEESH key-recovery Attacks Lightweight Block Cipher Security Evaluation

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Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification

Decomposition-based Multi-objective Genetic Programming for ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Zhang, Tuo Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University China State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical and Information Engineering Zhengzhou University Luoyang China School of Electrical and Information Engineering Zhengzhou University School of Electrical Engineering and Automation Henan Institute of Technology State Key Laboratory of Intelligent Agricultural Power Equipment China School of Engineering and Computer Science Centre for Data Science and Artificial Intelligence Victoria University of Wellington New Zealand

ISBN: (纸本)9798400704956

Image classification is challenging due to the high dimensionality and large variations of the image data. As an effective method in image classification, feature learning can be considered a multiobjective problem of maximizing the classification accuracy and minimizing the number of learned features. Existing multi-objective genetic programming (MOGP) methods directly apply the multiobjective techniques to GP without considering the characteristics of feature learning tasks. Therefore, this paper proposes a decomposition-based MOGP approach with a global replacement strategy to feature learning in image classification. The proposed approach is compared with existing MOGP methods and the experimental results demonstrate the effectiveness of the proposed approach. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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