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14th Asian control Conference, ASCC 2024

作者： Chen, Chen Li, Shaoyuan Yin, Xiang Shanghai Jiao Tong University Department of Automation Shanghai200240 China The Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9789887581598

In this paper, we investigate the challenge of con-trol synthesis for Signal Temporal Logic (STL) specifications within workspaces harboring unknown obstacles. We consider a sensing-based control setting, where robots are equipped with sensors with a limited sensing range to acquire obstacle locations with prior information. The objective is to synthesize a control strategy that maximize the robustness degree of the STL tasks while simultaneously minimizing the probability of collisions with obstacles during its operation. To address this, we present a receding horizon reactive control framework, tackling the problem by solving an uncertainty-aware optimal planning problem at each time instant. A key aspect of our approach is the avoidance of explicit computation of collision probability. Instead, we transform the uncertainty-aware synthesis problem into a non-probabilistic optimization problem, and the results of which still provide probabilistic guarantees. Simulation results are provided to illustrate the effectiveness of our approach. © 2024 Asian control Association.

关键词： Temporal logic

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Safe Deep Learning-Based Offset-Free MPC for Green Hydrogen Injected PEMFC Systems

Safe Deep Learning-Based Offset-Free MPC for Green Hydrogen ...

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2023 China Automation Congress, CAC 2023

作者： Wang, Han Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai China

ISBN: (纸本)9798350303759

Safe and stable operation of a proton exchange membrane fuel cell (PEMFC) system requires stringent control of oxygen excess ratio (OER). However, the OER regulation in PEMFC is challenging due to frequent fluctuations of current, various modeling nonlinearities, constrained manipulated variable, and real-time requirements. Offset-free model predictive control (MPC) provides a useful means for controlling systems with disturbances and constraints, but suffers from the heavy computational burden of repeatedly solving an optimization problem in real time. Such computational issue precludes the possibility of meeting the real-time requirements of PEMFC. In this paper, a PEMFC cathode gas supply model is firstly established. Next, we develop a safe deep learning-based offset-free MPC algorithm. Based on the nominal offset-free MPC, the proposed MPC not only reserves the ability of disturbance rejection, but also leverages deep neural networks for approximating the explicit solution to the MPC problem to greatly reduce online computational time. Furthermore, a gauge map is used to guarantee the satisfaction of safe constraints regarding compressor voltage. The simulation results show that the proposed MPC is an order of magnitude faster than the nominal offset-free MPC. © 2023 IEEE.

关键词： Model predictive control

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

ISBN: (纸本)9798350368741

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. © 2025 IEEE.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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Provably Safe Deep Learning-Based Offset-Free Model Predictive control and its Application to Safety-Critical Autonomous Driving 6

Provably Safe Deep Learning-Based Offset-Free Model Predicti...

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6th International Conference on Robotics, control and Automation Engineering, RCAE 2023

ISBN: (纸本)9798350315301

Offset-free model predictive control (MPC) provides a useful means for controlling systems with uncertainties and constraints, but suffers from the heavy computational burden of repeatedly solving an optimization problem in real time. Such computational issue precludes the possibility of its application in systems requiring high realtime requirements, such as autonomous driving system. To address this problem, we develop a provably safe deep learning-based offset-free MPC framework. Based on the nominal offset-free MPC, the proposed MPC not only reserves the ability of disturbance rejection, but also leverages deep neural networks for approximating the explicit MPC solution to greatly reduce online computational time. Furthermore, a gauge map is used to guarantee the satisfaction of safe constraints. The proposed MPC is used in trajectory tracking control for smart autonomous driving. The simulation results show that the proposed MPC is an order of magnitude faster than the nominal offset-free MPC in safety-critical systems. © 2023 IEEE.

关键词： Model predictive control

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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

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Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal UniversityHangzhou 311121China Tongda College of Nanjing University of Posts and Telecommunications Yangzhou 225127China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi'an 710071China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXining 810008China Faculty of Software Fujian Normal UniversityFuzhou 350108China

Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute ***,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption *** this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access *** attacks are thwarted by providing unauthorized users with invalid *** heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud ***,users can verify the correctness of multiple outsourced decryption results *** analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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Dense Convolution Siamese Network for Hyperspectral Image Target Detection 5

Dense Convolution Siamese Network for Hyperspectral Image Ta...

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5th International Conference on information Communication and Signal processing, ICICSP 2022

作者： Shen, Kun Xie, Weixin Tang, Haojin Li, Yanshan Shenzhen University Atr National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781665485890

Compared with grayscale and RGB images, hyperspectral image (HSI) can provide both spatial and spectral information of ground targets, which makes it possible to improve the efficiency and accuracy of target detection. Therefore, the research of HSI target detection algorithms has attracted widespread concern in recent years. With the development of hardware devices and the arrival of big data era, deep learning algorithms have been successfully applied to image processing, text recognition and other fields. However, due to the complex gathering environment of HSI, it is so difficult to obtain a large number of labeled samples, which limits the application of deep learning algorithms in HSI target detection. Therefore, a dense convolution Siamese network (DCSN) is proposed for HSI target detection, which improves the accuracy in the scenery of small-scale training samples. The main contributions of this paper include the following three points. First, we design a target sample generation method based on improved autoencoder to enhance target training data. Then, a background selection method based on density estimation is presented, which can acquire typical background samples effectively. Finally, a spectral feature extraction method based on dense convolution is proposed to extract the more discriminative spectral features. The experimental results of HSI target detection on Muufl Gulfport and San Diego datasets indicate that our proposed DCSN is able to achieve superior performance than the existing target detectors. © 2022 IEEE.

关键词： Convolution

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Lightweight Multilevel Feature Fusion Network for Hyperspectral Image Classification 5

Lightweight Multilevel Feature Fusion Network for Hyperspect...

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5th International Conference on information Communication and Signal processing, ICICSP 2022

作者： Huang, Quanyu Tang, Haojin Xie, Weixin Li, Yanshan Shenzhen University Atr National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781665485890

Hyperspectral image (HSI) classification is the key technology of remote sensing image processing. In recent years, convolutional neural network (CNN), which is a powerful feature extractor, has been introduced into the field of HSI classification. Since the features of HSI are the basis of HSI classification, how to effectively extract the spectral-spatial features from HSI with CNN has become a research hotspot. The HSI feature extraction network, based on two-dimensional (2D) and three-dimensional (3D) CNN which can extract both spectral and spatial information, may lead to the increase of parameters and computational cost. Compared with 2D CNN and 3D CNN, the number of parameters and computational cost of one-dimensional (1D) CNN will be greatly reduced. However, 1D CNN based algorithms can only extract the spectral information without considering the spatial information. Therefore, in this paper, a lightweight multilevel feature fusion network (LMFFN) is proposed for HSI classification, which aims to achieve efficient extraction of spectral-spatial features and to minimize the number of parameters. The main contributions of this paper are divided into the following two points: First, we design a hybrid spectral-spatial feature extraction network (HSSFEN) to combine the advantages of 1D, 2D and 3D CNN. It introduces the idea of depthwise separable convolution method, which effectively reduces the complexity of the proposed HSSFEN. Then, a multilevel spectral-spatial feature fusion network (MSSFFN) is proposed to further obtain more effective spectral-spatial features, which effectively fuses the bottom spectral-spatial features and the top spectral-spatial features. To demonstrate the performance of our proposed method, a series of experiments are conducted on three HSI datasets, including Indian Pine, University of Pavia, and Salinas Scene datasets. The experimental results indicate that our proposed LMFFN is able to achieve better performance than the manual f

关键词： Convolution

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A Pricing Strategy for Smart Grid Based on PSO and Distributed Iterative Algorithm

A Pricing Strategy for Smart Grid Based on PSO and Distribut...

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International Conference on Communications, Signal processing, and Systems, CSPS 2023

作者： Wang, Xiaocheng Ma, Haogang Yin, Yuan Li, Zelong Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9789819975556

This paper proposes a pricing strategy for the Market by using a Stackelberg game-based bi-level programming model. In the model, the wholesale price and the retail price is optimized by the Market to increase its profit. In the model, the Energy Provider (EP) optimize optimizes the adjustment coefficient to increase the trading probability with the Utility Company (UC). Besides, the Market can sell the power to the UC directly. Then, the UCs determine their retail price by non-cooperative game to increase their profit. The simulation results reveal that the Market determines the optimal price to maximize profits. On the other hand, the proposed price strategy of EP can increase the trading probability, which promotes more UCs to trade with EP, thus increasing its benefit. Finally, UCs get a bash equilibrium through the non-cooperative game. The research proves that the proposed strategy is a win–win strategy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Profitability

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A Secure Parameter Self-Tuning control Scheme for Vehicle Platoons under DoS Attacks 13

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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13th IEEE International Conference on CYBER Technology in Automation, control, and intelligent Systems, CYBER 2023

作者： Han, Qiaoni Cheng, Guowen Tang, Zhenning Liu, Chang School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China

ISBN: (纸本)9798350315196

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security. © 2023 IEEE.

关键词： Fuzzy control

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Method for identifying vital nodes in the scientific-citation double-layer hypernetwork based on the h-index

Method for identifying vital nodes in the scientific-citatio...

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2024 International Conference on Computer Application and information Security, ICCAIS 2024

作者： Guo, Lei Liu, Wei Zhang, Xin Hu, Feng School of Computer Qinghai Normal University Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9781510689312

Existing studies on scientific collaboration networks and citation networks are often limited to the analysis of a single relational dimension, making it challenging to comprehensively capture the interactions between collaboration and citation relationships within multilayered networks and their characteristics in knowledge dissemination. At the same time, traditional vital node identification methods are mostly based on evaluation metrics from single-layer networks, making it difficult to comprehensively and accurately identify vital nodes in multivariate, multilayer and multidimensional relationship hypernetworks. Therefore, based on the characteristics of scientific collaboration and citation networks, and in combination with hypernetwork theory based on hypergraphs, this paper proposes the construction of a scientific-citation double-layer hypernetwork model. Subsequently, based on the concept of aggregated networks, the double-layer hypernetwork is aggregated into a weighted hypernetwork. A method for identifying vital nodes in the hypernetwork based on the h-index is proposed, where the h-index is applied to the weighted aggregated hypernetwork and combined with the K-shell algorithm and hyperdegree metric to identify vital nodes. Finally, this method is applied to the empirical analysis of the scientific-citation double-layer hypernetwork constructed from the dataset of Acta Physica Sinica from 2014 to 2024. The results, based on the analysis of network propagation dynamics, network statistical characteristics, and AUC accuracy evaluation metrics, demonstrate that the proposed identification method is accurate and effective. This study provides valuable insights into how to construct multilayer hypernetwork models based on the characteristics of real-world networks, as well as the application of multilayer hypernetworks in real networks and the identification of vital nodes. © The Authors.

关键词： Multivariant analysis

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