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26th International Conference on Information Fusion, FUSION 2023

作者： Zuo, Kezhu Li, Xinde Dezert, Jean Dong, Yilin Southeast University School of Cyber Science and Engineering Nanjing China Southeast University Key Laboratory of Measurement and Control of Cse School of Automation Nanjing China The French Aerospace Lab Onera DTIS/MIDL Palaiseau France Shanghai Maritime University College of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798890344854

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster's rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods. © 2023 International Society of Information Fusion.

关键词： Frequency domain analysis

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HRPDF: A Software-Based Heterogeneous Redundant Proactive Defense Framework for Programmable Logic controller

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Journal of computer science & Technology 2021年第6期36卷 1307-1324页

作者： Ke Liu Jing-Yi Wang Qiang Wei Zhen-Yong Zhang Jun Sun Rong-Kuan Ma Rui-Long Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China School of Information Systems Singapore Management UniversitySingapore 689867Singapore

Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called Heterogeneous Redundant Proactive Defense Framework(HRPDF).We propose a heterogeneous PLC architecture in HRPDF,including multiple heterogeneous,equivalent,and synchronous runtimes,which can thwart multiple types of attacks against PLC without the need of external *** ensure the availability of PLC,we also design an inter-process communication algorithm that minimizes the overhead of *** implement a prototype system of HRPDF and test it in a real-world PLC and an OpenPLC-based device,*** results show that HRPDF can defend against multiple types of attacks with 10.22%additional CPU and 5.56%additional memory overhead,and about 0.6 ms additional time overhead.

关键词： industrial control system programmable logic controller proactive defense heterogeneous redundant architecture

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Degree of Spiking Neural P Systems Without Delay

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Chinese Journal of Electronics 2023年第3期21卷 419-424页

作者： Keqin JIANG Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China School of Computer and Information Anqing Teachers College Anqing China

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called “spikes”). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).

关键词： Computational modeling Neurons Parallel processing Delays

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Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

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

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 IEEE.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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A Loopless Distributed Algorithm for Personalized Bilevel Optimization

A Loopless Distributed Algorithm for Personalized Bilevel Op...

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IEEE Conference on Decision and control

作者： Youcheng Niu Ying Sun Yan Huang Jinming Xu College of Control Science and Engineering Zhejiang University Hangzhou China School of Electrical Engineering and Computer Science The Pennsylvania State University State College PA

This paper studies a class of personalized distributed bilevel optimization problems over networks, where nodes aim at jointly optimizing the sum of outer-level objectives that depend on the solution of inner-level optimization problems. The existing algorithms for distributed bilevel optimization problems usually require extra computation loops for estimating hypergradients. To facilitate computational efficiency, we develop a loopless distributed algorithm that employs certain steps to approximate the optimal solution of innerlevel optimization problems, and track Hessian-inverse-vector products in a recursive manner. We prove that for stochastic nonconvex-strongly-convex problems, the proposed algorithm achieves the state of the art O(∊ −2 ) communication cost, while improving the computational cost by O(1og(1/∊)). Numerical experiments validate our theoretical findings.

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Incomplete Multi-view Clustering via Diffusion Contrastive Generation

arXiv

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arXiv 2025年

作者： Zhang, Yuanyang Lin, Yijie Yan, Weiqing Yao, Li Wan, Xinhang Li, Guangyuan Zhang, Chao Ke, Guanzhou Xu, Jie School of Computer Science and Engineering Southeast University Nanjing210096 China College of Computer Science Sichuan University China School of Computer and Control Engineering Yantai University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China College of Computer National University of Defense Technology China College of Computer Science and Technology Zhejiang University China Department of Control Science and Intelligence Engineering Nanjing University China School of Economics and Management Beijing Jiaotong University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates;2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Federated Multi-View Multi-Label Classification

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IEEE Transactions on Big Data 2024年

作者： Meng, Hongdao Deng, Yongjian Zhong, Qiyu Wang, Yipeng Yang, Zhen Lyu, Gengyu Beijing University of Technology College of Computer Science China Ministry of Education Engineering Research Center of Intelligence Perception and Autonomous Control Beijing100124 China

Multi-view multi-label classification is a crucial machine learning paradigm aimed at building robust multi-label predictors by integrating heterogeneous features from various sources while addressing multiple correlated labels. However, in real-world applications, concerns over data confidentiality and security often prevent data exchange or fusion across different sources, leading to the challenging issue of data islands. To tackle this problem, we propose a general federated multi-view multi-label classification method, FMVML, which integrates a novel multi-view multi-label classification technique into a federated learning framework. This approach enables cross-view feature fusion and multi-label semantic classification while preserving the data privacy of each independent source. Within this federated framework, we first extract view-specific information from each individual client to capture unique characteristics and then consolidate consensus information from different views on the global server to represent shared features. Unlike previous methods, our approach enhances cross-view fusion and semantic expression by jointly capturing both feature and semantic aspects of specificity and commonality. The final label predictions are generated by combining the view-specific predictions from individual clients and the consensus predictions from the global server. Extensive experiments across various applications demonstrate that FMVML fully leverages multi-view data in a privacy-preserving manner and consistently outperforms state-of-the-art methods. © 2015 IEEE.

关键词： Federated learning

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Metric localization for lunar rovers via cross-view image matching

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Visual Intelligence 2024年第1期2卷 1-14页

作者： Chen, Zihao Li, Kunhong Li, Haoran Fu, Zhiheng Zhang, Hanmo Guo, Yulan School of Electronics and Comunication Engineering the Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen 510275 China Peng Cheng Laboratory Guangdong Shenzhen 518055 China Department of Computer Science and Software Engineering The University of Western Australia Perth 6009 Australia Department of Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Aerospace Control Technology Institute Shanghai 201109 China College of Electronic Science and Technology National University of Defense Technology Hunan Changsha 410073 China

Accurate localization is critical for lunar rovers exploring lunar terrain features. Traditionally, lunar rover localization relies on sensor data from odometers, inertial measurement units and stereo cameras. However, localization errors accumulate over long traverses, limiting the rover’s localization accuracy. This paper presents a metric localization framework based on cross-view images (ground view from a rover and air view from an orbiter) to eliminate accumulated localization errors. First, we employ perspective projection to reduce the geometric differences in cross-view images. Then, we propose an image-based metric localization network to extract image features and generate a location heatmap. This heatmap serves as the basis for accurate estimation of query locations. We also create the first large-area lunar cross-view image (Lunar-CV) dataset to evaluate the localization performance. This dataset consists of 30 digital orthophoto maps (DOMs) with a resolution of 7 m/pix, collected by the Chang’e-2 lunar orbiter, along with 8100 simulated rover panoramas. Experimental results on the Lunar-CV dataset demonstrate the superior performance of our proposed framework. Compared to the second best method, our method significantly reduces the average localization error by 26% and the median localization error by 22%. © The Author(s) 2024.

关键词： Aerial patch Cross-view metric localization (CVML) Lunar rover Panorama Perspective projection

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Measurement-Device-Independent Deterministic Quantum Key Distribution 9

Measurement-Device-Independent Deterministic Quantum Key Dis...

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9th Annual International Conference on Network and Information Systems for computers, ICNISC 2023

作者： Zhang, Ting Liu, Jie Kang, Guodong Jishou University School of Physics and Mechanical and Electrical Engineering Jishou China Jishou University School of Computer Science and Engineering School of Physics and Mechanical and Electrical Engineering Jishou China Hunan Normal University Key Laboratory of Low Dimensional Quantum Structures and Quantum Control Ministry of Education College of Physics and Electronic Science Changsha China

ISBN: (纸本)9798350306682

Improve the secure key rate and the limited communication distance is the main goal in the field of quantum key distribution. Measurement-device-independent quantum key distribution, not only removes all detector side channel attacks, but also doubles the secure transmission distance. However, most of measurement-device -independent quantum key distribution protocols are non-deterministic. In this paper, we propose a measurement-device-independent deterministic quantum key distribution protocol that doubles the secure distance and amends the non-deterministic factor of current measurement-device -independent quantum key distribution. And with imperfect devices model assumption, we derived the formula of the performances for it and find that the limited distance can be extended over 200km with relatively high secure key rate by employing parameters of current technologies. © 2023 IEEE.

关键词： Quantum cryptography

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Research on coking diagnosis based on semi-supervised Bayesian T-distribution mixed model 4

Research on coking diagnosis based on semi-supervised Bayesi...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Yang, Hanmei Cui, Delong Li, Qirui Peng, Zhiping Zuo, Jinglong School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Electronic and Information Engineering Guangdong University of Petrochemical Technology Maoming525000 China School of Computer Science Guangdong University of Petrochemical Technology Maoming525000 China Jiangmen Polytechnic College Jiangmen529090 China

ISBN: (纸本)9781510681637

In the process of ethylene production, the operating data of ethylene cracking furnace not only has the problems of time variability, non-gauss, strong noise, and outlier influence but also the collected data is incomplete, presenting the problem of scarce data with labels and abundant data without labels. To solve the above problems, this paper proposes a soft measurement model based on a semi-supervised Bayesian T-distribution mixture for the coking diagnosis of ethylene cracking furnaces. The model adopts a semi-supervised structure to solve the problem of the small number of data labels, adopts a T-distribution model to ensure the robustness of the model to strong noise and outliers under the Bayesian framework, and solves the solid variation of process data through the mixed model—complexity, non-Gaussian, and nonlinear problems. Finally, the experiment was carried out on the coking data of an ethylene cracking furnace in a petrochemical plant to verify the feasibility and prediction accuracy of the model under the condition of limited label data, and also to verify the robustness of the model to outliers. © 2024 SPIE.

关键词： Statistics

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