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43rd Chinese Control Conference, CCC 2024

作者： Yuan, Haoran Wu, Jiande Xiong, Xin Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Yunnan Kunming650500 China

ISBN: (纸本)9789887581581

In order to explore the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion of slurry pipe, a numerical simulation method approach grounded in Computational Fluid Dynamics - Dense Discrete Phase Model (CFD-DDPM) is proposed. Firstly, the geometric models of bends with different pipe diameters, bend-diameter ratios, and bend angles are constructed;secondly, the slurry pipe erosion model based on Dense Discrete Phase Model (DDPM) is constructed;then, model validation is conducted on the slurry pipe erosion model to affirm the accuracy of the model;finally, the computational fluid dynamics method is employed to simulate the erosion and wear behavior of the pipeline, to investigate the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion and wear of the slurry pipeline. At the same time, gray correlation analysis was also used to analyze the significant relationship between the three pipeline factors and erosion and wear. The results show that the main erosion area is not determined, with the change of the pipeline parameters, the most serious erosion area will move;the effect of different pipeline parameter variations on the erosion rate is different, in which the pipe diameter of the largest impact, the second largest bend-diameter ratio, the smallest impact of the bend angle. Therefore, in the design of slurry pipeline, should first consider the influence of pipe diameter factors. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Slurry pipelines

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Neural Descriptors 24

DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Xiaze Ding, Ziheng Jing, Qi Cheng, Ying Ding, Wenchao Feng, Rui Fudan University Shanghai China The School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China The Academy for Engineering and Technology Fudan University China The Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9798400706868

Simultaneous Localization and Mapping (SLAM) plays a pivotal role in autonomous driving and robotics. Existing methods often rely on hand-craft feature extraction and cross-modal fusion techniques, resulting in limited feature representation capability and reduced robustness. To address this challenge, we introduce DeepPointMap2, a novel learning-based LiDAR-Visual SLAM architecture that leverages neural descriptors to tackle multiple SLAM sub-tasks in a unified manner. Our approach employs neural networks to extract multi-modal tokens, which are then adaptively fused by the Visual-Point Fusion Module to generate sparse 3D neural descriptors, ensuring precise and robust performance. As a pioneering work, our method achieves state-of-the-art localization performance among various Visual-, LiDAR-, and Visual-LiDAR-based methods in widely-used benchmarks, as shown in the experiment results. Furthermore, the approach proves to be robust in scenarios involving camera failure and LiDAR obstruction. © 2024 ACM.

关键词： SLAM robotics

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QSM: A Quick Shuffling Method for Defending Against Low-Rate Denial of Service Attacks in Microservice Systems

QSM: A Quick Shuffling Method for Defending Against Low-Rate...

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Fan, Qiqi Zhao, Chunchang Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Microservice architecture, renowned for its flexible scalability and loose coupling, has seen widespread adoption. However, this architecture has also become a prime target for network attackers, with low-rate denial of service (LDoS) attacks posing significant threats to microservice systems. Due to their covert nature, traditional passive defense methods often fall short in countering LDoS attacks. To address this challenge, this paper introduces a microservice-oriented quick shuffling method (QSM), based on the moving target defense (MTD) principle. The QSM framework leverages the dynamic and flexible properties of microservices to implement a rapid shuffling mechanism, ensuring the protection of innocent clients. Experimental results demonstrate the effectiveness of QSM in robustly defending microservice systems against LDoS attacks. © 2025 SPIE.

关键词： Denial-of-service attack

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Polynomial stacked-attention network for nationality classification

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Frontiers of Computer Science 2022年第4期16卷 215-217页

作者： Kunyan Li Jie Zhang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing100190China University of Chinese Academy of Sciences Beijing100049China

1Introduction In recent years,nationality and region inference has atracted more and_more *** anthropological researches indicate that human faces are influenced by genes,and other external factors,e.g-,geographical environment[1].This relevance provides possible solutions for geographical area inference by ***,it remains a challenging task due to the tiny differences on faces for various countries,which is even hard for human beings to tell.

关键词： Polynomial s. network

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ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Motor Imagery Recognition

ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Mo...

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Xue, Haixiao Huang, Yuwen Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the brain's topological characteristics and the correlations between EEG channels, resulting in suboptimal decoding. To address this, we propose an adaptive spatio-temporal graph neural network (ASTGNN), which constructs a brain graph topology and adaptively learns the topological adjacency matrix, exploring both common and individual electrode connections. The spatial features are extracted using a graph convolutional network, while a gated position-aware self-attention mechanism captures movement information and global dependencies, enhancing temporal feature extraction. Experiments show that ASTGNN significantly improves recognition, achieving an average accuracy of 87.36%. © 2025 SPIE.

关键词： Electroencephalography

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K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION ALGORITHM FOR DOA ESTIMATION

K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION A...

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IET International Radar Conference 2023, IRC 2023

作者： Guo, Qiang Jiang, Hanyu Xiang, Jianhong Zhong, Yu College of Information and Communication Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

Aiming at the problem that traditional greedy class compressed sensing algorithms are influenced by noise in the estimation of direction of arrival (DOA), this paper proposes the K-Means Clustering based Sparse Cross-Entropy Minimization (KMCSCEM) algorithm. First the algorithm selects atoms by minimizing the sparse cross-entropy to improve the accuracy of atom selection at the low signal-to-noise ratio (SNR). Subsequently, K-mean clustering is added in each iteration to reduce the range of atom selection, which further improves the estimation accuracy. Finally, the speed of convergence is improved by introducing an orthogonal least squares (OLS) criterion. Simulation experiments show that the proposed algorithm has an exact estimation ratio close to 90% when the SNR is less than -5 dB. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASYMMETRIC RESIDUAL CROSS TRANSFORMER

JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASY...

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IET International Radar Conference 2023, IRC 2023

作者： Sun, Yaxiu Wang, Minhui Sun, Rui Hao, Lihong Xiang, Jianhong National Key Laboratory of Communication Anti-Jamming Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

With the increasing amount of remote sensing (RS) data obtained from satellites or airborne platforms, The combined use of multimodal remote sensing data to improve classification performance has been widely concerned. Therefore, a new transformer-based hyperspectral and synthetic aperture radar (HS-SAR) data joint classification network ACCT was proposed in the paper. Combining the strengths of convolutional neural networks (CNN) and transformer, focus on extracting specific and common features from HS and SAR data. In addition, adding layer scale to the output of each residual block can learn diagonal matrix and improve the dynamics of training. Then, combining the advantages of residual join and asymmetric convolution kernel, the parameters are reduced while the feature information is fully learned. Experiments in this paper are carried out on two famous multimodal fusion datasets: Augsburg (AU), and Berlin (BL) datasets. Results show that ACCT achieves better classification accuracy than several existing models. OA reaches 97.20%, and 85.02% on the two datasets. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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DTS: Dynamic Traffic Scrubbing Against Link Flooding Attacks with Programmable Data Plane

DTS: Dynamic Traffic Scrubbing Against Link Flooding Attacks...

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Liu, Fang Meng, Zhaoran Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Link flood attacks (LFA) represent a significant cybersecurity threat due to their ability to cause extensive network disruptions. Traditional traffic scrubbing methods, relying on static rules, often struggle to counter dynamic LFAs effectively. This paper introduces the dynamic traffic scrubbing (DTS) method, leveraging programmable data planes. DTS integrates moving target defense principles into traffic scrubbing, dynamically deploying and adjusting scrubbing rules to thwart attackers. Game theory is employed to analyze the distributed denial of service attack and defense strategies. Experimental results demonstrate that DTS effectively mitigates dynamic LFAs with minimal overhead, showcasing its robustness in defending against sophisticated cyber threats. © 2025 SPIE.

关键词： Denial-of-service attack

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