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16th Asian Conference on Computer Vision, ACCV 2022

作者： Su, Peng Liu, Rui Dong, Jing Yi, Pengfei Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9783031262838

Cross-modality person re-identification between visible and infrared images has become a research hotspot in the image retrieval field due to its potential application scenarios. Existing research usually designs loss functions around samples or sample centers, mainly focusing on reducing cross-modality discrepancy and intra-modality variations. However, the sample-based loss function is susceptible to outliers, and the center-based loss function is not compact enough between features. To address the above issues, we propose a novel loss function called Heterocentric Sample Loss. It optimizes both the sample features and the center of the sample features in the batch. In addition, we also propose a network structure combining spatial and channel features and a random channel enhancement method, which improves feature discrimination and robustness to color changes. Finally, we conduct extensive experiments on the SYSU-MM01 and RegDB datasets to demonstrate the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Infrared imaging

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Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection against Multipath Routing

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： Network intrusion

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An Improved Correlation-Based Just-in-Time Modeling Method Using Dynamic Partial Least Squares and Adaptive Local Domain Partition 29

An Improved Correlation-Based Just-in-Time Modeling Method U...

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第29届中国控制与决策会议

作者： Xiaolong Chen Zhizhong Mao Runda Jia Dong Xiao Xiaojun Wang College of Information Science & Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Advanced Design and Intelligent Computing Dalian university

ISBN: (纸本)9781509046584

This paper proposes an improved correlation-based just-in-time modeling method,referring to as the ICo JIT,for improving the prediction accuracy and real-time performance of the conventional correlation-based just-in-time(CoJIT) modeling *** achieve this objective,a novel adaptive local domain partition method has been developed based on the moving window technique and the fitting precision,which takes into account the input and output information simultaneously and has potentially the capabilities of obtaining the optimal local domain partition adaptively and capturing new process states by adding new local *** dynamic partial least squares and adaptive local domain partition method,multiple local domains and corresponding local models can be obtained during the offline operation *** online computation burden is reduced compared to CoJIT modeling *** addition,the proposed ICoJIT modeling method can efficiently deal with nonlinearity and time-varying behavior of processes as well as the CoJIT modeling *** effectiveness of the proposed method is demonstrated through a real industrial process dataset in sulfur recovery unit process.

关键词： Soft sensor Correlation-based just-in-time modeling Partial least squares Local domain partition Moving window

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3D Ear Shape Matching Using Joint -Entropy

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Journal of Computer Science & Technology 2015年第3期30卷 565-577页

作者：孙晓鹏李思慧韩枫魏小鹏 Institute of Computer Systems School of Computer and Information Technology Liaoning Normal University Dalian 116029 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China School of Mechanical and Engineering Dalian University of Technology Dalian 116024 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian 116622 China

In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree （MST） on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.

关键词： joint a-entropy minimum spanning tree local shape feature ear matching ear recognition

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End-to-end speech emotion recognition based on one-dimensional convolutional neural network 2019

End-to-end speech emotion recognition based on one-dimension...

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3rd International Conference on Innovation in Artificial Intelligence, ICIAI 2019

作者： Gao, Mengna Zhang, Qiang Dong, Jing Yang, Deyun Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116024 China School of Information Science and Technology Taishan University Tai'an Shandong Province271000 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116024 China

ISBN: (纸本)9781450361286

Real-time speech emotion recognition has always been a problem. To this end, we proposed an end-to-end speech emotion recognition model based on one-dimensional convolutional neural network, which contains only three convolution layers, two pooling layers and one full-connected layer. Through Adam optimization algorithm and back propagation mechanism, more discriminative features can be extracted continuously. Our model is quite simple in structure and easy to quickly complete the emotional classification task. Compared with traditional methods, there is no need to carry out the complex process of manually extracting features, and the model can automatically learn the emotional features from raw speech signals. In the emotional recognition experiments with EMODB, CASIA, IEMOCAP, and CHEAVD four speech databases, relatively high recognition rates were obtained. Experiments show that the proposed algorithm is of great benefit to the implementation of real-time speech emotion recognition. © 2019 ACM

关键词： Emotion Recognition

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Palmprint recognition based on sparse two-dimensional local discriminant projections

Palmprint recognition based on sparse two-dimensional local ...

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2014 International Conference on Applied Sciences, Engineering and Technology, ICASET 2014

作者： Zhang, Jian Xin Zhang, Zhan Ge Liu, Jian Yang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (纸本)9783038351849

A novel palmprint recognition method based on sparse two-dimensional local discriminant projections (S2DLDP) is proposed. After a description of the basic theory and resolution method for S2DLDP, the paper presents the detail palmprint feature extraction method based on S2DLDP, and tests the algorithm performance by various non-zero elements size and neighborhood size. S2DLDP considerers the class information, local separability, two-dimensional image inherent properties of training samples and sparse projection, which provides an intuitive, semantic and interpretable feature subspace for palmprint representation. The optimal recognition accuracy of EER=2.2% is obtained on PolyU palmprint database, which also illuminates the effectiveness of the proposed algorithm. © (2014) Trans Tech Publications, Switzerland.

关键词： Palmprint recognition

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

arXiv

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arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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GA based optimal design for megawatt-class wind turbine gear train 8th

GA based optimal design for megawatt-class wind turbine gear...

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8th International Conference on Image and Graphics, ICIG 2015

作者： Zhang, Jianxin Zhang, Zhange Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (纸本)9783319219622

In this paper, we propose a novel megawatt-class wind turbine gear train optimal design method based on genetic algorithm. Firstly, we construct an objective function to obtain the optimal quality of gear train with the constrain conditions of ratio, adjacency, assemble, gear bending fatigue and gear surface contact fatigue limitations. Then, genetic algorithm is applied to achieve the optimal parameter of the objective function. Finally, the finite element analysis method is taken to verify the feasibility of the optimization results. One type of wind turbine gearbox is given as the detail example to verify the effective of proposed method, and the verification results show that the presented method can achieve good effect for wind turbine gearbox. © Springer International Publishing Switzerland 2015.

关键词： Wind turbines

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Research on orbit maneuver strategies of onorbit service vehicle for multispacecraft rendezvous mission

Research on orbit maneuver strategies of onorbit service veh...

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2014 International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SMTA 2014

作者： Li, Yue Zhang, Jianxin Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China

ISBN: (纸本)9781845649821

In consideration of the different servicing types and various orbital spatial relationships, the paper proposes an orbital maneuver strategies model of on-orbit service vehicle (OSV) based on multiple decision variables for the problem of multispacecraft rendezvous mission. We establish a variety of mathematical models of orbit maneuver strategies, and formulate the appropriate time energy’s numerical model for different service types, adding orbital spatial relationship and service type as design variables, finally determine the optimal maneuver strategies of OSV. The paper selects two interim circle orbits as example. We select the orbit transfer strategies for service vehicle. We also calculate the relationship of transfer time and energy consumption according to the time energy’s consumption requirements. © 2014 WIT Press.

关键词： Vehicles

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Classification of ECG signals based on 1D convolution neural network 19

Classification of ECG signals based on 1D convolution neural...

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19th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2017

作者： Li, Dan Zhang, Jianxin Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (纸本)9781509067046

Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features automatically. This model realizes the classification of 5 typical kinds of arrhythmia signals, i.e., normal, left bundle branch block, right bundle branch block, atrial premature contraction and ventricular premature contraction. The experimental results on the public MIT-BIH arrhythmia database show that the proposed method achieves a promising classification accuracy of 97.5%, significantly outperforming several typical ECG classification methods. © 2017 IEEE.

关键词： Neural network models

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