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3D Ear Shape Matching Using Joint -Entropy

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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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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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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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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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Trajectory tracking control of autonomous heavy-duty mining dump trucks with uncertain dynamic characteristics

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Science China(Information Sciences) 2023年第10期66卷 246-262页

作者： Liang CHEN Zhaobo QIN Manjiang HU Hongbo GAO Yougang BIAN Biao XU Xiaoyan PENG State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Wuxi Intelligent Control Research Institute of Hunan University Department of Automation University of Science and Technology of China

The proportion of mines using autonomous mining trucks is still very low at present. To promote the development of intelligent mining, it is urgent to proceed with the drive-by-wire modification to common mining trucks and design a motion control algorithm considering uncertain dynamic *** paper proposes a trajectory tracking control method for autonomous heavy-duty mining dump trucks(AHMDTs) with uncertain dynamic characteristics. In this method, a driving/braking force compensation algorithm based on an inverse dynamic model is designed to guarantee accurate longitudinal control with less control gain tuning. Using road curvatures, a modified rear-wheel position feedback control method is proposed to deal with reverse-path tracking, which can simultaneously reduce the lateral error and yaw angle error. A modified Stanley controller considering the collaborative preview based on speed and curvature is constructed to achieve accurate path tracking in the forward gear. Moreover, the proposed method focuses on the practice of trajectory tracking control in an open-pit mine condition with the adverse effects caused by uncertain vehicle dynamics, huge variable load, and large actuator lag. Real vehicle tests show that the proposed methodology can control AHMDTs with a low tracking error.

关键词： vehicle control trajectory tracking control driving/braking force compensation rear-wheel position feedback

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View-dependent fast real-time generating algorithm for large-scale terrain

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Procedia Earth and Planetary Science 2009年第1期1卷 1147-1151页

作者： Jin, Hai-liang Lu, Xiao-ping Liu, Hui-jie Key Laboratory of Mine Spatial Information Technologies State Bureau of Surveying and Mapping Henan Polytechnic University Jiaozuo 454000 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian 116622 China

In order to eliminate the popping effect of switching among levels of detail and to increase the frame rates with high image quality during the fly-through of large-scale terrain, this paper put forward a new bottom-up modeling strategy, which constructed simplified terrain triangle mesh globally and updated mesh nodes dynamically. Employed hybrid culling technique based on blocks and triangle faces and simplified computing method for screen-space errors to select appropriate terrain nodes rapidly. Then updated the Delaunay terrain mesh by adding nodes, deleting nodes and modifying locally. At the same time achieved self-adaptive control for screen-space error tolerance during the terrain fly-through. Results of simulation experiments demonstrate that the algorithm eliminates popping effect effectively, and has a higher frame rate compared with other algorithms. So it is particularly suitable for close-distance fly-through simulation of large-scale terrain. © 2009.

关键词： Landforms Image quality Visualization Bottom up modeling computing methods Delaunay Error tolerance Frame rate High image quality Large scale terrain Levels of detail Mesh nodes Other algorithms Real time visualization Self adaptive control Simulation experiments Terrain meshes Triangle mesh View dependent

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