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Analytical algorithm of program quaternion in the arbitrary spacecraft attitude-adjusting control

Journal of Information and Computational Science 2012年第3期9卷 751-759页

作者： Ni, Shaobo Wu, Jianwu Liu, Lei Wang, Yongji Shan, Jiayuan Su, Mao School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China Beijing Aerospace Automatic Control Institute Beijing 100854 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

The analytical algorithm of program quaternion is studied, aiming at the problem of the arbitrary space vehicle attitude-adjusting control. The analytical constructor method of the program quaternion is provided for arbitrary given initial quaternion, initial angle velocity, terminal quaternion and terminal angle velocity. The presented method has advantages as: good adaptability, the initial and terminal conditions are both arbitrary;reduced operation, the calculation process involves the fundamental algebraic operation;direct optimization, the time consumption in the process of space vehicle attitude adjustment and the maximum angular velocity can be optimized directly by using the program quaternion model. Finally, according to the simulation results, the program quaternion tracking can be realized by a PD control law. © 2012 Binary Information Press.

关键词： Attitude control

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The application of support vector machines and improved particle swarm optimization

The application of support vector machines and improved part...

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2012 International Conference on Modelling, Identification and control, ICMIC 2012

作者： Zhang, Hu Wang, Min Huang, Xinhan Key Laboratory of Image Processing and Intelligent Control of Education Department of Control Science and Engineering Huazhong University of Science and Technology NO.1037 Luoyu Road 430074 Wuhan China

ISBN: (纸本)9780956715715

Parameters selection of support vector machine is the key issue that impacts its accurate performance. A method for support vector regression machine with standard particle swarm optimization (SPSO) algorithm is proposed in this paper. Furthermore, in order to improve the performance of the SPSO algorithm, the concept of the particles' average distance and fitness variance is proposed to make the efficiency of algorithm better. So, the improve algorithm was also applied in this paper. The two different models using SPSO and IPSO respectively were used to forecast the density of the acid-lead battery electrolyte. The experimental results indicate that both SPSO and IPSO have high prediction accuracy and efficiency. The time of the parametric searching by IPSO is obviously decreased to that of SPSO. The mean squared error (MSE) of the prediction model using SPSO is about 2.02056×10-4, Meanwhile, the MSE of the model using IPSO is only about 1.9324×10-4. So, the IPSO algorithm has more superior performance on convergence speed and global optimization. © 2012 Huazhong Univ of Sci & Tec.

关键词： Support vector machines

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Bagging and Boosting Fine-Tuning for Ensemble Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1728-1742页

作者： Zhao, Changming Peng, Ruimin Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 China and alsowith the ShenzhenHuazhong University of Science and Technology Research Institute Shenzhen 518029 China

Ensemble learning aggregates outputs from multiple base learners for better performance. Bootstrap aggregating (bagging) and boosting are two popular such approaches. They are suitable for integrating unstable base learners with large variance and weak base learners with large bias, respectively, but not base learners with small variance and/or bias, e.g., support vector machine, regularized logistic regression, and ridge regression. This article proposes two novel ensemble-learning-based fine-tuning approaches, boosting fine-tuning (BF) and bagging and boosting fine-tuning (BBF), to fine-tune learners with small variance and/or bias for better performance. BF embeds boosting in a single hidden layer neural network. In each iteration, BF first uses the Newton's method to generate a temporary training set, and then trains a boosting learner on it. BBF combines BF and bagging. It first uses bootstrap to obtain multiple replicas of the training set, and then trains a BF learner on each replica. Extensive experiments on 46 real-world datasets demonstrated that BBF is flexible, robust, and effective, and can fine-tune many popular classifiers to achieve better generalization performance. © 2020 IEEE.

关键词： Support vector machines

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3D ultrasound data acqusition system based on back end scan mode

3D ultrasound data acqusition system based on back end scan ...

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2011 IEEE International conference on intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Qi, Jian Ding, Mingyue Yuchi, Ming Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Life Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769546230

The data acquisition of 3D-Ultrasound includes array scan and mechanical scan, and the later one is more easy to realize. Currently, the traditional probe scanning mode is Front-end scanning. Under the above scanning mode, when it scans over the breast, 2D-Ultrasound probe slides through the surface of the patients' bodies, the image will be influenced strongly by the human *** this paper we propose a new scanning mode to solve the above problem: Back-End scan, the back end rotates while the front end contacts the patients' skin without slide. The device designed using dual stepper motors which are under the synchronization control. Experiment results show the effectiveness of the proposed device. © 2011 IEEE.

关键词： Scanning

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A novel estimation strategy for the eye-in-hand depth-independent visual servoing

A novel estimation strategy for the eye-in-hand depth-indepe...

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2011 International Conference on Consumer Electronics, Communications and Networks, CECNet 2011

作者： Liang, Xinwu Huang, Xinhan Wang, Min Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Dept. of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China

ISBN: (纸本)9781612844572

As we know, a novel adaptive visual servoing strategy has been proposed for the control of robot manipulators with an eye-in-hand configuration, where an adaptive law is used to estimate the unknown parameters determined by the products of the unknown 3D coordinates and the unknown camera parameters. From a practical point of view, in this paper, we propose a novel strategy for the estimation of these two types of unknown parameters. The new strategy is very useful in that it can reduce the number of the unknown parameters to be estimated. To demonstrate the feasibility of the proposed method, preliminary simulation results based on a two-link planar robot manipulator are presented in this paper. © 2011 IEEE.

关键词： Parameter estimation

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Solving 0-1 planning problem based on self-assembly of dna tiles

Solving 0-1 planning problem based on self-assembly of dna t...

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作者： Huang, Yufang Cheng, Zhen Xu, Jin Shi, Xiaolong Zhou, Kang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology 430074 Wuhan China Ministry of Education 10087 Beijing China

DNA tile self-assembly has been proved to enable programmable manipulation of biological systems as a tool of molecular computation. It is mainly based on the property that is the spontaneous self-ordering of substructure into superstructure driven by annealing of Watson-Crick base-pairing DNA sequences. We take full advantage of the superiority of DNA tile self-assembly to construct a molecular computing system that implements a solution for the 0-1 planning problem. This algorithm can independently and simultaneously yield the data pool, containing all possible solutions when all basic operation tiles are designed beforehand. Then we can use some advanced bio-chemistry techniques to select the optimization solutions of the 0-1 planning problem. Our work has shown that it is possible to work with an exponential number of components to solve NP-complete problems. The method proposed here also can reduce the number of laboratory steps required for computation so that it can improve the computation speed. Copyright © 2010 American Scientific Publishers. All rights reserved.

关键词： Self assembly

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A Discrete Artificial Bee Colony Algorithm for Stochastic Vehicle Scheduling

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Complex System Modeling and Simulation 2022年第3期2卷 238-252页

作者： Yuanyuan Li Yindong Shen Jingpeng Li Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computer Science and Mathematics University of StirlingStirlingFK94LAUK

Vehicle scheduling plays a profound role in public ***,stochastic vehicle scheduling may lead to more robust *** solve the stochastic vehicle scheduling problem(SVSP),a discrete artificial bee colony algorithm(DABC)is *** to the discreteness of SVSP,in DABC,a new encoding and decoding scheme with small dimensions is designed,whilst an initialization rule and three neighborhood search schemes(i.e.,discrete scheme,heuristic scheme,and learnable scheme)are devised individually.A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.

关键词： public transportation stochastic vehicle scheduling discrete artificial bee colony search scheme

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Blind noisy image separation based on a new robust independent component analysis network

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Chinese Optics Letters 2006年第10期4卷 573-575页

作者：纪建田铮 Department of Computer Science & Technology Northwestern Polytechnical University Xi'an 710072 Department of Applied Mathematics Northwestern Polytechnical UniversityXi'an 710072 Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhan 430074

The separation of noisy image is a very exciting area of research, especially when no prior information is available about the noisy image. In this paper, we propose a robust independent component analysis （ICA） network for separation images contaminated with high-level additive noise or outliers. We reduce the power of additive noise by adding outlier rejection rule in ICA. Extensive computer simulations confirm robustness and the excellent performance of the resulting algorithms.

关键词： Algorithms Computer simulation Independent component analysis

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Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning 24

Enhancing Deep Learning-based Depression Level Estimation Ba...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Ye, Qingxin Xie, Zhenming Sun, Hao Xin, Luyao Chen, Youwen Song, Jian Chen, Yen-Wei Provincial key Laboratory of Computational Science Huaqiao University Quanzhou China Intelligent Image Processing Lab Zhejiang University Hangzhou China Intelligent Image Processing Lab Ritsumeikan University Osaka Japan

ISBN: (纸本)9798400717833

In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an urgent issue that needs to be addressed. Currently, many experts and scholars propose using deep learning methods to assess the level of depression. Compared to other deep learning models, Transformer models have parallel computing capabilities with their self-attention mechanism, enabling them to handle entire sequences and capture long-distance dependencies. After the success of Transformers in natural language processing, similar architectures have been introduced into the field of computer vision, promoting advancements in multi-modal research. However, how to integrate other information or tasks to improve assessment accuracy remains a challenge. In our team's preliminary work, we proposed a multi-modal adaptive fusion Transformer network for assessing depression levels. Inspired by multi-task learning, we introduced gender classification as a second auxiliary task alongside the main task of depression regression, forming a three-task learning framework including depression classification. This was done to enhance the model's performance. We validated the effectiveness of the proposed method by testing it on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge). The experimental results show that our proposed method achieved a Concordance Correlation Coefficient (CCC) of 0.60448, which is 1.688% higher than the accuracy of the dual-task learning method (CCC = 0.5876). © 2024 Copyright held by the owner/author(s).

关键词： Multi-task learning

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiang Wang, Xiaoping Lei, Shanglin Zeng, Zhigang Wuhan430074 China Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan430074 China

ISBN: (纸本)9798350303759

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge. © 2023 IEEE.

关键词： Emotion Recognition

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