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Chinese control and Decision Conference, CCDC

作者： Jing Wang Yuan Wei Yisong Liu Weichao Huang Ming Hu Faculty of Printing Packaging Engineerring And Digital Media Technology Xi’an University of Technology Xi’an China Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China

The kernel correlation filtering algorithm cannot judge the occlusion by itself when the object is occluded by foreign objects, and the tracking performance is greatly affected. Aiming at this problem, this paper proposes an anti-occlusion object tracking algorithm that fuses response templates and combined features. The peak sidelobe ratio (PSR) is used to determine whether the object is occluded. When the object is partially occluded, a combined feature representation object model is introduced to enhance robustness, and a multi-response template with anti-occlusion performance is constructed to improve the performance of the object when it is occluded by foreign objects. The problem of reducing the available information, thereby improving algorithm performance. Experiments in the OTB-100 data set can prove that the success rate and accuracy of the algorithm have competitive results compared with the comparison algorithms.

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

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information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xianzhi Qi Wenxing Fu Pei An Bingli Wu Jie Ma National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

关键词： Three-dimensional displays Laser radar Filtering Filtering algorithms Spatial resolution Task analysis Measurement by laser beam

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MOSFET/TFET Mixed Power Clamp Circuit For ESD and Surge Protection

MOSFET/TFET Mixed Power Clamp Circuit For ESD and Surge Prot...

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Design and system (IEDS), International EOS/ESD Symposium on

作者： Zhaonian Yang Yaping Yue Shi Pu Xiaohan Yang Yue Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Shandong Inspur Intelligence Research Institute Co. Ltd. Xi'an China Xi'an Xiangteng Microelectronics Co. Ltd. Xi'an China

ISBN: (数字)9781585373383

ISBN: (纸本)9781665469814

In this paper, a high sensitive voltage detector is proposed and its electrical characteristics are investigated using technology computer-aided design (TCAD) simulation. Germanium (Ge) source and line configuration TFET is adopted to obtain an ultra sensitive detection capability. Corresponding operating principle is explained and the impact of the key parameters on the voltage detector is also discussed. The simulation results show that the new proposed Ge source line TFET based voltage detector has a low leakage current and high detection sensitivity under the electrostatic discharge (ESD) and surge events compared to traditional detectors.

关键词： TFETs Sensitivity Simulation Surge protection Detectors Voltage Germanium

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Architectural distortion recognition based on a subclass technique and the sparse representation classifier

Architectural distortion recognition based on a subclass tec...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xiaoming Liu Leilei Zhai Ting Zhu Zhou Yang School of Automation Huazhong University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781509037117

Architectural distortion is the third most common sign of breast cancer in mammograms. The accurate recognition is important for computer aided diagnosis of breast cancer. However, due to the subtle symptom and complex structures in the mammogram images, it is difficult to recognize whether a region of interest (ROI) is truly an architectural distortion. In this paper, we proposed a new method for architectural distortion recognition. In the proposed method, several texture features are extracted for each region of interest, including features from GLCM matrix, spiculated related features, entropy features, etc. Feature selection is obtained by a sub-classes clustering based multi-task learning method (SMTL), which can utilize the discriminative label information and reflect the multi-clustering characteristic of the data samples. Finally, the powerful sparse representation based classifier is used for the classification of AD or non-AD. The proposed method has been tested on DDSM dataset and compared with several other methods, the experimental results showed the effectiveness of the proposed method.

关键词： Feature extraction Distortion Entropy Training Mammography Delta-sigma modulation Breast cancer

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Network Parameter Generation for One-Shot Object Detection

Network Parameter Generation for One-Shot Object Detection

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IEEE International Conference on Unmanned systems (ICUS)

作者： Kang Wang Jiajia Zhao Yang Wang Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineer Institute Beijing China School of Information Engineering Nanchang University China

ISBN: (纸本)9781728137933

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection.

关键词： Object detection Detectors Head Training Feature extraction Task analysis Training data

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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An Approximated Domination Relationship based on Binary Classifiers for Evolutionary Multiobjective Optimization

An Approximated Domination Relationship based on Binary Clas...

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Congress on Evolutionary Computation

作者： Hao Hao Aimin Zhou Hu Zhang Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China

Preselection is an important strategy to improve evolutionary algorithms’ performance by filtering out unpromising solutions before fitness evaluations. This paper introduces a pre-selection strategy based on an approximated Pareto domination relationship for multiobjective evolutionary optimization. For each objective, a binary relation between each pair of solutions is constructed based on the current population, and a binary classifier is built based on the binary relation pairs. In this way, an approximated Pareto domination relationship can be defined. When new trial solutions are generated, the approximated Pareto domination is used to select promising solutions, which shall be evaluated by the real objective functions. The new preselection is integrated into two algorithms. The experimental results on two benchmark test suites suggest that the algorithms with preselection outperform their original ones.

关键词： Filtering Computational modeling Sociology Training data Evolutionary computation Approximation algorithms Linear programming

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Application of Case Teaching Method in Artificial Intelligence Teaching

Application of Case Teaching Method in Artificial Intelligen...

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作者： Li Huang Wei Hu Chong Tan College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology

Aiming at the current situation of poor quality of artificial intelligence teaching and weak students' interest in learning, a case teaching method is proposed. This paper analyzes the teaching status of artificial intelligence firstly, and then systematically describes the case teaching method. Finally, the necessity of introducing case teaching method into artificial intelligence teaching is studied. Practice shows that the case teaching method can effectively enhance students' self-learning ability, improve students' comprehensive innovation ability, and promote the development of artificial intelligence teaching.

关键词： Case teaching method Artificial intelligence Teaching case Course

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Water supply prediction based on initialized attention residual network

arXiv

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arXiv 2019年

作者： Long, Yuhao Wang, Jingcheng Wang, Jingyi Automation Department Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an Shaanxi710021 China

Real-time and accurate water supply forecast is crucial for water plant. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply prediction. In this paper, we address a generic artificial neural network, called Initialized Attention Residual Network (IARN), which is combined with an attention module and residual modules. Specifically, instead of continuing to use the recurrent neural network (RNN) in time-series tasks, we try to build a convolution neural network (CNN) to recede the disturb from other factors, relieve the limitation of memory size and get a more credible results. Our method achieves state-of-the-art performance on several datasets, in terms of accuracy, robustness and generalization ability. Copyright © 2019, The Authors. All rights reserved.

关键词： Water supply

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An improved distributed nonlinear observer for leader-following consensus via differential geometry approach

arXiv

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arXiv 2020年

作者： Xu, Haotian Wang, Jingcheng Wang, Bohui Wang, Hongyuan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an Shaanxi710021 China School of Aerospace Science and Technology Xidian University Xi'an710071 China

This paper is concerned with the leader-following output consensus problem in the framework of distributed nonlinear observers. In stead of certain hypotheses on the leader system, a group of geometric conditions is put forward to develop a novel distributed observer strategy with less conservatism, thereby definitely improving the applicability of the existing results. To be more specific, the improved distributed observer can precisely handle consensus problems for some nonlinear leader systems which are invalid for the traditional strategies with the certain assumption, such as Elastic Shaft Single Linkage Manipulator (ESSLM) systems and most of first-order nonlinear systems. We prove the sufficient conditions for the exponential stability of our distributed observer's error dynamic by proposing two pioneered lemmas to show the relationship between the maximum eigenvalues of two matrices appearing in Lyapunov type matrices. Then, a partial feedback linearization method with zero dynamic proposed in differential geometry is employed to design a purely decentralized control law for the affine nonlinear multiagent system. With this advancement, the existing results can be regarded as a specific case owing to that the followers can be chosen as an arbitrary minimum phase affine smooth nonlinear system. At last, the novel distributed observer and the improved purely decentralized control law are applied in the distributed control framework to construct a closed-loop system. We also prove the stability of closed-loop system to achieve leader-following consensus, i.e., the distributed control framework is proved to satisfy certainty equivalence principle. Our method is illustrated by ESSLM system and Van der Pol system as leader. Copyright © 2020, The Authors. All rights reserved.

关键词： Feedback linearization

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