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World Congress on Intelligent control and Automation (WCICA)

作者： Wei Zhang Jun-fei Qiao College of Electronic Information and Control Engineering Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligence System Beijing China

In this paper, a direct adaptive neural network control (DANNC) method is developed to deal with the multi-variable (dissolved oxygen concentration and nitrate concentration) tracking control problem in wastewater treatment processes (WWTPs), which avoids the perplex issue of establishing the plant model of WWTP and has the excellent adaptive ability. The DANNC system is composed of neural controller and compensation controller. The neural controller is employed to approximate an ideal control law, and the compensation controller is designed to offset the network approximation error. The controller parameters' adaptive laws are deduced by the Lyapunov theorem. Simulation results, based on the international benchmark simulation model No.1 (BSM1), show that the control accuracy and dynamic performance of the DANNC method are improved nicely.

关键词： Effluents Mathematical model Neural networks Biological system modeling Inductors Process control

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DFPC-SLAM:A Dynamic Feature Point Constraints-Based SLAM Using Stereo Vision for Dynamic Environment

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Guidance, Navigation and control 2023年第1期3卷 46-60页

作者： Bo Zeng Chengqun Song Cheng Jun Yuhang Kang CAS Key Laboratory of Human-Machine Intelligence-Synergy System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P.R.China University of Chinese Academy of Sciences Beijing P.R.China The Chinese University of Hong Kong Hong Kong P.R.China

Visual SLAM methods usually presuppose that the scene is static, so the SLAM algorithm formobile robots in dynamic scenes often results in a signicant decrease in accuracy due to thein°uence of dynamic objects. In this paper, feature points are divided into dynamic and staticfrom semantic information and multi-view geometry information, and then static region featurepoints are added to the pose-optimization, and static scene maps are established for dynamicscenes. Finally, experiments are conducted in dynamic scenes using the KITTI dataset, and theresults show that the proposed algorithm has higher accuracy in highly dynamic scenes comparedto the visual SLAM baseline.

关键词： SLAM stereo vision semantic segmentation multi-view geometry dynamic scenes.

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An input-output clustering approach for structure identification of T-S fuzzy neural networks

An input-output clustering approach for structure identifica...

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IEEE Symposium on Computational intelligence in control and Automation, CICA

作者： Wei Li Honggui Han Junfei Qiao College of Electronic Information and Control Engineering Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligence System Beijing China

ISBN: (纸本)9781479945290

This paper proposes a novel input-output clustering approach for structure identification of T-S fuzzy neural networks. This approach consists of two phases. Firstly, k-means clustering method is applied to the input data to provide the initial clusters of the input space. Secondly, check whether the sub-clustering is needed for each input cluster by considering the corresponding output variation and then apply the k-means method to further partition those input clusters needed sub-clustering. Applying the above process recursively leads to the structure identification of a T-S fuzzy neural network and then the parameter identification is completed by using the gradient learning algorithm. The experiments by applying the proposed method to several benchmark problems show better performance compared with many existing methods and then verify the effectiveness and usefulness of the proposed method.

关键词： Clustering methods Fuzzy neural networks Accuracy Clustering algorithms Context Fuzzy control Partitioning algorithms

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A Novel Multi-scale Fourier Descriptor Based on Plane Orthogonal Vectors for Fast Shape Retrieval

A Novel Multi-scale Fourier Descriptor Based on Plane Orthog...

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IEEE International Conference on Robotics and Biomimetics

作者： Jiajun Ma Jiafeng Zhu Guoyuan Liang Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System & CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China

Shape retrieval is an interesting and challenging issue in computer vision and robot vision. The recent works employing Fourier-based multi-scale descriptors achieve some success in shape retrieval, especially make real-time application possible by improving the efficiency. However, these methods are commonly adopting really complex shape descriptors to present the shape information which is not intuitive enough. In our work, we propose a new Fourier-based method with novel multi-scale descriptor which is a combination of contour feature and regional feature. This novel descriptor has two main characteristics: First, utilize six different invariant signatures which are based on plane orthogonal vectors; Second, leverage multiple scale structures to describe local and global information of shapes. Except the easiness in understanding, this descriptor approach also produces a more distinct topology of shapes than other approaches which means the property of stability on variations, linear or nonlinear. The method is tested on some popular databases that provides a promising retrieval rate(87.46% and 99.27%) with constant time complexity optimization. The results confirm that our method is more capable of representing the topology of shapes than most other methods based on Fourier descriptor.

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An online measurement method for noise-source impedance of electrical equipment

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Progress In Electromagnetics Research C 2020年 100卷 95-103页

作者： Du, Mingxing Li, Yang Wang, Hongbin Ouyang, Ziwei Wei, Kexin Tianjin Key Laboratory of Control Theory & Applications in Complicated System Tianjin University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

The paper presents a method of extracting noise source impedance of electrical equipment under working condition. Firstly, based on the theory of two-port network, the measurement method of noise impedance is analyzed theoretically, and the injection probe and receiving probe are calibrated by two known resistors. No special calibration fixture is needed to calibrate the injection probe and receiving probe. Secondly, the port structure of the noise impedance measurement method is analyzed, and the noise source impedance is calculated by using the theory of microwave transmission. Compared with the traditional method, this method does not require calibration fixture and simplifies the experimental process. Finally, passive devices and active systems are tested respectively, and the experimental results show that the method is effective and feasible. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Probes

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Hopf bifurcation analysis and control of a bacteria-immunity system with delayed quorum sensing 8

Hopf bifurcation analysis and control of a bacteria-immunity...

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8th International Symposium on Computational intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Liu, Feng Dai, Xiangjuan Mei, Qicheng Ling, Guang Sun, Fenglan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligence Automation for Complex System Wuhan430074 China School of Science Wuhan University of Technology Wuhan430070 China Research Center of System Theory and Application Chongqing University of Posts and Telecommunications Chongqing400065 China

A bacteria-immunity system model is formulated to describe the interaction between bacteria and immune cells with quorum sensing. Time delay is introduced to study this system. The stability and the conditions for the existence of Hopf bifurcation are obtained. Finally, numerical simulations are used to verify the theoretical conclusions. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Bacteria

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An improved calculation method for static capacitance in inductor windings

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Progress In Electromagnetics Research C 2020年 104卷 25-36页

作者： Du, Mingxing Zhang, Yuxiao Wang, Hongbin Tian, Ye Ouyang, Ziwei Wei, Kexin Tianjin Key Laboratory of Control Theory & Applications in Complicated System Tianjin University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

This paper proposes an improved method for calculating static capacitance between two conductors with circular cross-sections in inductor windings. It considers the effects of electric field coupling and energy distribution on static capacitance. In this paper, the capacitance between two conductors in inductor windings is calculated by the improved calculation method and finite-element method (FEM), respectively. The relative error of the improved calculation method is between 0.11% and 4.51% compared to the FEM. In order to verify the effectiveness of this method for inductor winding, the orthogonal stacking winding and staggered stacking winding are chosen as calculation examples to accurately predict the static capacitance of multi-layer circular-section induction coils. Finite element models for the two types of windings are built to determine the capacitances for our 3 × 3 array arrangement winding. The results show that the improved calculation method proposed in this paper highly conforms to FEM. Finally, we adopt an air-cored cylindrical inductor winding for experimental verification, and the improved calculation method is proved to be correct. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Capacitance

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Short-time Fourier transform covariance and selection, a feature extraction method for binary motor imagery classification

Short-time Fourier transform covariance and selection, a fea...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Ma, Yue Zheng, Liangsheng Yi, Zhengkun Xiao, Yang Wang, Can Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chongqing University of Posts and Telecommunications

ISBN: (纸本)9781665436786

The brain-computer interfaces (BCI) technology is able to help dysfunctional people recover their motor functions. Electroencephalography (EEG) is an effective noninvasive method to construct BCI. Motor imagery (MI) paradigm can directly reflect the motor intention of user without additional stimulation equipment in EEG-BCI. The feature extraction methods are the key components to improve the accuracy of MI classification. Traditional feature extraction methods like CSP that only extract features in a single domain or two domains. In this study, we propose two novel feature selection method, short-time Fourier transform covariance and its selection method, which are aiming to extract spatial-time-frequency features simultaneously. In order to evaluate the proposed features, the BCI Competition IV Data Set IIb is employed to test the classification accuracy. By comparing the average accuracy of novel TSGSP method, the proposed method is more stable than TSGSP about 6% and the accuracy is just decrease about 0.5% at the same time. The average accuracy of 83.8% over all subjects is obtained. Superior classification performance results show that our proposed method has great potential, which is helpful for the further development and application of BCI technology for motor imaging in the field of neurorehabilitation. © 2021 IEEE.

关键词： Electroencephalography

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Prognostic Prediction for Non-small-Cell Lung Cancer Based on Deep Neural Network and Multimodal Data 17th

Prognostic Prediction for Non-small-Cell Lung Cancer Based o...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Zhang, Zhong-Si Xu, Fei Jiang, Han-Jing Chen, Zhan-Heng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University No. 4800 Caoan Road Shanghai201804 China

ISBN: (纸本)9783030845315

Non-small-cell lung cancer (NSCLC) is the most common lung cancer with poor prognosis. Prognostic prediction is significant in improving the prognosis of NSCLC patients. Clinical information and multi-omics data including gene expression, miRNA, copy number variations, and DNA methylation are closely related to NSCLC prognosis. In this study, we propose a deep neural network to conduct prognostic prediction for NSCLC patients based on all five types of data. Given the high dimensional features of the omics data, past works reduce the feature dimension by regression analysis or correlation sorting algorithms. A shortcoming of these methods is that only a small number of features are considered. To overcome it, we propose a convolution neural network-based feature transformation method, which considers all features and extracts the abstract representations of the omics data. Based on the representations, we predict the five-year survival status of NSCLC patients. The results show that our method achieves a more precise prediction than previous work, with the areas under the curve improved from 0.8163 to 0.8508 and the accuracy improved from 0.7544 to 0.8096. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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