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5th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2021

作者： Tang, Zanyu Zeng, Tianyu Li, Zhenyu Chen, Jianrong Zhang, Yunong Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China College of Information Science and Engineering Jishou University Jishou416000 China School of Public Health Administration Youjiang Medical University for Nationalities Baise533000 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

The problem of Takagi factorization (TF) for complex time-varying symmetric matrix is proposed in this paper. To solve this difficult and challenging problem, a continuous-time TF (CTTF) model of derivative equation system form is proposed via Zhang neural network (ZNN) method. For the sake of digital hardware implementation, we also build up the corresponding discrete-time (DT) ZNN solution models with different precisions of the derivative equation system discretization. Specifically, an eight-instant DTTF (EIDTTF) model are proposed. We also comparatively use two other multi-instant (e.g., four-instant and six-instant, i.e., FIDTTF and SIDTTF, respectively) models by discretizing the CTTF model. The effective solution step-size intervals of the proposed DTTF models are further discussed lastly. The results of simulations and experiments substantiate the correctness, effectiveness and superiority of the proposed CTTF and DTTF models. © 2021 IEEE.

关键词： Complex networks

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Adaptive Positional Encoding for Bundle-Adjusting Neural Radiance Fields

Adaptive Positional Encoding for Bundle-Adjusting Neural Rad...

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International Conference on Computer vision (ICCV)

作者： Zelin Gao Weichen Dai Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province and School of Computer Science Hangzhou Dianzi University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province

Neural Radiance Fields have shown great potential to synthesize novel views with only a few discrete image observations of the world. However, the requirement of accurate camera parameters to learn scene representations limits its further application. In this paper, we present adaptive positional encoding (APE) for bundle-adjusting neural radiance fields to reconstruct the neural radiance fields from unknown camera poses (or even intrinsics). Inspired by Fourier series regression, we investigate its relationship with the positional encoding method and therefore propose APE where all frequency bands are trainable. Furthermore, we introduce period-activated multilayer perceptrons (PMLPs) to construct the implicit network for the high-order scene representations and fine-grained gradients during backpropagation. Experimental results on public datasets demonstrate that the proposed method with APE and PMLPs can outperform the state-of-the-art methods in accurate camera poses and high-fidelity view synthesis.

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Self-Organized Criticality in C. Elegans Neural Network 42

Self-Organized Criticality in C. Elegans Neural Network

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42nd Chinese control Conference, CCC 2023

作者： Gu, Xiao Liu, Jian Yuan, Ye Zhao, Peng Xin, Kuankuan Fang, Tao Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai200240 China Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai200093 China Queensland Brain Institute The University of Queensland BrisbaneQLD4072 Australia

ISBN: (纸本)9789887581543

The self-organized criticality is a fundamental property for organisms to perform normal functional activities. Extensive large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Calcium

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Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

arXiv

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arXiv 2020年

作者： Wu, Dongrui Jiang, Xue Peng, Ruimin Kong, Wanzeng Huang, Jian Zeng, Zhigang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence Hangzhou Dianzi University Hangzhou310018 China

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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Diffusion Posterior Proximal Sampling for Image Restoration 24

Diffusion Posterior Proximal Sampling for Image Restoration

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Hongjie He, Linchao Zhang, Mingqin Chen, Dongdong Luo, Kunming Luo, Mengting Zhou, Ji-Zhe Chen, Hu Lv, Jiancheng College of Computer Science Sichuan University Chengdu China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China Heriot-Watt University Edinburgh United Kingdom Hong Kong University of Science and Technology Hong Kong Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education China College of Computer Science Sichuan University China

ISBN: (纸本)9798400706868

Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep. Additionally, we start the restoration process with an initialization combined with the measurement signal, providing supplementary information to better align the generative process. Extensive experimental results and analyses validate that our proposed method significantly enhances image restoration performance while consuming negligible additional computational resources. © 2024 ACM.

关键词： Image reconstruction

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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Trajectory Tracking Multi-mode Predictive control Based on Softswitching for Unmanned Surface Vehicle

Trajectory Tracking Multi-mode Predictive Control Based on S...

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第43届中国控制会议

作者： Kunpeng Duan Shanling Dong Meiqin Liu Senlin Zhang College of Electrical Engineering Zhejiang University National Key Laboratory of Industrial Control Technology Zhejiang University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an Jiaotong University Jinhua Institute of Zhejiang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The unmanned surface vehicle(USV) plays a vital role in ocean exploration and utilization. Its primary tasks include navigating designated routes and safely avoiding obstacles in complex environments, ensuring efficient and secure arrival at destinations. This paper proposes a soft-switching-based multi-mode predictive control method. Specifically, A two-stage control model is defined to categorize the control modes, and a nonlinear model predictive controller(NMPC) embedding relevant obstacle avoidance constraints is developed. Then combined with NMPC framework, a sigmoid function is introduced to handle the multi-mode control problem. In addition, we apply the proposed algorithm successfully to the trajectory tracking control of USV. Simulation results show the strength and reliability of the proposed algorithm, which reduces the errors and improves the control accuracy effectively.

关键词： Unmanned surface vehicle model predictive control multi-mode control trajectory tracking

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Short-Term and Mid-Term Population Projections Especially of Germany via WASD Neural Network: Theoretical Parts

Short-Term and Mid-Term Population Projections Especially of...

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第40届中国控制会议

作者： Zhiwen Yuan Siyuan Feng Chengxu Ye Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Computer Science Qinghai Normal University

Population has a direct impact on the demands of consumption, resource, and employment. Population factors, such as size and growth rate, are crucial in making decisions and plans of trade, environment protection, resource allocation and human resource development. Thus, population projection is an essential part of strategy development for companies as well as governments, and draws the attention of many researchers. Some projection methods have been developed, but most of the popular ones require a vast knowledge in demography and only can be operated by skilled professionals. In this paper, as an improvement, we theoretically develop a regression model and a subtrend model of WASD（weights and structure determination）neural network in a simultaneous manner to make population projections especially of Germany in the mid term and near ***, we aim at utilizing the learning capabilities of the WASD neural-network models with single input and multiple inputs, having been very popular recently, to propose these easy-used and high accuracy models for verifications and projections via later computer experiments.

关键词： Germany population Projections Neural network WASD（weights and structure determination） Multiple inputs

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One-Iteration-per-Update 94LVI Algorithm Solving Time-Varying Quadratic Programming in Real Time with Numerical and Graphical Results

One-Iteration-per-Update 94LVI Algorithm Solving Time-Varyin...

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第34届中国控制与决策会议

作者： Yunong Zhang Zhiwen Yuan Tangke Xu School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Research Institute of Sun Yat-sen University in Shenzhen

Recently,there have been many methods to solve quadratic programming(QP) *** this work,on the basis of linear variational inequality(LVI),a simple and effective algorithmic method(i.e.,one-iteration-per-update 94 LVI algorithm)is proposed for solving the time-varying QP problem,which is subject to time-varying linear equality,inequality and bound constraints at the same *** that the normal 94 LVI algorithm is usually used to find the optimal solution of the static QP problem in the ***,we transform the QP problem into a piecewise-linear projection equation(PLPE).Then,the continuous time-varying QP problem is divided into many static QP problems via sampling time interval ***,we use the normal 94 LVI algorithm to update with only one iteration based on the solution of previous sampling time instant,and finally we get the solution of each sampling time *** numerical experiments,as a novel approach,the one-iterationper-update 94 LVI is able to get the solution of each sampling time instant much quickly and quite accurately when solving the time-varying QP problem.

关键词： time-varying QP problem linear variational inequality piecewise-linear projection equation one-iteration-perupdate 94LVI numerical algorithm

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Research on Optimization control of Deep Hole Machining Based on Capuchin Search Algorithm to Optimize Fuzzy PID

Research on Optimization Control of Deep Hole Machining Base...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Chang Qin Hongzhi Hu HuaiZhi Chen BaoXian Yan Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Key Laboratory of Automatic Testing Guilin University of Electronic Science and Technology Guilin China School of Electronic Engineering and Automation Guilin University of Electronic Science and Technology Guilin China

A small deep hole drilling control system based on the capuchin search algorithm to optimize fuzzy PID is proposed to improve the efficiency of small deep hole drilling. Based on the difference between the actual axial force and the expected axial force, the system adaptively adjusts the quantization factor and scale factor of the fuzzy PID controller by using the capuchin search algorithm and adjusting the parameters of the controller in real-time to improve the system performance. The experimental results show that the CapSA-optimized fuzzy PID algorithm achieves the shortest stabilization time and slight overshoot. When the algorithm is applied to actual machining, the proposed algorithm has a faster response speed and more stable axial force. It can be adaptive to Change the PID drilling parameters to meet the production requirements of intelligent manufacturing..

关键词： Drilling PI control System performance Simulation Force Process control Ubiquitous computing

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