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Systematic review of digital twin technology and applications

Visual computing for Industry,Biomedicine,and Art 2023年第1期6卷 119-138页

作者： Jun-Feng Yao Yong Yang Xue-Cheng Wang Xiao-Peng Zhang Center for Digital Media Computing School of FilmXiamen UniversityXiamen 361005China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and TourismXiamen 361005China State Key Laboratory of Multimodal Artificial Intelligence Systems the Institute of AutomationChinese Academy of SciencesBeijing 101408China

As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application *** phenomenon has been widely studied in academia and *** this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are *** internal association between DT and related technologies is *** four stages of DT development history are *** fundamentals of the technology,evaluation indexes,and model frameworks are ***,a conceptual ternary model of DT based on time,space,and logic is *** technology and application status of typical DT systems are ***,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.

关键词： Digital twin Digitalization Virtual modeling Information technology Simulation

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ResFFont: Few-Shot Font Generation based on Reversible Network 43

ResFFont: Few-Shot Font Generation based on Reversible Netwo...

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43rd Chinese Control Conference, CCC 2024

作者： Ju, Chuanying Zhang, Ziying Chen, Xin China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China China University of Geosciences School of Arts and Communication Wuhan430073 China

ISBN: (纸本)9789887581581

The technique of generating fonts with few-shot learning has become a popular research topic. The goal of this technique is to transform the source-to-target domain by using a small number of sample images to generate characters for new fonts. However, current research is limited by model frameworks and migration algorithms, which pose challenges for maintaining content integrity and stroke-level style consistency when generating Chinese characters. This paper proposes ResFFont, a reversible network-based model that uses a unique reversible framework and Global and Local Feature Fusion Module (GaLSFM) to ensure complete delivery of content information and stroke-level style consistency. The experimental results confirm the effectiveness and superiority of the ResFFont model, providing a new solution to the font generation problem. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Few-Shot Font Generation Reversible Network Style Transfer

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Visual Explanation of Object Detectors via Saliency Maps 18

Visual Explanation of Object Detectors via Saliency Maps

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Xiao, Jin Liu, Wenrui Hu, Xiaoguang Jiang, Hao Wang, Weipeng Yahang, Li Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Beijing China The Third Academy of Casic Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350312201

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators. © 2023 IEEE.

关键词： Object detection

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A repetitive-control method based on sliding-mode control for nonlinear systems with external disturbances 41

A repetitive-control method based on sliding-mode control fo...

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第41届中国控制会议

作者： Yibing Wang Min Wu Manli Zhang Luefeng Chen Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents a two-dimensional(2 D) repetitive-control method based on sliding-mode control for a nonlinear system with external *** a sliding-mode controller has a powerful ability to handle nonlinearity and external disturbances during the sliding motion,the reference-tracking and disturbance-suppression problems of the original nonlinear system are decomposed into two subproblems:reference tracking for a linear system and disturbance suppression for a nonlinear modified-repetitive control system(MRCS) with external *** the first one,a repetitive controller constructed with two positive time-delay loops is employed to increase the flexibility of the system design and guarantee high accuracy reference tracking.A continuous-discrete 2 D model of the linear MRCS is established to describe continuous control and discrete learning by using the 2 D property of repetitive control.A sufficient stability condition,in which three parameters are used to adjust control and learning,is given to guarantee the stability of the linear *** design of the system is transformed into parameter *** optimization algorithm is used to yield the optimal *** the other,a sliding-mode controller is developed to eliminate the effect of both nonlinearity and external disturbances on the system.A linear-matrix-inequality(LMI)-based sufficient stability condition is derived to guarantee the stability of the sliding *** condition is used to design the sliding-mode controller,which has a simple structure and does not require any specific information of nonlinearity and external ***,a numerical example and a comparison demonstrate the validity and advantage of the presented method.

关键词： Repetitive control sliding-mode control two-dimensional model

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Confidence-Driven Semi-Supervised Partial Label Learning

Confidence-Driven Semi-Supervised Partial Label Learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Chengkun Liu Jun Zhang Jing Chai School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming City China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Semi-supervised Partial Label Learning (SPLL) aims to learn from a dataset comprised of both partial label examples each of which is associated with a candidate label set and unlabeled examples. The mainstream of SPLL methods usually construct a confidence matrix for training examples and by which to operate label disambiguation and classifier training. However, they treat examples with different confidence levels in the same strategy during training, which might result in degenerated learning performance. In this work, a novel method named COnfidence-DRiven (CODR) is proposed to deal with the above drawback. In specific, we iteratively update the confidence matrix and predictive network, and employ different strategies to deal with high-confidence and remaining low-confidence examples. Extensive experiments on real-world datasets demonstrate the superiority of CODR in classification accuracy compared with several other state-of-the-art methods.

关键词： Training Measurement Accuracy Sensitivity analysis Noise Neural networks

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Hardware-in-the-loop Simulation and Experimental System for Process Control of Marine Resources Exploration 43

Hardware-in-the-loop Simulation and Experimental System for ...

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43rd Chinese Control Conference, CCC 2024

作者： Wang, Zeyi Wang, Chenxuan Ma, Zhejiaqi Zeng, Kanghui Lu, Chengda Wang, Yawu Wu, Min China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China China University of Geosciences School of Future Technology Wuhan430074 China

ISBN: (纸本)9789887581581

Effective control techniques are essential for offshore platform systems to suppress disturbances caused by the complex marine environment and ensure the stable operation of the systems during the exploration of marine resources. This paper establishes a hardware-in-the-loop based marine resource exploration process control simulation and experiment system to simulate the effects of marine environmental factors on the offshore platform. The marine environment is simulated using JONSWAP spectra and real marine environment data. The dynamics of offshore platform is modeled using Lagrange's equation method. And the control effect is demonstrated using virtual reality technology. Ultimately, the dynamic positioning system control experiment, which uses the equivalent input disturbance method, validates the experimental system. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Hardware-in-the-loop simulation

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Dilated Autoformer: An Improved Model for Predicting the State of Transformer Bushing 43

Dilated Autoformer: An Improved Model for Predicting the Sta...

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43rd Chinese Control Conference, CCC 2024

作者： Huang, Xiangjie Lang, Xun Liu, Songhua Li, Peng Guo, Tao Yu, Li Yunnan University Department of Electronic Engineering Yunnan Kunming650091 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Honghe Power Supply Bureau Yunnan Power Grid Corporation Yunnan Honghe China

ISBN: (纸本)9789887581581

Electric transformers play a crucial role in power systems, and extensive research has been conducted on transformer state prediction. However, despite the importance of transformer bushing as a vital component of transformers, there has been limited research focused specifically on its state prediction. This work builds upon the state-of-the-art predictive network, the Autoformer, and introduces an enhanced functionality module (the Dilated Loss module) for efficient state prediction of transformer bushing. More specifically, the aim is to predict three essential parameters of the bushing: dielectric loss, end shield current, and equivalent capacitance. By forecasting future trends in these parameters, potential bushing or transformer faults can be detected at an early stage. To evaluate the effectiveness of the proposed method, we conducted comparative experiments with attention-based frameworks (Autoformer Pyrafomer, Informer), traditional deep learning algorithms such as Temporal Convolutional Network and Long Short-Term Memory, as well as the statistical methods (Moving Average). The results show that our Dilated Autoformer outperforms the aforementioned techniques in terms of four key metrics: mean square error, mean absolute error, root mean squared error, and coefficient of determination. Our proposed model reveals the potential of attention-based algorithms in predicting the status of transformer bushings. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dielectric losses

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A Survey on IMU-and-Vision-based Human Pose Estimation for Rehabilitation 41

A Survey on IMU-and-Vision-based Human Pose Estimation for R...

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第41届中国控制会议

作者： Yuan Niu Jinhua She Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of human pose estimation,the human-pose-estimation method applied in rehabilitation therapy has attracted more and more attention in today's aging *** them,inertial measurement units(IMU) and computer vision are two significant methods for human pose *** mainly pay attention to the application of these two methods in the rehabilitation therapy *** this paper,we first summarize the research status of IMU and computer vision in the field of rehabilitation ***,we find that combining the two methods can produce better ***,the main problems of the current research and possible development directions are identified.

关键词： Human Pose Estimation IMU Computer Vision Rehabilitation Therapy

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Shuaizheng Feng, Chaolu Li, Wei Yang, Jinzhu Zhao, Dazhe Northeastern University School of Computer Science And Engineering Shenyang China Ministry of Education Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China

ISBN: (纸本)9798350386226

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction. © 2024 IEEE.

关键词： Self-supervised learning

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