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第三十九届中国控制会议

作者： Ming Zeng Xiangzhe Lu Wenkang Xu Tongxi Zhou Yinbo Liu Institute of Robotics and Automation Systems School of Electrical and Information EngineeringTianjin University

Intelligent garbage classification is an important technique for garbage harmless, reducing and recycling treatment. In this paper, we propose a public garbage classification algorithm based on the CNN architecture, namely Public Garbage Net. The proposed algorithm is a multi-task classification algorithm in which one task identifies four major categories of domestic garbage,and the other task achieves recognition of 10 subclasses garbage. The two classification tasks are related to each other, and the joint loss function is helpful to improve the accuracy of garbage recognition. Considering the fact that the existing garbage datasets are incomplete in classes and small in quantity, we constructed a new pubic garbage dataset including 10 subclasses and a total of 10624 images. In order to obtain better performance, systematically studies such as backbone optimization selection,data augmentation, learning rate optimization, and label smoothing have been made, and finally the accuracy of the optimized model reaches 96.35%.

关键词： garbage classification public garbage dataset multi-task learning data augmentation

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Controlling CAN Servo Step Drives and Their Remote Monitoring by Using Protocol OPC UA

Controlling CAN Servo Step Drives and Their Remote Monitorin...

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2019 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2019

作者： Martinov, G. Issa, A. Martinova, L. Institute of Automation and Robotics Dept. Computer Control Systems Moscow Russia

ISBN: (纸本)9781728100616

The diverse requirements of customers in automation systems increase the need for improved adaptability, flexibility and of simplicity production processes. which creates new horizons for modern production systems. Fortunately, the achievements of the Industry 4.0 (fourth industrial revolution) have provided many solutions to these problems. In this study, an OPC UA-based solution is proposed for monitoring the working process of CAN servo drives. By integrating C++ based OPC UA libraries to implement common OPC UA functionality and developing protocol of interaction between the kernel of CNC system and OPC UA server. The aim of this work is to ensure the connection mechanism between OPC UA Server of CNC system and OPC UA clients which are currently existed on the market. This flexible model allows users to remotely tune and set up CAN servo drives and monitor their work. © 2019 IEEE.

关键词： Digital storage

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mvEchoSeg: One-shot In-context Learning for Multi-view Echocardiography Segmentation

mvEchoSeg: One-shot In-context Learning for Multi-view Echoc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuting Guo Ya Duan Wenfeng Song Nannan Li Aili Li Shuai Li State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Science Beijing Information Science and Technology University Beijing China School of Information Science and Technology Dalian Maritime University Dalian China China-Japan Friendship Hospital Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Echocardiography is the clinical standard for evaluation of cardiac morphology, and function, and providing hemodynamic parameters in patients with known or suspected heart disease. Due to the diversity and complexity of diagnostic tasks, comprehensive interpretation of echocardiography often requires multi-view imaging for integrated metric analysis. Deep learning methods have become the mainstream approach for echocardiography segmentation. However, achieving segmentation of multi-view echocardiography still requires a substantial amount of annotation. To remedy this, we propose a one-shot in-context learning network mvEchoSeg for multi-view echocardiography segmentation. This network requires only one annotated image for each view. Specifically, we propose a Task Prompt Identifier (TPI) module to identify the task type of the image and allocate the most precise task prompt for it, with minimal adaptation of CLIP and few-shot strategies. Additionally, we leverage a unified In-Context Model (ICM) capable of performing a diverse set of echocardiography segmentation tasks automatically. Using fine-tuning and low-rank adapters improved the performance of the pre-trained model, achieving significant results with minimal training cost. Furthermore, we collect a multi-view echocardiography dataset (MVECD) with 8 views to evaluate our method. The results show an improvement of more than 10% in the DICE score compared to the SOTA foundational medical image segmentation models. To our knowledge, this is the first exploration of a one-shot model for multi-view echocardiography segmentation. Our codes and models are available at https://***/stellating/mvEchoSeg.

关键词： Training Measurement Image segmentation Adaptation models Echocardiography Biological system modeling Morphology Hemodynamics Standards Medical diagnostic imaging

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Code generation technology and reliability analysis based on structure flow chart 2

Code generation technology and reliability analysis based on...

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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Zhang, Tao Sun, Xuyang Shao, Zhong School of Electrical Engineering Dalian University of Technology Dalian China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Software College Shenyang University of Technology Liaoning Shenyang China

ISBN: (纸本)9783800757398

With the rapid iterative development of software development technology, the increasing scale of software and the increasing demand for reliability, the research on automatic code generation and reliability prediction technology has increasingly become a hot issue in the field of software engineering. The purpose of this paper is to obtain data from the user modeling process and store it, and then use software metrics to analyze and judge the corresponding data, and obtain a method for optimizing the model, so as to prevent some errors made by users in the modeling phase. And incomplete information, and then iteratively improve and repair the model. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Reliability analysis

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Online Triangular Constraint Calibration for LiDAR and Cameras in Open-Pit Mines

IEEE Transactions on Field Robotics

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IEEE Transactions on Field robotics 2024年 2卷 131-146页

作者： Yuchen Li Luxi Li Siyu Teng Zhenshan Bing Alois Christian Knoll Zhe Xuanyuan Long Chen Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China Faculty of Science Hong Kong Baptist University Hong Kong SAR China Research and Development Center WAYTOUS Inc. Beijing China Department of Information Technische Universität München Munich Germany State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Artificial Intelligence Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China

Extrinsic calibration between a LiDAR and a camera is a crucial task for intelligent vehicles and mobile robots. However, most of the current extrinsic calibration methods are offline and target-based, making it hard to achieve real-time calibration depending on the characteristics of the environments. Although a few online calibration strategies can solve the above drawbacks, they still rely on a large amount of labeled data and struggle to achieve satisfactory performance in extrinsic calibration in unstructured scenarios where valid targets are scarce. In this work, we propose an effective online calibration strategy for mining scenarios, one of the typical unstructured scenarios. Based on the analysis of mining area data, we adopt an approach that integrates image and point cloud segmentation to accomplish the entire calibration process. An initialization method supporting a single rigid target is proposed, effectively addressing the initialization failure caused by the scarcity of targets in mining areas. Incorporating the segmentation results from the right camera to establish a left-right camera-LiDAR triangular constraint effectively reduces translation errors. Temporal data with confidence scores are then used to update and optimize the extrinsic parameters. Our online extrinsic calibration method has been validated on mining scenarios data and has achieved less than 0.2 m in data collected from an SUV and 0.3 m from trucks in translation errors within 10 frames. Besides, we provide a detailed discussion on the challenges of calibration in mining areas and the limitations of the proposed method.

关键词： Calibration Image segmentation Cameras Sensors Point cloud compression Laser radar Translation Image edge detection Accuracy Mining industry

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A Multi-Model Fusion Approach for Transformer Fault Detection

A Multi-Model Fusion Approach for Transformer Fault Detectio...

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

作者： Ke Zhou Wei Zhang Ligang Li Shaoming Pan Chengwei Huang Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid CO. LTD. Nanning State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd.

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model.

关键词： Vibrations Support vector machines Training Accuracy Biological system modeling Fault detection Predictive models Transformers Prediction algorithms Data models

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Research on fault feature extraction of rotating machinery based on adaptive VMD and optimized CYCBD

Research on fault feature extraction of rotating machinery b...

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Chinese Control and Decision Conference, CCDC

作者： Ran Xie Ling Xiong Binbin Dan Zeyu Ren Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control of Ministry of Education Wuhan China School of Physics and Electronic Science Hunan Institute of Science and Technology Yueyang China

ISBN: (纸本)9781665478977

To solve the problem that early fault features of rotating machinery are difficult to extract, an adaptive k-value hierarchical variational mode decomposition (H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) fault feature extraction method is proposed in this paper. Mode decomposition of the vibration signal is performed with H-VMD. and then the noise dominant component is denoised by wavelet threshold denoising (WTD). Furthermore, the improved autocorrelation-weighted correlated kurtosis (ACK) when CYCBD enhances the periodic shock component of the denoised signal is the fitness function of ChOA, and the envelope demodulation analysis of the feature-enhanced signal is performed using the teager energy operator (TEO). Simulation analysis and experimental results show that the interference of background noise can be effectively removed and the periodic shock component of the vibration signal be enhanced by the proposed method, which is a new feature extraction method for the fault diagnosis of rotating machinery.

关键词： Vibrations Fault diagnosis Electric shock Noise reduction Interference Feature extraction Linear programming

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An EMG-Based Objective Function for Human-in-the-Loop Optimization

An EMG-Based Objective Function for Human-in-the-Loop Optimi...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： María Alejandra Díaz Sander De Bock Philipp Beckerle Jan Babič Tom Verstraten Kevin De Pauw Human Physiology and Sports Physiotherapy Research Group Brussels Human Robotic Research Center Vrije Universiteit Brussel Brussels Belgium Department of Electrical Engineering and the Department of Artificial Intelligence in Biomedical Engineering Institute of Autonomous Systems and Mechatronics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Automation Biocybernetics and Robotics Laboratory for Neuromechanics and Biorobotics Jožef Stefan Institute Ljubljana Slovenia Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Robotics and Multibody Mechanics Research Group Vrije Universiteit Brussel and Flanders Make Brussels Belgium

Advancements in wearable robots aim to improve the users' motion, performance, and comfort by optimizing, mainly, energetic cost (EC). However, EC is a noisy measurement with a physiological delayed response that requires long evaluation periods and wearing an uncomfortable mask. This study aims to estimate and minimize an EMG-based objective function that describes the natural energetic expenditure of individuals walking. This objective is assessed by combining multiple electromyography (EMG) variables from the EMG intensity and muscle synergies. To evaluate this objective function simply and repeatedly, we prescribed step frequency (SF) via a metronome and optimized this frequency to minimize muscle activity demands. Further, a linear mixed-effects model was fitted for EC, with the EMG variables as fixed-effects and a random intercept that varies by participant. After the model was fitted to the data, a cubic polynomial was used to identify the optimal SF that reduces the overall EMG-based objective function. Our analysis outlines that the proposed objective function is comparable to the EC during walking, the primary objective function used in human-in-the-loop optimization. Thus, this EMG-based objective function could be potentially used to optimize wearable robots and improve human-robot interaction.

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A distributed integrated energy trading solution

A distributed integrated energy trading solution

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Computer, Blockchain and Financial Development (CBFD), International Conference on

作者： Jingya Dong Chunhe Song Zeyu Zheng Tao Zhang Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing University of Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665429641

Blockchain technology can bring profound changes to the energy industry and is now becoming a new driving force for the rapid development of this sector. In order to build a secure, reliable, and stable energy service system, it is necessary to study the framework and interaction mechanism of the energy trading system. This paper introduces a distributed integrated energy trading solution, proposes a new overall architecture, and analyses the blockchain model. This paper presents an algorithm to calculate the score directly in the client. Through this algorithm, users can choose service providers, get rid of cloud control, and realize users' free choice of electricity purchase and interaction between users through the smart contract. The test results show that the system has high performance and efficiency, can meet the needs of integrated energy transactions, and provides a solution for the application of blockchain in energy trading.

关键词： Industries Cloud computing Analytical models Costs Smart contracts Force Computer architecture

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Method for detecting surface defects of ceramic tile based on improved Cascade RCNN

Method for detecting surface defects of ceramic tile based o...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Yu Cao Yu Wang Haiwen Feng Ting Wang School of Software College Shenyang University of Technology Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

In view of the problems of small scale and low contrast of ceramic tile defects, various types of ceramic tile surface defects, and difficulty in realizing high-precision ceramic tile defect detection, a ceramic tile surface defect detection model based on improved Cascade RCNN is proposed to locate and identify the types of ceramic tile surface defects in different texture backgrounds. The improved ResNest network is used to improve the classification ability of the algorithm and optimize the performance of the model. The improved feature pyramid enhanced the feature extraction ability of the algorithm, and improved the accuracy of detecting small-scale defects and low-contrast defects of ceramic tiles. The whole connection structure of the last layer of cascade detectors was modified to double-head structure, which improved the ability of detectors to perform classification and regression tasks, and solved the problem of various kinds of defects on the surface of ceramic tiles. After data collection, 2810 tiles defect pictures were obtained, and then 7934 tiles defect slices were obtained by image segmentation, and the slices were made into tiles defect data sets. Experiments show that this method can achieve 77.8 % MAP and 93.9% average positive detection rate, which is higher than Faster RCNN and original Cascade RCNN.

关键词： Training Image segmentation Tiles Detectors Feature extraction Data models Ceramics

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