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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Liu, Jingyu Yang, Chen Li, Zijin Jiang, Wei State Key Laboratory of Media Convergence and Communication Communication University of China 100024 China Ministry of Culture and Tourism Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System 100024 China Communication University of China School of Information and Communication Engineering 100024 China Central Conservatory of Music Ai Music and Music Information Technology Department 100032 China

ISBN: (纸本)9798350380347

With the acceleration of modernization and the rapid development of popular music, the splendid culture of Guqin, a type of Chinese traditional musical instrument which has lasted for nearly 1,000 years, is facing a serious inheritance crisis. Therefore, how to make full use of modern technical means to digitally store and manage the valuable traditional music cultural resources is the current *** response to the aforementioned challenges, this study focuses on the Guqin, an emblematic Chinese traditional musical instrument. By employing advanced audio feature extraction techniques, this research systematically analyzes the diverse playing techniques of Guqin, thereby establishing a comprehensive acoustic dataset. This endeavor not only facilitates the digital preservation and management of the Guqin's musical heritage but also pioneers a standardized framework for the creation of acoustic datasets. This framework serves as a benchmark for the development of similar datasets, thereby promoting the conservation and standardization of resources associated with Chinese traditional musical instruments. This will provide a scientific basis for the inheritance, standardized management and construction of more acoustic datasets of ethnic musical instruments in the future. © 2024 IEEE.

关键词： Musical instruments

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A Hybrid Communication Framework Based Remote Management Architecture with OPC UA information Model Construction 48

A Hybrid Communication Framework Based Remote Management Arc...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chen, Yuchao Zhang, Jinglong Xu, Qimin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Cloud manufacturing aims to carry out large-scale collaborative production through remote equipment management. To improve management efficiency, it is necessary to construct a unified information model and an adaptive communication framework for manufacturing lines with heterogeneous (high reliable/ low load) needs. OPC UA has become a prominent solution for remote configuration with extensible information models and various transport mechanisms. However, the existing solutions based on OPC UA focus on the information model and communication framework for the machine level, which is hard to support efficient remote management of the production line. In this paper, we construct the model of the process and parameter information in production-line level with a unified manner, which enables unified scheduling, configuration, and monitoring. The operational data are modeled hierarchically to reduce the amount of data transmission during monitoring. The hybrid communication framework is designed by integrating the Client-Server and PubSub models with edge assistance, which is to meet the heterogeneous communication requirements. We implement the management architecture on the physical platform to verify the performance. The experiments show that our architecture is suitable for remote configuration in terms of the combined performance of packet loss rate and CPU load with a massive connection. © 2022 IEEE.

关键词： information theory

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Mapless Path Planning of Multi-robot systems in Complex Environments via Deep Reinforcement Learning 4

Mapless Path Planning of Multi-robot Systems in Complex Envi...

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4th International Conference on Data-Driven Optimization of Complex systems, DOCS 2022

作者： Han, Wanbin Fang, Chongrong He, Jianping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

As mobile robots are becoming more and more widely used, it is of great significance to design an efficient path planning method for multi-robot systems (MRS) that can adapt to complex and unknown environments. In this paper, we present a deep reinforcement learning (DRL) based method to navigate the MRS with collision avoidance in unknown dynamic environments, which consists of centralized learning and decentralized executing paradigm. The proposed policy maps original laser information into robot control commands without constructing global maps. The learned policy is tested in Gazebo environments with three robot systems, which shows the effective performance in terms of success rate, extra time rate, and formation maintenance rate. © 2022 IEEE.

关键词： Multipurpose robots

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Online Voltage Regulation of Distribution Networks in Energy Sharing Markets: a Feedback-Based Method With Parameter Estimation

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IEEE Transactions on Smart Grid 2025年

作者： Wang, Wenyue Liu, Yiming Wang, Zhaojian Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

With the number of prosumers rising quickly, energy sharing markets in distribution networks are emerging. The trading of active power influences the power flow in the distribution network, which will cause voltage fluctuation when the line resistance is not trivial. In this paper, we investigate the voltage regulation problem of distribution networks with the consideration of electricity markets. We first formulate the problem in the form of a non-cooperative game. The uniqueness of generalized Nash Equilibrium (GNE) is proved. Then, a feedback-based GNE seeking algorithm is proposed with online parameter estimation without knowing accurate values of resistance and reactance. The estimation error is proved to be accurate in expectation. The convergence of the algorithm is also guaranteed theoretically. Finally, numerical results in the IEEE 33-bus system validate the effectiveness of the algorithm. © 2010-2012 IEEE.

关键词： Power distribution networks

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Soft Robotics:Morphology and Morphology-inspired Motion Strategy

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IEEE/CAA Journal of Automatica Sinica 2021年第9期8卷 1500-1522页

作者： Fan Xu Hesheng Wang Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China

Robotics has aroused huge attention since the *** of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental ***,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured *** decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing *** presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific *** paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.

关键词： Soft continuum manipulator soft gripper soft mobile robot soft robot control method soft robot modeling method soft robotics

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Lifespan Prediction of Electronic Card in Nuclear Power Plant Based on Few Samples

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Journal of Shanghai Jiaotong University (Science) 2023年 1-7页

作者： Xu, Yong Cai, Yunze Song, Lin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Fujian Fuqing Nuclear Power Co. Ltd. Fujian Fuqing350318 China Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

A lifespan prediction model was developed based on a few samples to provide decision-making information for operation and maintenance, as well as improve the economy and safety of nuclear power plant (NPP) operations. This paper applies a Weibull model to forecast the lifespan of electronic cards with a few samples in NPPs. Relationship between the lifespan prediction of electronic cards and the ambient temperature is revealed using the Arrhenius equation. Censored samples are used to compensate for the lack of fault electronic card data. Scale parameter and shape parameter of the Weibull model are optimized by adjusting the weight ratio between the censored data and the fault data. Characteristic life is then obtained using the rank regression fitting equation. Parameters of the Arrhenius equation can be calculated by dividing the samples into groups according to the ambient temperature. A case study of the intermediate range high-voltage electric card of ex-core neutron detectors demonstrates that the lifespan prediction of electronic cards in NPPs can be successfully predicted with a few samples by combining the Weibull model and the Arrhenius model. This can help provide preventive maintenance recommendations for electronic cards. Finally, operation suggestions for the electronic card’s ambient temperature can be made by utilizing the temperature-life model. © 2023, Shanghai Jiao Tong University.

关键词： Nuclear power plants

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Self-Supervised Depth Estimation Based on the Consistency of Synthetic-real Image Prediction 8

Self-Supervised Depth Estimation Based on the Consistency of...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Tong, Wei Gao, Yubing Wu, Edmond Q. Zhu, Li-Min School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu210014 China Ministry of Education Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China

ISBN: (纸本)9798350300178

Learning-based multi-view stereo aims to restore the real scene from multiple images with overlapping areas. The mainstream self-supervised MVS method trains the model based on the assumption that spatial points from different perspectives share the same color information. To further suppress the interference from specular reflection and illumination noise, this work proposes a self-supervised MVS network based on the consistency of synthetic-real image prediction. The network first applies the coarse-to-fine manner to gradually refine the depth map, and the source images are projected to the reference view to generate the synthesized reference image. Then the synthesized image with real source images are re-input into the network to form a cycled network, and the consistency constraint of the prediction results of the two periods is introduced to improve the color anti-interference of the self- supervised MVS model. The comprehensive experiments on the public dataset show that the proposed work can further improve the reconstruction performance of the benchmark model, which verifies the effectiveness of the proposed work. © 2023 IEEE.

关键词： Forecasting

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 IEEE.

关键词： Resource allocation

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Gas-Liquid Two-Phase Flow Measurement Models for Coriolis Mass Flowmeters Based on SVM and ANN 42

Gas-Liquid Two-Phase Flow Measurement Models for Coriolis Ma...

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42nd Chinese control Conference, CCC 2023

作者： Zhuang, Xuan Sun, Lijun Zhao, Zihui Shao, Xin School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China School of Intelligent Manufacturing Tianjin Sino-German University of Applied Sciences Tianjin300350 China

ISBN: (纸本)9789887581543

The flow measurement under the conditions of gas-liquid two-phase flow is an urgent problem to be solved. Coriolis mass flowmeters are introduced into the two-phase flow measurement due to its superior performance in single-phase flow measurement. Due to the large error in the two-phase flow measurement using Coriolis mass flowmeters, this paper uses the Support Vector Machine (SVM) based on the PSO-grid search method and Back Propagation - Artificial Neural Network (BP-ANN) based on PSO algorithm to model the liquid mass flowrate correction and the gas volume fraction (GVF) prediction respectively. Besides, this paper employs the backward stepwise selection method combined with the correlation analysis to select the appropriate input variables. Two indicators, accuracy and generalization ability, are used to evaluate the quality of the model. According to the experimental results, the appropriate model is selected, and the effectiveness of the model is verified. Finally based on the Baker flow regime map, the reason why a better model can be obtained from the training set after excluding some flow points is analyzed. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Support vector machines

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet 42

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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42nd Chinese control Conference, CCC 2023

作者： Wang, De-Xiu Meng, Qing-Hao Han, Rui-Xue Li, Hong-Yue Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581543

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electronic nose

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