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Chinese control and Decision Conference, CCDC

作者： Yujun Liu Xinrui He Shutong Duan Bo Wang Hongqing Wang College of Science Beijing Forestry University Beijing China School of Economics & Management Beijing Forestry University Beijing China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China

Based on the weather feature clustering, an optimized algorithm called Adaboost-GA-BP is presented for solving the problem that BP neural network is easy to fall into local extremum and over-fitting in PV power short-term prediction. Initially, the K-means algorithm is applied to cluster the historical data into sunny, cloudy and overcast&rainy day. It is then the GA that optimize initial weights, forming the GA-BP neural network. The idea of Adaboost algorithm is applied to train GA-BP as different weak predictors so that the Adaboost-GA-BP strong predictor is combined. Finally, this paper provides a detailed performances comparison of various models. The Adaboost-GA-BP predicts a 4.4% decrease in MAE and a 4.1% decrease in RMSE for the sunny test set, a 9.0% decrease in MAE and a 13.5% decrease in RMSE for the cloudy test set, and a 2.7% decrease in MAE and a 9.1% decrease in RMSE for the rainy test set, outperforming the BP model, the GA-BP model, and the Adaboost-BP models. After comparative analysis, the model has better prediction accuracy and generalization ability especially for predicting cloudy weather.

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A Novel Dynamically Differentiated Access Scheme for Massive Grant-Free NOMA

A Novel Dynamically Differentiated Access Scheme for Massive...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yitian Wang Meng Zheng Haibin Yu Wei Liang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665454698

Facing the dual challenges of massive access and time-sensitive traffics, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a promising technology for implementing massive ultra-reliable and low-latency communications (mURLLC). In this paper, we propose a differentiated power level access (DPLA) policy that exploits the correlations among power levels of GF-NOMA, and implement DPLA by a dynamically distributed GF-NOMA framework. Further, a closed-form expression to the reliability of DPLA is analytically derived and the optimal framework parameters to maximize reliability are obtained. Finally, considering the traffic variation over time, we propose a dynamically-distributed differentiated-layered transmission $(\mathrm{D}^{3}$ LT) algorithm to improve the reliability online. Simulation results show that the proposed scheme in this work outweighs existing schemes in transmission reliability.

关键词： NOMA Vehicular and wireless technologies Correlation Closed-form solutions Simulation Heuristic algorithms Reliability

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A Conceptual Framework of Subjective and Objective Fusion Assessment System for Surgical Robot Surgeons

A Conceptual Framework of Subjective and Objective Fusion As...

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control Conference (ANZCC), Australian and New Zealand

作者： Haoxin Cui Nan Zheng Xiaoyu Chang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of International Economics and Management Beijing Technology and Business University Beijing China

With the continuous advancement of technology, Robot-Assisted Minimally Invasive Surgery (RAMIS) has gained increasing recognition and is considered one of the important trends in the future of surgery. However, there is still a lack of standardized, systematic, and comprehensive assessment systems for assessing the skills of surgeons in RAMIS. In this paper, we propose a conceptual framework of assessment system that combines subjective and objective measures, which focuses on the design of the assessment system and the design of the experimental paradigm. It is expected that through a scientifically designed comprehensive assessment system, the training efficiency in the field of robot-assisted minimally invasive surgery can be improved, enabling the rapid development of highly skilled surgeons in hospitals, thus promoting the advancement of robot-assisted minimally invasive surgery.

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Fast accurate position control of light-responsive liquid crystal elastomer actuator

Fast accurate position control of light-responsive liquid cr...

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Asian control Conference

作者： Xinyu Nie Jundong Wu Yawu Wang Chun-Yi Su Daiki Sato Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST) Tokyo Institute of Technology Yokohama Kanagawa Japan The School of Engineering Tokyo University of Technology Tokyo Japan

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Light-responsive liquid crystal elastomers (LCEs) are promising for soft robot actuators due to their ability to respond to light stimuli and produce deformation. In order to realize their application in soft robots, fast and accurate position control is essential. Based on the intrinsic physical nature of LCE actuator's deformation, a terminal sliding mode controller (TSMC) is designed to efficiently adjust the temperature of the actuator, thus directly controlling the actuator deformation. In order to achieve the target temperature, a prediction model is established to predict the target temperature corresponding to the target displacement. Moreover, a proportional integration controller is applied to eliminate model errors and disturbances. The feasibility of the control strategy is experimentally verified, and compared with conventional PID controller. The results prove that the proposed control strategy can achieve fast and accurate control of displacement.

关键词： Heating systems Actuators Accuracy Deformation Position control Process control Elastomers

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Semi-supervised Long-tail Endoscopic Image Classification

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Chinese Medical Sciences Journal 2022年第3期37卷 171-180,I0002页

作者： Runnan Cao Mengjie Fang Hailing Li Jie Tian Di Dong School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Advaneed Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited *** We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse ***-supervised learning algorithm FixMatch was applied based on consistency regularization and *** splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited *** The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall *** algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,*** a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average *** analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail *** SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume ***,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

关键词： endoscopic image artificial intelligence semi-supervised learning long-tail distribution image classification

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin 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 Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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Autonomous transfer robot system for commercial vehicles at Ro-Ro terminals

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Expert systems with Applications 2025年 289卷

作者： Zhang, Lin Xu, Yongkang Si, Jinge Bao, Runjiao An, Yichen Wang, Shoukun Wang, Junzheng School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

With the rapid development of smart port infrastructure, ports such as Rotterdam and Ningbo have made significant progress in automating container handling. However, traditional manual transfer methods at roll-on/roll-off (Ro-Ro) terminals can no longer meet the growing demands for low-cost, high-efficiency, and standardized operations, driven by increased port throughput, labor shortages, and operational complexity. This study introduces a novel autonomous robot transfer system comprising commercial vehicle transfer robots, a cloud scheduling system, and robotic operation systems. An electric transfer robot with independent four-wheel drive and steering was developed for heavy-duty commercial vehicle transfers, featuring a modular software architecture with cloud-based scheduling and planning, and robot perception and control modules. To support multi-robot cooperative transfer in open yard environments, we propose a task allocation algorithm based on an adaptive particle swarm genetic algorithm and an enhanced conflict-aware path planning method under kinematic constraints. For precise and safe vehicle pick-up and drop-off under complex conditions, a multi-stage fusion algorithm is introduced for vehicle body localization, orientation estimation, and wheel alignment, together with a predictive docking control algorithm using virtual transfer vehicle tracking. The system was deployed at the Ro-Ro terminal of Yantai Port, Shandong Province, China, where targeted experiments were conducted. Results demonstrate centimeter-level accuracy in vehicle handling, a transfer efficiency of 91 % compared to manual operations, and the operating time reaches 2–3 times. These findings validate the effectiveness and practical value of the proposed robot system and its key technologies. © 2025 Elsevier Ltd

关键词： Four wheel steering

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

arXiv

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arXiv 2022年

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo The University of Science and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better real-time performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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