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A Brief Overview of ChatGPT:The History,Status Quo and Potential Future Development

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1122-1136页

作者： Tianyu Wu Shizhu He Jingping Liu Siqi Sun Kang Liu Qing-Long Han Yang Tang the Key Laboratory of Smart Manufacturing in Energy Chemical Process East China University of Science and TechnologyShanghai 200237China the Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China the Research Institute of Intelligent Complex Systems Fudan UniversityShanghai 200437China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

ChatG PT,an artificial intelligence generated content (AIGC) model developed by OpenAI,has attracted worldwide attention for its capability of dealing with challenging language understanding and generation tasks in the form of *** paper briefly provides an overview on the history,status quo and potential future development of ChatGPT,helping to provide an entry point to think about ***,from the limited open-accessed resources,we conclude the core techniques of ChatGPT,mainly including large-scale language models,in-context learning,reinforcement learning from human feedback and the key technical steps for developing *** further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various *** it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields,mankind should still treat and use ChatG PT properly to avoid the potential threat,e.g.,academic integrity and safety ***,we discuss several open problems as the potential development of ChatGPT.

关键词： AIGC ChatGPT GPT-3 GPT-4 human feedback large language models

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Obstacles Avoidance Path Planning for Mobile Robot Based on Cellular Automata 6

Obstacles Avoidance Path Planning for Mobile Robot Based on ...

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6th International Conference on robotics, intelligent Control and Artificial Intelligence, RICAI 2024

作者： Li, Lili Li, Wenwei Huang, Jinglun School of Automation Science and Engineering South China University of Technology Guangzhou China Institute of Intelligent Manufacturing Shunde Polytechnic Foshan China Department of Technical Research South China Robotics Innovation Research Institute Foshan China Foshan Zhiyouren Technology Co. Ltd. Foshan China School of Electronics and Information Engineering Wuyi University Jiangmen China

ISBN: (纸本)9798331541699

In order to quickly plan the shortest path for mobile robot in two-dimensional space, a two-stage obstacles avoidance path planning algorithm based on cellular automata was presented in this paper. For the first stage, the distance between each free cell to the ending cell was gradually generated by the evolution of cellular automata, and the algorithm’s cellular space, cellular states, Moore type neighbors and evolutionary rules were described, respectively. For the second stage, by using the idea of the negative gradient, a method of generating the shortest path based on the cells’ state values was proposed. Accordingly, the corresponding flow chart and Matlab programme of the algorithm were designed. The effectiveness of the algorithm was verified by the simulation and the physical experiment. The results showed that the shortest path in complex environment can be efficiently planned by using our cellular automata algorithm. © 2024 IEEE.

关键词： Microrobots

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Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-LSTM-Attention Network from Surface Electromyography

Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Wang, Zhuo Chen, Hui Yang, Fangliang Wang, Xiangyang Wu, Xinyu Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

In various fields such as robotics, rehabilitation, biomechanics, human-machine interfaces, and clinical research, human motion prediction has extensive applications. A hybrid networks model based on Convolutional Neural Network-Long Short-Term Memory-Attention (CNN-LSTM-Attention) was employed in this paper to extract features from the high-dimensional space and time series of surface electromyography (sEMG) data for continuous prediction of knee angles. Five able-bodied subjects participated in the study, walking at a self-selected pace while six surface electromyography (sEMG) signals from muscles involved in knee flexion and extension, along with knee angles, were synchronously recorded. The performance was assessed using the root mean square error (RMSE), Pearson Correlation Coefficient (r), and coefficient of determination (R2) between estimated and measured knee joint angles. Compared to the CNN, LSTM, and CNN-LSTM algorithms, the adopted method estimated knee angles more accurately, achieving an average RMSE, ρ, and R2 of 3.09± 0.90,\ 0.9911 ± 0.0052, and 0.9817± 0.0108, respectively. As the advance time increased, the accuracy of knee joint angle estimation decreased, particularly when the advance time reached 100 ms, at which point the RMSE is 4.83 degress. In addition, the computing time of the prediction model was less than 1 ms, which was shorter than the advance time 100 ms it could predict, thereby facilitating real-time computation in practical environments. Compared to previous studies, the proposed method excelled at extracting features directly from raw signals, avoiding the need for handcrafted feature extraction. Consequently, the model effectively achieved continuous and accurate predictions of joint motion angles and demonstrated potential to reduce latency in the human-machine interface. © 2024 IEEE.

关键词： Clinical research

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Design of Regenerative Braking Strategy for Hybrid Electric Vehicle Based on Sensitivity Ratio

Design of Regenerative Braking Strategy for Hybrid Electric ...

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第35届中国控制与决策会议

作者： Qinyu Xu Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology

Based on the braking system of hybrid electric vehicles(HEVs),a method based on sensitivity ratio decisionmaking is proposed to coordinate the multi-objective requirements of stability and economy in vehicle regenerative ***,the grid method is used to calculate the fitness of the braking force distribution coefficient of each front and rear wheel under each braking intensity,and the Pareto solution set is obtained through non-dominated *** the decision method of nonlinear variable weight sensitivity ratio is adopted offline to obtain the best *** the one-dimensional mapping table(1-D table) of and is built,and embedded into the braking energy strategy to achieve the coordination of stability and ***,the simulation is carried out in ADVISOR simulation software,and the results show that the stability of this strategy is better than the traditional ADVISOR strategy,and the brake recovery rate is increased by 43.55%.

关键词： Hybrid electric vehicle regenerative braking strategy multi-objective optimization nonlinear variable weight sensitivity ratio decision

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Non-Contact Measurement Algorithm of Load Swing Angle for Bridge Crane Based on Contour Detection

Non-Contact Measurement Algorithm of Load Swing Angle for Br...

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Chinese Control Conference (CCC)

作者： Zhongyan Liu Biao Lu Jing Zhang Institute of Robotics and Automatic Information System Nankai University Tianjin Institute of Intelligence Technology and Robotics Systems Shenzhen Research Institute of Nankai University Shenzhen

The traditional measurement method of the load swing angle for the bridge crane is easily disturbed by the environmental noise, which leads to inaccurate measurement and delayed feedback. In this paper, the computer vision method is used to realize the non-contact measurement of the load swing angle. A rectangular light source is used as a marker and it is detected using an industrial camera. In order to improve the accuracy and real-time performance of the detection, the method of color separation combined with rectangular contour detection is used to quickly identify the marker. Then the swing angle is calculated according to the recognition results by using the camera geometry. It has been demonstrated by experiments that, the algorithm can quickly identify the marker and can accurately calculate the swing angle. In particular, the proposed method has strong independence and anti-interference ability, which is very suitable for bridge crane system.

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A Novel Gas Classification Method Based on The Improved GasNet

A Novel Gas Classification Method Based on The Improved GasN...

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第43届中国控制会议

作者： Xiaolin Qin Lei Cheng Xiang Tao Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Accurately and promptly identifying harmful gases in the environment can effectively prevent many accidents. Most existing pattern recognition methods are focused on addressing gas classification problems in static scenarios, but the real world is a dynamic and changing environment. Therefore, researching gas classification in continuous learning scenarios will better meet the needs of suddenly emerging gas classification tasks in real-world environments. To address this issue, an incremental learning model was introduced into gas classification, and an improved deep convolutional network was proposed for gas image feature extraction. The proposed model and other baseline methods experimented on an open-source gas dataset. The proposed model reduces knowledge loss due to catastrophic forgetting and effectively classifies the 10 gas samples in the dataset in continuous learning scenarios compared to non-incremental methods. It outperforms other baseline incremental methods by optimizing the network's shortcut connections, making the feature extraction network easier to train and improving gas classification performance. In dynamic learning scenarios, the proposed model achieved a classification accuracy of 86.30% for 10 gas *** experimental results demonstrate that the proposed method effectively mitigates catastrophic forgetting interference and completes gas classification tasks in continuous learning scenarios.

关键词： Gas classification Incremental learning Shortcut connection Catastrophic forgetting

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Low-light image enhancement based on Transformer and CNN architecture

Low-light image enhancement based on Transformer and CNN arc...

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第35届中国控制与决策会议

作者： Keyuan Chen Bin Chen Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology

Affected by low photon counts and noise,images captured in low-light environments commonly have low signalto-noise ratios,details losing,low contrast and other ***-light image enhancement which restores clear images from low-light images with unsatisfactory quality is *** this paper,we propose a strong baseline model based on transformer and CNN architecture for low light image enhancement(TrCLLE).In TrCLLE,the advantages of transformer in effectively modeling long-distance dependence and convolutional neural network in modeling local features through inductive bias are *** consists of three parts:shallow feature extraction module,deep feature extraction module and high-quality image reconstruction *** last,the factors affecting performance are analyzed in detail and the experimental results demonstrate that the proposed method achieves enhanced images with higher quality compared to other popular approaches.

关键词： transformer self-attention mechanism convolutional neural network low-light image enhancement

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Gas Source Localization for UAV Using Infotaxis-Based Three-Dimensional Algorithm and Particle Filtering

Gas Source Localization for UAV Using Infotaxis-Based Three-...

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第43届中国控制会议

作者： Duan He Cheng Lei Wang Rong Zhang Yu Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Unmanned aerial vehicles(UAVs) are extensively utilized in gas source localization tasks due to their payload capacity and maneuverability. This paper presents a UAV gas source localization algorithm that utilizes three-dimensional Infotaxis and particle filter. The algorithm is designed for gas source localization of UAVs in dynamic outdoor airflow *** source localization process comprises UAV search area partitioning, particle filter initialization, observation and update of probability map, entropy calculation and direction selection, particle direction and weight update, and UAV movement. To validate the effectiveness of the algorithm, we build a CFD gas environment and UAV source localization platform, and compare it with traditional Infotaxis-based source localization algorithms. The proposed algorithm based on three-dimensional Infotaxis and particle filter demonstrates higher accuracy.

关键词： infotaxis particle filtering gas source localization olfactory drone path planning

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Point cloud segmentation network based on multi-scale feature fusion

Point cloud segmentation network based on multi-scale featur...

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2024 China Automation Congress, CAC 2024

作者： Deng, Hao Cheng, Peng Cheng, Shengmei Liu, Cheng Du, Shaoyi Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Service School of Information Science and Technology Northwest University Xi’an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

Aiming at improving the local feature extraction for point cloud learning, we introduce a point cloud segmentation network that enhances the PointNet++ framework with a local feature transformation module and a multi-scale feature fusion module. The local feature transformation module employs operations akin to convolution to restructure the point cloud's local feature dimensions, thus boosting the network's ability to extract detailed local features. Additionally, the multi-scale feature fusion module integrates semantic features of point clouds across various scales using a layered network design, aiming to heighten segmentation precision. Experimental results indicate that the proposed network can achieve a robust point cloud segmentation accuracy, reaching an accuracy rate of 85.8%. © 2024 IEEE.

关键词： Cloud platforms

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