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第43届中国控制会议

作者： Tian-Shu Song Zhen-Peng Chen Hui-Rang Hou Hong-Yue Li Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Detecting ignitable liquids(ILs) at the scene of a fire is crucial for fire investigation. The electronic nose(e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network(FMG-Net).This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network(SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs(diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural Network Cross Platform

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PI2-CMA Based Trajectory Planning Method for the Tractor-Trailer Vehicle 6

PI2-CMA Based Trajectory Planning Method for the Tractor-Tra...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Wang, You Wang, Zehao Wang, Jingchuan Guo, Rui Xiao, Peng Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Institute of Medical Robotics and Department of Automation Shanghai200240 China State Grid Intelligence Technology CO. LTD China

ISBN: (纸本)9798350325485

The motion primitive-based state lattice trajectory planning method has been widely used in the field of trajectory planning. However, due to the limitations of the search space and resolution of motion primitives, and the lack of consideration of scenario information during offline generation of motion primitives, the tractor-Trailer vehicles can easily fall into "jackknife phenomenon"when making large pose adjustments in narrow scenarios, which brings dilemma for trajectory planning. In this paper, a trajectory planning algorithm is proposed based on a general model of tractor-Trailer vehicle, which combines global planning and local optimization. The method first generates a series of motion primitives, including forward and backward driving conditions, and then an A based global state lattice planner is used to obtain a planning result, which is imported as the initial solution into the local trajectory optimizer based on PI2-CMA (Path Integral Policy Improvement with Covariance Matrix Adaptation) algorithm and dynamic potential method. Experiments show that this method significantly improves the performance in the obstacle avoidance, "jackknife phenomenon"avoidance and curvature compared to traditional motion primitive-based state lattice planning methods. © 2023 IEEE.

关键词： Trajectories

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Comparison Study of Different Types of Setting Reference Movements for Mechatronic Objects by Parametric Splines

Comparison Study of Different Types of Setting Reference Mov...

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2022 International Conference on Ocean Studies, ICOS 2022

作者： Gubankov, Anton Gornostaev, Igor Institute of Automation and Control Processes Feb Ras Sevastopol State University Robotics Laboratory Sevastopol Vladivostok Russia Institute of Marine Technology Problems Feb Ras Laboratory of Robotic Systems Vladivostok Russia

ISBN: (纸本)9781665473729

The paper presents a comparative study of various ways of specifying program movements for mechatronic objects (MO) along complex spatial trajectories defined by parametric Bezier splines, B-splines and third-order Catmull-Rom splines. An expression is obtained to describe the dependence of the rate of change of the parameter setting the desired position of the MO with the program speed of its movement, taking into account information about the type of trajectory. As a result, using this expression, which is universal for all the specified parametric splines of the third order, it is ensured that the MO moves along the trajectory exactly at a given speed while simultaneously changing its orientation as desired. The application of the methods considered in this paper does not require complex iterative calculations and allows one to perform all the necessary calculations in real time, even on low-power on-board computers of various robots and robotic complexes. Numerical modeling was carried out to analyze the features of the use of these splines in solving practical problems. © 2022 IEEE.

关键词： Trajectories

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang 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 China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision trees

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From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models

arXiv

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arXiv 2024年

作者： Pulli, Tessa Thalhammer, Stefan Schwaiger, Simon Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department UAS Technikum Vienna Austria

Robots are increasingly envisioned to interact in real-world scenarios, where they must continuously adapt to new situations. To detect and grasp novel objects, zero-shot pose estimators determine poses without prior knowledge. Recently, vision language models (VLMs) have shown considerable advances in robotics applications by establishing an understanding between language input and image input. In our work, we take advantage of VLMs zero-shot capabilities and translate this ability to 6D object pose estimation. We propose a novel framework for promptable zero-shot 6D object pose estimation using language embeddings. The idea is to derive a coarse location of an object based on the relevancy map of a language-embedded NeRF reconstruction and to compute the pose estimate with a point cloud registration method. Additionally, we provide an analysis of LERF’s suitability for open-set object pose estimation. We examine hyperparameters, such as activation thresholds for relevancy maps and investigate the zero-shot capabilities on an instance- and category-level. Furthermore, we plan to conduct robotic grasping experiments in a real-world setting. © 2024, CC BY.

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Weld Defects Detection by Neural Networks Based on Attention Mechanism 7

Weld Defects Detection by Neural Networks Based on Attention...

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7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Yang, Guang Liu, Zhifeng Wang, Jianjun Cui, Shuping State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9798350334197

Welding technology plays an indispensable role in ship construction, and its maturity has a direct impact on the quality of shipbuilding. The weld seam, being the weakest part of a welded structure, significantly affects the structural strength, service life, and safety reliability of ships. However, the current identification of weld defects in the ship industry relies on manual methods, which are subjective and prone to error. To address this issue, this paper presents a novel approach by collecting weld images from actual ship scenes, preprocessing them to eliminate noise and highlight defect features, and proposing a Transformer-based improved target detection model (T-OBJDETC) to enhance the recognition accuracy of the model. © 2023 IEEE.

关键词： Ships

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A Miniaturized Pneumatic Electrode Gripper for Robotic Cochlear Implant Surgery

A Miniaturized Pneumatic Electrode Gripper for Robotic Cochl...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Gao, Hongyan Zou, Yun Huang, Shaoping Chen, Weidong Gao, Anzhu Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Institute of Medical Robotics and Department of Automation Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

Cochlear implant surgery is one of the most challenging surgeries due to the limited workspace and delicate inner structure. A traditional electrode implant instrument is a passive elastic forceps, which needs another forceps to help grip or release the electrode. It is not convenient to achieve delicate operations in a confined space. This paper develops a miniaturized pneumatic electrode gripper at the tip of forceps to achieve active gripping. It transmits the pressurized air through 3D-printed inner lumens to form two inflated soft bubbles to push the electrode to the rigid shell for accurate positioning. The finite element analysis is carried out to evaluate the shape of inflated soft bubbles with variable soft films. A prototype of a gripper with inner tortuous airways is fabricated using micro 3D printing technique, and experiments are carried out to validate its accuracy and demonstrate its ability to grip and release. Finally, robotic teleoperation using a robotic arm and a joystick is implemented for cochlear implant surgery. Results demonstrate that the developed miniaturized pneumatic gripper has successfully achieved electrode implantation in a 3D-printed cochlear phantom. © 2022 IEEE.

关键词： Electrodes

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Small Photoresist Defect Samples Augmentation Based on Generative Adversarial Network 6

Small Photoresist Defect Samples Augmentation Based on Gener...

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6th IEEE Information Technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Yang, Guang Li, Zhihang Yang, Zhijia Cui, Shuping State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9781665460033

Photoresist coating technology is an important part of the surface treatment of semiconductor wafers. The presence of bubbles in photoresist can seriously affect the quality of wafers, however, the lack of sufficient bubble samples makes intelligent automatic detection techniques impossible. To solve such a problem, we propose B-GAN based on adversarial generative network, and design a mapping network for potential encoding mapping and a synthetic network for high-quality bubble image generation, so that defective bubble samples can be automatically generated. Experiments prove that our method achieves excellent results. © 2023 IEEE.

关键词： III-V semiconductors

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UWIA-AODV: Improved Ad-Hoc On-Demand Distance Vector Routing Protocol for Underwater Acoustic Sensor Networks 42

UWIA-AODV: Improved Ad-Hoc On-Demand Distance Vector Routing...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Xueyi Liu, Meiqin Zhang, Senlin Dong, Shanling Zhejiang University National Key Laboratory of Industrial Control Technology Hangzhou310027 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9789887581543

In the past a few years, underwater acoustic networks have developed as a powerful technique used for variety of applications. Ad-Hoc On-Demand Distance Vector (AODV) is a popular and effective routing protocol, while it is not suitable for underwater usage due to the features of underwater acoustic channel such as low data rate, long delay, limited bandwidth and energy. In this paper, we propose an improved AODV based on implicit acknowledgement which employs broadcast features of nodes to overhear packets and retransmit those that fail to be transmitted. In addition, residual energy is considerded for energy balancing. Finally, the in-field experiment results shows that our proposed protocol can select the route with more residual energy for energy balancing. And our proposed protocol also outperforms AODV in terms of throughput and packet discovery ratio. So it is more suitable for underwater scenarios that require high transmission reliability. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Routing protocols

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

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

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy 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 Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

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. © 2023 IEEE.

关键词： Long short-term memory

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