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5th International Conference on robotics and Computer Vision, ICRCV 2023

作者： Song, Fei Li, Jinyu Tang, Fengzhen Tang, Yandong Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

ISBN: (纸本)9798350326369

The field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. Through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. This article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. These phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques. © 2023 IEEE.

关键词： Navigation

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Industrial Internet for intelligent manufacturing:past, present, and future

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Frontiers of Information Technology & Electronic Engineering 2024年第9期25卷 1173-1192页

作者： Chi XU Haibin YU Xi JIN Changqing XIA Dong LI Peng ZENG State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China

Industrial Internet, motivated by the deep integration of new-generation information and communication technology(ICT) and advanced manufacturing technology, will open up the production chain, value chain, and industry chain by establishing complete interconnections between humans, machines, and things. This will also help establish novel manufacturing and service modes, where personalized and customized production for differentiated services is a typical paradigm of future intelligent manufacturing. Thus, there is an urgent requirement to break through the existing chimney-like service mode provided by the hierarchical heterogeneous network architecture and establish a transparent channel for manufacturing and services using a flat network architecture. Starting from the basic concepts of process manufacturing and discrete manufacturing, we first analyze the basic requirements of typical manufacturing tasks. Then, with an overview on the developing process of industrial Internet, we systematically compare the current networking technologies and further analyze the problems of the present industrial *** this basis, we propose to establish a novel “thin waist” that integrates sensing, communication, computing, and control for the future industrial Internet. Furthermore, we perform a deep analysis and engage in a discussion on the key challenges and future research issues regarding the multi-dimensional collaborative sensing of task–resource, the end-to-end deterministic communication of heterogeneous networks, and virtual computing and operation control of industrial Internet.

关键词： Intelligent manufacturing Industrial Internet Thin waist Transparent service Manufacturing as a service

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Optimizing Handwritten Character Recognition systems: Neural Networks vs. Statistical Methods

Optimizing Handwritten Character Recognition Systems: Neural...

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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Agrawal, Vikash K. Bogireddy, Srinivasa Rao Patil, Lalit N. Sonekar, Mahesh Patil, Yashraj M. Panwar, Vikas Singh Dr. D. Y. Patil Institute of Technology Department of Automation and Robotics Engineering Pune India Horizon Systems Inc PhoenixAZ United States Dr. D. Y. Patil Institute of Technology Department of Mechanical Engineering Pune India

ISBN: (纸本)9798331539948

This investigation focuses on the identification of handwritten characters using neural network technology combined with statistical methods. Handwriting recognition has gained popularity due to its wide range of applications in document processing, signature authentication, and digitized communication. The use of neural networks, particularly deep learning models, has greatly enhanced the accuracy and efficiency of character recognition systems. This study provides a comprehensive analysis aimed at evaluating the effectiveness of neural networks in recognizing handwritten characters. Various neural network architectures, including convolution and recurrent neural networks, are explored;each designed to address specific challenges in character recognition. Additionally, statistical methods are employed for feature extraction, further improving recognition accuracy. Extensive testing and benchmarking are conducted on diverse handwritten datasets to assess the performance of the proposed approaches. The results demonstrate the significant benefits of combining artificial neural networks with statistical techniques to achieve high levels of accuracy in character recognition, paving the way for practical applications. This study makes substantial contributions to the field of handwritten character recognition by highlighting the synergistic advantages of neural networks and statistical methods, which will aid in the development of more accurate and robust authentication systems for future applications. © 2024 IEEE.

关键词： Convolution

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The integration of egocentric and allocentric spatial information is important for navigation 3

The integration of egocentric and allocentric spatial inform...

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3rd International Conference on Biomedical and Intelligent systems, IC-BIS 2024

作者： Song, Fei Tang, Fengzhen Tang, Yandong Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China School of Systems Science Beijing Normal University Beijing100091 China Chinese Institute for Brain Research Beijing102206 China

ISBN: (纸本)9781510681279

In the realm of advanced cognitive processes, animals employ both egocentric and allocentric reference frames for navigation. While prior studies have largely concentrated on differentiating and transitioning between these frames, the present research explores the concurrent use of both strategies in navigation tasks. By simulating the entorhinalhippocampal neural circuit and applying reinforcement learning (RL), we developed three agents characterized by egocentric, allocentric, and a novel integrated strategy. Testing across diverse navigation scenarios revealed that the integrated model substantially improved navigation adaptability and efficiency. Despite the challenges associated with the "black box"nature of RL, the artificial combination of individual reference frames notably enhanced performance. These results shed light on novel mechanisms of biological spatial information processing and the flexible organization of navigation strategies. © 2024 SPIE.

关键词： Navigation

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Transfer Learning in Subsurface Flow Surrogate Model with Physics-Guided Neural Network

Transfer Learning in Subsurface Flow Surrogate Model with Ph...

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2024 SPE Annual Technical Conference and Exhibition, ATCE 2024

作者： Cheng, H.B. Qiao, J.H. Wei, Y.C. Li, S.C. Zeng, P. Yu, H.B. 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 University of Chinese Academy of Sciences Beijing China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China

ISBN: (纸本)9781959025375

It is a great challenge for reservoir engineers to accurately and quickly model the subsurface flow surrogate for oil and gas reservoirs. The traditional numerical simulation methods are high computational complexity and time-consuming. The widely used pure data- driven flow surrogate methods require massive high-quality data due to lack of theoretical foundation. Therefore, it is necessary to develop transfer learning methods integrating prior knowledge with data-driven deep learning methods to solve complex seepage problems. To reduce data requirements and improving modeling efficiency, a theory-guided and data-driven transfer learning method is proposed to build the fast and accurate subsurface flow surrogate model. Specifically, a data-driven machine learning method is first proposed to simulate and predict the flow processes and build the primary surrogate model. In addition, physical mechanism and constraints are embedded into the data-driven model to make the prediction results satisfy the prior domain knowledge. The transfer learning method based on physics-guided neural network (TL-PG) integrates the seepage theory with sparse spatial data to improve the prediction accuracy of the surrogate model. The proposed TL-PG method is verified by a subsurface flow problem in heterogeneous reservoir models. First, we build a flow field to simulate 50 years of production history. A physics-guided neural network model is trained based on the samples from the first 30 years and tested based on the last 20 years of data. The relative L2 loss and the coefficient of determination R2 are used for comparison. Compared with the purely data-driven method, the relative L2 loss of physics-guided neural network is increased by 16%, and the mean coefficient of determination R2 is up to 0.8932, which means the embedded physical mechanism can greatly improve the performance of neural networks. Secondly, the physics-guided neural network is selected as the pretraining model. Then, th

关键词： Seepage

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A Model Integrating Wavelet Convolution and Linear Attention Mechanism for Inventory Management in Power Warehouses 4

A Model Integrating Wavelet Convolution and Linear Attention...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Li, Tao Zhang, Zhengli Li, Ligang Du, Guozheng State Grid Shandong Electric Power Company Material Company Jinan China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China

ISBN: (纸本)9798350363586

Accurate inventory management in power warehouses is crucial for maintaining consistency between records and actual stock levels. However, the complex storage layout and the diversity of stored materials often lead to human errors during manual inventory checks, resulting in inefficiencies. To address these challenges, we collected 2,000 images from power warehouse environments and created a binary classification dataset containing images of correctly and incorrectly placed items. We propose a novel network, CWAMNet, specifically designed for visual inventory management in complex power warehouse environments. CWAMNet integrates wavelet convolution and a linear attention mechanism to overcome the limitations of traditional convolutional neural networks (CNNs) and standard attention mechanisms, achieving a balance between model capacity and computational efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Description and fault diagnosis in autonomous underwater vehicles based on ontologies

Description and fault diagnosis in autonomous underwater veh...

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2022 International Conference on Ocean Studies, ICOS 2022

作者： Alexander, Timoshenko Alexander, Zuev Eduard, Mursalimov Valeria, Gribova Alexander, Inzartsev Far Eastern Federal University Institute of Marine Technology Problems Feb Ras Laboratory of Intelligent Systems for Marine Robots Department of Automation and Robotics Vladivostok Russia Institute of Automation and Control Feb Ras Laboratory of Intelligent Systems Vladivostok Russia Institute of Marine Technology Problems Feb Ras Laboratory of Control Systems Vladivostok Russia

ISBN: (纸本)9781665473729

The paper proposes new ontologies for describing and diagnosing malfunctions of subsystems of autonomous underwater vehicles (AUV) as a part of the development of a previously created approach to the intelligent diagnosis of these vehicles using knowledge bases. To implement this approach, it is proposed to use two knowledge bases, one of which describes malfunctions, features for their identification and actions necessary to adapt to the negative consequences caused by the appearance of these malfunctions. The other database contains information about the main subsystems of the AUV, devices and replaceable modules, as well as the states they may be in. Knowledge bases are used in the work of the intelligent control-emergency system (ICES) on board the AUV, which allows for comprehensive functional diagnostics of all subsystems of the device, including payload modules, as well as adaptation to the consequences of emerging malfunctions, taking into account the actual cause-and-effect relationships between them. To test the system's operability, a prototype was developed. The results of the prototype simulation showed that ICES performs the tasks and can be implemented on the onboard computers of existing AUV using well-known programming technologies. © 2022 IEEE.

关键词： Autonomous underwater vehicles

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Design of a Mesh Crawling Robot with Beetle-Inspired Variable Stiffness Tarsus

Design of a Mesh Crawling Robot with Beetle-Inspired Variabl...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Chen, Hao Hu, Kai Hu, Longlong Zhou, Ziqi Li, Yao Li, Bing Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

Compared with large robots, micro-robots have the advantages of small size, low power consumption, and high concealment. For complex terrain, the crawling performance of micro-robots will be significantly reduced. In this study, the mesh surface was used as the crawling base of the robot, and a variable stiffness tarsus was designed with the beetle as a reference. This tarsus composed of multiple bending parts achieves a large range of stiffness changes, 0.070.495 N/mm. We have achieved stiffness control of the tarsus in a single actuation by trajectory planning of the claw ends. The proposed robot can achieve a big stiffness of the tarsus to resist the deformation of the tarsus due to external forces, which means that the robot can increase the adhesion ability when crawling at the stance phase. Therefore, even when the load was increased from 10g to 40g, the average crawling speed of the robot was not significantly reduced. This new tarsus can improve the crawling performance of the micro-robot on the mesh. © 2024 IEEE.

关键词： Microrobots

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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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