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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jianxiong Qiu Zhaoshui He Cheng Yan Hailong Zhou Guangdong University of Technology Guangzhou China School of Automation Guangdong University of Technology Key Laboratory of Information Processing and System Integration of IoT Ministry of Education Guangzhou China Huizhou Metal Cutting Processing Unit Intelligent Manufacturing Engineering Technology Research Center Huizhou China Tianjin Bonuo Intelligent Robot Technology Co. Ltd. Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In order to carry out the intelligent production of typical chemical products for the application of order-based flexible production system, based on intelligent measurement and control technology, a new generation of information technology such as industrial Internet, intelligent management and control, data visualization, and informatization is integrated. In this paper, we study the establishment of an order-based flexible production process with definable configuration according to the intelligent processing mode of process automation. After selecting the sensor reasonably, Configuration of the industrial intelligent detection system, System signal processing and digitalization, Industrial information network construction and debugging, Programming operation and debugging of intelligent measurement and control systems, Production tasks such as visualization and remote monitoring of the production process. The study found,The sensor plays a vital role in the control system control of the DCS -based controller, Liquid level sensors and flow/weight sensors and other key sensors can monitor various parameters in the production process in real time, provide accurate data support for the control system to ensure the accuracy and stability of the ingredient system. And realize the flexible ingredients controlled by program control, and then make the feeding at the same time to improve production efficiency. This research provides a strong technical guarantee for actual production, and provides a feasible implementation method for our intelligent manufacturing process industrial control.

关键词： Temperature measurement Temperature sensors Accuracy Data visualization Process control Production Control systems

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Mobilenetv2-SSD Target Detection Method Based on Multi-scale Feature Fusion 8th

Mobilenetv2-SSD Target Detection Method Based on Multi-scale...

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8th International Conference on cognitive Systems and Information Processing, ICCSIP 2023

作者： Li, Boao Jiang, Du Tang, Xinjie Sun, Ying Weng, Yaoqing Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Longzhong Laboratory Hubei Xiangyang441000 China

ISBN: (纸本)9789819980208

Most of the deep learning networks used in target detection algorithms are very complex, which poses difficulties for edge devices with limited storage capacity and computing power, such as mobile terminals. A MobileNetv2-SSD target detection algorithm based on multi-scale feature fusion is proposed in this paper. We use MobileNetv2 to improve the SSD algorithm by replacing the original network’s backbone network VGG16 with the lightweight network MobileNetv2 to perform the model streamlining. The problem of accuracy loss due to model compression is improved by the designed feature fusion module and finally tested on the dataset. The experimental results show that the mAP and FPS are 90.37% and 50FPS, respectively, and the detection accuracy is improved by 3.24% compared with the original SSD network, the detection speed is 1.9 times of the original one, and the size of the model is only 28.5M, which is only 30.5% of the original model. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Look from Coarse to Fine: A Hierarchical Lane Detection Network

Look from Coarse to Fine: A Hierarchical Lane Detection Netw...

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Artificial Intelligence Innovation (ICAII), International Conference on

作者： Xichen Yu Yanduo Zhang Tao Lu Xun Li Jun Chang Hubei Provincial Key Laboratory of Intelligent Robot School of Computer Science and Engineering & School of Artificial Intelligence Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and School of Computer Science Wuhan University Wuhan China

Lane detection involves identifying a high-level semantic identifier with a slender structure, which requires both high-level feature detection for existence and low-level features for shape and position determination. The objective of this study is to introduce a framework called hierarchical lane detection (HLDNet), which from coarse to fine established stronger relationships between the global and local lanes to achieve stable and accurate lane detection. Specifically, we reframe the existing lane identification method by expressing lanes as a combination of lane instances (for existence detection) and lane point sets (for shape detection). Our proposed Lane IoU loss improves the model performance by regressing the lane instance as a whole. To enhance stability, we capture the complete lane with high-level features using multiple receptive fields. Additionally, our model adaptively fuses high- and low-level features, propagates key features between channels, and further enhances lane local feature representation. Our experimental results show significant improvements in the stability and accuracy of lane detection across multiple benchmark datasets and can run in realtime at a speed of 91 FPS.

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ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification

arXiv

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arXiv 2023年

作者： Lei, Chunyu Chen, C.L. Philip Guo, Jifeng Zhang, Tong The Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou510335 China

Deep learning generally suffers from enormous computational resources and time-consuming training processes. Broad Learning System (BLS) and its convolutional variants have been proposed to mitigate these issues and have achieved superb performance in image classification. However, the existing convolutional-based broad learning system (C-BLS) either lacks an efficient training method and incremental learning capability or suffers from poor performance. To this end, we propose a convolutional broad learning system (ConvBLS) based on the spherical K-means (SKM) algorithm and two-stage multi-scale (TSMS) feature fusion, which consists of the convolutional feature (CF) layer, convolutional enhancement (CE) layer, TSMS feature fusion layer, and output layer. First, unlike the current C-BLS, the simple yet efficient SKM algorithm is utilized to learn the weights of CF layers. Compared with random filters, the SKM algorithm makes the CF layer learn more comprehensive spatial features. Second, similar to the vanilla BLS, CE layers are established to expand the feature space. Third, the TSMS feature fusion layer is proposed to extract more effective multi-scale features through the integration of CF layers and CE layers. Thanks to the above design and the pseudo-inverse calculation of the output layer weights, our proposed ConvBLS method is unprecedentedly efficient and effective. Finally, the corresponding incremental learning algorithms are presented for rapid remodeling if the model deems to expand. Experiments and comparisons demonstrate the superiority of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Paradigm shift in psychiatric neuroscience:Multidimensional integrative theory

Medicine Plus

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Medicine Plus 2024年第2期1卷 1-5页

作者： Zhu-Qing Gong Bharat B.Biswal Xi-Nian Zuo State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain ScienceBeijing Normal UniversityBeijing 100875China National Basic Science Data Center Beijing 100190China

Abnormal structure and function of the human brain cause various mental and neurological *** search for neurobiological mechanisms and biomarkers associated with psychiatric disorders has always been a focal point and a challenging issue in *** magnetic resonance imaging(fMRI)allows the noninvasive re-cording of high spatial-temporal resolution brain activity in patients,making it one of the primary research methods in integrative neuroscience.

关键词： dimension shift siona multi integrative neuroscience paradigm psychiatric theory

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Neural Rendering and Flow-assisted Unsupervised Multi-view Stereo for Real-time Monocular Tracking and Scene Perception

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IEEE Transactions on Automation Science and engineering 2025年

作者： Tong, Kevin W. Cai, Yandong Jie, Yu-Wen Duan, Ya Hou, Yuhong Wu, Edmond Q. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Shanxi University School of Automation and Software Engineering Shanxi 237016 China Flight Test Technology and Engineering Center of the Chinese Flight Test Establishment Xian710089 China China Civil Aviation Shanghai Aircraft Airworthiness Certification Center Shanghai200335 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work. © 2025 IEEE.

关键词： Stereo image processing

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Admittance Based robot Force Control Framework for Server Board Assembly

Admittance Based Robot Force Control Framework for Server Bo...

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IEEE International Conference on Industrial technology (ICIT)

作者： Yunlong Ma Yaonan Wang Yiming Jiang Xianen Zhou Ge Zhu Qing Zhu Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Xiangjiang Laboratory Hunan China College of Electrical and Information Engineering Hunan University Changsha China School of Robotics Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan University Changsha China ZTE Corporation Shenzhen China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

In server board assembly tasks, the effect of vision-based robot assembly schemes is not ideal due to the small installation gap and the blocking of vision. Adding force sensors and force controllers can be a good solution to the above problems and increase the flexibility in the assembly process. In this paper, we propose a force control strategy applied to a server board assembly task. The method is divided into two main parts. In the first part, the zero offset of the force sensor and the load gravity are calibrated and compensated, so that the external force on the load is accurately obtained. In the second part, the admittance controller is designed to achieve compliant behavior between the robot and the environment. Finally, the experimental verification of the board insertion is carried out on the experimental platform. Experimental results verify the effectiveness and practicability of the proposed method.

关键词： Process control robot sensing systems Manipulators Servers Force sensors Admittance Force control

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Deep Visible and Thermal Image Fusion with Cross-Modality Feature Selection for Pedestrian Detection 17th

Deep Visible and Thermal Image Fusion with Cross-Modality Fe...

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17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020

作者： Li, Mingyue Shao, Zhenzhou Shi, Zhiping Guan, Yong College of Information Engineering Capital Normal University Beijing China Beijing Key Laboratory of Light Industrial Robot and Safety Verification Beijing China Beijing Advanced Innovation Center for Imaging Technology Beijing China

ISBN: (纸本)9783030794774

This paper proposes a deep RGB and thermal image fusion method for pedestrian detection. A two-branch structure is designed to learn the features of RGB and thermal images respectively, and these features are fused with a cross-modality feature selection module for detection. It includes the following stages. First, we learn features from paired RGB and thermal images through a backbone network with a residual structure, and add a feature squeeze-excitation module to the residual structure;Then we fuse the learned features from two branches, and a cross-modality feature selection module is designed to strengthen the effective information and compress the useless information during the fusion process;Finally, multi-scale features are fused for pedestrian detection. Two sets of experiments on the public KAIST pedestrian dataset are conducted, and experimental results show that our method is better than the state-of-the-art methods. The robustness of fused features is improved, and the miss rate is reduced obviously. © 2021, IFIP International Federation for Information Processing.

关键词： Feature Selection

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation

arXiv

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arXiv 2023年

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

Gland instance segmentation is an essential but challenging task in the diagnosis and treatment of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a displacement-field assisted graph energy transmitter (DFGET) framework to solve these problems. Specifically, a novel message passing manner based on anisotropic diffusion is developed to update the node features, which can distinguish the isomorphic graphs and improve the expressivity of graph nodes for complex samples. Using such graph framework, the gland semantic segmentation map and the displacement field (DF) of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module based on diffusion theory is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands from the semantic segmentation maps. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the superiority of DFGET and effectiveness of the proposed anisotropic message passing manner and clustering method. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.5% and 3.4% respectively, while decreases the object-HD by 32.4%, achieving state-of-the-art performance. © 2023, CC BY-NC-ND.

关键词： Message passing

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PanoVPR: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows across the Panoramic View

arXiv

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arXiv 2023年

作者： Shi, Ze Shi, Hao Yang, Kailun Yin, Zhe Lin, Yining Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China School of Robotics and with the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai SUPREMIND Technology Co Ltd Shanghai China

— Visual place recognition has gained significant attention in recent years as a crucial technology in autonomous driving and robotics. Currently, the two main approaches are the perspective view retrieval (P2P) paradigm and the equirectangular image retrieval (E2E) paradigm. However, it is practical and natural to assume that users only have consumer-grade pinhole cameras to obtain query perspective images and retrieve them in panoramic database images from map providers. To address this, we propose PanoVPR, a perspective-to-equirectangular (P2E) visual place recognition framework that employs sliding windows to eliminate feature truncation caused by hard cropping. Specifically, PanoVPR slides windows over the entire equirectangular image and computes feature descriptors for each window, which are then compared to determine place similarity. Notably, our unified framework enables direct transfer of the backbone from P2P methods without any modification, supporting not only CNNs but also Transformers. To facilitate training and evaluation, we derive the Pitts250k-P2E dataset from the Pitts250k and establish YQ360, latter is the first P2E visual place recognition dataset collected by a mobile robot platform aiming to simulate real-world task scenarios better. Extensive experiments demonstrate that PanoVPR achieves state-of-the-art performance and obtains 3.8% and 8.0% performance gain on Pitts250k-P2E and YQ360 compared to the previous best method, respectively. Code and datasets will be publicly available at PanoVPR. Copyright © 2023, The Authors. All rights reserved.

关键词： Query processing

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