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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jie Peng, Gang Yan, Shiqian Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Hubei Eya Medical Investment Management Co. Ltd Wuhan430061 China

ISBN: (纸本)9781665465335

Dentists judge the quality of root canal therapy for each patient very time-consuming, and inefficient, lack of quantitative evaluation criteria, easy to cause judgment errors. At the same time, the traditional method of extracting the root canal image features based on experience is difficult to accurately extract the filling area of the root canal and the tooth area where it is located, resulting in low accuracy of root canal target segmentation, and thus affecting the accuracy of root canal treatment quality evaluation. In this paper, for the real root canal treatment images of patients, the CenterNet network was used to detect the root canal target of root canal images. Then, according to the test results, the U-Net full convolution neural network was used to segment the root canal filling region and its tooth region. Finally, the segmented images were quantitatively evaluated according to the professional evaluation index of doctors. The experimental results show that the coincidence rate between the evaluation results of this method and the evaluation results of professional doctors is 83.3 %, which can better complete the quality evaluation of root canal therapy for patients, effectively improve the work efficiency of doctors, and has reference significance for the application of artificial intelligence in medical field. © 2022 IEEE.

关键词： Patient treatment

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LPV modeling of the missile attitude control system based on small deviation equation

LPV modeling of the missile attitude control system based on...

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2011 International Conference on Electrical, Information Engineering and Mechatronics, EIEM 2011

作者： Ni, Shaobo Wu, Jianwu Shan, Jiayuan Liu, Lei Wang, Yongji Hu, Kun College of Aerospace Beijing Institute of Technology Beijing 100081 China Beijing Institute of Aerospace Automatic Control Beijing 100854 China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China

With a concern about the missile attitude control system, this paper attempts to solve the online calculation method of the coefficient of the small deviation equation, on which a LFT-based LPV model of the missile at... 详细信息

ISBN: (纸本)9781447124665

关键词： Attitude control

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LIGHT YOLO FOR HIGH-SPEED GESTURE RECOGNITION

LIGHT YOLO FOR HIGH-SPEED GESTURE RECOGNITION

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IEEE International Conference on image processing

作者： Zihan Ni Jia Chen Nong Sang Changxin Gao Leyuan Liu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

This paper proposes an efficient model named Light YOLO for hand gesture recognition on the embedded platforms. Light YOLO improves accuracy, speed, and model size, in three aspects. To deal with the small scale gestures in practical applications, we strengthen the YOLOv2 with a spatial refinement module to obtain fine-grained features. To accelerate the refined network, we propose a selective-dropout channel pruning approach to prune the redundancy convolution kernels in the network. Moreover, we introduce a dataset for hand gesture recognition in complex scenes. The experimental results on this dataset show that the proposed Light YOLO significantly improve the YOLOv2 network, i.e., accuracy from 96.80% to 98.06%, speed form 40PFS to 125FPS, and size form 250M to 4MB.

关键词： Gesture recognition Feature extraction Detectors Acceleration Taylor series Kernel Cost function

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Vehicle re-identification by fusing multiple deep neural networks

Vehicle re-identification by fusing multiple deep neural net...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Chao Cui Nong Sang Changxin Gao Lei Zou Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Vehicle re-identification has become a fundamental task because of the growing explosion in the use of surveillance cameras in public security. The most widely used solution is based on license plate verification. But when facing the vehicle without a license, deck cars and other license plate information error or missing situation, vehicle searching is still a challenging problem. This paper proposed a vehicle re-identification method based on deep learning which exploit a two-branch Multi-DNN Fusion Siamese Neural Network (MFSNN) to fuses the classification outputs of color, model and pasted marks on the windshield and map them into a Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. In order to achieve this goal, we present a method of vehicle color identification based on Alex net, a method of vehicle model identification based on VGG net, a method of pasted marks detection and identification based on Faster R-CNN. We evaluate our MFSNN method on VehicleID dataset and in the experiment. Experiment results show that our method can achieve promising results.

关键词： image color analysis Feature extraction Neural networks Task analysis Licenses Automobiles Automotive components

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Adaptive pinning cluster synchronization of fractional-order complex dynamical networks

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Applied Mathematics and Computation 2014年 231卷 347-356页

作者： Guan-Sheng Wang Jiang-Wen Xiao Yan-Wu Wang Jing-Wen Yi School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 PR China

The problem about cluster synchronization of fractional-order CDNs is studied via a pinning adaptive approach in this paper. Based on the stability theory of fractional differential equations, some sufficient criteria for local and global cluster synchronization of fractional-order CDNs are derived. In this paper, the coupling configuration matrix can be asymmetric as well as reducible and the inner coupling matrix can also be asymmetric. Moreover, the number of pinning nodes in each cluster can be evaluated. Especially, when the coupling strength is large enough and the coupling configuration matrix is symmetric, cluster synchronization can be achieved via pinning a single node in each cluster. Finally, some typical examples are given to illustrate the correctness and effectiveness of our results, a surprising finding is that the synchronization performance will become better as the fractional order decreases in this simulation.

关键词： Complex dynamical networks Cluster synchronization Fractional-order Pinning adaptive control

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Robotic dynamic target recognition and tracking based on the monocular vision

Robotic dynamic target recognition and tracking based on the...

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26th Chinese control Conference, CCC 2007

作者： Lei, Liu Yongji, Wang Huazhong University of Science and Technology Intelligent and Complex System Laboratory Key Laboratory of Image Processing and Intelligent Control Ministry of Education

ISBN: (纸本)7900719229

Gather the information of the environment by the monocular vision. Using the H and S weight of the HSV color model, separate the target from the environment with a certain color, by a fast clustering algorithm for two-value image segmentation. Calculating the distance between the camera and target by the 3D reconstruction algorithm and sub-control strategy, and raise its veracity by laser information fusion. Furthermore, a vision servo system has been designed and utilized to achieve the robot's dynamic track. At last, some experiments were used to certification its availability.

关键词： Robot applications

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DISCOVERING DISTINCTIVE ACTION PARTS FOR ACTION RECOGNITION

DISCOVERING DISTINCTIVE ACTION PARTS FOR ACTION RECOGNITION

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IEEE International Conference on image processing

作者： Feifei Chen Nong Sang Changxin Gao Xiaoqin Kuang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology

ISBN: (纸本)9781479957521

Recent methods based on mid-level visual concepts have shown promising capability in human action recognition field. Automatically discovering semantic entities such as parts for an action class remains challenging. In this paper, we focus on discovering distinctive action parts for recognition of human actions by learning and selecting a small number of discriminative part detectors directly from training videos. We initially train a large collection of candidate Exemplar-LDA detectors from clusters obtained by clustering spatiotemporal patches in whitened space. A novel Coverage-Entropy curve is proposed as a means of measuring the representative and discriminative capabilities of part detectors, and used to select a set of compact and meaningful detectors out of the vast candidates. By integrating these mined detectors into "bag of parts" representation, our approach demonstrates state-of-the-art performance on the UCF50 dataset.

关键词： Action recognition Action part Exemplar LDA

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Handling multi-objective in an HTN planner for emergency decision-making

Handling multi-objective in an HTN planner for emergency dec...

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IEEE Conference Anthology

作者： Minglei Li Hongwei Wang Chao Qi Dan Liu Jian Wang Zhe Wang Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science & Technology Wuhan China

There are many multi-objective planning problems in emergency decision-making domain. HTN planners were widely used in emergency decision-making, while they have limited ability to solve optimization, especially multi-objective optimization. Aimed to handling multi-objective in HTN planners, this paper proposes a novel method based on SHOP2, which is a domain-independent state-based forward HTN planner. The method uses a weighted vector to mirror the decision maker's performance for various objectives and an anytime search algorithm to improve the ability to find the best solution plan. Finally, a case of emergency evacuation is given to testify the effectiveness of the method.

关键词： Planning Decision making Floods Optimization Vectors Measurement Search problems

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Laser and Force Sensors Based Human Motion Intent Estimation Algorithm for Walking-aid Robot

Laser and Force Sensors Based Human Motion Intent Estimation...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Qingyang Yan Wenxia Xu Jian Huang Suping Cao Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Automation Huazhong University of Science and Technology

ISBN: (纸本)9781479987313

A human motion intent estimation algorithm based on laser ranger finder and force sensors is designed for a walking-aid robot. By applying the proposed algorithm to the robot, the user can operate the walking-aid robot more fluently, comfortably and safely. A walking-aid or rehabilitation robot normally estimates the user's motion intention by measuring the interactive forces between human and robot. Whereas, the human motion should be represented as a coordination between the arms and the legs movements. This study therefore proposes a human motion intent estimation algorithm by fusing the motion intentions detected by the force sensors and the laser ranger finder, which are used to monitor the movements of arms and legs respectively. A special arrangement of push-pull force sensors is implemented for estimating the human motion intention I from user's operations on the handles. Meanwhile the human motion intention II is extracted by a laser ranger finder observing the user's movement of legs. A multi-sensor fusion algorithm is employed to fuse these two estimates. Experimental results are presented to show the validity of the proposed human motion intent estimation algorithm.

关键词： Human motion force sensors Robot Laser selectors Rehabilitation robotics Arms Arm spiral arm

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Application Scenarios of Wireless Sensor Networks for Urban Transportation: a Survey

Application Scenarios of Wireless Sensor Networks for Urban ...

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第三十四届中国控制会议

作者： Xiaoya Hu Wei Xiong Wei Li Li Ke School of Automation Huazhong University of Science & Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control

ISBN: (纸本)9781467374439

The rapid progress in the research and development of electronics, sensing, signal processing, and communication networks has significantly advanced the state of applications of intelligent transportation systems(ITS). However, efficient and low-cost methods for gathering information in large-scale roads are lacking. Consequently, wireless sensor network(WSN) technologies that are low cost, low power, and self-configuring are a key function in ITS. The current state of the art of application scenarios of WSNs for urban transportation is captured in this paper, where solutions are discussed under their related application schemes. This paper also points out the potential application scenarios and design requirements of WSN for urban transportation.

关键词： WSN,ITS,urban transportation,application scenarios

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