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International Conference on Digital Image Computing: Techniques and Applications, DICTA 2010

作者： Zou, Li-Hui Chen, Jie Zhang, Juan García, Narciso Education Ministry Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China Grupo de Tratamiento de Imágenes E.T.S.I. Telecomunicación Universidad Politécnica de Madrid Madrid Spain

ISBN: (纸本)9780769542713

Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs. © 2010 IEEE.

关键词： Diagnosis

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A Fast Quantity and Position Detection Method Based on Monocular Vision for a Workpieces Counting and Sorting system

A Fast Quantity and Position Detection Method Based on Monoc...

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第三十八届中国控制会议

作者： Xin Wang Baokui Li Hongbin Ma Man Luo School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

With the rapid development of machine vision, many technologies have been applied to the robots for improving the efficiency in the industrial field. This paper concerns the industrial sorting and counting technology problems in a workpieces counting and sorting system, and puts forward a solution using monocular vision. The main process consists of three parts. The rough positioning is accomplished first by using the pixel intensity comparison-based object detection(PICO). Then, image preprocessing and extracting geometric features are established, composing of binarization, morphological operation, optimizing the foreground,finding inner as well as outer contours, and calculating areas. Finally, the center coordinates and categories of workpieces are obtained. We choose nuts and gears as experimental objects, and complete the fast detection. The results of counting nuts and locating gears illustrate that the proposal solution not only has high speed, but also can ensure a high accuracy.

关键词： Monocular Vision the PICO Detector Workpieces Counting Fast Positioning

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A New Modeling Reference Direct Adaptive Sliding Mode control for Electromechanical Actuator

A New Modeling Reference Direct Adaptive Sliding Mode Contro...

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Joint 48th IEEE Conference on decision and control and 28th Chinese control Conference

作者： Hao Li Lihua Dou Zhong Su Jie Chen School of Automation and the Key Laboratory of Complex System Intelligent Control and DecisionMinistry of EducationBeijing Institute of Technology School of Automation Beijing Information Science & Technology University

A new model reference direct adaptive sliding mode control(MRDASMC) approach for electromechanical actuator(EMA) is *** with parameter variation,external load disturbance,nonlinear structure and unmodelled dynamics in the system are treated as a lumped *** lumped uncertainty is estimated without the bounds known in advance.A switching function is employed to compensate for the estimated error of the lumped *** the issue of chattering in classical SMC is suppressed as the amplitude of the switching function is tuned with the tracking error by adaption *** adaption law contains a fading factor which can prevent saturation of the control *** stability of the control system is guaranteed by Lyapunov *** results show that the proposed controller can provide favorable performance and is robust to system uncertainties.

关键词： Model reference adaptive sliding mode control Electromechanical actuator Fading factor Saturation Chattering.

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Multimodal Multilevel Attention for Semi-Supervised Skeleton-Based Gesture Recognition

SSRN

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SSRN 2024年

作者： Liu, Jinting Gan, Ming-Gang He, Yuxuan Guo, Jia Hu, Kang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

Although skeleton-based gesture recognition based on supervised learning has made promising achievements, the reliance on large amounts of annotation for training poses a significant cost. This paper addresses semi-supervised gesture recognition, aiming to learn feature representations effectively from unlabeled data. To resolve this problem, we propose a novel multimodal multilevel attention network to achieve semi-supervised learning. This model uses an attention mechanism to polymerize multimodal and multilevel complementary semantic information of the hand skeleton, designing a multimodal multilevel contrastive loss to measure the similarity of features for semi-supervised learning. Specifically, our method explores the relationships between joint, bone, and motion to learn more discriminative feature representations. Considering the hierarchy of the hand skeleton, the skeleton data is divided into multilevel (i.e., hand-level, finger-level, and joint-level) to capture complementary semantic information. Furthermore, a multimodal contrastive loss measure similarity among multilevel representations. The proposed method outperforms the semi-supervised skeleton-based gesture recognition tasks, according to extensive experimentation on two challenging datasets from SHREC-17 and DHG 14/28 datasets. © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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An Adaptive Sliding Mode controller with The Exponential and Power Reaching Law for Discrete systems

An Adaptive Sliding Mode Controller with The Exponential and...

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第37届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhuang Li Wenchao Zao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which combines the advantages of the exponential reaching law and the power reaching law, is introduced. Second, the chattering of the sliding mode controller（SMC） with the exponential and power reaching law for discrete systems is investigated. Finally, the adaptive sliding mode controller（ASMC） with the exponential and power reaching law is introduced. The stability of the ASMC with the exponential and power reaching law for discrete systems is analyzed, and the simulation of this approach on one joint of a six degrees of freedom robot is carried out. The experimental results indicate that the ASMC with the exponential and power reaching law is effective in reducing the time of arriving the sliding mode surface. The experimental results also indicate that the ASMC with the exponential and power reaching law may make output error reach zero in a shorter time.

关键词： adaptive sliding mode controller reaching law

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Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine

Convolutional Neural Network Features Comparison Between Bac...

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第37届中国控制会议

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Recently deep learning based architectures have been widely deployed in many problems of artificial *** deep learning models, Convolutional Neural Networks（CNN） have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation（BP） algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine（ELM） to train any convolutional neural network has been proposed. The developed framework（ELM-CNN） is based on the concept of autoencoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method（BP-CNN） has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.

关键词： Convolutional Neural Network Extreme Learning Machine Features Comparison Visualization

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Adaptive Temporal Aggregation Network for Online Video Visual Relation Detection

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SSRN 2023年

作者： He, Yuxuan Gan, Ming-Gang Ma, Qianzhao Wang, Chenyu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

Nowadays, more and more researchers pay attention to scene perception of artificial robot. Video visual relation detection is an essential task for scene perception but existing methods are all offline methods which are unsuitable for real-time application. Thus, we first introduce online video visual relation detection task which aims at localizing and classifying relations in the current frame. Then, we propose Adaptive Temporal Aggregation Network for online video visual relation detection. In this network, we design content-based adaptive sampling method to sample historical frames which contain dynamic scene information. In order to extract history knowledge from these discrete features, we propose adaptive spatio-temporal activation module which consists of adaptive spatial activation unit and adaptive temporal aggregation unit to extract history knowledge from history memory. After obtaining history knowledge and current state, the proposed attention-based knowledge-state fusion module is used to fuse these dynamic and static visual information. We also construct spatial location features and semantic embedding features to improve final relation representations. Experiments show the effectiveness and efficiency of our method. Moreover, we implement our network on fetch robot and visualize the online video visual relation detecting process. © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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Improving Constraint Handling for Multiobjective Particle Swarm Optimization

Improving Constraint Handling for Multiobjective Particle Sw...

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第三十三届中国控制会议

作者： Erdong Yu Qing Fei Hongbin Ma Qingbo Geng School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology

ISBN: (纸本)9781479947249

In this paper,a novel particle swarm algorithm for solving constrained multiobjective optimization problems is *** new algorithm is able to utilize valuable information from the infeasible region by intentionally keeping a set of infeasible solutions in each *** enhance the diversity of these preserved infeasible solutions,a modified version of adaptive grid is *** addition,a voting mechanism is designed to balance the preference of infeasible solutions with smaller constraint violation and the exploration of the infeasible *** effectiveness of the proposed method is validated by simulations on several commonly used benchmark *** using the hypervolume indicator,it is shown that the proposed algorithm is more powerful than two other state-of-the-art algorithms.

关键词： multiobjective,constraint handling,particle swarm optimization,adaptive grid,voting mechanism

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3D Point Cloud-Based Lithium Battery Surface Defects Detection Using Region Growing Proposal Approach 8th

3D Point Cloud-Based Lithium Battery Surface Defects Detecti...

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8th International Workshop on Advanced Computational Intelligence and intelligent Informatics, IWACIII 2023

作者： Rehman, Zia Ur Wang, Xin Alsumeri, Abdulrahman Abdo Ali Khan, Malak Abid Ali Ma, Hongbin State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975891

Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper. The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product. This paper offers a novel strategy using 3D point clouds to get beyond the labeling and training challenges involved with conventional 2D approaches. This 3D point cloud-based approach for lithium battery fault identification makes use of feature-based methods to improve the point cloud data and lessen the computing burden. In our work, the experiments show that the feature-based technique precisely detects the affected surface of the battery. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium batteries

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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第42届中国控制会议

作者： Malak Abid Ali Khan Hongbin Ma Ying Jin Jingxiang Ma Zia Ur Rehman Mizanur Rahman School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products,and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its *** technologies provide connectivity and a fast-updating system to eliminate the paper-based *** is one of the contenders to design the system for electronic shelf labels(ESLs).In this paper,LoRa has been used to minify data losses and guarantee the successful decoding of the carrier *** data parallelism at the network server(NS) is used to distribute the data packets among the gateways(GWs) for concurrent transmissions to the end devices(EDs).The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and *** data rate(DR) and spreading factors(SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated *** orthogonality principles follow industrial,scientific,and medical regulations(ISM) to avoid data traffic *** under different duty cycles(DC) and bandwidth(BW) are examined to minify the network saturation and reduce the energy harvesting of the tags.

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