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A Comparative Study on the Data-driven Based Prognostic Approaches for RUL of Rolling Bearings

A Comparative Study on the Data-driven Based Prognostic Appr...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Zhai, Xiaojie Wei, Xiukun Yang, Jihong Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China Beijing Res Ctr Urban Traff Informat Intelligent Beijing Peoples R China CRRC Qingdao Sifang Co Ltd Qingdao Peoples R China

ISBN: (纸本)9781728124858

With the condition monitoring equipment becoming more sophisticated, data-driven based prognostic approaches for remaining useful life (RUL) are emerging. This paper introduces three classical prognostic approaches and verifies the effectiveness through the whole-life cycle experimental data of degenerated rolling bearings. The result shows that the prediction of the methods based on probability statistics will be greatly affected, if the prior parameters are inaccurate. And the degradation model cannot be adapted to individual bearing accurately. The prognostic method based on artificial intelligence and condition monitoring is more accurate in the case of a small number of training samples, and it will output a remaining useful life prediction interval with higher reliability. Therefore, by combining with other models, improving the intelligent algorithm to enhance the accuracy of its RUL prediction is the key to solve the problem about online prognostic.

关键词： rolling bearings data-driven prognostic RUL prediction

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Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition

Cascade Network with Boundary-Adaptive Filter for Surgical G...

引用

2021 International Joint Conference on Neural Networks, IJCNN 2021

作者： Chen H.-B. Ni Z.-L. Li Z. Bian G.-B. Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780738133669

Automatically recognizing surgical gestures plays a key role in computer-assisted surgery and automatic skill assessment. Solutions to this task that only rely on video without additional sensor hardware have attracted great attention since they are economical and feasible. However, it is still a challenging problem due to the variable gesture durations and the oversegmentation problem that occurred in a long gesture duration. To address the above problems, we propose a cascade network with boundary-adaptive filter to dynamically capture the long-range temporal dependencies and reduce the over-segmentation errors obviously. A cascade module is designed to enhance the temporal modeling abilities in different timescales, in which multiple similar temporal convolution networks are divided into cascade stages and a fusion stage. Every cascade stage with different temporal receptive fields has a frame-specific weight to adjust the distribution of each stage and they will be aggregated into the fusion stage to refine the predictions. The frame-specific model architecture may incur a more serious over-segmentation problem. A boundary-adaptive filter is designed to leverage gesture boundary information to smooth the prediction results adaptively, which maintains consistency within a gesture duration while keeping the gesture-crossing discrepancy. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The proposed method reaches the state-of-the-art frame-wise accuracy (83.79%) and comparable segment-level performance (83.35% F1@50). © 2021 ieee.

关键词： action segmentation surgical gesture recognition temporal convolutional network video understanding

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Arduino mobile robot with Myo Armband gesture control 13

Arduino mobile robot with Myo Armband gesture control

引用

13th ieee International symposium on Applied computational intelligence and Informatics, SACI 2019

作者： Kristof, Robert Ciupe, Valentin Moldovan, Cristian Maniu, Inocentiu Banda, Magdalena Stoian, Ana-Maria Politehnica Univeristy Timisoara Mechatronics Department Timisoara Romania

ISBN: (纸本)9781728106854

In this paper we present our work regarding possibilities to control a mobile robot with the help of an innovative myoelectric sensing device called Myo, produced by Thalmic Labs. The proposed mobile structure is built upon the Arduino development board and it results in a tracked mobile robot. There are presented some design aspects that enable the system to be functional. Since Myo can interpret hand gestures, these are used to control the mobile robot, and described in the paper. The final goal of the project is to enable realtime control of the mobile robot using direct visual feedback. Also, an analysis of the system's delay in command execution is presented in the paper. © 2019 ieee.

关键词： Mobile robots

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Leader-Follower Bearing-based Formation System with Exogenous Disturbance 13

Leader-Follower Bearing-based Formation System with Exogenou...

引用

ieee 13th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Bae, Yoo-Bin Ahn, Hyo-Sung Lim, Young-Hun Gwangju Inst Sci & Technol GIST Sch Mech Engn Gwangju South Korea Gyeongnam Natl Univ Sci & Technol GnTECH Dept Elect Engn Jinju South Korea

ISBN: (纸本)9781728106854

This paper presents the leader-follower bearing-based formation system under the bearing-only formation controller in the presence of the exogenous disturbance. Under the assumption that each agent has access to a common global coordinate frame, we consider this problem for an undirected formation topology in an arbitrary dimensional space. The main contribution of this work is the first robust stability analysis on the bearing-based formation system. Based on the Lyapunov approach, we show the global exponential ultimate boundedness of the formation error for the bounded exogenous disturbance. Numerical simulation example validates the theoretical results.

关键词： Bearing-only formation control law bearing localizability multi-agent system disturbance

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Deep Domain Adaptive Object Detection: a Survey

Deep Domain Adaptive Object Detection: a Survey

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Wanyi Li Fuyu Li Yongkang Luo Peng Wang Jia sun Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented.

关键词： Object detection Detectors Automobiles Feature extraction Training Task analysis Adaptation models

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Study on a road surface estimation method based on big data analysis 13

Study on a road surface estimation method based on big data ...

引用

ieee 13th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Fenyes, Daniel Nemeth, Balazs Gaspar, Peter Pup, Daniel Szauter, Ferenc Budapest Univ Technol & Econ Dept Control Transportat & Vehicle Syst Stoczek Utca 2 H-1111 Budapest Hungary Hungarian Acad Sci Syst & Control Lab Inst Comp Sci & Control Kende U 13-17 H-1111 Budapest Hungary Szechenyi Istvan Univ Res Ctr Vehicle Ind Egyet Ter 1 H-9026 Gyor Hungary

ISBN: (纸本)9781728106854

The paper presents a new method to classify the road surfaces according to the adhesion coefficient between the tire and road surface using big data approach. In this research, three different categories of the road surface are considered, such as dry, wet and icy. The purpose of classification is to create a model, which is able to determine the type of the actual road surface using only the measured data of the vehicle. The classification method is, basically, based on the C4.5 decision tree algorithm, while the data is provided the high-fidelity simulation software, CarSim. Finally, the efficiency of the resulted model is demonstrated through a complex simulation.

关键词： Big data

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The Monitoring System of Aquaculture Environment

The Monitoring System of Aquaculture Environment

引用

International symposium on computational intelligence and Design, ISCID

作者： Chunxia JIN Qiuchan BAI Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huaian China Faculty of Automation Huaiyin Institute of Technology Huaian China

ISBN: (数字)9781728184463

ISBN: (纸本)9781728184470

According to the needs of the aquaculture industry, this article proposes to design an aquaculture environment monitoring system for collecting temperature, PH, and dissolved oxygen parameters that affect fishery water quality environment, real-time tracking and monitoring, remote transmission and abnormal handling, etc. The system has played an important role in the application of fish breeding industry, providing technical support for the development of the breeding industry, and bringing greater economic profits to farmers.

关键词： Aquaculture Monitoring Temperature sensors Water quality Temperature measurement Fish Temperature control

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AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition

AdaSGN: Adapting Joint Number and Model Size for Efficient S...

引用

International Conference on Computer Vision (ICCV)

作者： Lei Shi Yifan Zhang Jian Cheng Hanqing Lu NLPR & AIRIA Institute of Automation Chinese Academy of Sciences Meituan School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665428132

Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there are some works trying to speed up the skeleton modeling by designing light-weight modules. However, in addition to the model size, the amount of the data involved in the calculation is also an important factor for the running speed, especially for the skeleton data where most of the joints are redundant or non-informative to identify a specific skeleton. Besides, previous works usually employ one fix-sized model for all the samples regardless of the difficulty of recognition, which wastes computations for easy samples. To address these limitations, a novel approach, called AdaSGN, is proposed in this paper, which can reduce the computational cost of the inference process by adaptively controlling the input number of the joints of the skeleton on-the-fly. Moreover, it can also adaptively select the optimal model size for each sample to achieve a better trade-off between the accuracy and the efficiency. We conduct extensive experiments on three challenging datasets, namely, NTU-60, NTU-120 and SHREC, to verify the superiority of the proposed approach, where AdaSGN achieves comparable or even higher performance with much lower GFLOPs compared with the baseline method.

关键词： Adaptation models Computer vision computational modeling Process control Manuals Skeleton Data models

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Defects Detection of Catenary Suspension Device Based on Image Processing and CNN

Defects Detection of Catenary Suspension Device Based on Ima...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Li, Yongguang Wei, Dehua Wei, Xiukun Wu, Kuan Liang, Yong Shen, Xing Shengzhou Tongzhen Technol Co Beijing Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China CRRC Changchun Rail Transit Bus Co Ltd Changchun Peoples R China

ISBN: (纸本)9781728124858

Catenary suspension device (CSD) is a critical part of the pantograph-catenary system, which is a fundamental power equipment to supply electricity to urban rail transit vehicles. Whether the CSD is in a normal state is of great significance to the safe operation of operating vehicles. Therefore, it is important to detect the defects of CSD in time and automatically. In this paper, innovative and intelligent methods using image processing technologies and convolutional neural network (CNN) are proposed. Firstly, the insulators and bolts of CSD are extracted in the detected images using template matching algorithm. After that, an improved Bag of Features (BOF) model is proposed for the defect detection of CSD. Furthermore, in order to further improve the detection efficiency, AlexNet is trained for the defect detection and identification of CSD. The experimental results show that the proposed methods can detect the defects of CSD in time, with high robustness and accuracy.

关键词： defect detection catenary suspension device image processing alexNet CNN

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Simplified Space Based Neural Architecture Search

Simplified Space Based Neural Architecture Search

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ieee symposium Series on computational intelligence (SSCI)

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781728124858

In this paper, we propose Simplified Space based Neural Architecture Search (SSNAS), an efficient approach for automatic architecture search. Inspired by the popular convolutional neural networks which have strong capability for feature extraction with small convolutional kernels, we design a simplified search space of convolutional operations with small kernel and construct a large model based on it. Furthermore, we use an Long Short-Term Memory (LSTM) to sample child model from the large model in a way of selective activation. The probability distribution of selective activation is obtained by training the LSTM with reinforcement learning for maximizing the expected reward of selected child model on the validation set. Moreover, the trained weights are saved in a large model which concludes all child models. Each weight of new sampled child model will be restored when being selected by another one again instead of training from scratch so that SSNAS can greatly reduce the expensive computation than traditional approaches for automatic model design. Extensive experiments on CIFAR-10, ImageNet and VOC show that the proposed approach excels in discovering transferable architectures with high performance.

关键词： neural architecture search simplified space image classification object detection

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