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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是461-470 订阅

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A Lateral Active Collision Avoidance System Based on Fuzzy-PID and Sliding Mode control for Electric Vehicles

A Lateral Active Collision Avoidance System Based on Fuzzy-P...

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data driven control and learning systems (ddcls)

作者： Yu Tian Yufeng Lian Taotao Zhang Chonghe Tang Shi Qi School of Electrical and Electronic Engineering Changchun University of Technology Changchun China Siemens Factory Automation Engineering Ltd. (SFAE) Changchun China FAW TOOLING DIE MANUFACTURING COMPANY LTD Changchun China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

A lateral active collision avoidance system based on fuzzy and slide mode control is presented for electric vehicles in this paper. It can be consisted of the safety distance model, the upper level controller, and the lower level controller. the safety distance model can judge and give current vehicle's steering collision avoidance safety logic. When an emergency danger occurs, the steering collision avoidance can be considered first, rather than braking collision avoidance. the upper level controller can be designed based on fuzzy control. It can calculate the desired trajectory. the lower level controller can be signed based on slide mode control to realize the direct yaw moment control. Simulation experiments can be conducted with different preview times and different control methods. Simulation results demonstrate that the lateral active collision avoidance system based on fuzzy and slide control is in good agreement with the actual driving process for electric vehicles.

关键词： Wheels Collision avoidance Tires Safety Mathematical model Vehicles Stability analysis

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Real-time Indoor Object Detection Based on Deep learning and Gradient Harmonizing Mechanism

Real-time Indoor Object Detection Based on Deep Learning and...

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data driven control and learning systems (ddcls)

作者： Min Chen Xuemei Ren Zhanyi Yan School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Due to the indoor environment is complicated, and the proportion of the background is much larger than the object, the positive and negative categories of the sample are not balanced. this paper proposes that the imbalance of sample categories is due to the imbalance of difficult and easy samples which can be reflected by the gradient norm of the sample. the gradient density is introduced to solve the uneven distribution of gradient norm. the parameter "gradient density" is added to the Yolov3 loss function which is one of the best one-stage object detection frameworks that can balance accuracy and speed in order to improve the sample category imbalance. Considering the high computational complexity of the sample gradient density, the gradient norm is divided into equal-width intervals, and the same gradient density is adopted for the samples falling in same region so as to simplify the calculation and improve the training efficiency. the experimental results demonstrate that the improved approach achieves best detection accuracy and obtains the most accurate bounding boxes of indoor objects to be classified with quick speed. therefore, the gradient harmonizing mechanism of samples can improve the sample category imbalance. the improved indoor object detection algorithm can be applied in the field of intelligent indoor monitoring, indoor service robots.

关键词： Object detection Training Probability Feature extraction Real-time systems Proposals Logistics

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Incremental Gaussian Mixture Model for Time-varying Process Monitoring

Incremental Gaussian Mixture Model for Time-varying Process ...

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data driven control and learning systems (ddcls)

作者： Qingyang Dai Chunhui Zhao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

With the increasing complexity of industrial production, data-driven based monitoring methods attract more attention. However, the conventional static process monitoring methods may show poor performance for the time-varying processes since they fail to track the time-varying characteristics. As Gaussian Mixture Model (GMM) has been widely used for process monitoring, this paper presents a new incremental GMM model for monitoring time-varying processes. First, an incremental GMM (IGMM) model is proposed, which can recursively update model parameters, adaptively add new Gaussian components and discard the irrelevant component based on the shifting samples online. then the Bayesian Inference Probability (BIP) is introduced for monitoring statistics and a two-level partition strategy that can separate normal shifting samples from fault samples is proposed, which reduces the possibility of adding fault samples to the model. On the basis of IGMM model, an adaptive monitoring scheme is developed, which can track the time-varying characteristics of processes. Finally, a time-varying numerical example and the Tennessee Eastman process are adopted to validate the feasibility of the proposed monitoring model. Experimental results clearly demonstrate the adaptiveness of the monitoring model to time-varying processes and the ability to avoid false updates.

关键词： Adaptation models Process monitoring Mathematical model Gaussian mixture model Numerical models data models Production

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data driven control and learning systems (ddcls)

作者： Songjun Zhong Jinglin Zhou College of Information Science & Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Quality-related fault detection has become a research hotspot in recent years, and its goal is to maximize the alarm rate for quality-related faults in process monitoring and minimize the alarm rate for faults that are irrelevant or self-adjustable. the traditional principal component analysis(PCA) and partial least squares(PLS) method use covariance to extract the principal component. As a second-order statistic, covariance can only extract Gaussian information without considering that the data may contain higher-order non-Gaussian information, so its statistical alarm rate is not the most accurate. To solve above problems, this paper proposes a quality-related fault detection method based on weighted mutual information. the first step is to extract the set of process variables that contain the most information about the quality variables through Bayesian fusion mutual information, and then use the fusion mutual information for the extracted process variables to eliminate some process variables. For the remaining process variables, use PLS algorithm based on maximum mutual information to extract principal components for statistical modeling. Finally, it is applied to Tennessee Eastman process(TEP) to simulate the feasibility and effectiveness of the proposed method.

关键词： Mutual information Feature extraction Fault detection data mining Principal component analysis Production Correlation

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Yarn-dyed Fabric Defect Detection using U-shaped De-noising Convolutional Auto-Encoder

Yarn-dyed Fabric Defect Detection using U-shaped De-noising ...

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data driven control and learning systems (ddcls)

作者： Hong-wei Zhang Quan-lu Tan Shuai Lu Zhi-qiang Ge De Gu School of Electronic Information Xi’an Polytechnic University Xi’an China Institute of Industrial Process Control Zhejiang University Hangzhou China Institute of Mathematical Beijing University of Chemical Technology Beijing China Institute of Automation Jiangnan University Wuxi China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Practical factors such as high labor cost of labelling defect samples and scarcity of defect samples make it difficult for supervised machine learning models to solve the problem of yarn-dyed fabric defect detection. To solve this problem, this paper proposes an unsupervised yarn-dyed fabric defect detection method based on U-shaped de-noising convolutional auto-encoder (UDCAE). Firstly, for tested samples of yarn-dyed fabric, the training dataset was constructed by collecting the non-defect yarn-dyed fabric samples. then, the non-defect dataset is utilized to model and train the proposed UDCAE model. Finally, the defective area can be quickly detected by calculating the residual between the original tested yarn-dyed fabric image and its reconstructed item correspondingly. the experiment results show that the proposed method can accurately detect defects of yarn-dyed fabrics with different patterns.

关键词： Fabrics Image reconstruction Training Noise reduction Feature extraction Testing Inspection

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A Parallel Feature Extraction Model with Channel Attention for Button Defect Detection

A Parallel Feature Extraction Model with Channel Attention f...

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data driven control and learning systems (ddcls)

作者： Jiahao Wang Kuangrong Hao Bing Wei Lei Chen Xuesong Tang Ministry of Education College of Information Science and Technology Donghua University Shanghai P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Defect detection is a vital task for production process, which greatly affects the quality of the product. Manual inspection is the most commonly used defect detection method, but is very time-consuming and not reliable. Currently, many image processing algorithms have been used for defect detection to partially replace manual detection. However, due to the complexity of various defects, how to correctly and quickly detect whether the button is defective still faces great challenges. To address these challenges, we propose a button defect detection model based on parallel feature extraction network. the model extracts feature by two parallel CNN structures, one of which is deeper and stronger (CNN-B), and the other is relatively shallow and weak (CNN-A). the CNN-B is trained on ImageNet firstly. Meanwhile, only the three layers of CNN-B but all layers in CNN-A are trained. We further add channel attention module to perform feature maps recalibration in CNN-A. the experimental results show that the parallel feature extraction model can identify the defect of button effectively.

关键词： Feature extraction Convolution Manuals Standards Neurons Kernel data mining

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Application of NARX dynamic neural network in blood glucose prediction model

Application of NARX dynamic neural network in blood glucose ...

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data driven control and learning systems (ddcls)

作者： Menglin Xu Ruizhuo Song Yong Zhao Biao Song Juan Tang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing P. R. China Beijing Institute of Computer Application University of Science and Technology Beijing Beijing P. R. China Ericsson (China) Commodity Co. Ltd ARIIX CHINA P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Establishing a blood glucose prediction model and controlling the amount of insulin injected according to the predicted blood glucose value can effectively reduce the harm caused by high and low blood glucose to the human body. Because the dynamic process of glucose-insulin metabolism in human body is a typical non-steady-state and non-linear process, and has a large time-lag dynamic characteristic, it is difficult for linear models to accurately describe the blood glucose-insulin model. With the development of artificial neural network and its application in all aspects of modern times, this paper proposes to use NARX dynamic neural network to build blood glucose-insulin model, and use the data generated by UVa/Padova simulation platform for network training. Finally, through MATLAB simulation verification, by calculating the mean square error and Clark error grid analysis method, it is shown that the blood glucose can be effectively predicted.

关键词： Blood Glucose Neural networks data models Predictive models Insulin Mathematical model

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Fault-Tolerant control for Flexible Air-Breathing Hypersonic Vehicle Based on Tube Robust Model Predictive control

Fault-Tolerant Control for Flexible Air-Breathing Hypersonic...

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data driven control and learning systems (ddcls)

作者： Xiaohe Yang Weijie Lv Chaofang Hu Yongtai Hu School of Electrical and Information Engineering Tianjin University Tianjin China Aviation Key Laboratory of Science and Technology on Aircraft Control Facri China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper adopts Tube robust model predictive control (RMPC) approach to achieve the trajectory tracking control for flexible air-breathing hypersonic vehicle (FAHV) with lose efficiency fault of actuator. First of all, Jacobian linearization and tensor-product transformation are used to build the polytopic linear parameter varying (LPV) model for FAHV. then, the FAHV faulty model is obtained by transforming lose efficiency fault of actuator into the additional disturbance term. thirdly, fault-tolerant control is realized by composite control law. the error system is obtained by the difference between the actual and the nominal system. By designing the minimum robust positive invariant set (mRPI), the error system state is limited to the mRPI set. the additional disturbance term in mRPI set is suppressed by the auxiliary robust feedback control law. the nominal control law provides a reasonable trajectory for the actual system. And it is calculated by the convex optimization problem, which is constrained by linear matrix inequality (LMI). the fault-tolerant controller guarantees that the actual trajectory is located in the mRPI set all the time and centered on the nominal trajectory. Finally, the designed Tube RMPC controller is validated by simulation.

关键词： Fault tolerant systems Fault tolerance Electron tubes Actuators Trajectory Feedback control Tensors

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An Abrasion Detection Method for Elevator Traction Wire Rope based on Template Matching

An Abrasion Detection Method for Elevator Traction Wire Rope...

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data driven control and learning systems (ddcls)

作者： Zehua Li Zheng Chai Chunhui Zhao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou

Elevators are gradually becoming an indispensable part of people9;s daily life. At the same time, various safety problems caused by elevators are also seriously threatening people9;s life and property security. ... 详细信息

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Elevators are gradually becoming an indispensable part of people's daily life. At the same time, various safety problems caused by elevators are also seriously threatening people's life and property security. Elevator traction wire rope damage is a common type of potential safety hazard. At present, the defect detection method for elevator traction wire rope damage generally has high complexity and labor cost. this paper presents a method to detect the abrasion of elevator traction wire rope by taking full use of the texture feature of the traction wire rope images. First, the interference factors in the process of image acquisition are removed by the pretreatment methods of graying and denoising. then, the abrasion area of the traction wire rope is determined according to the similarity measurement criteria by combining edge detection and template matching. Finally, the image is finely segmented and the abrasion rates of different areas are calculated. this method is convenient and intuitive to detect the changes of elevator wire rope abrasion. On one hand, the complexity of the detection and the loss of the human labor can be reduced; on the other hand, valuable reference can be provided for elevator maintenance. Experiment on actual wire rope images validate the effectiveness of the proposed method.

关键词： Wires Elevators Image edge detection Noise reduction Steel Gray-scale Magnetic resonance imaging

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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2020 ieee International conference on Networking, Sensing and control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 ieee.

关键词： Curricula

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