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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是141-150 订阅

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Sliding Mode control of the Penicillin Fermentation System Based on Nonlinear Disturbance Observer

Sliding Mode Control of the Penicillin Fermentation System B...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhang Tengfei Fang Xing Liu Fei Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781538626184

A feed-forward compensation strategy for the disturbance is proposed for the control problem of the bacteria in the fermentation process of the penicillin. Firstly, the nonlinear disturbance observer is designed to estimate the lumped disturbance of the system. then, a sliding mode control law is designed for the system. the design of the control law guarantees the closed-loop system is asymptotically stable and achieves the purpose of tracking control for the bacteria's concentration in the system. the simulation results show that this method can enhance the anti-disturbance capability and improve the control performance of the system.

关键词： penicillin fermentation nonlinear disturbance observer sliding mode method tracking control

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Improved Kernel Fisher Discriminant Analysis for Nonlinear Process Fault Pattern Recognition 7

Improved Kernel Fisher Discriminant Analysis for Nonlinear P...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Deng, Xiaogang Sun, Baowei Wang, Lei China Univ Petr Coll Informat & Control Engn Qingdao 266580 Peoples R China

ISBN: (纸本)9781538626184

Kernel Fisher discriminant analysis (KFDA) has emerged as an well-known nonlinear fault pattern recognition method. However, traditional KFDA method does not consider the utilization of the high order statistical information of process variables, and ignores the mining of the local data structure characteristic. To achieve better fault pattern recognition performance, this paper proposes an improved KFDA method, called statistics local KFDA(SLKFDA). In the proposed method, two techniques, including statistics pattern analysis (SPA) and local structure analysis (LSA), are combined to enhance the basic KFDA method. Firstly, SPA is applied to extract the original process variables' statistics with different orders. then the KFDA optimization objective is modified by considering the local structure preserving. Lastly, a fault classifier is developed to recognize fault pattern. Simulations on one benchmark process demonstrate that the proposed SLKFDA method has a superior fault pattern recognition performance.

关键词： Fault Recognition Kernel Fisher Discriminant Analysis Local Structure Analysis Statistics Pattern Analysis

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On Closed - loop control of Matrix Converter with Double Voltage control 7

On Closed - loop Control of Matrix Converter with Double Vol...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Ma, Xinghe Zhang, Zhendong Xu, Dan Wang, Kunchao Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454000 Peoples R China

ISBN: (纸本)9781538626184

A novel closed-loop control strategy is designed and researched for a matrix converter with dual voltage synthesis control. the closed-loop control strategy is based on the dual-voltage control of the matrix converter duty cycle calculation characteristics, the deviation between the ideal input voltage duty cycle and the equivalent input voltage duty cycle is calculated, and the calculated deviation is added as the negative feedback variable of the closed loop system to the next duty cycle calculation period. In order to achieve the purpose of closed-loop control, the closed-loop control strategy proposed in this paper is used to solve the problem that the output side voltage performance of the matrix converter is affected by the input side voltage distortion and the performance of the internal components of the matrix converter is not ideal. To ensure that the actual output voltage of the matrix converter can better meet the desired voltage, improve its disturbance after the output voltage quality reduction and improve the output performance. the experimental results show that the output voltage of the matrix converter with this new closed - loop control method is closer to the desired voltage, the output current waveform is smoother and the output voltage quality is more ideal.

关键词： dual voltage control strategy matrix converter closed loop control duty cycle

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Anode Effect prediction based on Expectation Maximization and XGBoost model 7

Anode Effect prediction based on Expectation Maximization an...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhang, Zhixin Xu, Gaofeng Wang, Hongting Zhou, Kaibo Huazhong Univ Sci & Technol Sch Automat MOE Key Lab Image Proc & Intelligence Control Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781538626184

Anode Effect Prediction problem has been drawing great research interest of scientists, due to its significant values in reducing energy consumption and improving the efficiency of aluminum electrolysis. However, a large number of missing values contained in the collected data from the aluminum reduction cell are always neglected in the works, resulting in a decline in prediction accuracy and generalization ability. To solve this problem, a combined model of Expectation Maximization and XGBoost (EM-XGBoost) is proposed. Firstly, the original incomplete samples collected from the aluminum cells are recovered by Expectation Maximization (EM) algorithm. Afterwards, the XGBoost model trains on the recovered data, and then predicts the result for new samples. the more comprehensive metrics accuracy and F1 Score are introduced for evaluation. the results in the experiment show that the proposed model improves the accuracy to 99.7% and the F1 Score can achieve 99.8% under the premise of forecasting 30 minutes in advance. the proposed model not only has a high prediction accuracy, but also owns an excellent generalization ability.

关键词： aluminum electrolysis anode effect EM-XGBoost F1 Score

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Short-Term Traffic Flow Prediction Based on XGBoost 7

Short-Term Traffic Flow Prediction Based on XGBoost

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Dong, Xuchen Lei, Ting Jin, Shangtai Hou, Zhongsheng Beijing Jiaotong Univ Adv Control Syst Lab Beijing 100044 Peoples R China

ISBN: (纸本)9781538626184

Fast and accurate short-term traffic flow prediction is an important precondition for traffic analysis and control. Due to the fact that the short-term traffic flow has nonlinear characteristic and changes randomly, concurrent computation is difficult for traditional machine learning algorithms. In this paper, a traffic flow prediction model combining wavelets decomposition and reconstruction with the extreme gradient boosting (XGBoost) algorithm is proposed to predict the short-term traffic flow. First, in the training part, wavelet de-noising algorithm is utilized to obtain the high and low frequency information of target traffic flow. Secondly, the high frequency information of traffic flow is processed by threshold method. After that, the high and low frequency information is reconstituted as the training label. Finally, the de-noised target flow is sent to the XGBoost algorithm for training to predict traffic flow. In this way, the trend of the traffic flow in each sample period is retained, and the influence of the short-term high frequency noise is reduced. the proposed traffic flow prediction method is tested base on the traffic flow detector data collected in Beijing, and the proposed method is compared with support vector machine (SVM) algorithm. the result shows that the prediction accuracy of the proposed algorithm is much higher than SVM, which is of great importance in the field of traffic flow prediction.

关键词： Short-term traffic prediction wavelet de-noising XGBoost

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Fast Positioning of Rotating Center Based on Correction of Finite Angle Deviation of CT System 7

Fast Positioning of Rotating Center Based on Correction of F...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Duan, Deyu Zhai, Fahui Cao, Yuqin Hou, Huaqiong Yang, Shuguo Qingdao Univ Sci & Technol Sch Math & Phys Res Ctr Math Modeling Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Sch Math & Phys Inst Intelligence Sci & Data Technol Qingdao 266061 Peoples R China

ISBN: (纸本)9781538626184

Note that it is very important to determine accurately the position of the Center of Rotation (COR) to the image reconstruction in the CT scanning system, in this paper, we establish the model of fast determining COR by using the correction of finite angle deviation, moreover apply certain algorithms to achieve the center of rotation calibration. By simulating original signal or the original signal with noise, we obtain that the artifact of the reconstructed image is significantly less and the image quality is also raised, thus the center of rotation can be accurately determined. Compared with the known algorithms, the model in this paper has a small amount of calculation and strong resistance to random noise. Consequently, our model and algorithm are helpful for determining COR.

关键词： 2D-CT Finite Angle Deviation Improved Inverse Transformation of Radon Least Square Method White Gaussian Noise

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Modified P-Type ILC for High-Speed Trains with Varying Trial Lengths 7

Modified P-Type ILC for High-Speed Trains with Varying Trial...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Yu, Qiongxia Bu, Xuhui Chi, Ronghu Hou, Zhongsheng Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454000 Peoples R China Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266042 Peoples R China Beijing Jiaotong Univ Adv Control Syst Lab Beijing 100044 Peoples R China

ISBN: (纸本)9781538626184

High-speed trains always operate from the same departure station to the same terminal station and hence iterative learning control (ILC) is an appropriate approach for automatic train control. However, due to complex environment and unknown uncertainties, the train may not arrive at the terminal station on time, or earlier and later than the schedule time in each operation. To address this problem, a modified proportional-type (P-type) ILC is presented where the trial length in each operation can be randomly varying. Moreover, the convergence condition in 2-norm is also derived through rigorous analysis. the effectiveness of the modified P-type ILC is further verified through simulations.

关键词： High-Speed Train ILC Varying Trial Length

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A Second-Order Hidden Markov Model Based Web Services Selection 7

A Second-Order Hidden Markov Model Based Web Services Select...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Lu, Yuan Jia, Zhichun Li, Xiang Xing, Xing Bohai Univ Coll Informat Sci & Technol Jinzhou 121013 Peoples R China Bohai Univ Key Lab Digital Publishing Big Data Min Governanc Jinzhou 121013 Peoples R China Bohai Univ Inst Automat Jinzhou 121013 Peoples R China

ISBN: (纸本)9781538626184

Over the last few decades, the cloud computing is rapidly developing. How to find the suitable web services for users quickly and accurately is facing more and more challenges. the quality of service becomes an essential parameter to discriminate web services with the same function. In this paper, we propose an effective services selection method based on Quality of Service (QoS) parameters. Our method uses the second-order Hidden Markov Model (HMM) to model the business process of web services and selects the optimal web services for the execution of user requests. the technique we present can solve the measurement problem of the web service behaviors according to the given threshold values of the throughput and response time. By ranking the candidate services with the similar functionality, the top service is selected to run in the business process for meeting the user needs. Finally, we conduct the simulation experiments to demonstrate our method using QWS database. the result shows that our method is effective.

关键词： Second-order Hidden Markov Model Quality of Service (QoS) Web Service Selection

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Big data Mining Method of thermal Power Based on Spark and Optimization Guidance 7

Big Data Mining Method of Thermal Power Based on Spark and O...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Song, Mingcheng Jia, Li Shanghai Univ Coll Mechatron Engn & Automat Dept Automat Shanghai 200072 Peoples R China Shanghai Univ Coll Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Dept Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9781538626184

With the increasing degree of information technology in the electric-power industry, the amount of big data in thermal power has increased geometrically. To address the problem of the computational bottlenecks in traditional data mining deal with big data of thermal power, big data mining of thermal power method based on Spark is presented in this paper. According to the characteristics of the actual operation of the unit, the proposed method determines the steady-state conditions of big data of thermal power and divides the working conditions based on external constraints. In addition, data mining method based on distributed computing is used to mine big data of thermal power to get the strong association rules, thus the best value of the parameters under each working condition can be got. Lastly, the historical knowledge base is established, which can guide the operation of the unit by the proposed method. this method is applied to a 300 MW unit in a power plant in Anhui Province, and mines the operation data of the unit for 10 days in a month. the results of simulation show that the proposed method can effectively mine big data of thermal power and has the advantage of computational efficiency compared with traditional data mining for big data.

关键词： Big data of thermal power Spark Big data mining Strong association rules Operation optimization

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A Novel Improved Grey Wolf Optimization Algorithm for Numerical Optimization and PID controller Design 7

A Novel Improved Grey Wolf Optimization Algorithm for Numeri...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhang, Tao Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

ISBN: (纸本)9781538626184

the grey wolf optimization (GWO) algorithm, one of the recently proposed bio-inspired algorithms, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. the GWO has a good performance in some optimization tasks, but its search capacity decreases with the increasing search scope and dimension. this paper proposes an improved GWO (IGWO) algorithm, in which Levy flight strategy and a sine cosine operator with adaptive step are incorporated to significantly improve the performance of the algorithm. the Levy flight strategy is used to strengthen the efficiency of global search. the adaptive sine cosine operator is introduced to improve the local search ability. Experimental results based on twenty unconstrained benchmark problems show the superiority of the proposed IGWO. Furthermore, the IGWO is utilized in PID controller design. the comparison results show that the IGWO algorithm is better than, or at least comparable to, other well-established swarm intelligence algorithms.

关键词： Grey wolf optimization Levy flight strategy Adaptive sine cosine operator PID controller design

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