检索结果-内蒙古大学图书馆

8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Zhou, Hao Ding, Min Li, Danyun Wang, Qingyi Tan, Zhili Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China

With the large-scale application of wind power, the wind power prediction has been highlighted. In order to improve the prediction accuracy of wind power and ensure the resource utilization rate, a wind power prediction method based on support vector machine regression and particle swarm optimization based on multi-source NWP (numerical weather prediction) is proposed. Firstly, the SVR (support vector regression) algorithm is introduced to model the multi-source wind speed data, which can effectively extract the main features and improve the wind speed prediction accuracy. Then, the PSO (particle swarm optimization) is used to model the historical predicted power data and the actual power data for power prediction. Finally, the proposed approach provides wind power forecasts for future 1 day in steps of 15 min. The effectiveness of the proposed method is verified by experiments on wind farms and the results show that the proposed method is more accurate and effective than SVR. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Weather forecasting

来源：评论

学校读者我要写书评

暂无评论

Design of intelligent Drilling System Software Framework and Data Architecture Based on MVC Pattern

Design of Intelligent Drilling System Software Framework and...

引用

第三十八届中国控制会议

作者： Peilun Ouyang Weihua Cao Min Wu Chao Gan Fawen Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Hubei Institute of Urban Geological Engineering

Drilling system software is crucial for the complex geological drilling process due to its vital role in improving the drilling efficiency and safety. In this paper, a Model-View-controller(MVC) pattern-based software framework and data architecture of an intelligent drilling system are proposed. First, four main functional requirements, namely, data fusion and visualization, safety assessment and efficiency calculation, efficiency and safety optimization, and coordination control have been analyzed. After that, a four-layers is established based on the above analysis and MVC pattern. Finally, a new online data architecture has been presented at the end of this paper. The proposed software framework and data architecture form a basis for the monitoring of the drilling process.

关键词： Model-View-controller intelligent Drilling Software Framework Data Architecture

来源：评论

学校读者我要写书评

暂无评论

Sensorless control of a Tubular Oscillatory LPMSM based on MARS and Deadbeat Current Predictive control

引用

IOP Conference Series: Materials Science and Engineering 2020年第6期768卷

作者： Kang Lu Tao Wu Haonan Chen He Wang Jia Gao School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China

In the vector control system of the tubular oscillatory linear permanent magnet (PM) synchronous motor (LPMSM), it is difficult for traditional mechanical sensors to accurately obtain the feedback information under harsh conditions. In this work, a speed sensorless vector control system is designed and applied to the tubular oscillatory LPMSM which adopts the model reference adaptive system (MRAS), and the deadbeat current predictive control (DCPC) is also adopted to replace the traditional PI current loop to establish the coordination controller, which overcomes the difficulties of global optimization and PI control parameter setting. And the method is proved by the simulation results and it gets better dynamic performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of Deviation Correction control System in Vertical Drilling Process

Design of Deviation Correction Control System in Vertical Dr...

引用

第三十八届中国控制会议

作者： Dian Zhang Luefeng Chen Chengda Lu Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Software and Electrical Engineering Swinburne University of Technology

This paper is concerned with design of a deviation correction control system for vertical drilling process. Notice that only inclination angle is considered by traditional deviation correction systems, which may cause that the drilling trajectory extends around the wellhead's plumb line or gets away from the line. In this paper, focusing on the drilling trajectory itself, a deviation correction control scheme is provided to improve the vertical precision of the vertical drilling while maintaining small inclination angles. A hybrid modeling method for establishing the trajectory extension model and a model predictive control strategy based on the model are integrated in the scheme. It is expected that the phenomenon of spiral-like drilling trajectory can be attenuated by our control system over the traditional ones.

关键词： Geological Exploration Vertical Drilling Deviation Correction control Trajectory Extension Model Model Predictive control

来源：评论

学校读者我要写书评

暂无评论

intelligent control System Design for Electric-drive Rig in complex Geological Drilling Process

Intelligent Control System Design for Electric-drive Rig in ...

引用

第三十八届中国控制会议

作者： Xiao Wu Xuzhi Lai Jianglong Zhu Hongbo Huang Luefeng Chen Sheng Du Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Geological Equipment Group Co. Ltd

The complex geological drilling process is full of uncertain factors because of the invisibility of the bottom hole. The factors cause the problems of trajectory control and condition monitoring. In order to solve these problems, an intelligent control system is designed for complex geological drilling process by taking the XD-35 DB electric-drive rig as a study case. At first,the hardware structure of the rig is described in detail, and the working mechanism is analyzed. Then, based on its working mechanism, an intelligent control system is designed, which includes the basic automation layer and the intelligent control *** intelligent control layer is designed from three aspects: intelligent optimization of drilling trajectory, tracking control of drilling trajectory, and condition monitoring of the drilling process. The intelligent control system provides a feasible solution to improving drilling efficiency and ensuring drilling safety.

关键词： Condition monitoring Drilling process Electric-drive rig intelligent control system intelligent optimization

来源：评论

学校读者我要写书评

暂无评论

Bifurcation Analysis and control of Fractional-Order Quorum Sensing Network 37

Bifurcation Analysis and Control of Fractional-Order Quorum ...

引用

37th Chinese control Conference, CCC 2018

作者： Feng, Liu Xiangjuan, Dai Qicheng, Mei Guang, Ling Xinmei, Wang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Science Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789881563941

In this paper, bifurcation analysis and control of fractional-order quorum sensing network regulated by sRNA is studied. The dynamics of fractional quorum sensing network with time-delay is analyzed. The stability criterion and bifurcation conditions of the fractional quorum sensing network are given. In order to control the dynamic properties of fractional order systems, a feedback controller is designed, and the complex dynamics of controlled fractional-order networks are further investigated. The simulation results show that this control method can achieve the ideal control effect. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Stability criteria

来源：评论

学校读者我要写书评

暂无评论

Artificial Neural Network Based Maximum Power Point Tracking for PV System

Artificial Neural Network Based Maximum Power Point Tracking...

引用

第三十八届中国控制会议

作者： Al-wesabi Ibrahim Min Ding Xiaoling Jin Xianzhong Dai Mohammad Abu Sarhan Muhammed Badeaa Shafik Hao Zhou School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System State Grid Energy Research Institute School of electrical engineering and automation Wuhan university

Nowadays PV systems is suffering from two main problems: high production cost and low efficiency especially under variable weather conditions. Therefore, to reduce such associated problems, PV systems are being connected with various optimization controllers such as maximum power point tracking. In this paper, an artificial neural network maximum power point method has been designed to be linked between a PV system and DC-DC buck converter, and each part of the system is fully explained. The whole system was modeled under MATLAB/Simulink environment, simulation results demonstrated the efficiency of the proposed method. The proposed method has been examined to track the maximum power point under different weather conditions which shows a rapid and accurate tracking of the maximum power point(MPP) of PV system. The whole system which consists of PV arrays, Buck converter, ANN tracker, and load has been modeled using MATLAB/Simulink environment. This Simulink model has been examined using different levels of temperature and irradiation and the output results of both P-V and I-V characteristics, output power, and the system efficiency clearly demonstrate the validation of the modeled system. This demonstration has been done by comparing between the result of experimental platform and the results of simulated model which approves that ANN is highly recommended to be used in the field of maximum power point tracking of PV system.

关键词： Artificial neural network(ANN) Photovoltaic(PV) Maximum power point tracking(MPPT) Buck converter

来源：评论

学校读者我要写书评

暂无评论

EEG emotion recognition based on ensemble learning 8

EEG emotion recognition based on ensemble learning

引用

作者： Ding, Xue-Wen Liu, Zhen-Tao Feng, Zhi-Yong Xie, Qiao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Automotive Engineering Wuhan University of Technology Wuhan430070 China

EEG emotion recognition based on ensemble learning is proposed, in which three classification models including k-Nearest Neighbor, Decision Tree and Logistic Regression are integrated. In feature extraction, four kinds of features including Mean, Skewness, Sample Entropy and Lempel-Ziv complexity (LZC) are extracted from eight channels’ EEG signals, and Kernel Principal Component Analysis (KPCA) is employed to reduce the dimension of the feature set. The proposal is tested on the DEAP dataset in which the EEG emotional states are accessed in Arousal-Valence emotion space. The result shows that the average emotion recognition accuracies of 77.73% in Arousal dimension and 75.34% in Valence dimension are obtained, which demonstrates its feasibility. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Identification of Protein complex Based on a Novel Multi-Objective Method 37

Identification of Protein Complex Based on a Novel Multi-Obj...

引用

37th Chinese control Conference, CCC 2018

作者： Yuan, Zhu Xiaoyu, Peng Chong, Wu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789881563941

Systematic identification of protein complexes is an important application of data mining in bioscience. The existing computation methods of detecting protein complexes are usually based on the topological properties of protein-protein interaction(PPI) network data. However, limited by the inherent single structure of the PPI network, the mining of protein complexes may not be fully resolved. In this paper, we propose an original multi-objective optimization strategy based protein complex detection method, which is used Adaptive Multi-Objective Black Hole algorithm, called AMOBH. In the selection of objective function strategy, we integrate the topological structure of PPI network data with semantic similarity based on GO (Gene Ontology) annotation data. Experiments demonstrate that the method we adopt provide more convinced results and higher computational efficiency compared with the existing prediction measures. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Gene Ontology

来源：评论

学校读者我要写书评

暂无评论

Adaboost-kNN for dynamic emotion recognition 8

Adaboost-kNN for dynamic emotion recognition

引用

作者： Li, Min Su, Wanjuan Chen, Luefeng Wu, Min Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Tokyo Institute of Technology Yokohama226-8502 Japan

— K-nearest Neighbor based adaptive boosting (AdaBoost-KNN) is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression. It can make robots capable of understanding human emotions and make HRI run smoothly. The Candide-3 model was adopted to extract the two-dimensional coordinate values of the key points of facial emotion, as the basis for emotion classification. The emotion classification is based on AdaBoost-KNN, which sets up seven basic KNN classifier. Then it iterates seven times to update the weight of samples, and combines the weak classifier with coefficient to establish the final classifier. Finally, the performance of the proposal is verified by using K-fold cross validation. Results show that the proposal achieves higher accuracy than that of a single KNN. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：