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Annual Conference of the IEEE Industrial Electronics Society

作者： Feng Wang Jinhua She Yasuhiro Ohyama Min Wu School of Engineering Tokyo University of Technology Tokyo Japan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Effective influential spreaders prediction in online social networks is critical for a variety of network services such as viral marketing, online recommendation, and many more. The conventional machine learning methods mainly adopted various hand-crafted features in the prediction models. However, their effectiveness relies on the domain knowledge heavily, and thus it is difficult to generalize these models to different domains. In this paper, we propose an Influence Deep Learning (IDL) model to learn users' latent feature representation for predicting influence spread. Our IDL model is fully data-driven, and it takes sampling subnetworks as input to the deep neural networks for learning users' latent vector representation. Moreover, we design a strategy to incorporate user-specific features and network structure into the graph convolutional neural network to overcome the imbalance problem of labeled training data. The experimental results show that our model outperforms other baselines in online social networks, and our model is more practical in large-scale data sets.

关键词： Social network services Machine learning Predictive models Neural networks Integrated circuit modeling Support vector machines Training

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Development of Sheet Palletizing Manipulator Based on Multi-link Mechanism

Research Square

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Research Square 2020年

作者： Chen, Zhihua Wang, Shoukun Liu, Quan Wang, Junzheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China New Energy Laboratory College of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China

In view of the disadvantages of Traditional tandem joint type thin-plate palletizing robots using push-down adsorption sorting, such as moving path complexity, large occupied space, joint error stack and low end control precision, a novel multi-link mechanism thin-plate palletizing robot is developed. The kinematics analysis is carried out based on the analysis of the overall structure for the manipulator, a genetic algorithm based on the stroke speed ratio K, the transmission angle γ and the four-bar size is proposed to realize the optimal design of the four-link rocker arm mechanism. Secondly, a finite element based overall structure analysis method is proposed, which obtains the most affected part of the vibration process of a manipulator and performs load verification to prevent early warning and reference of faults caused by mechanical resonance. To solve the control problem of nonlinear strongly coupled dynamic systems, an iterative learning fuzzy adaptive control algorithm is proposed to achieve the smooth running of the manipulator and the high-precision tracking of the end centroid trajectory. The experimental results verify the feasibility and effectiveness of the proposed multi-link controllable thin-plate palletizing manipulator's overall structural design and iterative learning control method, which is well applied in practical engineering. © 2020, CC BY.

关键词： Manipulators

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Load Curve Modeling Based on Behavior Analysis of Distributed Power Customers

Load Curve Modeling Based on Behavior Analysis of Distribute...

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2019 4th Asia Conference on Power and Electrical Engineering (ACPEE 2019)

作者： Xinyang HAN Yu ZHANG Yuqiao JIAO Yue ZHANG Yanjing QU Min DING State Grid Energy Research Institute School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System State Grid Shanghai Municipal Electric Power Company

As renewable power generation directly affects the customers' traditional electricity behavior and then offsets the power load,this paper proposes a load curve modeling method for renewable power customers based on the behavior ***,customers' active behavior is represented by the quantity of active customer *** on the analysis of customer behaviors,a modeling method for the quantity of active customer households is proposed based on Markov Chain Monte Carlo ***,with the inputs as the quantity of active customer households and time of photovoltaic power generation,an inference model based on fuzzy logic is proposed to get the quantity of customer household starting electrical *** combing the average usage time of electrical appliances,load characteristics are analyzed based on usage state of electrical appliance of distributed power ***,the simulation results verify the effectiveness of the proposed method.

关键词： Carlo Monte Markov Load Curve Modeling Based on Behavior Analysis of Distributed Power Customers

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Dynamic Security Assessment of Small-Signal Stability for Power systems using Windowed Online Gaussian Process

arXiv

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arXiv 2019年

作者： Zhai, Chao School of Automation China University of Geo-sciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Due to the evolving nature of power grids and model uncertainty, the online stability assessment of electrical power systems is always a challenging problem. This paper aims to provide a theoretical framework for estimating the region of attraction for power systems in real time. By analyzing and learning the measurement data in a given time duration, a windowed online Gaussian process (GP) approach is developed to provide the real-time security assessment and quantify the uncertainty caused by measurement errors and dynamic evolution of power systems. In addition, the theoretical analysis is conducted to ensure the conference level of the estimated region of attraction. Finally, numerical simulations are implemented on a microgrid model with 9 buses and 3 generators to validate the proposed approach. The proposed online assessment approach contributes to improving the situational awareness of human operators, thereby taking remedial actions before the emergency. Copyright © 2019, The Authors. All rights reserved.

关键词： Uncertainty analysis

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

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

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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

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Design of Deviation Correction control system in Vertical Drilling Process

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

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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

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

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

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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

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A survey of computer vision-based tracking registration technology for augmented reality 8

A survey of computer vision-based tracking registration tech...

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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： Li, Yuqing Hong, Xin Xu, Chi School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China

Augmented reality technology is a research hotspot in the field of computer vision in recent years. Tracking registration technology determines the performance of augmented reality systems. The accuracy of tracking registration directly affects the users experience. Therefore, the research of tracking registration technology is of great significance. In this paper, augmented reality and its core tracking registration technology are introduced firstly. Then, the visual tracking registration method based on marker and natural features are elaborated, and the more advanced visual SLAM-based tracking registration method in recent years is summarized. Finally, the main problems of the current research and possible development directions are analyzed. © 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： Augmented reality

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Short-term load forecasting based on GA-BPNN-SVR optimization algorithm 8

Short-term load forecasting based on GA-BPNN-SVR optimizatio...

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作者： He, Lianghua Ge, Lai Zhang, Hao Wang, Guowei School of automation China University of Geosciences Wuhan Hubei430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei430074 China

In order to improve the accuracy of short-term power load forecasting, an algorithm that simultaneously optimizes SVR parameters and optimizes the selection of input features is proposed. Firstly, the input features are selected by BP neural network. In order to avoid the influence of the randomness of the initial weights and thresholds on the optimization, the GA algorithm is used to optimize the weights and thresholds. Secondly, using GA algorithm to optimize the parameters of support vector regression, a short-term load forecasting model based on GA-BPNN-SVR optimization algorithm is obtained. Finally, the GA-BPNN-SVR model is applied to practical short-term power load forecasting, and the forecasting effect is compared with that of GA-SVM and BPNN-SVR models. The results show that the average absolute percentage error obtained by this method is only 1.74%, which is 1.64% and 3.11% lower than the average absolute percentage error obtained with GA-SVM model and BPNN-SVR. This algorithm can be effectively used for short-term load forecasting of power systems. © 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： Genetic algorithms

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An overview of sensing platform-technological aspects for vector magnetic measurement: A case study of the application in different scenarios

arXiv

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arXiv 2020年

作者： Liu, Huan Dong, Haobin Ge, Jian Liu, Zheng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Engineering University of British Columbia Okanagan KelownaBCV1V 1V7 Canada

Magnetic sensing platform techniques have been used in many years in an attempt to better evaluate the likelihood of recoverable hydrocarbon reservoirs by determining the depth and pattern of sedimentary rock formations containing magnetic minerals, such as magnetite. Utilizing airplanes, large-area magnetic surveys have been conducted to estimate, for example, the depth of igneous rock and the thickness of sedimentary rock formations. In this case, the vector magnetic survey method can simultaneously obtain the modulus and direction information of the Earth's magnetic field, which can effectively reduce the multiplicity on data inversion, contribute to the quantitative interpretation of the magnetic body and obtain more precise information and characteristics of magnetic field resource, so as to improve the detection resolution and positioning accuracy of the underground target body. This paper presents a state-of-the-art review of the application situations, the technical features, and the development of the vector magnetic sensing platform-technical aspects for different application scenarios, i.e., ground, wells, marine, airborne, and satellites, respectively. The potential of multi-survey sensing platform technique fusion for magnetic field detection is also discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Geological surveys

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