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5th International Workshop on Advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Wang, Yipeng Xin, Bin School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

Hybridization is termed as an efficient strategy to improve the performance of many optimizers, and the design of efficient hybrids for difficult optimization problems remains a challenging field. In this study, an automatic method is proposed to generate various hybrid optimizers based on particle swarm optimization and differential evolution for a given problem. Based on the unified framework for hybridization proposed in our recent work, the proposed method can provide plenty of hybrid optimizers for a specific problem with given parent optimizers. Besides, an approach which can provide the unified representation of different kinds of hybridization levels is developed. Moreover, all the generated hybrids are evaluated on the CEC2017 benchmark problems, and the results of the statistic analysis and the Wilcoxon's rank-sum tests demonstrate that the hybrid optimizer utilizing the hybridization strategy performs obviously better than the others on the test functions.

关键词： Particle swarm optimization (PSO)

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Simulation and Experimental Study on Piezoelectric Geophone Core Based on PMN-PT

Simulation and Experimental Study on Piezoelectric Geophone ...

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Chinese control Conference

作者： JunLei Song YanXia Huang ZhiHeng Yang MeiJuan Chen Yong Yang WenQin Mo KaiFeng Dong Fang Jin Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China School of Mathmatics and Physics China University of Geosciences Wuhan P. R. China

Piezoelectric geophones are vibration detectors that convert vibration acceleration signals into electrical signals. High performance piezoelectric materials can improve the sensitivity of piezoelectric geophone and meet the need of high-resolution seismic data acquisition. The comprehensive performance of relaxor piezoelectric single crystal PMN-PT is more superior than PZT, and it is potential to be applied to high sensitivity and small volume geophones. In this paper, the central compressed geophone core model based on PMN-PT was established and theoretically analyzed. Then, a multiphysics simulation model was set up in COMSOL for simulation calculation. Finally, experimental verification was carried out. The results show that using PMN-PT in geophone core design can improve the sensitivity of the model by more than 120% compared with the traditional PZT material. The PMN-PT has the potential to be applied to high sensitivity and small volume geophones.

关键词： Vibrations Sensitivity Acceleration Piezoelectric materials Sensors Earth Force

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Deep Forest-Based Classification of Hyperspectral Images

Deep Forest-Based Classification of Hyperspectral Images

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Chinese control Conference

作者： Xu Yin Ruilin Wang Xiaobo Liu Yaoming Cai Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China School of Computer Science China University of Geosciences Wuhan P. R. China

The classification of hyperspectral images (HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network (CNN) has achieved great success for HSI classification. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN, nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classification approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis (PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the first deep forest-based hyperspectral spectral information classification. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is significantly superior to the state-of-the-art methods.

关键词： Forestry Hyperspectral imaging Training Principal component analysis Vegetation Radio frequency

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Recognize User Intents in Online Interactions from Massive Social Media Data 2

Recognize User Intents in Online Interactions from Massive S...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Chenxi Cui Wenji Mao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

ISBN: (纸本)9781509036202

Online interactions,especially user generated contents on social events,reveal a variety of communicative purposes ranging from expressing feelings to proposing *** intents in users' online interactive behavior from massive social media data can effectively identify users' motives and intents behind communication and provide important information to aid monitoring,analysis and decision making for a variety of ***,user intents recognition from online communication is inherently challenging due to the ambiguity in semantic processing and diversity of syntax ***,the massive online data are usually unlabeled,which greatly hinders the usage of typical machine learning based methods that can automate the recognition *** this paper,we tackle this problem by proposing a Speech Act Theory guided classification scheme,which regards online communication as performative actions of users and classifies user utterances according to their pragmatic *** the basis of this,we construct a dictionary of performative words,expand it using external knowledge sources and refine it by word embedding and similarity *** then use this dictionary to automatically label the online textual data with *** a large amount of the labeled data,we train feature based classifiers to identify user intents in their online *** experimental study using a microblog dataset on social events from Sina Weibo shows the effectiveness of our proposed method.

关键词： user intent recognition speech act theory online communication social media big data

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A probabilistic learning algorithm for the shortest path problem 11th

A probabilistic learning algorithm for the shortest path pro...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Diao, Yiya Li, Changhe Ma, Yebin Wang, Junchen Zhou, Xingang School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China Communication Security Group of Eastern Theater Command Nanjing China

ISBN: (纸本)9783319687582

Ant colony optimization (ACO) has been shown effectiveness for solving combinatorial optimization problems. Inspired by the basic principles of ACO, this paper proposes a novel evolutionary algorithm, called probabilistic learning (PL). In the algorithm, a probability matrix is created based on weighted information of the population and a novel random search operator is proposed to adapt the PL to dynamic environments. The algorithm is tested on the shortest path problem (SPP) in both static and dynamic environments. Experimental results on a set of carefully designed 3D-problems show that the PL algorithm is effective for solve SPPs and outperforms several popular ACO variants. © Springer International Publishing AG 2017.

关键词： Ant colony optimization

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Design of AI + Curriculum for Primary and Secondary schools in Qingdao

Design of AI + Curriculum for Primary and Secondary Schools ...

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2018 Chinese automation Congress, CAC 2018

作者： Han, Xiao Hu, Fang Xiong, Gang Liu, Xiwei Gong, Xiaoyan Niu, Xiaojie Shi, Wanruo Wang, Xinzhu Qingdao Institute of Education Sciences Qingdao China Qingdao No.2 Middle School of Shandong Province Qingdao China Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Faculty of Education Beijing Normal University Institute of Educational Technology Beijing China

ISBN: (纸本)9781728113128

We are entering a new AI era, and the development of AI technology and popularization of AI education has become the national strategy of China. The primary and secondary school students are the builder of future society, and have strong curiosity and passion to learn new things;therefore, we should make a good preparation of AI education for them and cultivate their basic AI literacy and computational thinking to adapt to the future. In this paper, from the viewpoint of thinking mode and literacy and skill training, a curriculum including basic, project, and practical types is designed. Integrating with the iSTREAM educational system proposed by Chinese Academy of Sciences, exploratory works were shown with some case studies. The applications show that AI education is helpful to enrich the study of primary and secondary school students and to prompt comprehensive quality. © 2018 IEEE.

关键词： Curricula

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intelligent coordinating control between burn-through point and mixture bunker level in an iron ore sintering process

Intelligent coordinating control between burn-through point ...

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作者： Du, Sheng Wu, Min Chen, Xin Lai, Xuzhi Cao, Weihua School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems No. 388 Lumo Road Hongshan District Wuhan Hubei430074 China

Sintering is a process that involves complex physical and chemical reactions. An intelligent coordinating control strategy is proposed for the strong coupling between the burn-through point (BTP) and the mixture bunker level (MBL). First, an intelligent integrated controller is established for the BTP by fusing the neural network, expert rules, and fuzzy logic. Moreover, an expert controller is designed for the MBL based on expert rules using the analysis of the main factors that affect the MBL. Furthermore, by employing the soft switching control algorithm, an intelligent coordinating controller for the BTP and the MBL is designed. The optimal operation parameters are obtained from the algorithm, which realize the multi-objective control of the sintering process. Finally, a simulation and an experiment of the intelligent coordinating control between the BTP and the MBL are carried out, where the models of the BTP and the MBL are the Takagi-Sugeno (T-S) fuzzy model and the linear model, respectively. And the results show that the proposed approach is feasible and effective.

关键词： Mixtures

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An Improved-WM Method Based on Optimization of Centers of Output Fuzzy Subsets for Fuzzy Rules

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IFAC-PapersOnLine 2017年第1期50卷 7369-7374页

作者： Jin Y. Cao W. Wu M. She J. Yuan Y. 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 School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

The generation of fuzzy rules from samples is significant for fuzzy modelling. To improve the robustness of Wang-Mendel (WM) method, an improved WM method to extract fuzzy rules from all the regularized sample data was proposed. However, the accuracy of the model with this method is degraded for the conflicting rules with small difference between support degrees. And the output subsets can only be chosen from the pre-defined ones. To solve these problems, we develop an improved-WM method based on optimization of centers of output fuzzy subsets for fuzzy rules (COiWM). This method adopts the fuzzy c-means (FCM) clustering algorithm to divide the input and output spaces, and the improved WM method which replaces the original data by regularized data is used to calculate the support degrees. Then the support degrees are used as weights to optimize the centers of output fuzzy subsets with a method of weighted averages, so as to enhance the accuracy of a fuzzy model. Experimental results of a case study on short term daily maximum electric load forecasting prove that our proposed method enhances the accuracy of a fuzzy model. © 2017

关键词： Accuracy Conflicting rules Fuzzy c-means clustering algorithm Fuzzy rules Wang-Mendel method

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Simulation and Evaluation of CAVs Behavior in an Isolated Signalized Intersection Equipped with Dynamic Wireless Power Transfer System

Simulation and Evaluation of CAVs Behavior in an Isolated Si...

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International Conference on intelligent Transportation

作者： Qingyun Liu Simon Hu Panagiotis Angeloudis Yibing Wang Lihui Zhang Qiang Yang Yongfu Li ZJU-UIUC Institute Zhejiang University Zhejiang China Department of Civil and Environmental Engineering Imperial College London London UK Institute of Intelligent Transportation Systems School of Civil Engineering and Architecture Zhejiang University Zhejiang China College of Electrical Engineering Zhejiang University Hangzhou China Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781538670255

This paper proposes an idea of charging eco-driving connected autonomous vehicles (CAVs) which drive smoothly passing signalized intersection above a dynamic wireless power transfer (DWPT) system consisting of segmented coils at the upstream of approaching lanes. Mixed traffic streams with different penetration levels of eco-driving CAVs passing through a DWPT system-equipped signalized intersection are simulated using microscopic traffic simulation software PTV-VISSIM. Energy stored, which is defined as the result of energy transferred to the vehicle battery by DWPT system subtracting energy consumed by the vehicle battery whilst travelling the same distance, is used to evaluate the total impact of eco-driving behavior on energy consumption and charging capacity. The simulation results show that at full CAV penetration rate, there are 62% reduction of average energy consumption of all types of vehicles and 10% increase of average energy stored per battery electric vehicle compared to full HVs case. This indicates that the eco-driving driving behavior has a positive impact on the energy stored powered by the DWPT system designed in this paper.

关键词： Coils Energy consumption Biological system modeling Batteries Computational modeling Layout Wireless power transfer

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An Online Modeling Method for Formation Drillability Based on OS-Nadaboost-ELM Algorithm in Deep Drilling Process

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IFAC-PapersOnLine 2017年第1期50卷 12886-12891页

作者： Gan, Chao Cao, Weihua Wu, Min Chen, Xin Hu, Yule Wen, Guojun Gao, Hui Ning, Fulong Ding, Huafeng School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Adavanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences Wuhan430074 China School of Mechanical Engineering & Electronic Information China University of Geosciences Wuhan430074 China

To achieve safety, high quality, and efficiency in deep drilling, it is necessary to get formation drillability around the borehole during drilling-trajectory planning and intelligent drilling control. Since the drilling data have the characteristics of low value density and noise in the process of deep drilling, it is difficult to model formation drillability in deep drilling. In this paper, a new online modeling method for formation drillability based on online sequential nadaboost extreme learning machine (OS-Nadaboost-ELM) algorithm has been proposed. Firstly, the well logging parameters are chosen as the inputs of the model, whose output is formation drillability. Then, several ELM models are established and the outputs of these models are as weak learners. Then the weak learners are combined by Nadaboost algorithm in order to get a strong learner. Finally, the recursive least squares algorithm is used to adjust the model. The numerical test results show that, in both prediction accuracy and training efficiency aspects, the proposed method is better than other prediction methods such as multiple regression, gray method, back-propagation neural networks, Nadaboost extreme learning machine and online sequential extreme learning machine. Thus the prediction model serves as the online geological model to develop intelligent drilling systems. © 2017

关键词： E-learning

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