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The evaluation of urban human settlements based on fuzzy matter-element model: Taking 11 prefecture-level cities in Jiangxi Province as examples 1

The evaluation of urban human settlements based on fuzzy mat...

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1st International Conference on Physics, Mathematics and Statistics, ICPMS 2018

作者： Liu, Zuhan Wang, Lili Zhang, Bin Yu, Xiang Zhou, Xideng Jiangxi Province Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang Jiangxi China School of Science Nanchang Institute of Technology Nanchang Jiangxi China Land and Resources College China West Normal University Nanchong Sichuan China Business Administration College Nanchang Institute of Technology Nanchang Jiangxi China

A case study is presented in which the fuzzy matter-element model (FMEM) is used to assess the evaluation of urban human settlements (UHS) of the basin of the 11 prefecture-level cities in Jiangxi Province. The regional samples and evaluation standards are all treated as the matter-element, and the compound fuzzy matter-element is constructed with the evaluation indicators and the corresponding fuzzy values of the indicators. Then the Euclid approach degree of the evaluated fuzzy matter-element to the standard fuzzy matter-element is calculated, thus attaining the grade classification of human settlements of the evaluated samples. The study results show that the fuzzy matter-element method is practical for comprehensive evaluation of regional human settlements. © Published under licence by IOP Publishing Ltd.

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A WOA-based algorithm for parameter optimization of support vector regression and its application to condition prognostics 36

A WOA-based algorithm for parameter optimization of support ...

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第36届中国控制会议

作者： LI Sai FANG Huajing School of Automation Key Laboratory of Image Processing and Intelligent ControlMinistry of EducationHuazhong University of Science and Technology

ISBN: (纸本)9781538629185

Condition monitoring is very important for system safety and condition-based *** series prediction capabilities of machine learning like support vector regression(SVR) can be utilized for ***,choosing optimal parameters for SVR is an important step in SVR model design,which heavily affects the performance of ***,a whale optimization algorithm(WOA) based algorithm is proposed for SVR parameters *** proposed algorithm has been evaluated through some benchmark ***,the proposed method with moving window technology is used to condition prognostics of the Tennessee Eastman *** and engineering application show that the SVR-WOA method is effective,by noting that the computation time is shortened in some application scenarios.

关键词： Support vector regression Whale optimization algorithm Parameter optimization Condition prognostics

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The Design of Multi-mode Examinees Identity Verification System for Colleges and Universities

The Design of Multi-mode Examinees Identity Verification Sys...

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2018 International Conference on Computer Modeling, Simulation and Algorithm（CMSA2018）

作者： Jia Chen Cong Chen Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University

With the rapid development of information management in colleges and universities, traditional eyeobserving verification methods cannot satisfy today's requirement for examination management any more. Examinees' identities need to be verified more effectively and accurately. In recent years, biometric recognition has been becoming an essential component of effective person identification solutions because biometric identifiers intrinsically represent individuals' bodily identity. In this paper, we present a multi-mode verification system that not only takes advantage of biometric recognition to improve efficiency and accuracy, but also incorporates eye-observing verification to deal with the case when biometric recognition fails. Accordingly, an original portable verification device was designed to implement this multi-mode verification system. Our experiment shows that this verification system can meet the practical requirement of examination management in colleges and universities.

关键词： identity verification multi-mode verification comprehensive judgment original verification device

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DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information

DeepDock: Enhancing Ligand-protein Interaction Prediction by...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhirui Liao Ronghui You Xiaodi Huang Xiaojun Yao Tao Huang Shanfeng Zhu School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China School of Computing and Mathematics Charles Sturt University Albury Australia State Key Laboratory of Quality Research in Chinese Medicines Macau University of Science and Technology Macau China Shenzhen Zhiyao Information Technology Ltd. Shenzhen China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China

The prediction of precise protein-ligand binding activities can accelerate drug discovery by virtual screening-a computational technique that predicts whether a small molecule ligand is able to bind to a specific target. Thus, it is crucial to improve the performance of virtual screening. However, previous models for solving this problem are either ligand-based or structure-based. In this paper, we propose a universal deep neural network model called DeepDock that predicts protein-ligand interaction by using both ligand and structure information. Using the combination of two types of information, our model consists of embedding, convolution, max pooling, and fully-connected layers. In particular, different types of inputs are concatenated before being fed into the fully-connected layers. In the experiments, we compare our approach to the competing methods against two benchmark datasets under different settings. The experiment results have demonstrated that DeepDock can improve predictive performance by more than 4% on both DUD-E and MUV datasets in terms of AUPR.

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A battery management system with two-stage equalization 29

A battery management system with two-stage equalization

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第29届中国控制与决策会议

作者： Shengping Hong Jianhua Jiang Xi Li School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & Technology

ISBN: (纸本)9781509046584

This paper presents a lithium-ion battery pack equalization system and method. The batteries are divided into several groups, which are connected in parallel with the bidirectional DC-DC converters respectively, and the output of DC-DC converters ends are connected in series with each other as a DC bus. The scheme divides equalization of the cells into two stages: intra-group equalization and inter-group equalization, and the two stages are respectively realized by battery time-sharing-access structure and stack energy-sharing structure. Then equalization strategy of the distributed battery energy storage system under two stages is proposed, especially the Single Cell Battery Access Timing Algorithm and MPC Algorithm. The simulation results show that the proposed battery management structure and control strategy can realize fast and accurate SOC equalization.

关键词： Battery management system(BMS) cell equalization battery time-sharing-access structure stack energy-sharing structure Single Cell Battery Access Timing Algorithm MPC Algorithm

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Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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Application of Deep Learning in Software Security Detection

Application of Deep Learning in Software Security Detection

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2018 International Conference on Computational Science and Engineering(ICCSE 2018)

作者： Lin Li Ying Ding Jiacheng Mao College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System

Machine learning is knowledge of learning rules from data through computational models and algorithms. It has been applied in various fields that require mining rules from complex data, and has become one of the most core technologies in the field of artificial intelligence in the broad sense. In recent years, a variety of deep neural networks have made remarkable achievements in a large number of machine learning problems, forming a new branch of machine learning, deep learning, and also raising a new climax of machine learning theory, method and application research. Software security is a highly concerned issue at present. Vulnerability detection technology is an important measure to improve software security. In order to make software application more secure, it is necessary to discuss the application of deep learning in software security detection.

关键词： Deep learning Software security Detection RNN

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Relative influence maximization in competitive social networks

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Science China(Information Sciences) 2017年第10期60卷 252-254页

作者： Dingda YANG Xiangwen LIAO Huawei SHEN Xueqi CHENG Guolong CHEN College of Physics and Information Engineering Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Institute of Computing Technology Chinese Academy of Sciences

In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achiev... 详细信息

关键词： net RIM Relative influence maximization in competitive social networks Figure

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A Wind Turbine Fault Diagnosis Method Using Adaptive Weighting Algorithm and D-S Evidence Theory

A Wind Turbine Fault Diagnosis Method Using Adaptive Weighti...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Xianhao Shen Kangyong Liu Zhu Liu Miao Ye College of Information Science and Engineering GuiLin University Of Technology GuiLin China Guangxi Key Laboratory of Embedded Technology and Intelligent Information Processing GuiLin China

Since the system of Wind Turbine is complex and the relationships between faults and symptoms are not simple or linear, ordinary detection can not meet the diagnostic requirements, Data Fusion is an important problem in the context of wireless sensor networks. This paper proposed a data fusion method on Wind Turbine's state monitoring and fault diagnosis. First, take Adaptive Weighting Algorithm into signal data fusion which reduces data redundancy and energy consumption of network; then, take Gauss membership function to obtain the Basic Probability Assignment which can improve the data reliability in D-S Evidence Theory. The improved D-S Evidence Theory in feature data fusion can enhance the ability of fault identification. The experimentation shows the proposed method has a high diagnostic accuracy and reliability.

关键词： Data integration Wind turbines Sensor fusion Vibrations Shafts Fault diagnosis

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Reform and Construction of Computer Major on Local Colleges under the Background of Emerging Engineering Education

Reform and Construction of Computer Major on Local Colleges ...

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3rd International Conference on Politics,Economics and Law(ICPEL 2018)

作者： Yaojie Chen Xin Yuan Hai Zhou Zhijing Wan School of Computer Science and Technology Wuhan university of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System

In recent years, with the accelerated development of the global new economy, the country desperately need the support of new engineering talents, which raises higher requirements for the development of higher education in *** in 2016, the "emerging engineering education" was first proposed in China, which is more significant on colleges across the country. Computer major that is an enormous engineering category has trained a large number of talents for the ***, how to pay attention to the new needs of social development and cultivate more high-quality computer professionals is the key for computer major to take the road of connotation development in the future. In this paper, under the background of emerging engineering education, how to carry out teaching reform in local colleges, how to promote the cross-integration of computer subject, and how to strengthen the integration of research, education and industry are discussed in depth. Therefore, the construction of a new project education concept is the future core direction for local colleges.

关键词： emerging engineering education computer major local colleges interdisciplinary innovation

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