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2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical EngineeringTaiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform(WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the ***, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine(weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our institute,and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

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The comparative analysis of estimates of the time discontinuous parameter of the random pulse synthesized using Bayesian and maximum likelihood approaches

The comparative analysis of estimates of the time discontinu...

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ELEKTRO

作者： Alexandra V. Salnikova Oleg V. Chernoyarov Leila A. Golpayegani Michal Kuba Dept. of Radio Engineering Devices and Antenna Systems “Moscow Power Engineering Institute” (MPEI) Moscow Russia Dept. of Radio Engineering Devices and Antenna Systems National Research University “Moscow Power Engineering Institute” (MPEI) Moscow Russia Dept. of Telecommunications and Multimedia University of Zilina (ZU) Zilina Slovakia Dept. of Information Technologies and Computer-aided Design Voronezh State University of Architecture and Civil Engineering Voronezh Russia

The algorithms for optimal and quasi-optimal estimation of the arrival time of a Gaussian random pulse with a priori known and inaccurately known duration, which is observed against the background of white Gaussian noise, is synthesized. The comparative analysis of the Bayesian (quasi-Bayesian) and maximum likelihood (quasi-likelihood) measurers of the arrival time is performed. The results of the statistical computer simulation of estimation algorithms are presented.

关键词： Maximum likelihood estimation Bayes methods Time measurement Signal to noise ratio Pulse measurements Signal processing algorithms

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Estimating communication skills based on multimodal information in group discussions

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Transactions of the Japanese Society for Artificial Intelligence 2016年第6期31卷

作者： Okada, Shogo Matsugi, Yoshihiro Nakano, Yukiko Hayashi, Yuki Huang, Hung-Hsuan Takase, Yutaka Nitta, Katsumi School of Computing Tokyo Institute of Technology Japan Department of Computational Intelligence and Systems Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology Japan Department of Computer and Information Science Seikei University Japan College of Sustainable System Sciences Osaka Prefecture University Japan Department of Information and Communication Science College of Information Science and Engineering Ritsumeikan University Japan

This paper focuses on developing a model for estimating communication skills of each participant in a group from multimodal (verbal and nonverbal) features. For this purpose, we use a multimodal group meeting corpus including audio signal data and head motion sensor data of participants observed in 30 group meeting sessions. The corpus also includes the communication skills of each participant, which is assessed by 21 external observers with the experience of human resource management. We extracted various kinds of features such as spoken utterances, acoustic features, speaking turns and the amount of head motion to estimate the communication skills. First, we created a regression model to infer the level of communication skills from these features using support vector regression to evaluate the estimation accuracy of the communication skills. Second, we created a binary (high or low) classification model using support vector machine. Experiment results show that the multimodal model achieved 0.62 in R2 as the regression accuracy of overall skill, and also achieved 0.93 as the classification accuracy. This paper reports effective features in predicting the level of communication skill and shows that these features are also useful in characterizing the difference between the participants who have high level communication skills and those who do not. © 2016, Japanese Society for Artificial Intelligence. All rights reserved.

关键词： Human resource management

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2016 IEEE International Conference on systems, Man, and Cybernetics, SMC 2016

作者： Nguyen, Linh Anh Nguyen, Thi Hong Khanh Nguyen, Ngoc-Thanh Ha, Quang-Thuy Division of Knowledge and System Engineering for ICT Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Institute of Informatics University of Warsaw Banacha 2 Warsaw02-097 Poland Electric Power University 235 Hoang Quoc Viet Tu Liem Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Management Wroclaw University of Technology Poland Faculty of Information Technology VNU University of Engineering and Technology 144 Xuan Thuy Hanoi Viet Nam

ISBN: (纸本)9781509018970

We introduce comparisons with respect to information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. As bisimilarity is a natural notion for characterizing indiscernibility in modal and description logics, it is useful for concept learning in description logics also when inconsistencies occur. We give preservation results and the Hennessy-Milner property for comparisons with respect to information in paraconsistent description logics. As consequences, we also obtain invariance results and the Hennessy-Milner property for bisimilarity in paraconsistent description logics. © 2016 IEEE.

关键词： Data description

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Efficient mining of class association rules with the itemset constraint

Efficient mining of class association rules with the itemset...

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作者： Nguyen, Dang Nguyen, Loan T.T. Vo, Bay Pedrycz, Witold Division of Data Science Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Knowledge and System Engineering for ICT Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ho Chi Minh City University of Technology Viet Nam College of Electronics and Information Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw Poland

Mining class association rules (CARs) with the itemset constraint is concerned with the discovery of rules, which contain a set of specific items in the rule antecedent and a class label in the rule consequent. This task is commonly encountered in mining medical data. For example, when classifying which section of the population is at high risk for the HIV infection, epidemiologists often concentrate on rules which include demographic information such as gender, age, and marital status in the rule antecedent, and HIV-Positive in the rule consequent. There are two naive strategies to solve this problem, namely pre-processing and post-processing. The post-processing methods have to generate and consider a huge number of candidate CARs while the performance of the pre-processing methods depend on the number of records filtered out. Therefore, such approaches are time consuming. This study proposes an efficient method for mining CARs with the itemset constraint based on a lattice structure and the difference between two sets of object identifiers (diffset). Firstly, a lattice structure is built to store all frequent itemsets in the dataset. To reduce memory usage, instead of the entire set of object identifiers, the diffset is used. Secondly, the lattice is traversed to generate only rules which satisfy the itemset constraint. The experimental results show that the proposed algorithm outperforms existing methods in terms of both the mining time and memory usage. © 2016 Elsevier B.V. All rights reserved.

关键词： Data mining

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Data Acquisition system with Low-Accuracy Sensors

Data Acquisition System with Low-Accuracy Sensors

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International Conference on Development and Application systems

作者： Su JUN Oleksiy Roshchupkin Volodymyr KOCHAN Anatoliy SACHENKO Nataliia ROSHCHUPKINA School of Computer Science Hubei University of Technology Wuhan China Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Research Institute of Intelligent Computer Systems Ternopil National Economic University Ternopil Ukraine Department of Computer Science and Econometrics Silesian University of Technology Gliwice Poland Computer System and Network Department Chernivtsi National University Chernivtsi Ukraine

ISBN: (纸本)9781509019946

The paper deals with improving the accuracy of the sensors under influence of non-informative factors. There are described the generalized block diagram of the data acquisition system and its measurement channels which provide the sensors signal processing. The generalized block diagram of the calculation process is proposed, and the structure of used neural networks is shown. Both scheme and algorithm of data processing are described. The measurement error of the proposed system was evaluated. It confirmed that proposed system adds the error of 0.32% which is very small in comparison with the etalon mean of 1%. The proposed system allows use of the sensors with a significant influence of non-informative factors.

关键词： Multisensor Sensor Neural Network Individual Transfer Function Data Acquisition system Measuring Channels Algorithm

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A Reduced Reachability Tree for a Class of Unbounded Petri Nets

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IEEE/CAA Journal of Automatica Sinica 2015年第4期2卷 345-352页

作者： Shouguang Wang Mengdi Gan Mengchu Zhou Dan You School of Information and Electronic Engineering Zhejiang Gongshang University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Ministry of Education(MoE)Key Laboratory of Embedded System and Service Computing Tongji University MoE Key Laboratory of Embedded System and Service Computing Tongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

As a powerful analysis tool of Petri nets, reachability trees are fundamental for systematically investigating many characteristics such as boundedness, liveness and reversibility. This work proposes a method to generate a reachability tree, called ωRT for short, for a class of unbounded generalized nets called ω-independent nets based on new modified reachability trees (NMRTs). ωRT can effectively decrease the number of nodes by removing duplicate and ω-duplicate nodes in the tree, and verify properties such as reachability, liveness and deadlocks. Two examples are provided to show its superiority over NMRTs in terms of tree size. © 2014 Chinese Association of Automation.

关键词： Petri nets

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Link prediction of LOD by multiple label propagation algorithm considering semantic distance

Link prediction of LOD by multiple label propagation algorit...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Toshitaka Maki Kazuki Takahashi Akihiro Yamaguchi Toshihiko Wakahara Toru Kobayashi Akihisa Kodate Noboru Sonehara Intelligent Information System Engineering Fukuoka Institute of Technology FIT Fukuoka Japan Communication & Information Networking Fukuoka Institute of Technology FIT Fukuoka Japan Information and Systems Engineering Fukuoka Institute of Technology FIT Fukuoka Japan Computer and Information Sciences Program Nagasaki University Nagasaki Univ. Nagasaki Japan Department of Computer Science Tsuda College Tsuda Tokyo Japan Information and Society Research Division National Institute of Informatics NII Tokyo Japan

ISBN: (纸本)9781509023349

This paper presents link prediction of Linked Open Data (LOD) by Multiple Label Propagation Algorithm (MLPA). The current LOD do not have enough links. Therefore, the LOD have not been able to exert so much semantic characteristics. In order to solve this problem, we proposed the MLPA considering semantic distance. The MLPA can expand potential links of each data contained in the LOD. The experimental result of the MLPA shows the good performance and the validity of this algorithm is confirmed.

关键词： Prediction algorithms Semantics Metadata Algorithm design and analysis Conferences Consumer electronics Resource description framework

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Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L (vol 50, pg 1565, 2018)

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NATURE GENETICS 2018年第12期50卷 1754-1754页

作者： Zhang, Jisen Zhang, Xingtan Tang, Haibao Zhang, Qing Hua, Xiuting Ma, Xiaokai Zhu, Fan Jones, Tyler Zhu, Xinguang Bowers, John Wai, Ching Man Zheng, Chunfang Shi, Yan Chen, Shuai Xu, Xiuming Yue, Jingjing Nelson, David R. Huang, Lixian Li, Zhen Xu, Huimin Zhou, Dong Wang, Yongjun Hu, Weichang Lin, Jishan Deng, Youjin Pandey, Neha Mancini, Melina Zerpa, Dessiree Nguyen, Julie K. Wang, Liming Yu, Liang Xin, Yinghui Ge, Liangfa Arro, Jie Han, Jennifer O. Chakrabarty, Setu Pushko, Marija Zhang, Wenping Ma, Yanhong Ma, Panpan Lv, Mingju Chen, Faming Zheng, Guangyong Xu, Jingsheng Yang, Zhenhui Deng, Fang Chen, Xuequn Liao, Zhenyang Zhang, Xunxiao Lin, Zhicong Lin, Hai Yan, Hansong Kuang, Zheng Zhong, Weimin Liang, Pingping Wang, Guofeng Yuan, Yuan Shi, Jiaxian Hou, Jinxiang Lin, Jingxian Jin, Jingjing Cao, Peijian Shen, Qiaochu Jiang, Qing Zhou, Ping Ma, Yaying Zhang, Xiaodan Xu, Rongrong Liu, Juan Zhou, Yongmei Jia, Haifeng Ma, Qing Qi, Rui Zhang, Zhiliang Fang, Jingping Fang, Hongkun Song, Jinjin Wang, Mengjuan Dong, Guangrui Wang, Gang Chen, Zheng Ma, Teng Liu, Hong Dhungana, Singha R. Huss, Sarah E. Yang, Xiping Sharma, Anupma Trujillo, Jhon H. Martinez, Maria C. Hudson, Matthew Riascos, John J. Schuler, Mary Chen, Li-Qing Braun, David M. Li, Lei Yu, Qingyi Wang, Jianping Wang, Kai Schatz, Michael C. Heckerman, David Van Sluys, Marie-Anne Souza, Glaucia Mendes Moore, Paul H. Sankoff, David VanBuren, Robert Paterson, Andrew H. Nagai, Chifumi Ming, Ray Fujian Agriculture and Forestry University and University of Illinois at Urbana-Champaign School of Integrative Biology Joint Center for Genomics and Biotechnology National Sugarcane Engineering Technology Research Center Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology Key Laboratory of Genetics Breeding and Multiple Utilization of Corps Ministry of Education Fujian Agriculture and Forestry University Fuzhou China Department of Plant Biology University of Illinois at Urbana-Champaign Urbana USA Hawaii Agriculture Research Center Kunia USA Institute for Plant Physiology and Ecology Chinese Academy of Sciences Shanghai China Department of Plant Biology University of Georgia Athens USA Department of Horticulture Michigan State University East Lansing USA Department of Mathematics and Statistics University of Ottawa Ottawa Canada Department of Microbiology Immunology and Biochemistry University of Tennessee HSC Memphis USA Chinese Academy of Sciences–Max-Planck-Gesellschaft Partner Institute for Computational Biology Chinese Academy of Sciences Shanghai China China Tobacco Gene Research Center Zhengzhou Tobacco Research Institute of CNTC Zhengzhou China Division of Biological Sciences University of Missouri Columbia USA Department of Agronomy University of Florida Gainesville USA Texas A&M AgriLife Research Texas A&M University System Dallas USA Centro de Investigación de la Caña de Azúcar de Colombia (Cenicaña) Cali Colombia Department of Crop Sciences University of Illinois at Urbana-Champaign Urbana USA Departments of Computer Science and Biology Johns Hopkins University Baltimore USA Microsoft Research Redmond USA Departamento de Botânica Instituto de Biociências Universidade de São Paulo São Paulo Brazil Departamento de Bioquímica Instituto de Química Universidade de São Paulo São Paulo Brazil

Modern sugarcanes are polyploid interspecific hybrids, combining high sugar content fromSaccharum officinarumwith hardiness, disease resistance and ratooning ofSaccharum spontaneum. Sequencing of a haploidS. spontaneum, AP85-441, facilitated the assembly of 32 pseudo-chromosomes comprising 8 homologous groups of 4 members each, bearing 35,525 genes with alleles defined. The reduction of basic chromosome number from 10 to 8 inS. spontaneumwas caused by fissions of 2 ancestral chromosomes followed by translocations to 4 chromosomes. Surprisingly, 80% of nucleotide binding site-encoding genes associated with disease resistance are located in 4 rearranged chromosomes and 51% of those in rearranged regions. Resequencing of 64S. spontaneumgenomes identified balancing selection in rearranged regions, maintaining their diversity. IntrogressedS. spontaneumchromosomes in modern sugarcanes are randomly distributed in AP85-441 genome, indicating random recombination among homologs in differentS. spontaneumaccessions. The allele-definedSaccharumgenome offers new knowledge and resources to accelerate sugarcane improvement.

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Capacity-aware Cost-efficient Network Reconstruction for Post-Disaster Scenario

Capacity-aware Cost-efficient Network Reconstruction for Pos...

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IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications

作者： Xiaoyan Wang Hao Zhou Lei Zhong Yusheng Ji Kiyoshi Takano Shigeki Yamada Guoliang Xue Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan School of Computer Science University of Science and Technology of China Hefei Anhui China Earthquake Research Institute The University of Tokyo Tokyo Japan Ira A. Fulton Schools of Engineering Arizona State University Tempe USA

ISBN: (纸本)9781509032556

Natural disasters can result in severe damage to communication infrastructure, which leads to further chaos to the damaged area. After the disaster strikes, most of the victims would gather at the evacuation sites for food supplies and other necessities. Having a good communication network is very important to help the victims. In this paper, we aim at recovering the network from the still-alive mobile base stations to the out-of-service evacuation sites by using multi-hop relaying technique. We propose to reconstruct the post-disaster network in a capacity-aware way based on prize collecting Steiner tree. The purpose of the proposed scheme is to achieve high capacity connectivity ratio in a cost efficient way. To provide more accurate evaluation results, we evaluate the proposed scheme by using the real evacuation site and base station data in Tokyo area, and utilizing the big data analysis based post-disaster service availability model.

关键词： Base stations Relays Mobile communication Earthquakes Steiner trees Mobile computing Spread spectrum communication

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