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13th International Conference on Embedded Software and System, ICESS 2016

作者： Li, Junke Guo, Bing Shen, Yan Li, Deguang Huang, Yanhui College of Computer Science Sichuan University Chengdu China School of Control Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9781509037261

To cope with the increasing scale of data, GPU (Graphic Processing Unit) is integrated with more and more cores to improve processing performance. High performance, however, is at the expense of sustained power consumption. Although current GPUS support concurrent kernel execution to relieve this problem, hardware resource utilization, category of kernels and the order of concurrent kernels issued to GPU will significantly impact on energy consumption of concurrent multi-kernels execution. An approach for considering these factors is presented, with the goal of reducing energy consumption by selecting appropriate kernels to be executed concurrently. Experiments show our proposed approach is effective and the results show that our approach can achieve maximum energy saving by 20.76% compared with sequential execution and can get maximum energy saving by 11.27% to typical approach. © 2016 IEEE.

关键词： Scheduling

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Gaussian pedestrian proxemics model with social force for service robot navigation in dynamic environment 17th

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17th International Conference on Asia Simulation, AsiaSim 2017

作者： Chik, Sheng Fei Yeong, Che Fai Su, Eileen Lee Ming Lim, Thol Yong Duan, Feng Tan, Jeffrey Too Chuan Tan, Ping Hua Chin, Patrick Jun Hua Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Johor Bahru Malaysia Malaysia Japan Institute of Technolgy Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Automation College of Computer and Control Engineering Nankai University Tianjin China Institute of Industrial Science The University of Tokyo Tokyo Japan DF Automation and Robotics Sdn. Bhd Skudai Malaysia

ISBN: (纸本)9789811064623

Pedestrian motion behaves stochastically, causing difficulties in modelling the appropriate proxemics for effective and efficient service robot navigation. Intruding the pedestrian social space can affect the social acceptance of a service robot. In this paper, a new proxemics model, Social-Force Gaussian Pedestrian Proxemics Model is presented to model the pedestrian social space and to improve the service robot navigation in dynamic human environment. This model was simulated and validated in a pedestrian simulator with both low and high pedestrian density environments. Results showed that the proposed model (i) improved proxemics representation of pedestrians, (ii) enhanced the robot performance in respecting the social norm and (iii) increased the efficiency in achieving a given task. This paper also presents the methods for parameter selections for the model without the requirement of complex tuning. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Mobile robots

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Automatic K selection method for the K — Means algorithm

Automatic K selection method for the K — Means algorithm

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International Conference on Systems and Informatics (ICSAI)

作者： Xiuli Shao Huichao Lee Yiwei Liu Bo Shen College of Computer and Control Engineering Nankai University Tianjin China The Fu Foundation School of Engineering and Applied Science Columbia University New York NY USA Tianjin Port Information Technology co. ltd Tianjin China

The key problem of the K-means clustering algorithm is to ascertain the appropriate number of clusters, that is K value, usually by ceaseless experiments to obtain it, the voting mechanism method was introduced in this paper to automatically determine the optimal number of clusters in a dataset. The voting mechanism method was introduced in this paper to automatically determine the optimal number of clusters in a dataset. In this method, the symbol K is used to represent the number of clusters, and a series candidate sets of K were obtained by using different validity indexes. Then, the frequency of each K was calculated, and we chose the highest frequency as the final number of clusters, that is the value of K. Using the method proposed in this paper to divide the real dataset of the ship tonnage, the experimental result is basically consistent with the rules of the actual operation, so the experimental verify the reliability and effectiveness of the approach presented in this paper.

关键词： Informatics

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Nodes-weighted-graph approach for rsfMRI data classification: Application to schizophrenia 35

Nodes-weighted-graph approach for rsfMRI data classification...

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35th Chinese control Conference, CCC 2016

作者： Mei, Xue Li, Weiwei Chellali, Ryad Zhou, Yu Huang, Jiashuang Ma, Shilin College of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9789881563910

We propose a new method to analyze rsfMRI data to identify people with Schizophrenia. The method can be extended to identify other mental diseases. Although many graph-based methods have been applied to achieve similar goals, most of them only focus on using graph with a single feature. Our method not merely uses existing network properties, but also takes advantage of the content of nodes to improve the classification process. Next, we apply selected features a machine learning technique, namely multi-kernel learning approach to perform the classification. We tested our method on rsfMRI dataset about Schizophrenia and Health control. The results showed that our method does improve classification performances (classification accuracy of 93.1%. 7.39% outperform existing approach), and helps discovering disease-related brain regions. © 2016 TCCT.

关键词： Nodes-weighted-graph rsfMRI schizophrenia

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Short-term forecast of stock price of multi-branch LSTM based on K-means

Short-term forecast of stock price of multi-branch LSTM base...

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International Conference on Systems and Informatics (ICSAI)

作者： XiuLi Shao Doudou Ma Yiwei Liu Quan Yin College of Computer and Control Engineering Nankai University Tianjin China The Fu Foundation School of Engineering and Applied Science Columbia University New York NY USA Tianjin Port Information Technology co. ltd Tianjin China

In order to improve the short-term forecast accuracy of the stock, this paper first uses the time window to divide the stock price sequence data in a period of time into a number of equal sub-sequences. The K-means algorithm is used to cluster the stock price sub-sequences. Then according to the number of clusters, The same number of long short-term memory(LSTM) neural network models are constructed with the clustering results used to train the corresponding LSTM model and a multi-branch LSTM stock price short-term forecasting model is constructed. When the stock price of one day is predicted, the distances between the stock price sequence of the neighboring days and the centers of the K-means cluster are calculated, and then the stock price sequence of the neighboring days is inputted into the LSTM forecasting model corresponding to the shortest distance clustering center to predict the stock price. The experimental results show that the proposed model has higher prediction accuracy than the BP neural network prediction model and the single LSTM neural network prediction model, which is based on the actual closing price of one stock.

关键词： Conferences Informatics Neural networks Forecasting Predictive models

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Nuclear norm regularized coding with local position-patch and nonlocal similarity for face hallucination

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Digital Signal Processing 2017年 64卷 107-120页

作者： Guangwei Gao Jian Yang Zhihui Lai Pu Huang Jielin Jiang Hao Gao Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 PR China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 PR China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 PR China

Learning-based face hallucination methods have received much attention and progress in past few decades. Specially, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based ones. As opposed to the existing patch based methods, where the input image patch matrix is converted into vectors before combination coefficients calculation, in this paper, we propose to directly use the image matrix based regression model for combination coefficients computation to preserve the essential structural information of the input patch matrix. For each input low-resolution (LR) patch matrix, its combination coefficients over the training image patch matrices at the same position can be computed. Then the corresponding high-resolution (HR) patch matrix can be obtained with the LR training patches replaced by the corresponding HR ones. The nonlocal self-similarities are finally utilized to further improve the hallucination performance. Various experimental results on standard face databases indicate that our proposed method outperforms some state-of-the-art algorithms in terms of both visual quantity and objective metrics.

关键词： Face hallucination Position-patch Matrix based regression Nuclear norm Nonlocal similarity

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A structure based approach for accurate prediction of protein interactions networks 7

A structure based approach for accurate prediction of protei...

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7th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2016 - Part of 9th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2016

作者： Rehman, Hafeez Ur Zafar, Usman Benso, Alfredo Islam, Naveed Department of Computer Science National University of Computer and Emerging Sciences Hayatabad Peshawar Pakistan Department of Control and Computer Engineering Politecnico di Torino TorinoI-10129 Italy Department of Computer Science Islamia College University Peshawar Pakistan

ISBN: (纸本)9789897581700

In the recent days, extraordinary revolution in genome sequencing technologies have produced an overwhelming amount of genes that code for proteins, resulting in deluge of proteomics data. Since proteins are involved in almost every biological activity, therefore due to this rapid uncovering of biological "facts", the field of System Biology now stands on the doorstep of considerable theoretical and practical advancements. Precise understanding of proteins, specially their functional associations or interactions are inevitable to explicate how complex biological processes occur at molecular level, as well as to understand how these processes are controlled and modified in different disease states. In this paper, we present a novel protein structure based method to precisely predict the interactions of two putative protein pairs. We also utilize the interspecies relationship of proteins i.e., the sequence homology, which is crucial in cases of limited information from other sources of biological data. We further enhance our model to account for protein binding sites by linking individual residues in structural templates which bind to other residues. Finally, we evaluate our model by combining different sources of information using Naive Bayes classification. The proposed model provides substantial improvements in terms of accuracy, precision, recall when compared with previous approaches. We report an accuracy of 90% when tested for a protein interaction network of yeast proteome. Copyright © 2016 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved.

关键词： Proteins

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A Novel Technique for Quantifying Clusters Based on Small Squares in a Spatial Prisoner’s Dilemma

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Procedia computer science 2017年 112卷 2497-2505页

作者： Ryota Matsumoto Masahiro Tokumitsu Hironao Koshimizu Yoshiteru Ishida Department of Electrical and Control Engineering National Institute of Technology Yonago College 4448 Hikonacho Yonago Tottori 683-0854 Japan Department of Liberal Arts National Institute of Technology Yonago College 4448 Hikonacho Yonago Tottori 683-0854 Japan Department of Computer Science and Engineering Toyohashi University of Technology 1-1 Hibarigaoka Toyohashi Aichi 441-8580 Japan

A number of studies have revealed and investigated how the clusters organized by cooperators emerge in a spatial prisoner’s dilemma. However, a qualitative analysis of the clusters formed by the cooperators has not yet been studied. This paper aims to propose a new technique for quantifying clusters that consist of players with cooperative strategies. The proposed method can be applied to the spatial prisoner’s dilemma in a square lattice network. In the proposed method, a minimum cluster is defined as a group of the cooperators based on a small square (2 by 2). This paper also defines a degree of the clusters by extending the small squares that based on how the players organize themselves into coordinated groups. The authors analyze and compare with typical examples of the clusters in the spatial prisoner’s dilemma. By analyzing the examples, this paper demonstrates that the proposed method quantifies the degree of the clusters organized by the cooperators. The authors also examine the significance of the proposed method by the simulations of the spatial prisoner’s dilemma.

关键词： Spatial prisoner’s dilemma prisoner’s dilemma game theory clusters of cooperators cluster index of cooperators cooperative strategies

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A game theory-based trust measurement model for social networks

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Computational Social Networks 2016年第1期3卷 1-16页

作者： Wang, Yingjie Cai, Zhipeng Yin, Guisheng Gao, Yang Tong, Xiangrong Han, Qilong School of Computer and Control Engineering Yantai University Qingquan Road Yantai 244005 China College of Computer Science and Technology Harbin Engineering University Harbin 150001 China Department of Computer Science Georgia State University Atlanta 30303 United States School of Mathematics and Information Science Yantai University Qingquan Road Yantai 244005 China

Background: In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks. Methods: We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively. Results and conclusions: We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism. © 2016, The Author(s).

关键词： Feedback effectiveness Game theory Punishment mechanism Recommendation credibility Service reliability

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Data-based Networked Tracking control System with Time Delay in the Forward Channel 35

Data-based Networked Tracking Control System with Time Delay...

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第35届中国控制会议

作者： TONG Shiwen QIAN Dianwei WANG Song JIANG Shan TIAN Kuo FANG Jianjun DENG Xianrui College of Automation Beijing Union University School of Computer and Control Engineering North China Electric Power University Beijing Key Laboratory of Information Service Engineering China United Engineering Corporation Beijing BBEF Science & Technology Co. Ltd. Department of Computer Science Tangshan Normal University

ISBN: (纸本)9781509009107

Networked Tracking control(NTC) is a challenging problem,especially for the unknown model system that only input-output data are expected to be *** paper has proposed a Data-based Networked Tracking control(DNTC)*** method uses fuzzy cluster modeling technology to establish the predictive model of the process according to the input-output *** the differences between the desired outputs and the predicted outputs of the process are treated as inputs of a Fuzzy Sliding Mode controller(FSMC) to obtain a string of future control *** control actions are packed and transferred from the controller side to the plant ***,the time delays in the forward communication channel are compensated by selecting appropriate control action in the plant *** outputs show rapid response and good accuracy of the proposed method.

关键词： Networked tracking control fuzzy cluster modeling fuzzy sliding mode control time delay

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