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Communications in computer and Information science 2018年 872卷 367-378页

作者： Tahyudin, Imam Berlilana Nambo, Hidetaka Artificial Intelligence Laboratory Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kakumamachi KanazawaIshikawa Perfecture Japan Department of Information System STMIK AMIKOM Purwokerto Jl. LetjendPol Soemarto PurwokertoCentral Java Indonesia

Bioelectric potential of plant produces a low electrical signal because of the plant activities like photosynthesis and respiration. Furthermore, the electrical signal will change because of the environmental factors such as temperature, humidity and human behavior. Some authors successfully used the bioelectric potential of plant for detecting the various human activities, like walking, jumping, open the door, and etc. They used decision tree (DT) J48, multi layer perceptron (MLP) and convolution neural network (CNN) as the analysis method. However, the previous accuracy was no satisfied and estimating the human position globally not specifically an exact location. This research has aim to construct a time series model for bioelectric potential dataset which is SARIMA model and to build infrastructure design of human position estimation in an exact location. For constructing the SARIMA model we use one observation location and obtained the best model is SARIMA (1,0,0) with accuracy of 80%. In addition, this research successfully designed the infrastructure of human position estimation using three locations. © Springer Nature Switzerland AG 2018.

关键词： Location

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Initial-Parameter-Criterion based Gaussian Mixture Model Monitoring Method for Non-Gaussian Process

Initial-Parameter-Criterion based Gaussian Mixture Model Mon...

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第37届中国控制会议

作者： Ying Tian Wenli Du Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Gaussian mixture model(GMM) monitoring method is commonly used for non-Gaussian process monitoring. In GMM monitoring method, the final number of Gaussian components and the statistical distribution parameters are estimated through the improved Figueiredo-Jain(F-J) algorithm in the modeling phase. However, the data dimensionality of modern industry is high, the number of initial Gaussian components is large for the accuracy of the algorithm. Under this condition, the relative small number of samplings makes the denominator in the weight estimation formula of the improved F-J algorithm become zero and the algorithm suspends. To make the algorithm work well in an acceptable modeling time, we deduce the Initial-ParameterCriterion among initial Gaussian components, data dimension, sampling number and propose the Initial-Parameter-Criterion based GMM(IPC-GMM) monitoring method. The proposed monitoring method is used for TE process monitoring, simulation results reveal the effectiveness and necessity of IPC-GMM monitoring method.

关键词： non-Gaussian process monitoring Gaussian mixture model Initial-Parameter-Criterion

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Probability Analysis of Ship Collision and Grounding in Inland Waterway Based on Big Data Analysis

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Journal of Physics: Conference Series 2020年第5期1486卷

作者： Yuheng Tang Yuankang Mao Mengjie Wu Te Shi Cunlong Fan School of Energy and Power Engineering Wuhan University of Technology Wuhan Hubei 430063 China School of Computer and Science and Technology Wuhan University of Technology Wuhan Hubei 430070 China Intelligent Transportation System Research Center Wuhan University of Technology Wuhan Hubei 430063 China National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan Hubei 430063 China

With the rapid development of technology, mining valuable information from massive data has become an important part of the community. However, there are many deficiencies in the probability analysis of collision and grounding of ships in inland waterways, such as the insufficient use of data. The main purpose of this paper is to analyze the probability of collision and grounding of ships in inland waterways. Initially, big data analysis technology is used to preprocess the massive shipping data, and then the probability of the processed data is calculated by establishing the probability calculation model of collision and grounding. The results show that in the channel intersection area with poor shipping conditions, the increase of ship traffic volume will lead to the increase of ship collision probability; in the case of low visibility and long driving time, the probability of ship grounding will increase.

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Efficient Pose Machine Based on Parameter-Sensitive Hashing 5

Efficient Pose Machine Based on Parameter-Sensitive Hashing

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2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Shangchi Lin Bowen Liu Yang Wen Anum Masood Bin Sheng Ping Li Xin Liu Haoyang Yu Weiyao Lin Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Mathematics and Information Technology The Education University of Hong Kong Faculty of Information Technology Macau University of Science and Technology Shenzhen Tencent Computer System Co.Ltd. Department of Electronic Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781538619797;9781538619780

In this paper, we propose an efficient pose machine using Parameter-Sensitive Hashing(PSH) techniques. Based on the original pose machine, which is a sequential prediction framework, we employ the Convolutional Neural Network(CNN)to extract features. To handle the high dimensional feature vectors and conduct similarity search efficiently, we use the Parameter-Sensitive Hashing Function(PSHF) to map the feature vectors into binary values. The property of the PSHF ensures that the collisions happen when two vectors are near to each other and the search can be completed in a fractional power time. We apply our approach to the popular datasets including LSP and FLIC and make a comparison with previous methods based on a criterion of strict Percentage of Correct Parts(PCP).Experimental results reflect that our approach outperforms previous methods in accuracy.

关键词： Human pose estimation convolutional neural network parameter-sensitive hashing example-based method parameter estimation

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Information propagation in interaction networks 20

Information propagation in interaction networks

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20th International Conference on Extending Database Technology, EDBT 2017

作者： Kumar, Rohit Calders, Toon Department of Computer and Decision Engineering Universit Libre de Bruxelles Belgium Department of Service and Information System Engineering Universitat Polit cnica de Catalunya Spain Department of Mathematics and Computer Science Universiteit Antwerpen Belgium

ISBN: (纸本)9783893180738

We study the potential flow of information in interaction networks, that is, networks in which the interactions between the nodes are being recorded. The central notion in our study is that of an information channel. An information channel is a sequence of interactions between nodes forming a path in the network which respects the time order. As such, an information channel represents a potential way information could have flown in the interaction network. We propose algorithms to estimate information channels of limited time span from every node to other nodes in the network. We present one exact and one more efficient approximate algorithm. Both algorithms are one-pass algorithms. The approximation algorithm is based on an adaptation of the HyperLogLog sketch, which allows easily combining the sketches of individual nodes in order to get estimates of how many unique nodes can be reached from groups of nodes as well. We show how the results of our algorithm can be used to build efficient influence oracles for solving the Influence maximization problem which deals with finding top k seed nodes such that the information spread from these nodes is maximized. Experiments show that the use of information channels is an interesting data-driven and model-independent way to find top k influential nodes in interaction networks. © 2017, Copyright is with the authors.

关键词： Influence estimation Influence maximization Information flow mining

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Adventures of two student research computing facilitators 18

Adventures of two student research computing facilitators

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2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018

作者： Howard, Noah Nellutla, Saritha Colella, Nicholas McCoy, Evan Legaard, Kasey Whitsel, Larry Wilson, Chris Segee, Bruce School of Computing and Information Science University of Maine United States Bridgewater State University Department of Chemical Sciences United States Harvard University Department of Chemistry and Chemical Biology United States Department of Electrical and Computer Engineering University of Maine United States Research Scientist University of Maine United States Advanced Computing Group University of Maine System United States

ISBN: (纸本)9781450364461

The NSF-sponsored Northeast Cyberteam program (https://***) is matching student research computing facilitators with research projects at small and medium sized institutions that need help making use of high performance computing resources. Students are selected based on relevant domain knowledge and level of interest in exploring the Research Computing Facilitator role as a career path. Each student is paired with an experienced mentor, and each project lasts 3-5 months. The poster presents results from two of the ~10 Northeast Cyberteam projects that are either completed or in progress at the time of the conference. Students will be prepared to discuss the research projects that they supported, how their efforts advanced each project, reflections on what they learned about the Research Computing Facilitator role, and recommendations on how other sites might make best use of students as Research Computing Facilitators. In the first project, conducted at the University of Maine, improvements to application performance and parallelization have enabled production of forest attribute data at significantly higher volume, changing the philosophy of forest mapping from creating a single map to creating and assessing thousands of maps, making it possible to assess and (to some degree) trade off errors between maps. The second project expanded efforts to introduce computational chemistry into the undergraduate chemistry curriculum at Bridgewater State University. Having access to a high performance computer cluster allows for studying "real world" systems and also provides students with an opportunity to experience how research computing is done in academia and/or chemical/pharmaceutical industries. © 2018 Copyright held by the owner/author(s).

关键词： Students

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Research on Decision Optimization Method of Avoiding Collision of Inland Ships Based on Intelligent Calculation

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IOP Conference Series: Materials science and engineering 2020年第7期780卷

作者： Yuheng Tang Ruijia Zhang Yuankang Mao Te Shi Cunlong Fan School of Energy and Power Engineering Wuhan University of Technology Wuhan Hubei 430063 China School of Computer and Science and Technology Wuhan University of Technology Wuhan Hubei 430070 China Intelligent Transportation System Research Center Wuhan University of Technology Wuhan Hubei 430063 China National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan Hubei 430063 China

Shipping is one of the main modes of transportation, and the safety of ship operation is very important. The navigation conditions of the inland river are complex. The width of the river, the density of the ships and the height of the bridge all affect the navigation of the ships to a certain extent. Based on this, this paper proposes a decision-making optimization method for collision avoidance of inland ships based on intelligent calculation. By analyzing the process of ship collision, the genetic algorithm in intelligent calculation is used to avoid collision. The simulation results show that the best collision avoidance effect is achieved in the collision avoidance of the two ships and the collision avoidance of the ship and the bridge, which reduces the risk to the lowest level.

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URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

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IEEE International Geoscience and Remote Sensing Symposium

作者： Fan Mou Rui Kong Kai Li Zezhong Zheng Jun Xia Yong He Mingcang Zhu Guoqing Zhou Hongsheng Zhang Zhigang Liu Ankai Hou Ling Jiang Shengli Wang Jiang Li School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

In recent years, the big data industry chain has become more mature. Analyzing and managing cities by utilizing various big data in cities has become a hot research topic. Urban functional regions discovering is one of the important applications. The mainstream in urban functional regions discovering are probabilistic topic models, such as latent Dirichlet allocation (LDA) based topic model, which seeing the regions as documents and their functions are their topics. These methods require feature engineering by hand, which will construct features of limited expressiveness. To overcome these methods' shortcomings, we introduced a deep learning topic model called document neural autoregressive distribution estimation (DocNADE) into urban functional regions mining. And we did an experiment to test its effect. The experimental result shows that this DocNADE framework has achieved a considerable result in urban function inference compared with Dirichlet Multinomial Regression (DMR) based topic model which is a state of the art of urban functional regions discovering.

关键词： Biological system modeling Deep learning Probabilistic logic Roads Urban areas Laboratories Data mining

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Generative adversarial network based on chaotic time series

arXiv

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arXiv 2019年

作者： Naruse, Makoto Matsubara, Takashi Chauvet, Nicolas Kanno, Kazutaka Yang, Tianyu Uchida, Atsushi Department of Information Physics and Computing Graduate School of Information Science and Technology University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Mathematical Engineering and Information Physics Faculty of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Computational Science Graduate School of System Informatics Kobe University 1- 1 Rokkodai Nada Kobe Hyogo657-8501 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama Saitama338-8570 Japan

Generative adversarial network (GAN) is gaining increased importance in artificially constructing natural images and related functionalities wherein two networks called generator and discriminator are evolving through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution, highly realistic scenes, human faces, among others have been generated. While GAN in general needs a large amount of genuine training data sets, it is noteworthy that vast amounts of pseudorandom numbers are required. Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data. We show that the similarity in proximity, which is a degree of robustness of the generated images with respects to a minute change in the input latent variables, is enhanced while the versatility as a whole is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also discuss the impact of utilizing chaotic time series in retrieving images from the trained generator. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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First field-trial of a high capacity micro-comb based optical communications system

First field-trial of a high capacity micro-comb based optica...

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45th European Conference on Optical Communication (ECOC 2019)

作者： Bill Corcoran Mengxi Tan Xingyuan Xu Jiayang Wu Thach G. Nguyen Sai T. Chu Brent E. Little Roberto Morandotti Andreas Boes Arnan Mitchell David J. Moss Department of Electrical and Computer System Engineering Monash University Clayton Australia Centre for Micro-Photonics Swinburne University of Technology Hawthorn Australia School of Engineering RMIT University Melbourne Australia Department of Physics and Material Science City University of Hong Kong Hong Kong China Xi'an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi'an China INRS-Énergie Matériaux et Télécommunications Varennes Canada ITMO University St. Petersburg Russia Inst. of Fundamental and Frontier Sciences U. of Electronic Science and Tech. of China Chengdu China

ISBN: (纸本)9781839531859

We present the first demonstration of micro-comb based communications system in installed optical fibre. Robust soliton-crystal states enable open-loop control, and achieve 32.5 Tb/s (8.2 b/s/Hz spectral efficiency) over 72.8 km, providing a path to compact, high capacity transceivers for legacy fibre, metro-area systems.

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