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检索条件"任意字段=21st International Conference on Distributed Computing and Intelligent Technology"

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Hierachical Model of Behavior On-line Testing for distributed Information Systems

Hierachical Model of Behavior On-line Testing for Distribute...

引用

IEEE international conference on intelligent Data Acquisition and Advanced computing Systems

作者： Oleksandr Martynyuk Oleksandr Drozd Ahmesh Tamim Bui Van Thuong Anatoliy Sachenko Halyna Mykhailova Mykhaylo Dombrovskyi Odessa National Polytechnic University Ternopil National Economic University Raiffeisen Bank Aval

ISBN: (纸本)9781728140704

The paper presents a three-level decomposition model of behavioral on-line testing for distributed information systems of the application level. The model is based on the representation of distributed information systems by a three-tier composition of Petri nets, the identification of reference positions/transitions, recognition of behavioral reference fragments, hierarchical inheritance of the recognized behavior. In Petri nets space-time check models are distinguished, which allow the decomposition of the behavior of a distributed system. The following tasks are solved - definition of distributed information systems analytical models - hierarchical extended Petri nets with structural spatial and temporal decomposition of processes and construction of multi-level analytical models of behavioral online testing of distributed information systems - a multi-level fixed extended behavior of the hierarchical extended Petri nets with additional recognition and encapsulation operations, relations of preordering and inheritance, that are defined on it. Behavioral online testing assumes the preceding definition of recognition of reference positions/transitions, reference fragments and their structures and is applicable for model of project verification and verification of implementations for real distributed information systems.

关键词： distributed information system Behavioral online testing Petrinet Identifier Behavior fragment distributed information systems on line detection Computer based information systems Petri nets role model application layer

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Preface

Advances in Intelligent Systems and Computing

引用

Advances in intelligent Systems and computing 2020年 1032卷 v页

作者： Siergiejczyk, Miroslaw Krzykowska, Karolina Faculty of Transport Warsaw University of Technology Warsaw Poland

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Prediction of Machine Learning Base for Efficient Use of Energy Infrastructure in Smart City

Prediction of Machine Learning Base for Efficient Use of Ene...

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international conference on computing, Electronics & Communications Engineering (iCCECE)

作者： Guwon Yoon Sanguk Park Sangmin Park Tacklim Lee Seunghwan Kim Hyeonwoo Jang Sanghyeon Lee Sehyun Park Electrical and Electronics Engineering Chung-Ang University Seoul Republic of Korea

ISBN: (纸本)9781728121390

The 21st century, which we live in today, is rapidly changing based on the fourth industrial revolution and information and communication technology. According to change, existing urban models are emerging as smart cities. Smart City is a sustainable city that improves urban functions of various elements by applying information and communication convergence technology and environment friendly technology to urban space and solves urban problems that are occurring now. Energy, a core element of the city, enables efficient energy management of energy systems through an Internet of Things-based platform. The Internet of Things is a technology that sends and receives data by attaching sensors to various objects through a network. The goal is to use resources efficiently as the Internet of Things is spread and various industries are applied. This paper is a prediction study based on machine learning algorithms for efficient use of energy infrastructure in the Smart City. The proposed system builds the optimal Smart City by estimating energy efficiency of the Smart City and improving resource utilization.

关键词： Smart cities Internet of Things Machine learning Mathematical model intelligent sensors

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Anomaly Detection of Roads from Driving Data Using a statistical Discrepancy Measure 21

Anomaly Detection of Roads from Driving Data Using a Statist...

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21st IEEE international conference on intelligent Transportation Systems, ITSC 2018

作者： Ikeda, Kazushi Kubo, Takatomi Sasaki, Hiroaki Mori, Masataka Hitomi, Kentaro Takenaka, Kazuhito Muto, Kenji Division of Information Science Nara Institute of Science and Technology Takayama Ikoma Nara Japan DENSO CORPORATION Kariya Aichi Japan

ISBN: (纸本)9781728103235

Anomalies in roads such as obstacles and accidents often cause fatal accidents and should be detected as soon as possible. We formulated the problem of detecting obstacles from driving behaviors as a change-point detection, not an anomaly detection problem, because driving behaviors are too distributed to make a 'standard driving' model. To model the distribution of driving states at each location at each time, we employed a nonparametric method due to a wide variety of the distribution and applied a change-point detection method called the maximum mean discrepancy to our synthetic data made using a traffic simulator. As a result, we successfully detected the existence of obstacles with high accuracy. © 2018 IEEE.

关键词： Anomaly detection

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Event-driven anomalies in spatiotemporal taxi passseger demand 21

Event-driven anomalies in spatiotemporal taxi passseger dema...

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21st IEEE international conference on intelligent Transportation Systems, ITSC 2018

作者： Wittmann, Michael Kollek, Michael Lienkamp, Markus Automotive Technology Faculty of Mechanical Engineering Technical University of Munich Boltzmannstraße 15 Garching85748 Germany

ISBN: (纸本)9781728103235

We propose a grid-based approach of mining historical taxi GPS data to study anomalous pickup patterns in spatiotemporal passenger demand caused by public events. The developed method is applied by way of example to the city of Munich on a large-scale taxi dataset. Information about occurring public events is collected from different online and offline sources. For further analysis, the data is aggregated on an equally distributed grid level based on geohashes. In a first step, an extension of robust principle component analysis (RPCA) is developed and applied to the dataset. It is shown that extended RPCA can separate anomalous pickup patterns from base demand in the given time series. Secondly, the combination of anomaly detection and knowledge about occurring public events allows the exploration of correlations in demand fluctuations. The results show that the proposed method can be used to discover the potential impact of public events on spatiotemporal passenger demand in large-scale mobility datasets. © 2018 IEEE.

关键词： Anomaly detection

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distributed Text Feature Selection Based On Bat Algorithm Optimization

Distributed Text Feature Selection Based On Bat Algorithm Op...

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IEEE international conference on intelligent Data Acquisition and Advanced computing Systems

作者： Hongwei Chen Qiao Hou Lin Han Zhou Hu Zhiwei Ye Jun Zeng Jiansen Yuan School of Computer Science Hubei University of Technology Wuhan China Wuhan Fiberhome Technical Services Co. Ltd. Wuhan

The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature selection method to pre-select the original features, and then uses the bat group algorithm to optimize the preselected features in binary code form, and uses the classification accuracy as the individual fitness. However, when the amount of text information is large, the execution time of the single machine is long. According to this shortcoming, combining the Bat Algorithm and the Spark parallel computing framework, the text feature selection algorithm SBATFS is proposed. The algorithm combines the good search performance of the bat algorithm with the distributed and efficient calculation speed to realize the efficient solution of the text feature selection optimization model. The results show that compared with the traditional feature selection method, after SBATFS is used for feature optimization, the classification accuracy is effectively improved.

关键词： Feature extraction Optimization Classification algorithms Text categorization Sociology statistics Sparks

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Semantic Interoperability in Cyber-Physical Systems

Semantic Interoperability in Cyber-Physical Systems

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IEEE international conference on intelligent Data Acquisition and Advanced computing Systems

作者： Ingo Kunold Hendrik Wohrle Markus Kuller Nursi Karaoglan Fabian Kohlmorgen Jorg Bauer Institute of Communications Technology Dortmund University of Applied Sciences and Arts Dortmund Germany

The increasing diversity of cyber-physical systems (CPS) especially in Smart Homes and Smart Buildings and their communication interfaces leads to a low interoperability and high development complexity. Hence, there is an increasing need for methods to enhance the interoperability by using alternative, software-based techniques. In this contribution, a distributed platform based on established web standards is proposed, which provides semantically enriched building condition data and functions. This includes new and existing automation solutions and - if required also across buildings - external services and other higher-level technical systems. Here, we focus on the implementation and system architecture for smart homes/buildings under use of semantic data principles like linked data and ontologies e.g. SAREF.

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Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction 1

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1st international Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2019

作者： Dzyubachyk, Oleh Koolstra, Kirsten Pezzotti, Nicola Lelieveldt, Boudewijn P. F. Webb, Andrew Börnert, Peter Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands C.J. Gorter Center for High Field MRI Department of Radiology Leiden University Medical Center Leiden Netherlands Computer Graphics and Visualization Group Delft University of Technology Delft Netherlands Philips Research Eindhoven Eindhoven Netherlands Intelligent Systems Department Delft University of Technology Delft Netherlands Philips Research Hamburg Hamburg Germany

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-distributed stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences. © 2019, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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1st international conference on Big Data Analysis and Deep Learning, ICBDL 2018

1st International Conference on Big Data Analysis and Deep L...

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1st international conference on Big Data Analysis and Deep Learning, ICBDL 2018

ISBN: (纸本)9789811308680

The proceedings contain 42 papers. The special focus in this conference is on Big Data Analysis and Deep Learning. The topics include: Multi-label land cover indices classification of satellite images using deep learning;real-time hand pose recognition using faster region-based convolutional neural network;school mapping for schools of basic education in Myanmar;GBSO-RSS: GPU-based BSO for rules space summarization;Machine learning based live VM migration for efficient cloud data center;dynamic replication management scheme for distributed file system;Frequent pattern mining for dynamic database by using hadoop GM-Tree and GTree;investigation of the use of learning management system (moodle) in University of Computer studies, Mandalay;user preference information retrieval by using multiplicative adaptive refinement search algorithm;predictive big data analytics using multiple linear regression model;proposed framework for stochastic parsing of Myanmar language;FDTD based numerical calculation of electromagnetic wave radiation in multi-layer circular cylindrical human head;improved convergence in eddy-current analysis by singular value decomposition of subdomain problem;development and validation of parallel acoustic analysis method for the sound field design of a large space;secret audio messages hiding in images;Location based personal task reminder system using GPS technology;front caster capable of reducing horizontal forces on step climbing;mobile location based indexing for range searching;building travel speed estimation model for Yangon City from public transport trajectory data;comparison between block-encoding and quadtree compression methods for raster maps;evaluation for teacher’s ability and forecasting student’s career based on big data;behavior analysis for nursing home monitoring system.

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A Spark-based distributed Whale Optimization Algorithm for Feature Selection

A Spark-based Distributed Whale Optimization Algorithm for F...

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IEEE international conference on intelligent Data Acquisition and Advanced computing Systems

作者： Hongwei Chen Zhou Hu Lin Han Qiao Hou Zhiwei Ye Jiansen Yuan Jun Zeng School of Computer Science Hubei University of Technology Wuhan Fiberhome Technical Services Co. Ltd.

ISBN: (纸本)9781728140704

With the rapid development of the Internet and big data technologies, high-dimensional data generated in various fields has increased dramatically. Feature selection is an effective way to solve data processing problems caused by high dimensionality and high computational complexity. The traditional feature selection method shows the problem of insufficient classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. The traditional feature selection method shows low classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. This paper proposed a feature selection method based on Whale Optimization Algorithm to learn mining feature selection rules, then improve the accuracy of feature selection. However, when the data size is very large, the efficiency of single node execution is low. Therefore, this paper combined the Whale Optimization Algorithm with the parallel computing model of the Spark platform, and proposed a feature selection method based on the Spark platform for distributed Whale Optimization Algorithm. The results showed that the excellent result search ability of the Whale Optimization Algorithm combined with the distributed and efficient calculation speed can realize the efficient solution of the feature selection optimization model.

关键词： Feature selection Whale optimization algorithm Spark platform Parallel computing Feature extraction selection methods PARALLEL PROCESSING (COMPUTERS) Optimization algorithms Whale data size Distribute

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