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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

共 3243 条记录，以下是171-180 订阅

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Distributed computing for smart Meter data Management for Electrical Utility Applications 30

Distributed Computing for Smart Meter Data Management for El...

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30th international conference on Cybernetics and Informatics (K and I)

作者： Zainab, Ameema Refaat, Shady S. Abu-Rub, Haitham Bouhali, Othmane Texas A&M Univ Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Elect & Comp Engn Doha Qatar

ISBN: (纸本)9781728143811

With the advent of Internet-of-Things (IoT) devices, including smart meters and sensors in the smart grid, there has been immense research interest in big data management, analytics, and parallel processing of data. However, complex hardware and software parameters configurations and in-depth understanding of the data processing design are essential for efficient utilization of big data analytics platforms. In this work, we analyze the parallelization of load prediction by utilizing spark regression python library to assess the performance with workloads of up to 8 nodes. The results of different configurations have been studied and analyzed against the performance of Apache Spark. It was found that a trade-off between the number of nodes and cores is necessary to perform efficient parallel computing. Multiple sets of combinations of nodes and cores are considered in this paper to evaluate the performance. The work also signifies the importance of high-performance computing capability for smart meters big data management. The obtained results indicate that the computational time is not only dependent on the data size but also on the number of compute nodes and the number of cores assigned to run the job.

关键词： smart grid load forecast big data parallel computing Apache-Spark high performance computing

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CPAS: A Copyright-Protected Autonomous Sharing Scheme by Blockchain and smart Contract for BIM data 7

CPAS: A Copyright-Protected Autonomous Sharing Scheme by Blo...

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7th international conference on Cloud computing and Big data analytics, ICCCBDA 2022

作者： Guo, Peng Xiong, Ruoting Yu, Gang Gong, Peisong Xiong, Feng Ren, Wei Wuhan Municipal Road and Bridge Co Ltd Wuhan430050 China School of Computer Science Huazhong University of Science and Technology Wuhan430074 China School of Economics and Management China University of Geosciences Wuhan430074 China School of Business Administration Zhongnan University of Economics and Law Wuhan430073 China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430074 China

ISBN: (纸本)9781665487115

Building Information Model (BIM) has been envisioned as one of the most promising technology for smart construction industry, e.g., industry 4.0 or industry digitalization. BIM data is of the most importance in the smart construction since the data can cover the whole life cycle of the building, which is somewhat also called digital twins. BIM data usually stored in cloud servers and finally BIM big data is formed. The BIM data is always required to be shared among a large number of contributors, and re-use of those data will greatly decrease the development delay. As BIM data usually encloses the intellectual property of contributors, the sharing of BIM data may be unwilling and thus difficult, especially in open data sharing over specific platforms or markets. To promote the re-use and replication of BIM data yet protect the intellectual property, we propose a blockchain and smart contract based scheme in this paper. The blockchain technology can encourage the sharing by awarding of tokens and trace the sharing behaviors. smart contract can implement autonomous sharing with copyright registration, authentication, and verification. The experiment results justified that our proposed scheme is practical. © 2022 IEEE.

关键词： Cloud computing smart contracts Buildings Process control Intellectual property Big data Blockchains

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Graphical Perception for Immersive analytics 27

Graphical Perception for Immersive Analytics

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27th IEEE conference on Virtual Reality and 3D User Interfaces (IEEE VR)

作者： Whitlock, Matt smart, Stephen Szafir, Danielle Albers Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9781728156088

Immersive analytics (IA) uses immersive virtual and augmented reality displays for data visualization and visual analytics. Designers rely on studies of how accurately people interpret data in different visualizations to make effective visualization choices. However, these studies focus on data analysis in traditional desktop environments. We lack empirical grounding for how to best visualize data in immersive environments. This study explores how people interpret data visualizations across different display types by measuring how quickly and accurately people conduct three analysis tasks over five visual channels: color, size, height, orientation, and depth. We identify key quantitative differences in performance and user behavior, indicating that stereo viewing resolves some of the challenges of visualizations in 3D space. We also find that while AR displays encourage increased navigation, they decrease performance with color-based visualizations. Our results provide guidelines on how to tailor visualizations to different displays in order to better leverage the affordances of IA modalities.

关键词： Human-centered computing Visualization Empirical studies in visualization Human-centered computing Human computer interaction (HCI) Interaction paradigms Mixed / augmented reality Human-centered computing Human computer interaction (HCI) Interaction paradigms Virtual reality

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data Analytic Tools An Overview

Data Analytic Tools An Overview

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2020 international conference on smart Innovations in Design, Environment, Management, Planning and computing, ICSIDEMPC 2020

作者： Janardhanan, Jitha Umamaheswari, S. Dr.G.R.Damodaran College of Science Department of Computer Science Coimbatore India

ISBN: (纸本)9781728159706

The process of extracting interesting patterns from vast database hiding is known as data mining the choice of best one among abundant data mining tools accessible in the sooqis not easy. A numeral factors demand to be contemplated before formulating a financing in any commercial solution. This article stretches the complete and hypothetical analysis of a few open source data mining tools. The data mining tools and their utilities comparison are conferred here. The paper also discusses about data analytics and its importance © 2020 IEEE.

关键词： data analytics

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intelligent Deep Reinforcement Learning based Resource Allocation in Fog network 26

Intelligent Deep Reinforcement Learning based Resource Alloc...

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26th international conference on High Performance computing, data and analytics (HiPCW)

作者： Divya, V Sri, Leena R. Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India

ISBN: (纸本)9781728148946

The increase in the smart devices have brought about the intelligence to the applications at the edge. The current day devices are rich in computations and communications which are in most cases are left under-utilized. These devices can be used to provide edge intelligence when deployed for computations on the streaming data from the IoT devices. To reduce the latency of sending the data to the cloud in case of real-time applications, a new paradigm of edge computing was introduced which meets the emerging challenge of handling latency aware applications on the fly. The other research issue to be handled with utmost importance is the load balancing which can be realized effectively by proper proactive resource allocation. Our proposed work involves the construction of SDN based Fog infrastructure where in the research issue was tested and evaluated. The proposed Deep Reinforcement Learning algorithm helps in intelligent action selection based on the past experience data and the dynamic network parameters. Finally, the proposed work was compared with the state of the art OSPF algorithm in terms of service time and the load variance on increasing task allocation.

关键词： intelligent Fog Infrastructure Software Defined Networking Deep Reinforcement Learning Resource allocation

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Optimising Production through intelligent Manufacturing

Optimising Production through Intelligent Manufacturing

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2019 international conference on Power, Energy and Electrical Engineering, PEEE 2019

作者： Olalere, Isaac O. Olanrewaju, Oludolapo A. Durban Univ. of Technology Industrial Engineering Department Durban4001 South Africa

intelligent manufacturing system (IMS) has been the focus of most industries since Industry 4.0 revolution. IMS is being implemented through the integration of Internet of Things, (IoT), Cyber-Physical Systems (CPS), digital twin and big data analytics to optimize production through smart manufacturing. This research presents a conceptual approach of an adaptive clustering algorithm (ACA) for advanced manufacturing decision-making for smart machining manufacturing. The work considers product monitoring and assessment, machine health and operating parameters monitoring, as an important factor for intelligent decision making on a machining production line through the developed cyber twin of the machine tool for production optimisation. Cyber twin of the machine tool is developed which runs on a realtime sequence with the physical asset fussed with smart sensors and controllers enabled with cloud computing, IoT and data analytics. The ACA enables resources monitoring, production monitoring, machine condition monitoring, cloud feedback notification, product monitoring, and assessment, for intelligent decision-making from a cluster of similar machines using ANN clustering tool for self-aware, self-predict and self-reconfiguration in a smart machining production line to detect a cutting tool chipping of less than 0.25mm size. The method is proposed to optimise production by increasing productivity through intelligent decision and prediction for tool change, tool failure, maintenance, adjustment of operating parameters. © 2020 The Authors, published by EDP Sciences.

关键词： data analytics

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A data Driven Approach for Leak Detection with smart Sensors 16

A Data Driven Approach for Leak Detection with Smart Sensors

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16th IEEE international conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Liang, Bin Verma, Sunny Xu, Jie Liang, Shuming Li, Zhidong Wang, Yang Chen, Fang Univ Technol Sydney Fac Engn & IT Ultimo NSW 2007 Australia

ISBN: (纸本)9781728177090

Preventing water pipe leaks and breaks has high priority for water utilities. It is a critical task for the utility to reduce water loss through leaks and breaks detection in water mains. The failure prediction and data analytics research have been conducted for an Australian water utility over the last few years to enhance the prediction of leaks and breaks detection in water mains. intelligent sensing at sensitive locations with current research aids in prioritising investigation and prevention of potential breaks and leaks in water mains. The purpose of this work is to integrate the predictive analytics and intelligent sensing applications to identify high risk mains prior to failures. Predictive analytics and minimum night flow (MNF) analysis have been utilised to prioritise risky zones over the whole water network, and then risky pipes are identified to optimise sensors deployment. The sensing data is being collected for analysis and validation, and a machine learning model is being built based on the analysis results. This work is currently under progress and the planned outcomes will help the utility reduce water loss, improve leak detection, and enhance customer satisfaction by automating the process of leak detection using a data driven approach with smart sensors.

关键词： Predictive analytics

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Fault prediction in wireless sensor networks using soft computing

Fault prediction in wireless sensor networks using soft comp...

引用

2020 international conference on smart Technologies in computing, Electrical and Electronics, ICSTCEE 2020

作者： Ara, Tabassum Prabhakar, M. Bali, Manish School of Computing Information Technology Bangalore India Presidency University Dept. of CSE Bangalore India

ISBN: (纸本)9781728172132

With a deluge of data from various sources, sensors being resource constrained with less computing power, small memory and battery life leads them into getting compromised with various attacks and malfunction. Failures or Fault tolerance based on a single source of information has inaccuracies. Faults should be identified and described by multiple feature of data collected from different sources over a period of time. Generally, fault tolerance has been carried out by monitoring the health of each device which in itself consumes a lot of battery power. Also we need to know what should be an apt time interval or frequency to consider predictive analysis. Predicting faults in wireless sensor networks is a challenging proposition that requires deep study of the past data available. With advances in Machine learning and data analytics in particular have changed the way organisations can approach this issue to monitor their health and take preventive measures. In this paper, we propose a soft computing-based sensor node fault prediction based on time-series data. Using ARIMA, we predict failures by decomposing time series data into trend, seasonality and residual components and fit a model. A robust forecast estimation model with confidence bounds: 80% and 95% is developed which can help in implementing an intelligent IoT infrastructure in WSN. There is more ambiguity regarding the longer term forecast as the model is supposed to regress future values based on previously recorded predicted values. © 2020 IEEE.

关键词： Predictive analytics

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Predictive and Prescriptive analytics on Clinical data using Improved K-NNtechnique

Predictive and Prescriptive Analytics on Clinical Data using...

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2022 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2022

作者： Kunjachen, Liza M Kavitha, R. SRM Institute of Science & Technology Department of Information Technology Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In recent years, the impactof pandemic situation there is a strong requirementoflateral thinking within the Healthcare sector. There is a consistent hesitancy for people to use hospital services because of social distancing, covid 19 circumstances, people prefer telemedicine system are rapidly increased. Health care provider provides with better treatment. Anartificial intelligence based dataanalytics shared by the patient through the IoT devices provides high security. In case of any emergencies Health care provider must be able to access the AI based Electronic Health records immediately and consuming less time in taking decisions before the patient could arrive. In this proposed improved KNN technique, preprocessing step is to produce an accurateoutput with additional efficient data and classification enhancement. The experimental results show that the improvedK-Nearest Neighbor (KNN) algorithm improves the accuracy and efficiency of *** from this, Trend analysis can be done to understand the various disease outbreaks which come under the data *** experimental results of the proposed technique foradvanced data analytics in healthcaretreatment helps better treatment at low cost efficiently and effectively. © 2022 IEEE.

关键词： data analytics

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Two-way recommendation system for intelligent employment of college students based on data mining

Two-way recommendation system for intelligent employment of ...

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international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Zhang, Yifan Liu, Qian Meng, Qingpeng School of Liaoning Institute of Science and Technology Benxi China

ISBN: (纸本)9783030515553

Based on big data and the new era, the information employment service technology revolution in China’s smart universities brought about by high-tech applications such as mobile internet, internet of things and cloud computing has put forward new technological transformation points and demands for the construction of the ideology of information service for smart employment and the mode of employment, work and life of Chinese college students. In order to make the employment of college students and the recruitment of enterprises more efficient, we have designed a "two-way recommendation system for intelligent employment of college students" by means of data mining and other means, so as to achieve the effect of intelligent integration, reorganization and intelligent sharing of high-quality relevant employment resources and improve the efficiency of employment services for college students [1]. © Springer Nature Switzerland AG 2021.

关键词： data mining

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