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Optimising Production through intelligent Manufacturing

Optimising Production through Intelligent Manufacturing

引用

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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8th IFIP WG 12.6 international Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021 held in conjunction with international Joint conference on Artificial Intelligence, IJCAI 2020

8th IFIP WG 12.6 International Workshop on Artificial Intell...

引用

8th IFIP WG 12.6 international Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021 held in conjunction with international Joint conference on Artificial Intelligence, IJCAI 2020

ISBN: (纸本)9783030808464

The proceedings contain 15 papers. The special focus in this conference is on Artificial Intelligence for Knowledge Management. The topics include: Holistic Approach to smart Factory;development of Big data analytics in a Multi-site Enterprise on the Example of Supply Chain Management;analysing Natural Gas Prices for Turkey in the Light of a Possible Hub;predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts;machine Learning Methods in the Inclinometers Readings Anomaly Detection Issue on the Example of Tailings Storage Facility;ontologies Cooperation to Model the Needs of Disabled Persons;a Conceptual Framework of intelligent Management Control System for Higher Education;Curriculum Vitae (CVs) Evaluation Using Machine Learning Approach;university Students’ Research on Artificial Intelligence and Knowledge Management. A Review and Report of Multi-case Studies;Customer Churn Prediction in FMCG Sector Using Machine Learning Applications;crowdsourcing as a Tool Supporting Intra-city Communication;the Importance of the Internet of Things for smart Cities;developing a Knowledge Base on Climate Change for Metropolitan Cities.

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Hybrid security analysis based on intelligent adaptive learning in Big data

Hybrid security analysis based on intelligent adaptive learn...

引用

international conference on Advances in computing and Communication Engineering (ICACCE)

作者： Sasubilli, Satya Murthy Dubey, Ashutosh Kumar Kumar, Abhishek Huntington Columbus OH 43287 USA Chitkara Univ Inst Engn & Technol Kalujhinda HP India

ISBN: (数字)9781728163628

ISBN: (纸本)9781728163628

Big data provides a way to handle and analyze large amount of data or complex set. It provides a systematic extraction also. In this paper a hybrid security analysis based on intelligent adaptive learning in big data has been discussed with the current trends. This paper also explores the possibility of cloud computing collaboration with big data. The advantages along with the impact for the overall platform evaluation has been discussed with the traditional trends. It has been useful in the analysis and the exploration of future research. This discussion also covers the computational variability and the connotation in terms of data reliability, availability and management in big data with data security aspects.

关键词： Big data Adaptive learning intelligent analytics data security data management

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Innovation of Ideological and Political Education Model in the Context of Big data

Innovation of Ideological and Political Education Model in t...

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Pan, Xiangzhen School of Management Science and Engineering Shandong Technology and Business University Shandong China

ISBN: (纸本)9783030627423

Times are developing, technology is innovating, and big data thinking and methods promote the progress of human civilization, triggering all-round deep thinking and change in society. In the age of Internet communication, ideological and political education is currently confronting new chances and threats. It must advance with the times, reflect the times, grasp the laws, and be creative. Combined with the analysis of the innovation of ideological and political education course mode for college students in the Age of Big data, the paper proposes cognition, work and assessment approaches in favor of curricular innovation based on cloud media technology, agent technology and Internet of Things technology, and comes up with a model to innovate student ideology education course. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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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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A data Driven Approach for Leak Detection with smart Sensors 16

A Data Driven Approach for Leak Detection with Smart Sensors

引用

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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The design of comprehensive quality evaluation system under the reform of china’s college entrance examination

The design of comprehensive quality evaluation system under ...

引用

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

作者： Zhang, Xianglin Ge, Xiaolin Central University of Finance and Economics Beijing100081 China

ISBN: (纸本)9783030515553

To understand the comprehensive quality evaluation of high school students, the first thing is to go out of the three major misunderstandings and recognize the value of comprehensive quality evaluation, the quantifiable factuality and the necessity of continuous record. It still exists problems in current comprehensive quality evaluation such as single and rough means, serious homogenization, and lack of timely feedback. The root cause of these problems is that we can not find strong "evidence" for the content that is difficult to be standardized, such as ideological and moral character, artistic quality, etc. The lack of technical means make a comprehensive quality evaluation become a mere formality. The arrival of the era of big data provides a new solution to solve these problems. The fine-grained quality of the high school students in the big data system is presented to the corresponding subjects. It is preliminarily conceived that the comprehensive quality evaluation system is constructed under the background of big data to provide a revelation for the evaluation of the comprehensive quality of high school students. © Springer Nature Switzerland AG 2021.

关键词： Big data

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ERP in Industry 4.0 Context

ERP in Industry 4.0 Context

引用

IFIP WG 5.7 international conference on Advances in Production Management Systems (APMS)

作者： Majstorovic, Vidosav Stojadinovic, Slavenko Lalic, Bojan Marjanovic, Ugljesa Univ Belgrade Fac Mech Engn Belgrade Serbia Univ Novi Sad Fac Tech Sci Novi Sad Serbia

ISBN: (纸本)9783030579937

Industry 4.0 (I4.0) is a new model of technology systems automation, based on the convergence of technologies that make up the concept of intelligent manufacturing, integrated with information and communication technologies that have been applied in a new way (cloud computing, big data analytics and AI), and the basis of distributed control. This approach has brought unprecedented opportunities to manufacturing systems, especially in the fields of planning and control, from resource to supply chains levels. For this reason, enterprise resource planning (ERP) is being researched and developed, which is based on the use of I4.0 elements: internet of things (IoT), big data analytics (BDA), AI and cloud computing (CC), which in this paper show.

关键词： Industry 4.0 ERP smart factory smart ERP

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A Novel Approach to Fashion-Based Analysis and analytics Using an intelligent In-store Camera System 5

A Novel Approach to Fashion-Based Analysis and Analytics Usi...

引用

5th international conference on Computer Science and Engineering (UBMK)

作者： Pur, Sezai Furkan Sagiroglu, Seref Browzzin Pte Ltd Yapay Zeka Takimi Ankara Turkey Gazi Univ MF Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781728175652

Customer analysis and analytics at online and physical stores are used to decide customers' profiles and to develop selling strategy as an important approach in these days. Thanks to these approaches, stores can decide on their target customers and products quite easily. Deciding on these approaches can be made at online stores using some customer information such as profile data, shopping history, following products even though it cannot be done in an easy way at physical stores since they do not have similar information. In the current study;an end-to-end intelligent system using artificial intelligent models for fashion-based customer analysis and analytics in physical stores has been developed, and the processes have been conducted on the basis of personal data privacy. Edge computing and cloud computing have been used as a hybrid method, and such customer information as age, gender, ethnicity, emotion, clothes, clothes styles and clothes colors at physical stores have been detected. Following that, analysis and analytics information have been shown through a web interface. The results obtained have been able to be used for designs that are likely to be made in the future.

关键词： In-store intelligent Camera System Edge computing Cloud computing Customer Fashion Analysis Customer Fashion analytics

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An Effective Heart Disease Prediction Model Based on Machine Learning Techniques 20th

An Effective Heart Disease Prediction Model Based on Machine...

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20th international conference on Hybrid intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Ripan, Rony Chowdhury Sarker, Iqbal H. Hasan Furhad, Md. Musfique Anwar, Md Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Canberra Institute of Technology Canberra Australia Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9783030730499

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an optimal value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data analytics

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