• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• International Journal of Fluid Machinery and Systems
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• 제2권1호
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• pp.21-30
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• 2009

Slurry wear with sand particles in rivers is a serious problem for pump operation. Therefore, a technique to predict wear volume loss is required for selecting wear resistant materials and determining specifications for the maintenance period. This paper reports a method for predicting the wear depth distribution on the blade of an impeller. Slurry wear tests of an aluminum pump impeller were conducted. Prediction results of wear depth distribution approximately correspond with the results of slurry wear tests. This technique is useful for industrial application.

• Journal of the Korean Society of Manufacturing Process Engineers
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• 제1권1호
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• pp.133-140
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• 2002

The object of this paper is to predict the surface roughness using the experiment equation of surface roughness, which is developed with a full factorial design in turning. $3^3$ full factorial design has been used to study main and interaction effects of main cutting parameters such as cutting speed, feed rate, and depth of cut, on surface roughness. For prediction of surface roughness, the arithmetic average (Ra) is used, and stepwise regression has been used to check the significance of all effects of cutting parameters. Using the result of these, the experimental equation of surface roughness, which consists of significant effects of cutting parameters, has been developed. The coefficient of determination of this equation is 0.9908. And the prediction ability of this equation was verified by additional experiments. The result of that, the coefficient of determination is 0.9718.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.637-642
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• 2002

Generally, the use of robots in manufacturing industry has been increased during the past decade. GMA(Gas Metal Are) welding is an actively growing area and many new procedures have been developed for use with high strength alloys. One of the basic requirement for welding applications is to study relationships between process parameters and bead geometry. The objective of this paper is to develop a new approach involving the use of neural network and multiple regression methods in the prediction of bead geometry for GMA welding process and to develop an intelligent system that enables the prediction of bead geometry using Rapid Prototyping(RP) in order to employ the robotic GMA welding processes. This system developed using MATLAB/SIMULINK, could be effectively implemented not only for estimating bead geometry, but also employed to monitor and control the bead geometry in real time.

• Transactions of the Korean Society of Machine Tool Engineers
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• 제16권3호
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• pp.81-87
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• 2007

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. However, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis and artificial neural network were used as the research methods. And, the results of two prediction methods were compared and analyzed.

• Journal of the Korean Society of Systems Engineering
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• 제12권1호
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• pp.105-112
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• 2016

An aircraft must be designed to minimize system failure rate for obtaining the aircraft safety, because the aircraft system failure causes a fatal accident. The safety of the aircraft system can be predicted by analyzing availability, reliability, and maintainability of the system. In this study, the reliability and the maintainability of the hydraulic system are analysed except the availability, and therefore the reliability and the maintainability analysis process and the results are presented for a helicopter hydraulic system. For prediction of the system reliability, the failure rate model presented in MIL-HDBK-217F is used, and MTBF is calculated by using the Part Stress Analysis Prediction and quality/temperature/environmental factors described in NPRD-95 and MIL-HDBK-338B. The maintainability is predicted by FMECA(Failure Mode, Effect & Criticality Analysis) based on MIL-STD-1629A.

• Journal of Industrial Technology
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• 제28권B호
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• pp.215-226
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• 2008

MIL-HDBK-217 has widely been used for electronics reliability predictions. Recently, the $217Plus^{TM}$ has been developed by DoD RIAC and may replace MIL-HDBK-217. A overview of the $217Plus^{TM}$ has been performed in this paper. We first reviewed the overall concepts and reliability prediction procedures. We then explained the component models and the system level model with process grading concepts. Bayesian approach incorporating field data into the predicted failure rate is another feature of this methodology.

• Transactions of Materials Processing
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• 제14권3호
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• pp.215-223
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• 2005

A numerical analysis program is developed by FDM scheme for the prediction of microstructural transformation during heat treatment of steels. In this study, multi-phase model was used fur description of diffusional austenite transformations in low-alloy hypoeutectoid steels during cooling after austenitization. A fundamental property of the model consisting of coupled differential equations is that by taking into account the rate of austenite grain growth, it permits the prediction of the progress of ferrite, pearlite, and bainite transformations simultaneously during quenching and estimate the amount of martensite also by using K-M eq. In order to simulate the microstructural evolution during tempering process, another Avrami-type eq. was adopted and method for vickers hardness prediction was also proposed. To verify the developed program, the calculated results are compared with experimental ones of casting product. Based on these results, newly designed heat treatment process is proposed and it was proved to be effective for industry.

• IE interfaces
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• 제9권3호
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• pp.298-305
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• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. Most of rolling processes use mathematical models to predict rolling force which is very important to decide the resultant thickness of a coil. In general, these mathematical models are not flexible for variant coil types and cannot handle various elements which is practically important to decide accurate rolling force. A corrective neural network is proposed to improve the accuracy of rolling force prediction. Additional variables-composition of the coil, coiling temperature and working roll parameters-are fed to the network. The model uses an MLP with BP to predict a corrective coefficient. The test results using 1,586 process data collected at POSCO in early 1995 show that the proposed model reduced the prediction error by 30% on average.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1102-1105
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• 1993

A fuzzy expert system for prediction and mitigation of sludge bulking was developed for an activated sludge process which treats waste water from a food industry. The developed system is able not only to infer the degree of progress of sludge bulking but also to generate remedial operation guides which may be sent to the local controllers as remote set points. One of the important consequences through this study is the BI (Bulking Index) inferred by the bulking prediction expert system was found to have a close correlation with the SVI (Sludge Volume Index) which is a practical measure of degree of bulking but needs tedious chores for its measurement.