• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.389-390
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• 2010

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.5
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• pp.500-505
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• 2012

Recently, a high speed spindle is an essential part of machine tools to satisfy latest demand of high precision product and machining of hard materials. But, there are many disadvantages such as heat generation of built-in-motor, bearing friction, noise, vibration and displacement because of the high speed. Many researches on spindle systems have been conducted for solving these problems. In this study, technical trend of machine tools spindle systems are analyzed with patent PSM, mapping and grouping. The analysis is carried out for the applied patent during January 2000 and December 2009 in Korea, Japan, EU and U.S.A. And development of the direction, strategy and promising technologies of the spindle system are suggested.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.62-72
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• 1999

We consider a Direct Input Output Manufacturing System(DIOMS) which has a munber of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval(S/R) machine handles parts placed on pallets for the machine centers located at either one or both sides of the AS/RS. This paper deals with the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the S/R machine, machine center-based part type selection rule and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.977-980
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• 1993

The technique to design high performance small motors has been based on experimental data, which results from lots of cost, experience and time in manufacturing. Recently, as high-performance computer appears, many engineers use numerical methods to design and analyze electric machine. Since, the step motor which has very small air gap(0.02-0.05mm) is different from other electric machine in its structure. The shape of rotor teeth and stator teeth influence seriously on the torque characteristics. And it is operated over magnetic saturation point. Therefore, the design of step motor needs to solve nonlinear problem and to calculate magnetic field precisely. In this paper, we solve nonlinear problem by employing Finite Element Method and obtain torque-displacement characteristics for the design of step motor. We also manufacture VR step motor according to the obtained results, and measure some torque characteristics. Through comparing calculated results with experimental results, it is verified that FEM is very useful to design step motor, and the motor designed by our technique is improved in its step accuracy.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.571-578
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• 2001

Production of green pepper has increased for ten years in Korea, as customer's preference of a pepper tuned to fiesta one. This study was conducted to develop an on-line fading algorithm of green pepper using machine vision and aimed to develop the automatic on-line grading and sorting system. The machine vision system was composed of a professive scan R7B CCD camera, a frame grabber and sets of 3-wave fluorescent lamps. The length and curvature, which were main quality factors of a green pepper were measured while removing the stem region. The first derivative of the thickness profile was used to remove the stem area of the segmented image of the pepper. A new boundary was generated after the stem was removed and a baseline of a pepper which was used for the curvature determination was also generated. The developed algorithm showed that the accuracy of the size measurement was 86.6% and the accuracy of the bent was 91.9%. Processing time spent far grading was around 0.17 sec per pepper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1065-1066
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.537-538
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.483-484
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• 2011

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.