• 한국정밀공학회지
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• 제9권4호
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• pp.13-21
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• 1992

Standards concerning the determination of positioning accuracy and repeatability of numerically controlled(NC) machine tools have been published relentlessly over the last 20 years. Since the publication in 1988 of the International Standard 230-2 there has been a pronounced move, both at national and international standards level, to embrace further test procedures for a complete machine tool performance assessment. For example, measurements of angular (pitch, roll, and yaw) and straightness errors along linear axes are now commonplace and complement the existing positioning accuracy and repeatablity tests. More recently the subject of circularity evalutaion has also gained considerable interest. Here dynamic tests, using a kinematic ballbar or circular masterpiece, give an instant overview of the contouring ability of the machine in two axes at specific feedrates. This information is extremely important in optimising machining accuracy. This paper describes moves to complete the machine calibration package in national and international standardis- ation for the assessment of machine tool performance.

• 한국정밀공학회지
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• 제26권10호
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• pp.7-11
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• 2009

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.969-972
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• 2004

This paper describes the mechanism of parallel micro machine platform and its feedback control system for acquiring high accuracy. The parallel micro machine platform that has developed has 5x5x5 work-space and sub-micron accuracy. For the high accuracy, the feedback control system is important but errors in machining and assembling are inevitable. Kinematic calibration is important for this reason. In this paper, various error components are introduced and the effects of error component are analyzed.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.624-628
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• 2000

The performance of machine tools is qualified by many test procedures given by the national/international standards or respected organisations. Among them, test regarding circular-interpolation accuracy is getting to be one of the important acceptance tests at the production level. Machine tool systems are composed of many mechanical and electronical sub-systems so that it is not easy to improve dynamic performance by examining only one particular part. Instead, overall systematic approach encompassing all the contributing elements is necessary to achieve good results. In this study, measures taken in circular accuracy improvements will be explained case by case.

• 대한기계학회논문집A
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• 제31권10호
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• pp.993-998
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• 2007

To verify the 2D or 3D positioning accuracy of a multi-axes stage is not easy, particularly, in the case the moving path of the stage is not linear. This paper is a study on a measuring method for the curved path accurately. A machine vision technique is used to trace the moving path of two-axes stage. To improve the accuracy of machine vision, a zoom lens is used for the 2D micro moving path. The accuracy of this method depends of the CCD resolution and array align accuracy with the zoom lens system. Also, a further study for software algorithm is required to increase the tracing speed. This technique will be useful to trace a small object in the 2D micro path in real-time accurately.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.335-340
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• 2004

KS and ISO have proposed several evaluation methods of conventional machine tools. Even though the accuracy of the tools can be evaluated with those methods, there are still no proper evaluation methods of high speed machining. Because it is hard to evaluate characteristics of high speed machining such as decrease of cutting temperature, cutting force, and reduced machining time. Therefore, new evaluation method for high speed machine should be developed. In this paper, several shapes of model have been proposed to evaluate cutting accuracy of high speed machine.

• 한국정밀공학회지
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• 제11권6호
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• pp.5-11
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• 1994

We have investigated the variation of dwell and warm-up time for effects of positioning accuracy of the CNC machine tools with an laser measuring system. Also, we strdied the effect of improvement of the positioning accuracy by variation of the temperature for hollow ball screw, which mostly used as drive mechanism of CNC machine tools. We dbtained the effectiveness of cooling effect of the new cooling system, compared with the conventional cooling system.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.326-327
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• 2022

본 연구는 머신러닝을 이용한 임상적 의사결정을 위한 알고리즘으로 환자를 간호하는 간호인력이 장기간 누워있는 환자를 보살힐 경우 욕창예방간호 수행에 도움을 주기 위한 시스템 개발에 활용될 욕창 분류 알고리즘이다. 머신러닝을 실시한 결과 알고리즘의 learning accuracy는 82.14%, test accuracy는 82.58%로 나타났다.

• Geomechanics and Engineering
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• 제37권6호
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.