• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.396-400
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• 2003

This paper has studied thermal characteristics of machine tool to develope high speed spindle and optimum design condidering the thermal deformation. Comparing the test data of temperature measurement and structural analysis data using FEM, we verifiedthe test validity and predicted thermal deformation, influence of spindle generation of heat, and established cooling system to prevent the thermal deformation. 1) The temperature rise of spindle system depends on increasing number of rotation and shows sudden doubling increment of number of rotation over 7,000rpm. 2) Oil jacket cooling can be effective cooling method below 8,000rpm but, over 8,000rpm, it shows the decrement of cooling effect. 3) Comparing FEM analysis results and revolution test results, we can confirmn approximate temperature change consequently, it is possible to simulate temperature rise and thermal distribution on the inside of spindle system. 4) We can confirm that simulated approach by FEM analysis can be effective method in thermal-appropriate design..

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.498-503
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• 2002

This paper has studied thermal characteristics of machine tool to develope high speed spindle and optimum design considering the thermal deformation. Comparing the test data of temperature measurement and structural analysis data using FEM, we verified the test validity and predicted thermal deformation, influence of spindle generation of heat, and established cooling system to prevent the thermal deformation. 1) The temperature rise of spindle system depends on increasing number of rotation and shows sudden doubling increment of number of rotation over 7,000rpm. 2) Oil jacket cooling can be effective cooling method below 8,000rpm but, over 8,000rpm, it shows the decrement of cooling effect. 3) Comparing FEM analysis results and revolution test results, we can confirmn approximate temperature change consequently, it is possible to simulate temperature rise and thermal distribution on the inside of spindle system. 4) We can confirm that simulated approach by FEM analysis can be effective mettled in thermal-appropriate design.

• 한국기계가공학회지
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• 제9권1호
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• pp.49-54
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• 2010

An integrated machining center is developed for high precision and productivity manufacturing. The developed machine is composed of the high precision spindle using ball bearings, the high stiffness bed and the three axis CNC controller with the high resolution AC servo motor. In this paper, structural and modal analysis for the developed machine are carried out to check the design criteria of machine. The analysis is carried out by FEM simulation with using the commercial software, CATIA V5, ANSYS and ARMD. The simulation model of machine is made by shell and solid finite elements. This study also presents the measurement system on the modal analysis of an integrated machining center. The weak part of the machine is found by the analytical evaluation. The results provide with the structural modification data for good dynamic behaviors. And the safety of machine is confirmed by the modal analysis of modified machine design. As this study results can be trustworthy with the analysis of ANSYS and CATIA, integrated machining center can be successfully developed.

• International journal of advanced smart convergence
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• 제6권1호
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• pp.39-43
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• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• 한국정보통신학회논문지
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• 제24권2호
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• pp.225-230
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• 2020

기계학습은 데이터를 기반으로 한 컴퓨터를 학습시켜 컴퓨터 스스로 데이터의 경향성을 파악하게 하여 새로운 입력 데이터의 출력을 예측하도록 하는 알고리즘이다. 기계학습은 크게 지도학습, 비지도학습, 강화학습으로 나눌 수 있다. 지도학습은 데이터에 대한 레이블이 주어진 상태로 기계를 학습시키는 방법이다. 즉, 데이터 및 레이블의 쌍을 통해 해당 시스템의 함수를 추론하는 방법으로 새로운 입력 데이터에 대해서 추론한 함수를 이용하여 결과를 예측한다. 그리고 예측하는 결과 값이 연속 값이면 회귀분석, 예측하는 결과 값이 이산 값이면 분류로 사용된다. 새로운 붓꽃 데이터 Sepal length(5.01)과 Sepal width(3.43)을 이용하여 기초 데이터와 유클리드 거리를 분석하였다. 분석결과, 테이블 3의 8번(5, 3.4, setosa), 27번(5, 3.4, setosa), 41번(5, 3.5, setosa), 44번(5, 3.5, setosa) 그리고 40번(5.1, 3.4, setosa)의 데이터 순으로 유사도가 높은 붓꽃으로 분류되었다. 따라서 이론적 실무적 시사점을 제시하였다.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• International Journal of Contents
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• 제14권4호
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• Communications for Statistical Applications and Methods
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• 제30권2호
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• pp.215-226
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• 2023

Deep learning has made great strides in the field of unstructured data such as text, images, and audio. However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning. To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently. In this paper, we review the latest deep learning models for tabular data and compare the performances of these models using several datasets. In addition, we also compare the latest boosting methods to these deep learning methods and suggest the guidelines to the users, who analyze tabular datasets. In regression, machine learning methods are better than deep learning methods. But for the classification problems, deep learning methods perform better than the machine learning methods in some cases.

• 한국기계가공학회지
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• 제10권5호
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• pp.1-6
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• 2011

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. Therefore, it is very important to assess errors in machine tools. Volumetric error analysis has been developed by many researchers. This paper presents a useful technique for analyzing the volumetric errors in machine tools using the ball bar. The volumetric error model is proposed in specific vertical machining center and the program is developed for generating NC code, acquiring the ball bar data, and analyzing the volumetric errors. The developed system assesses the volumetric errors such as positional, straightness, squareness, and back lash. Also this system analyzes the dynamic performance such as servo gain mismatch. The radial data acquired by ball bar on 3D space is used for analyzing these errors. It is convenient to test the volumetric errors on 3D space because all errors are calculated at once. The developed system has been tested using an actual vertical machining center.

• International Journal of CAD/CAM
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• 제12권1호
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• pp.1-8
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• 2012

This study analyzes the characteristics of two different kinds of squat exercise through physical experiments and a computer simulation, i.e. one with a free weight and the other with a Smith machine are studied. This study also proposes a new design for the Smith machine, which has both the advantages of each type based on the results of the analysis. The muscle force and level of stimulation of the lower extremities during squatting were calculated by running an inverse dynamics analysis program on a musculoskeletal model together with the measured motion data. The calculated results were verified by comparing with the measured EMG data. The analysis showed that squatting using free weight is more effective than squatting using the Smith machine. Meanwhile, in order to design an improved Smith machine, which is the final goal of this study, the trajectory of the barbell of the subjects during free weight squatting was measured on the sagittal plane. The measurement showed that the average slope of the trajectory of the barbell is tilted backward by $10.7^{\circ}$. Based on this measurement, this study proposes a tilted design for an improved Smith machine.