• Proceedings of the KSME Conference
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• 2000.04a
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• pp.831-836
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• 2000

During machining, since more than 50% compliance of the cutting point in machine tool structures comes from headstocks, with the remainder coming from beds, slides and structural joints, the structural analysis of the headstock is very important to improve the static and dynamic performances. Especially, in case of machining hard and brittle materials such as glasses and ceramics with the grinding machine, the reinforced headstock with the high damping material is demanded. Since the fiber reinforced composite materials have excellent properties for structures, owing to its high specific modulus, high damping and low thermal expansion, it is expected that the dynamic and thermal characteristics of the headstock will be improved if they are employed as the materials fur headstock. In this paper, the design and the manufacturing methods as well as the static and dynamic characteristics of a steel-composite hybrid headstock were investigated analytically and experimentally to improve the performance of the grinding machine system.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.1-7
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• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.860-880
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• 2024

Breast cancer is a major health concern affecting women and men globally. Early detection and accurate classification of breast cancer are vital for effective treatment and survival of patients. This study addresses the challenge of accurately classifying breast tumors using machine learning classifiers such as MLP, AdaBoostM1, logit Boost, Bayes Net, and the J48 decision tree. The research uses a dataset available publicly on GitHub to assess the classifiers' performance and differentiate between the occurrence and non-occurrence of breast cancer. The study compares the 10-fold and 5-fold cross-validation effectiveness, showing that 10-fold cross-validation provides superior results. Also, it examines the impact of varying split percentages, with a 66% split yielding the best performance. This shows the importance of selecting appropriate validation techniques for machine learning-based breast tumor classification. The results also indicate that the J48 decision tree method is the most accurate classifier, providing valuable insights for developing predictive models for cancer diagnosis and advancing computational medical research.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.1
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• pp.10-15
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• 2001

Urgent increasing of the vehicles influence air pollution and the damage of the plants and animals. Particularly, exhaust-ing particulate of diesel vehicles give serious effect to human life. Therefore, this study aim to reduce amount of particulate and to contribute developing after-treatment in diesel engine. Through the experimental and theoretical study about charac-teristics of the electric heat regeneration, various results are obtained.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.24-29
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• 2005

Optimal heat treatment process in martensitic stainless steel such as SUS416 is investigated. The approach is based on the combination of the interpolation and extrapolation method of a standard heat treatment technology with the principle of quenching and tempering temperature difference. The relationship of the macroscopic structure, fracture toughness and ductility as well as the hardness and strength are considered to induce a simple rule to apply with feasibility. Consequently, Optimal heat treatment condition in martensitic stainless steel is proposed and is shown the better quality. It was found that the smaller pain size of microstructure gives the enhanced fracture toughness and ductility.

• Journal of The Korean Society of Grassland and Forage Science
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• v.7 no.2
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• pp.113-119
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• 1987

This experiment was carried out in laboratory to find out the effect of coating materials, minerals and insecticides on germination of pasture seed. Seed coating was made by specially made machine and seed germination in petridishes was also determined from Sep. 1986 to May 1987. The results were summarized as follows; 1. Coating seed gave bad germination compared with the control. There were significant difference between treatment, coating materials and adhesives in white and red clover while Phleum pratense germination was improved by coating materials. 2. In mineral coating treatments, on Mg alone or Mg mixed treatment, germination was deepressed. Generally germination was lowed by mineral treatment, but the difference was small. 3. Uncoating treatment with insecticide was superior to coating treatment in terms of germination. Among the insecticide Ballen gave more serious effect to seed germination.

• Tribology and Lubricants
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• v.13 no.2
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• pp.39-45
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• 1997

The shot peening treatment were conducted for improving the strength of rolling contact fatigue of machine element like a gear. This paper was undertaken to analyze the influence of shot peening treatment for inner race of ball bearing on the rolling contact fatigue. Shot peening treatment were applied to the full hardened and the carbonitrided bearing. And the rolling contact fatigue life test and X-ray diffraction test were carried out. The results of this study showed that the fatigue life of ball bearing in the clean and the contaminated oil could be improved by shot peening treatment. This effect was found to be more pronounced to the full hardened bearing. These facts might be due to the generation of compressive residual stress and the strain hardening of surface layer by shot peening treatment. The failure of the shot peened bearing were presumed to initiate at surface.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.835-838
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• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• Science of Emotion and Sensibility
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• v.8 no.1
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• pp.37-45
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• 2005

This paper reports the remedial effect measurement of an obesity remedy machine for home use which has been developed by H Co. and the authors. It is expected that the machine enhances it's remedial effect and usability by utilizing medium frequency and thermotherapy belt etc. In order to measure it's remedial effect, a clinical experiment, which participates eight young female subjects, has been conducted for one month. The experiment includes the measurements on the changes of Gas-Exchange Responses of subjects through Cardio-Pulmonary Exercise Testing. The experimental results show that the obesity remedy machine helps the subjects to reduce their weights, fat rates, and $VCO_2s$. Thus, it turns out that the machine can be a good candidate for medical treatment on the obesity.