• 한국기계가공학회지
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• 제11권4호
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• pp.147-153
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• 2012

The demands of the large scale machine tools, for instance, such as planomiller, turning machine, boring machine, NC machine, have been gradually increased in recent years. As the performances of machine tools and/or cutting tools are advanced, it is possible to perform high-speed and high-precision cutting works. The effective treatment of wet chip, which is discharged from cutting works, becomes very important problems. Therefore, this study is forced on the design of large scale machine tools using CATIA V5R18 and analysis of cutter, which is considered as essential equipment in large scale machine tools, using MSC.Nastran & MSC.Patran. Especially, the relations between tolerated load of cutter, driving horse power and rpm of driving shaft in chip processing system are investigated through analysis. As the results, the reliability of design could be improved by evaluating simulated numerical values, it showed that tolerated loads of supported part and edged part of cutter are 87,000N and 14,450N, respectively.

• 대한기계학회논문집
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• 제19권3호
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• pp.877-885
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• 1995

Cold forging die for metal scissor is made by electric discharge machine. The impression of female die is made by electric discharge machine, the heat treatment is applied, and the impression is polished. When we forge goods by using this kind of die, the abrasion is severe and the crack occurs after forging about 240 strokes. Because the die should be frequently produced in the case, the cost rises, the work is delayed, and the precesion of goods is not good. Therefore, the electric discharge machine was not used in this study. Main die was produced by making hob, installing the hob to cold hobbing press, indenting the die material, and cold hobbing the impression. The die life was increased to 5,000-6,000 strokes in this case. In the future study, the die life will be increased to 10,000 strokes by changing the following : (1) the pre-treatment of slug, (2) the structure of die block, (3) the heat treatment of die material

• 한국분말재료학회지
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• 제10권6호
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• pp.443-447
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• 2003

Nano-sized $TiB_2$ was in situ synthesized in copper matrix through self-propagating high temperature synthesis (SHS) with high-energy ball milled Ti-B-Cu elemental mixtures as powder precursors. The size of $TiB_2$ particles in the product of SHS reaction decreases with time of preliminary mechanical treatment ranging from 1 in untreated mixture to 0.1 in mixtures milled for 3 min. Subsequent mechanical treatment of the product of SHS reaction allowed the $TiB_2$ particles to be reduced down to 30-50 nm. Microstructural change of $TiB_2$-Cu nanocomposite during spark plasma sintering (SPS) was also investigated. Under simultaneous action of pressure, temperature and electric current, titanium diboride nanoparticles distributed in copper matrix move, agglomerate and form a interpenetrating phase composite with a fine-grained skeleton.

• 한국의학물리학회지:의학물리
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• 제28권3호
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• pp.129-133
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• 2017

The machine log files recorded by a scanning control unit in proton beam therapy system have been studied to be used as a quality assurance method of scanning beam deliveries. The accuracy of the data in the log files have been evaluated with a standard calibration beam scan pattern. The proton beam scan pattern has been delivered on a gafchromic film located at the isocenter plane of the proton beam treatment nozzle and found to agree within ${\pm}1.0mm$. The machine data accumulated for the scanning beam proton therapy of five different cases have been analyzed using a statistical method to estimate any systematic error in the data. The high-precision scanning beam log files in line scanning proton therapy system have been validated to be used for off-line scanning beam monitoring and thus as a patient-specific quality assurance method. The use of the machine log files for patient-specific quality assurance would simplify the quality assurance procedure with accurate scanning beam data.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.149-163
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• 2023

In this study we analyze research trends on machine learning healthcare based on papers from the United States, the United Kingdom, and Korea. In Elsevier's Scopus, we collected 3425 papers related to machine learning healthcare published from 2018 to 2022. Keyword frequency and centrality analysis were conducted using the abstracts of the collected papers. We identified keywords with high frequency of appearance by calculating keyword frequency and found central research keywords through the centrality analysis by country. Through the analysis results, research related to machine learning, deep learning, healthcare, and the covid virus was conducted as the most central and highly mediating research in each country. As the implication, studies related to electronic health information-based treatment, natural language processing, and privacy in Korea have lower degree centrality and betweenness centrality than those of the United States and the United Kingdom. Thus, various convergence research applied with machine learning is needed for these fields.

• 한국염색가공학회지
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• 제8권6호
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• pp.47-54
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• 1996

In order to develop the air flow finishing machine with low tension drying and surface modification by high impact effect, the treatment characteristics and the moving speed of fabrics was analysed and investigated in the newly developed system. The change of handle before and after treatment of fabrics measured using Kawabata system. The moving speed in air nozzle was affected by an air pressure of blower, and the maximum value was 1,000 m/min. In accordance with the change of handle, the treatment effect was very different compared with untreated fabrics. And the drape properties of fabrics increased about 10∼45% compared with untreated one.

• 한국기계가공학회지
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• 제9권6호
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• pp.7-15
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• 2010

Recently, with the high performance and efficiency of machine, there have been required the multi-functions in various machine parts, such as the heat resistance, the abrasion resistance and the stress resistance as well as the strength. Fatigue crack growth tests were carried out to investigate the fatigue characteristics of high carbon steel (SM53C) experienced by high-frequency induction treatment. The Cam nose part of the Automobile's Cam shaft is strongly bumped with rocker arm or valve-lift. Therefore abnormal wear such as unfair wear and early wear occur in the surface. This abnormal wear causes a defect that bad timing open and close actions of the engine valve happen in the combustion chamber so the fuel gas will be combustion imperfect. Therefore, the cam shaft demands high hardness and wear resistance. In this study, high frequency heat treatment has been accomplished while wear test for material SM53C.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 춘계학술대회 논문집(한국공작기계학회)
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• pp.53-57
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• 2001

The way to reduce wear quantity of numerical control machine tool slideway is highfrequency heat treatment process for slideway in GC300 By using this way mentioned above We can expect the increase of Rockwell hardness from Rockwell hardness 30 to 48∼55. The deformation quantity caused by high-frequency heat treatment results in increasing nonwork time at the following process grinding work so it is important that we minimize the deformation quantity as much as possible. This thesis will propose one of the ways to minimize nonwork time at grinding work we first prospect the deformation quantify and process the rough cutting considering the data.

• Journal of Oral Medicine and Pain
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• 제47권2호
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.