• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.1
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• pp.94-101
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• 2015

The heat treatment machine based on immersion was developed to reduce temperature difference during netting process and appraised it performance compared current heat treatment machine using high pressure. It was also reviewed the optimum heat treatment procedures for PBSAT monofilament net in accordance with the immersion time and temperature. The procedure was based on physical measurement such as breaking load, elongation and angle of the mesh for PBSAT monofilament. The water temperature gap of the treatment machine based on immersion was less than $1^{\circ}C$. and the energy consumption was also increased in high temperature condition. It was identified that the optimum temperature was $75^{\circ}C$ and its optimum processing time was between 15 minutes and 20 minutes to get qualified physical properties.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.3
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• pp.57-64
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• 2002

Finite element program(ANSYS) estimated thermal deformation quantity in high frequency heat treatment process of a machine tool fred drive system slideway and apply deformation quantity in roughing process. Having processed the heat treatment minimizing methods of the quantity of deformation heat treatment process. Having done heat treatment with high frequency after taper processing with considering the existed heat treatment generating the quantity of deformation, existed quantity of deformation can be reduced down to 80%, consequently productivity and material saving can be achieved. When high frequency heat treatment finite element method estimated deformation quantity at difference temperature and time, it is progress at cost don and saved time.

• Textile Coloration and Finishing
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• v.10 no.4
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• pp.37-44
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• 1998

The purpose of this study was to elucidate the effect of weight loss of polyethylene terephthalate(PET) fabrics on the mechanical properties such as bending and shear. In order to compare the effect of treatment machine on the mechanical properies of treated PET fabrics, PET fabrics were hydrolyzed with NaOH aqueous solution using Tank machine and Liquor flow machine, respectively. The results were as follows ： 1. The bending rigidity and shear stiffness of hydrolyzed PET fabric decreased markedly up to about 10％ weight loss regardless of treatment machines. At the above 10％ weight loss, the variation of these properties is nearly unchanged. In addition, the bending hysteresis and shear hysteresis also showed similar trend. 2. Weft density change of PET fabrics treated with Liquor flow machine decreased by 1pick/inch. It is assumed that this is attributed to the tension during the treatment of Liquor flow machine. On the other hand, the weft density change of PET fabrics treated with Tank machine is scarcely influeneced by the weight loss. While warp density of PET fabrics treated with Liquor flow machine had no change with weight loss, warp density of PET fabrics treated with Tank machine decreased by 6pick/inch due to the tension. 3. The bending rigidity and shear stiffness of PET fabrics hydrolyzed with liquor flow machine slightly higher than with Tank m/c at the above 10％ weight loss. It is assumed that this is caused by the increasement of the crossing pressure of warp and weft yarn and contact points of filaments in the yarns. Also, the bending and shear hysteresis of PET fabrics treated with Tank machine were higher than that of liquor flow machine.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.396-401
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• 2023

Aus-tempering heat treatment is suitable for thin and small-sized in precision parts. However, the heat treatment process relies on the experience and skill of the operator, making it challenging to produce precision parts due to the cold forging process. The aims of this study is to explore suitable machine learning models using data from the aus-tempering heat treatment process and analyze the factors that significantly impact the mechanic properties (e.g. hardness). As a result, the study analyzed, from a machine learning perspective, how hardness prediction varies based on the quenching temperature, carbon (C), and copper (Cu) contents.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.97-99
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• 2002

A radiation incident took place during treatment on MEDNIF Tele cobalt-60 therapy machine in B.P.KOIRALA MEMORIAL CANCER HOSPITAL in Bharatpur, Nepal. This Chinese made machine has activity of 6240 Curies of cobalt -60. This machine has fulfilled safety requirements. ICRP recommendations, safety rules are followed and practiced. The source was struck up during treatment and a technician was exposed to equivalent dose of 13.75 mSv. recorded by Personal film badge. Risks of workers are comparable to other safe industries. All exposures shall be kept as low as reasonably possible. The higher level of safety is achieved only when every one is dedicated to common goal. A lesson is learnt for future. Good practice is essential but not sufficient. A high demand for tele Cobalt therapy convinced management to replace Mednif machine with a new efficient Elite Tele Cobalt theratron Machine.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_2
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• pp.1083-1090
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• 2022

Since renal cell carcinoma(RCC) has various examination and treatment methods according to clinical stage and histopathological characteristics, it is required to determine accurate and efficient treatment methods in the clinical field. However, the process of collecting and processing RCC medical data is difficult and complex, so there is currently no AI-based clinical decision support system for RCC treatments worldwide. In this study, we propose a clinical decision support system that helps clinicians decide on a precision treatment to each patient. RCC standard big database is built by collecting structured and unstructured data from the standard common data model and electronic medical information system. Based on this, various machine learning classification algorithms are applied to support a better clinical decision making.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.67-76
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• 2024

Diabetic retinopathy is a threatening complication of diabetes, caused by damaged blood vessels of light sensitive areas of retina. DR leads to total or partial blindness if left untreated. DR does not give any symptoms at early stages so earlier detection of DR is a big challenge for proper treatment of diseases. With advancement of technology various computer-aided diagnostic programs using image processing and machine learning approaches are designed for early detection of DR so that proper treatment can be provided to the patients for preventing its harmful effects. Now a day machine learning techniques are widely applied for image processing. These techniques also provide amazing result in this field also. In this paper we discuss various machine learning and deep learning based techniques developed for automatic detection of Diabetic Retinopathy.

• Journal of the Korean Society for Heat Treatment
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• v.37 no.3
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• pp.103-113
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• 2024

Martensite volume fraction significantly affects the mechanical properties of alloy steels. Martensite start temperature (M_s), transformation temperature for martensite 50 vol.% (M₅₀), and transformation temperature for martensite 90 vol.% (M₉₀) are important transformation temperatures to control the martensite phase fraction. Several researchers proposed empirical equations and machine learning models to predict the Ms temperature. These numerical approaches can easily predict the Ms temperature without additional experiment and cost. However, to control martensite phase fraction more precisely, we need to reduce prediction error of the Ms model and propose prediction models for other martensite transformation temperatures (M₅₀, M₉₀). In the present study, machine learning model was applied to suggest the predictive model for the Ms, M50, M90 temperatures. To explain prediction mechanisms and suggest feature importance on martensite transformation temperature of machine learning models, the explainable artificial intelligence (XAI) is employed. Random forest regression (RFR) showed the best performance for predicting the Ms, M50, M90 temperatures using different machine learning models. The feature importance was proposed and the prediction mechanisms were discussed by XAI.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• Geomechanics and Engineering
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• v.36 no.4
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• pp.381-390
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• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.