• Design & Manufacturing
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• v.17 no.2
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• pp.15-21
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• 2023

In this study, a diamond turning machine and a laser-assisted machining module were utilized for the complex combined cutting of aspheric shapes and fine patterns on the surface of high-hardness brittle material, silicon. The analysis of material's form accuracy and corrective machining was conducted based on key factors such as laser output, rotational speed, feed rate, and cutting depth to achieve form accuracy below 1 ㎛ and surface roughness below 0.1 ㎛. The cutting condition and corrective machining methods were investigated to achieve the desired form accuracy and surface roughness. The rotational speed of the spindle and the linear feed rate of the diamond turning machine were varied in five stages for the cutting condition test. Surface roughness and form accuracy were measured using both a contact surface profilometer and a non-contact surface profilometer. The experimental results revealed a tendency of improved surface roughness with increased rotational speed of the workpiece, and the best surface roughness and form accuracy were observed at a feed rate of 5 mm/min. Furthermore, based on the cutting condition experiments, corrective machining was performed. The experimental results demonstrated an improvement in form accuracy from 0.94 ㎛ to 0.31 ㎛ and a significant reduction in the average value of the surface roughness curve from 0.234 ㎛ to 0.061 ㎛. This research serves as a foundation for future studies focusing on the machinability in relation to laser output parameters.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.85-98
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• 2009

Recently, there are a number of researches and developments for the personalized software platform for mobility based on SaaS. The platform requires an optimal virtual machine in order to satisfy the operating systems of various users for the software. In addition, the platform must guarantee the mobility of the users' working environments by supporting fast and secure services between internal and external networks in the platform operating systems. In order to verify the optimal behaviors of virtual machines for the platform, the performance of the virtual machines must be measured and analyzed in various perspectives. In the previous research, unfortunately, the performance of a virtual machine were conducted in the condition that a guest operating system was installed on the virtual machine and considered as a computer, by measuring the network traffic between the guest operating system and an external client operating system. This performance measurement was not suitable for a virtual machine for the platform since a number of different software must be handled in the virtual machine. In order to overcome this limitation, this paper presents a measurement method for network performance and proposes the most optimal virtual machine by the method.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.729-735
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• 2000

Moisture loss of fresh fruits and vegetables is a main cause of deterioration. It resulted not only in the direct quantitative loss, but also in change in appearance, texture and nutrition. To reduce the loss of moisture content during the distribution in the market, fresh products are packaged using plastic films. But, most of the fresh products are packaged manually in Korea. In order to minimize the labor requirement, the packaging machine for fruits and vegetables was developed and tested. Prototype was composed of film feeding unit, bag former, products feeding conveyor, film feeding roller, center sealer, end sealer and discharge conveyor. Green peppers, carrots and perilla leaves were tested with prototype. Prototype could pack 1780, 1390, 1780 bags per hour at the feeding speed of 0.08m/s respectively and 2250, 1810, 2640bags per hour at the feeding speed of 0.10m/s respectively. And packaging speed of green peppers and carrots was improved by 3.7 and 3.4 times compared with manual packaging. The packaging condition with the prototype was good and the products had almost no damages.

• Asian Journal of Business Environment
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• v.12 no.3
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• pp.1-12
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• 2022

Purpose: This study aims to investigate whether AIS data can be used as a supporting indicator or as an initial signal to describe Indonesia's export-import conditions in real-time. Research design, data, and methodology: This study performs several stages of data selection to obtain indicators from AIS that truly reflect export-import activities in Indonesia. Also, investigate the potential of AIS indicators in producing forecasts of the value and volume of Indonesian export-import using conventional statistical methods and machine learning techniques. Results: The six preprocessing stages defined in this study filtered AIS data from 661.8 million messages to 73.5 million messages. Seven predictors were formed from the selected AIS data. The AIS indicator can be used to provide an initial signal about Indonesia's import-export activities. Each export or import activity has its own predictor. Conventional statistical methods and machine learning techniques have the same ability both in forecasting Indonesia's exports and imports. Conclusions: Big data AIS can be used as a supporting indicator as a signal of the condition of export-import values in Indonesia. The right method of building indicators can make the data valuable for the performance of the forecasting model.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.185-189
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• 2001

In order to prevent the production defects the optimum design of product, jig and fixture putting in the field is very significant manufacturing method. Drilling Jig is the device according to industrial demand for multi manufacturing products on the growing at alarming rate. In the field of design and making for machine tool working, welding, assembling with jig and fixture for mass production is a specific division. They require analysis of many kinds of important factors, theory and practice of machine tool operating process and jig & fixture structure, machining condition for tool making, tool materials, heat treatment of jig & fixture components, know-how and so on. In this study we designed and constructed a drilling jig of mass production and performed tryout under the Auto CAD, Auto Lisp database, that we made by database, and window environment. Especially this study is reveals with the analysis of part drawing, jig planning, jig design etc, and then the result of drill jig's making try out.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.446-449
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• 1995

In this paper, the accurate end position control method of ultraprecision machine tool post using piezoelectric material as an micro positonong devics is presented. This method employs the classical PID feedback and uses an additional notch filter which eliminates the resonance characteristics of controlled plant. And the simple predictor is added to make use of the future value of desired input for better tracking performance. To show the feasibilty of proposed method, the PC-based experimental apparacy can be obtained. Using method, Al specimen of diameter 100mm was cut under practical machining condition to test the practicability of proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.833-834
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• 2014

In recent years, a pattern recognition method has been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov model has recently been used as pattern recognition methods in various fields. In this study, a HMM method for the fault diagnosis of a mechanical system is introduced, and a rotating machine with mass unbalance is selected for fault diagnosis. Moreover, a diagnosis procedure to identity the size of a defect is proposed in this study.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• Proceedings of the ESK Conference
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• 1995.10a
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• pp.208-213
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• 1995

The vehicle navigation system is developed for helping driver to retrieve driving information more easily and lastly. Navigation System informs driver many pieces of driving information - roadway structure and system, on-line traffic condition, the position of vehicle, route guidance, destination and other infor- mation service. As the style of information is diverse and the amount of information is large, driver may have mental and visual overload. The display of information can disturb the driver's attention and this can cause accidents. This state is caused by the defect of human-machine interactions. When the navigation system is designed, human factors - cognitive, judgment, operating -must be considered. The display style must be designed simply and easily, not to be obstacle of human - machine interface. In this study, outside- in view display style and inside-out view display style are compared each other. Tow factors are measured. One is cognitive factor-time of cognition on information that is displayed by screen display, cognition error rate. The other is image of screen display - subject's feeling about several styles of display, degree of subject's preference. The prototype of roadway is four kinds -Cross, T-cross and O-cross. Roadway display for test is taken from paper maps. Traffic condition display style, vehicle position display style and route guidance display style are taken from current display style. Traffic condition display style is symbol. vehicle position display style and route guidance display style are described as color and symbol. The test on screen display is implemented doing given tasks. Then the test is analyzed statistically. The result of test analysis gives the guideline to the designer for the map display of the vehicle navigation system.