• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.31-38
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• 2014

The purpose of this study is to make virtual machine using a 3D modeller to perform reverse engineering. Through the intuitive designer's ability, approximated model of the object is created and used to extract the data and NC tool path. The extracted data make approximated curve by using NURBS curve fitting. And the curve is used to remodel. From these series of process, the final reverse engineering data of the objects can be obtained. Two conclusions are drawn from this study. First, initial deviation of the intuitive model is one of the important factors that determine the number of repetitions of the experiment. Due to the characteristic of intuitive curve, after a certain number of repetitions the average deviation increase and radiate rather than decrease.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.118-127
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• 2016

An improved sample balanced genetic algorithm and Extreme Learning Machine (iSBGA-ELM) was designed for accurate diagnosis of Alzheimer disease (AD) and identification of biomarkers associated with AD in this paper. The proposed AD diagnosis approach uses a set of magnetic resonance imaging scans in Open Access Series of Imaging Studies (OASIS) public database to build an efficient AD classifier. The approach contains two steps: "voxels selection" based on an iSBGA and "AD classification" based on the ELM. In the first step, the proposed iSBGA searches for a robust subset of voxels with promising properties for further AD diagnosis. The robust subset of voxels chosen by iSBGA is then used to build an AD classifier based on the ELM. A robust subset of voxels keeps a high generalization performance of AD classification in various scenarios and highlights the importance of the chosen voxels for AD research. The AD classifier with maximum classification accuracy is created using an optimal subset of robust voxels. It represents the final AD diagnosis approach. Experiments with the proposed iSBGA-ELM using OASIS data set showed an average testing accuracy of 87%. Experiments clearly indicated the proposed iSBGA-ELM was efficient for AD diagnosis. It showed improvements over existing techniques.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.4
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• pp.54-60
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• 2005

In a $CO_2$ inverter welding machine, stable arcs can be generated and a welding performance that is a goal of welding can be improved when stable electric power with a low voltage and a high current is supplied to a electrode that is the secondary part (output load terminal) and the base metal. For such a stable power supply, therefore, the AC arc welding machine, the thyristor welder, and the inverter welder have been developed in order according to development of the power electronics techniques. Up to now, the thyristor welding machine is still broadly used but the application volume is gradually reduced by development of the inverter welder. Because the welding performance of the inverter welder is very good and the weight and size of the welder is remarkably light and small. The final goal of this research is to develop the voltage loss compensator that is a drawback of the inverter welder and improve the welding performance using the developed compensator.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.7
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• pp.450-454
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• 2018

Because of the rapidly changing environment and high uncertainties, the semiconductor industry is in need of appropriate forecasting technology. In particular, both the cost and time in the test process are increasing because the process becomes complicated and there are more factors to consider. In this paper, we propose a prediction model that predicts a final "good" or "bad" on the basis of preconditioning test data generated in the semiconductor test process. The proposed prediction model solves the classification and regression problems that are often dealt with in the semiconductor process and constructs a reliable prediction model. We also implemented a prediction model through various machine learning algorithms. We compared the performance of the prediction models constructed through each algorithm. Actual data of the semiconductor test process was used for accurate prediction model construction and effective test verification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.276-281
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• 2014

In this research, we describe a force feedback control for industrial robots has been proposed as a system which is suitable to work utilizing pressure sensitive alternative to human. Conventionally, polished surface of the workpiece are recognized, chamfer ridge, machining processes such as deburring, and it is most difficult to automate because of its complexity, has been largely dependent on the human. To aim to build automatic vacuum system robotic force control was gripping the grinding tool, the present study we examined the adaptability to the polishing process to understand the characteristics of the control system feedback signal obtained from the force sensor mainly. Furthermore, as a field, which holds the key to the commercialization, I went ahead with the application to robotic sweeping machine. As a result, the final sweeping utilizing a robot machine to obtain a very good grinded surface was revealed.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1643-1645
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• 2005

A newly-built inverter has to undergo a series of stress tests in the final stage of production line. This can be achieved by connecting it to a dynamometer consisting of a three-phase machine joined by a rigid shaft to a DC load machine. The latter is controlled to create some specific load characteristic needed for the test. In this paper a test method is proposed, in which no mechanical equipment is needed. The suggested test stand consists only of a inverter to be tested and a simulator converter. Both devices are connected back- to-back on the AC-side via smoothing reactors. The simulator operates in real-time as an equivalent load circuit, so that the device under test will only notice the behaviour of a three-phase machine under consideration of the load. In odor to wove rightness of the suggested test method, the simulation and actural experiment rallied out emulation for a 2.2kW induction motor.

• International Journal of Contents
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• v.15 no.1
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• pp.39-51
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• 2019

Spontaneous speech provides rich information defining the linguistic characteristics of individuals. As such, computational analysis of speech would enhance the efficiency involved in evaluating patients' speech. This study aims to provide a method to differentiate the persons with and without aphasia based on language usage. Ten aphasic patients and their counterpart normal controls participated, and they were all tasked to describe a set of given words. Their utterances were linguistically processed and compared to each other. Computational analyses from PCA (Principle Component Analysis) to machine learning were conducted to select the relevant linguistic features, and consequently to classify the two groups based on the features selected. It was found that functional words, not content words, were the main differentiator of the two groups. The most viable discriminators were demonstratives, function words, sentence final endings, and postpositions. The machine learning classification model was found to be quite accurate (90%), and to impressively be stable. This study is noteworthy as it is the first attempt that uses computational analysis to characterize the word usage patterns in Korean aphasic patients, thereby discriminating from the normal group.

• Journal of Korea Game Society
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• v.22 no.1
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• pp.33-42
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• 2022

In online games, interactions with other players may threaten player satisfaction. Therefore, matching players of similar skill levels is important for players' experience. However, with the current evaluation method which is only based on the final result of the game, newbies and returning players are difficult to be matched properly. In this study, we propose a method to improve matchmaking quality. We build machine learning models to predict the MMR of players and derive the basis of the prediction. The error of the best model was 40.4% of the average MMR range, confirming that the proposed method can immediately place players in a league close to their current skill level. In addition, the basis of predictions may help players to accept the result.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.42-47
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• 2004

Formed insert type tool satisfy both the surface roughness and geometric accuracy, so that cutting edge of formed tool can duplicate final feature. For experiment the formed tools with various clearance angles are machined. And the tools are evaluated with respect cutting force, flank rear and surface roughness to optimistic condition.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.4 no.2
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• pp.38-45
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• 1995

The diagnosis and monitoring system of abnormal cutting condition is necessary to realize precision machining proces and factory automation, which are final goal of metal cutting in order to develop this system, theimage processing technique has been investigated in machining process. In theis paper, the measurement system of tool wear using computer vision is designed to detect the wear pattern by non-contact and direct method and get the realiable wear information about cutting tool. We measured the area of the side and front part of the diamond core dril which is used in 40kHz ultrasonic vibration machine.