• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.11-14
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• 1996

Samsung Electronics has developed high performance velocity and position controller for induction motors and succeeded in mass production for first time in Lorea. Dynamic performance and final control accuracy of the controller are equivalent to those of AC servo motor controller. At present we adopted the controller as spindle motor drive for Samsung CNC systems and expect its wide use in industry as general purpose velocity and position controller for induction motor.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.319-321
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• 1997

This paper deals with multipolar magnetizing process which can exert a considerable influence on the final performance of permanent magnet machine. In combination with impulse discharge magnetizer, the analysis and design of magnetizing fixture using finite element method is required to obtain the accurate characteristics of permanent magnet for small-size step motor. Simulated result of flux density shows good agreement with measured one.

• Proceedings of the Optical Society of Korea Conference
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• 2008.07a
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• pp.403-404
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• 2008

This report described the development of Vision Inspector program which can simulate numerically the image acquisition process of Machine Vision System for automatic optical inspection of any products. The program consists of an illuminator, a product to be inspected, and a camera with image sensor, and the final image obtained by ray tracing.

• Journal of KIISE
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• v.41 no.11
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• pp.911-920
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• 2014

In this paper, we introduce an automatic scoring system for middle-school level writing test based on using machine learning techniques. We discuss overall process and features for building an automatic English writing scoring system. A "concept answer" which represents an abstract meaning of text is newly introduced in order to evaluate the elaboration of a student's answer. In this work, multiple machine learning algorithms are adopted for scoring English writings. We suggest a decision process "optimal combination" which optimally combines multiple outputs of machine learning algorithms and generates a final single output in order to improve the performance of the automatic scoring. By experiments with actual test data, we evaluate the performance of overall automated English writing scoring system.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.201-206
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• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• Korean Journal of Food Science and Technology
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• v.23 no.2
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• pp.212-216
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• 1991

Whipping and bandaekee making process were known to a bottle neck for yukwa(deep-fat fried waxy rice snack) making process. For mechanization of the process, a machine was designed and manufactured with conveyer. Some functions of the machine were compared. The continuous whipping and bandaekee making machine was developed by modification of chopper. The chopper was substituted with specially designed plates and die. The newly designed plates were suitable for continuous whipping of dough and making bandaekee without showing any quality different at the final stage. The width and thickness of bandaekee could be controlled by speed of conveyer. The proper conveyer speed was 87.3 mm/sec when amount of extrudate of dough was 221.8 g/sec (MW 51%) from chopper. A shape of knife and plate among components of chopper was not seriously influenced on whipping effect. Expectable thickness of bandaekee for good quality was $3.0{\sim}3.5\;mm$. The number of passing through the chopper was not effected on yukwa quality·but no whipping showed bulky volume with too soft texture.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.86-96
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• 2016

Recently, virtualization is becoming the critical issue in the cloud computing due to its advantages of resource utilization and consolidation. In order to efficiently use virtualization services, several issues should be taken into account, including data reliability, security, and performance. In particular, a high write bandwidth on the virtual machine must be guaranteed to provide fast responsiveness to users. In this study, we implemented a way of visualizing comparison results between the block write pattern of KVM-based virtual machine and that of the real machine. Our final objective is to propose an optimized virtualization environment that enables to accelerate the disk write bandwidth.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.346-347
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• 2021

For pattern recognition for machine learning, the larger the amount of learning data, the better its performance. However, it is not always possible to secure a large amount of learning data with the types and information of patterns that must be detected in daily life. Therefore, it is necessary to significantly inflate a small data set for general machine learning. In this study, we study techniques to augment data so that machine learning can be performed. A representative method of performing machine learning using a small data set is the transfer learning technique. Transfer learning is a method of obtaining a result by performing basic learning with a general-purpose data set and then substituting the target data set into the final stage. In this study, a learning model trained with a general-purpose data set such as ImageNet is used as a feature extraction set using augmented data to detect a desired pattern.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.