• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.761-762
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.743-744
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent(HAI) controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.731-732
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• 2006

The paper is proposed maximum torque control of IPMSM drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and artificial neural network(ANN). For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.2
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• pp.237-243
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• 2020

Listeria sp. is one of the pathogenic bacteria causes the infection listeriosis, through mainly raw food such as fishery food, dairy food and vegetables. Listeria sp. is a Gram-positive, non-spore-forming, motile, and facultative anaerobic bacterium. Because of the tolerance of Listeria sp. to low temperature and high salt concentration, it is very difficult to prevent them contaminated in the food, which do not require heating, especially, such as raw fishery products. So prevention and removal of bacterial contamination at the food manufacturing stage is the best method. In this study, therefore, several natural products including Psidium guajava and Geranium thunbergii were screened to investigate the antibacterial activity against Listeria sp., with expectation of fewer side effects and fewer resistance problems. Significant effects of two extracts were confirmed by well diffusion assay, MIC assay, and growth inhibition assay. P. guajava and G. thunbergii showed MIC values at 64-256 ㎍/mL meaning strong antibacterial activities against 6 kind of Listeria sp. tested. And the growth of Listeria sp. in the liquid media was actually inhibited by the addition of these two extracts.

• Journal of Engineering Education Research
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• v.17 no.4
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• pp.95-101
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• 2014

As other major, students have difficulties to be successfully adapted to computer science major without some interest and skill to computer programming. In this paper, we try to find successful factors for computer science major freshmen students to computer programming. The factors we focused are programming experience before college entrance, taking liberal arts/natural science courses at high school, application motivations to computer major, existence of mentors, satisfaction to his/her computer department, student's holland job aptitude code. After analysis, taking liberal arts/natural science courses at high school, satisfaction to computer department, some holland job aptitude code are significant to their successful adaptation to computer major. Also, we found the holland job apptitude code is closely related to student's satisfaction to their major for engineering students including computer science students. Our analysis results will be a suggestion for designing computer science education program with students who enters college without some aptitude or preparation to his major.

• Proceedings of the Zoological Society Korea Conference
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• 1997.10b
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• pp.224.2-224
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• 1997

No Abstract, See Full Text

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.416-416
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• 2005

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.153-160
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• 2021

Land Surface Temperature (LST) is the radiological surface temperature which observed by satellite. It is very important factor to estimate condition of the Earth such as Global warming and Heat island. For these reasons, many countries operate their own satellite to observe the Earth condition. South Korea has many landcovers such as forest, crop land, urban. Therefore, if we want to retrieve accurate LST, we would use high-resolution satellite data. In this study, we made LSTs with 4 LST retrieval algorithms which are used widely with Landsat-8 data which has 30 m spatial resolution. We retrieved LST using equations of Price, Becker et al. Prata, Coll et al. and they showed very similar spatial distribution. We validated 4 LSTs with Moderate resolution Imaging Spectroradiometer (MODIS) LST data to find the most suitable algorithm. As a result, every LST shows 2.160 ~ 3.387 K of RMSE. And LST by Prata algorithm show the lowest RMSE than others. With this validation result, we choose LST by Prata algorithm as the most suitable LST to South Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Annual Conference of KIPS
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• 2015.10a
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• pp.920-922
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• 2015

매 학기 교수별, 과목별, 강의실별 시간표를 학사규정에 맞추어 수작업으로 작성하였다. 이에 업무량 및 시간적인 효율성 증대가 요구된다. 수작업으로 진행되는 프로세스 과정을 데이터베이스와 웹페이지를 활용하여 전산화 시켜 1주일이라는 작업시간을 단 1일로 줄였으며, 교수들이 직접 웹페이지에 접속하여 연구일을 직접 입력하고, 배정 완료된 강의들의 시간 및 강의실을 확인하기 쉽게 구현되었으며, 추후 타 학교에서도 사용가능 하도록 시스템을 개선할 계획이다.