• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.990-1001
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• 2015

This paper presents analysis of Switched Reluctance Machine (SRM) using Geometry Based Analytical Model (GBAM), Finite Element Analysis (FEA) and Fourier Series Model (FSM) with curve fitting technique. Further a Transient Analysis (TA) technique is proposed to corroborate the analysis. The main aim of this paper is to give in depth procedure in developing a Geometry Based Analytical Model of Switched Reluctance Machine which is very accurate and simple. The GBAM is developed for the specifications obtained from the manufacturer and magnetizing characteristic of the material used for the construction. Precise values of the parameters like Magneto Motive Force (MMF), flux linkage, inductance and torque are obtained for various rotor positions taking into account the Fringing Effect (FE). The FEA model is developed using MagNet7.1.1 for the same machine geometry used in GBAM and the results are compared with GBAM. Further another analytical model called Fourier Series Model is developed to justify the accuracy of the results obtained by the methods GBAM and FEA model. A prototype of microcontroller based SRM drive system is constructed for validating the analysis and the results are reported.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 추계학술대회 논문집
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• pp.675-676
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• 2009

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.967-968
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• 2010

• 대한방사선기술학회지:방사선기술과학
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• 제46권2호
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• pp.131-139
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• 2023

High-dose I-131 used for the treatment of thyroid cancer causes localized exposure among radiology technologists handling it. There is a delay between the calibration date and when the dose of I-131 is administered to a patient. Therefore, it is necessary to directly measure the radioactivity of the administered dose using a dose calibrator. In this study, we attempted to apply machine learning modeling to measured external dose rates from shielded I-131 in order to predict their radioactivity. External dose rates were measured at 1 m, 0.3 m, and 0.1 m distances from a shielded container with the I-131, with a total of 868 sets of measurements taken. For the modeling process, we utilized the hold-out method to partition the data with a 7:3 ratio (609 for the training set:259 for the test set). For the machine learning algorithms, we chose linear regression, decision tree, random forest and XGBoost. To evaluate the models, we calculated root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) to evaluate accuracy and R² to evaluate explanatory power. Evaluation results are as follows. Linear regression (RMSE 268.15, MSE 71901.87, MAE 231.68, R² 0.92), decision tree (RMSE 108.89, MSE 11856.92, MAE 19.24, R² 0.99), random forest (RMSE 8.89, MSE 79.10, MAE 6.55, R² 0.99), XGBoost (RMSE 10.21, MSE 104.22, MAE 7.68, R² 0.99). The random forest model achieved the highest predictive ability. Improving the model's performance in the future is expected to contribute to lowering exposure among radiology technologists.

• 기술혁신연구
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• 제24권4호
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• pp.27-53
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• 2016

본 사례는 공작기계산업의 A사에서 수행한 R&D 개발완료 과제 79건을 대상으로 R&D 효율성을 DEA를 활용하여 분석하고, 그 개선방안을 제시한 것이다. DEA 분석에서 투입변수는 R&D 투자비와 연구인력 맨먼스, 산출변수는 개발기간 목표달성률과 예상매출액(5년간)으로 설정하였으며, 표본은 제품, 선행기술, 제어기술로 구분하여 분석하였다. 여기서 선행기술은 제품성능을 위한 요소기술과 응용프로그램을 개발하는 것이고 제어기술은 컴퓨터를 토대로 수치제어 프로그램을 설계하는 것이다. 분석결과에 따르면 제품, 제어기술, 선행기술 순으로 효율적으로 운영되고 있어 선행기술의 효율성이 가장 낮았다. 그 이유는 선행기술의 불학실성에 기인한다. 선행기술은 개발목표를 정하기 어렵고 개발계획도 수립하기 어렵다. 심지어 운영하는 과정에도 환경변화가 영향을 미친다. 투자효율성 분석결과에서 CRS는 제품 34.6%, 선행기술 14.3%, 제어기술 38.9%이다. IRS는 제품 53.8%, 선행기술 85.7%, 제어기술 38.9%이다. DRS는 제품 11.5%, 선행기술 0%, 제어기술 22.2%이다. 전체적으로 본 사례는 과다투입보다는 과소투입이 문제가 되고 있다. 이는 R&D 투자 부족을 의미한다. 주목할 부분은 기업의 미래 경쟁력이 될 수 있는 응용기술에 대한 과소투입이 심각하다는 것이다. 비효율적 DMU의 효율적 운영을 위해서는 최적의 투입량을 관리해야 하며, 이것은 준거집단과의 비교를 통해 구할 수 있다.

• 대한원격탐사학회지
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• 제39권5_1호
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• pp.669-681
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• 2023

과일 나무의 생육을 평가하는 중요한 지표인 엽록소 함량을 추정하는데 비교적 많은 노동력의 투입이 요구되고 오랜 시간이 소요되는 기존의 파괴 조사 대신 비파괴적 조사 방식인 원격탐사기술을 적용하기 위한 연구가 시도되고 있다. 이 연구에서는 2년(2021, 2022) 간 무인기 기반의 초분광 영상을 이용하여 배나무 잎의 엽록소 함량을 비파괴적으로 추정하는 연구를 수행하였다. 영상 처리로 추출된 배나무 캐노피(canopy)의 단일 band 반사율은 시간 변화에 따라 불안정한 복사 효과를 최소화하기 위해 밴드비화(band rationing) 되었다. 밴드비(band ratios)를 입력 변수로 머신러닝 알고리즘인 elastic-net, k-nearest neighbors (KNN)과 support vector machine을 사용하여 추정(calibration, validation) 모델들을 개발하였다. Full band ratios 기반 추정 모델들의 성능과 비교하여 계산 비용 절감과 재현성 향상에 유리한 key band ratios를 선정하였다. 결과적으로 모든 머신러닝 모델에서 full band ratios를 이용한 calibration에 coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9%)와 validation에 R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% 성능을 비교하였을 때, key band ratios 네 개가 선정되었다. 머신러닝 모델들 사이에 validation 성능에는 비교적 큰 차이가 없어 calibration 성능이 가장 높았던 KNN 모델을 기준으로 삼았으며, 그 key band ratios는 710/714, 718/722, 754/758, 758/762 nm가 선정되었다. Calibration에서 R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%와 validation에서 R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%를 나타내었다. Validation의 기준으로 한 성능 결과는 배나무 잎 엽록소 함량을 추정하기에 충분하지 않았지만, 앞으로의 연구에 기준이 될 key band ratios를 선정했다는 것에 의미가 있다. 추후 연구에서는 추정 성능을 향상하기 위해 지속적으로 추가 데이터세트를 확보하여 선정된 key band ratios의 신뢰성 검증과 함께 실제 과원에 재현 가능한 추정 모델로 고도화할 필요가 있다.

• Industrial Engineering and Management Systems
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• 제4권2호
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• pp.136-144
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• 2005

This research deals with an order picking problem in automated storage and retrieval systems (AS/RS). When retrieval requests consist of multiple items and the items are in multiple stock locations, the storage/retrieval (S/R) machine must travel to numerous storage locations to complete each order. The aim of this research is to propose algorithms for the resolution of order picking problems with multiple stock locations to minimize the total time traveled by the S/R machine. We present and compare three alternatives for solving the problem based on enumeration, ordinary heuristic and genetic algorithms. We used a set of 180 different problems that are solved by these three algorithms. The results show that our proposed genetic algorithm is more efficient than the other two.

• 산업공학
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• 제8권1호
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• pp.45-52
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• 1995

Automated Storage and Retrieval System (AS/RS), which is an element of Computer Integrated Manufacturing (CIM), is a widely used material handling equipment with conveyors and Automatic Guided Vehicles (AGVs). Until now the evaluation of operational policies of AS/RS and control algorithms is done theoretically or by computer simulations. In this study, a real-time control and communication software for an AS/RS is developed for actually moving AS/RS miniature. A PC-based real-time operational program can control the AS/RS directly through the communication port. The operational system has additional functions such as storage/retrieval management, inventory management, statistics management, and protocol simulation. The communication protocol simulator of S/R machine can be used for the controller of an S/R machine.

• 대한물리의학회지
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• 제3권4호
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• pp.235-246
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• 2008

Purpose : The purpose of this study was to evaluate the effects of indoor horseback-riding machine(SLIM $RIDER^{(R)}$) exercise on balance of the elderly with dementia. Methods : Subjects over 65 years of age in the nursing home were divided into three groups : Alzheimer's dementia group(n=7), vascular dementia group(n=6), and general elderly group(n=6). All groups(n=19) practiced indoor horseback-riding machine exercise for 20 min a day, three days a week during 6 weeks, and their balance were evaluated at before and 2, 4, 6 weeks after intervention, using the BPM. The level of statistical significance was .05. Results : After the 4weeks indoor horseback-riding machine exercise, balance was significantly increased in the all groups(p<.05). Conclusion : Indoor Horseback-riding machine exercise had a positive effect on subjects' balance.

• Geomechanics and Engineering
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• 제37권4호
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.