• Journal of Electrical Engineering and Technology
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• v.10 no.3
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.675-676
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.967-968
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• 2010

• Journal of radiological science and technology
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• v.46 no.2
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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.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.27-53
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• 2016

This case analyzed the efficiency of 79 R&D projects performed within one private research center in machine tools industry. DEA was used for efficiency analysis. Input variables were R&D investment expense and man-month. Output variables were achievement rate on target development period and expected net sales within 5-years. Samples are divided into product development, Prior technology development, and control technology development. The key result is that Prior technology showed the lowest efficiency because of high uncertainty. It was so difficult to determine its goals and to make its specific plans. With respect to scale, the proportions of CRS(constant returns to scale) were 34.6%, 14.3% and 38.9% for product development, prior technology, control technology respectively. As for IRS(increase returns to scale), they were 53.8%, 85.7% and 38.9% for product development, prior technology, control technology respectively. As for DRS(decrease returns to scale) they were 11.5%, 0% and 22.2% for product development, prior technology, control technology respectively. On the whole, in this case, insufficient input was more problematic than excessive input, which means the lack of investment in R&D. Prior technology can be the source of the future competitiveness of companies. To operate inefficient DMU efficiently, the optimal input should be managed and it is derived from comparison with the reference group.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Industrial Engineering and Management Systems
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• v.4 no.2
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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.

• IE interfaces
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• v.8 no.1
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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.

• Journal of the Korean Society of Physical Medicine
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• v.3 no.4
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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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• v.37 no.4
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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.