• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.478-487
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• 1996

A " plucking rolls device" was developed in this study to improve the quality of harvested tea leaves. In this report, the outline of the system and the results of performance experiments in our laboratory are discussed. Tow kinds of performance experiments were carried out. The first experiment checked harvesting accuracy by using a plucking unit that was developed for harvesting machine installation. The second experiment was a harvesting experiment which utilized a fron bar in order to prevent cutting of the tea buds which had been a problem in precious experiments . As a results of the first experiments , it was confirmed that selective harvesting obtained high quality tea leaves. but a cutting problem that, when the harvesting seed was faster than the working speed, which was non-selective harvesting , was also seen. In the second experiment, the cutting rate decreased to a maximum of 50% level, when tea buds most bent ahead by the front bar. The effect was seen that cutt ng problem was alleviated from this.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.3
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• pp.109-114
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• 2016

This study presents the optimal hardness range for a coated layer of a workpiece when the diamond tool cuts the corner-cube pattern on the coated plates using an ultra-precision diamond-turning machine. Two kinds of coated plates, which have the hardness range of 211~328 Vickers hardness, are used on the first experiments. The form accuracy for the corner-cube pattern could be achieved through the following experiments using the accumulated thin copper plates in second experiments, having optimal 265~275 Vickers hardness based on the basic first experiments without tool wear. When the number of machining adjustments was increased to seven times, having machining depth was reduced successively in second experiment, a fine surface could be achieved without tool wear.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1175-1180
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• 1991

The purpose of this paper is to improve the matching accuracy in identifying corresponding points in the area-based matching for the processing of stereo vision. For the selection of window size, a new method is proposed based on frequency domain analysis. The effectiveness of the proposed method is confirmed through a series of experiments. To overcome disproportionate distortion in stereo image pair, a new matching method using the warped window is also proposed. In the algorithm, the window is warped according to imaging geometry. Experiments on a synthetic image show that the matching accuracy is improved by 14.1% and 4.2% over the rectangular window method and image warping method each.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.7
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• pp.88-96
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• 2002

High-speed machining is one of the most effective technologies to improve productivity. Because of the high speed and high feed rate, high-speed machining can give great advantages for the machining of dies and molds. This paper describes on the improvement of machining accuracy in high-speed machining. Depth of cut, feed rate and spindle revolution are control factors. The effect of the control factors on machining accuracy is investigated using two-way factorial design.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.393-396
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• 1997

High-speed machining is one of the most effective technologies to improve productivity. Because of the high speed and high feed rate, high-speed machining can give great advantages for the machining of dies and molds. This paper describes on the improvement of machining accuracy in high-speed machining. Depth of cut and feed rate are control factors. The effect of the control factors on machining accuracy is investigated using two-way factorial design.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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• pp.1-9
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• 2006

As consumers in optics, electronics, aerospace and electronics industry grow, the demand for ultra-precision aspheric surface lens increases higher. To enhance the precision and productivity of ultra precision aspheric surface micro lens, the development of ultra-precision grinding system and process for the aspheric surface micro lens are described. In the work reported in this paper, an ultra-precision grinding system for manufacturing the aspheric surface micro lens was developed by considering the factors affecting the ground surface roughness and profile accuracy. This paper deals with mirror grinding of an aspheric surface micro lens by resin bonded diamond wheel and spherical lens of BK7. The optimization of grinding conditions on ground surface roughness and profiles accuracy is investigated using the design of experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.561-562
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• 2006

To enhance the precision and productivity of ultra precision aspheric surface micro lens, the development of ultra-precision grinding system and process for the aspheric surface micro lens are described. In the work reported in this paper, an ultra-precision grinding system for manufacturing the aspheric surface micro lens was developed by considering the factors affecting the grinding surface roughness and profile accuracy. This paper deals with mirror grinding of an aspheric surface micro lens by resin bonded diamond wheel and spherical lens of BK7. The optimization of grinding conditions on ground surface roughness and profiles accuracy is investigated using the design of experiments.