• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2118-2126
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• 2017

The large-scale power system blackouts have indicated that conventional protection relays that based on local signals cannot fit for modern power grids with complicated setting or heavily loaded-flow transfer. In order to accurately detect various faulted lines and improve the fault-tolerance of wide-area protection, a novel multi-section weighed fault matching and detecting algorithm is proposed. The real protection vector (RPV) and expected section protection vectors (ESPVs) for five fault sections are constructed respectively. The function of multi-section weighed fault matching is established to calculate the section fault matching degrees between RPV and five ESPVs. Then the fault degree of protected line based on five section fault degrees can be obtained. Two fault detecting criterions are given to support the higher accuracy rate of detecting fault. With the enumerating method, the simulation tests illustrate the correctness and fault-tolerance of proposed algorithm. It can reach the target of 100% accuracy rate under 5 bits error of wide-area protections. The influence factors of fault-tolerance are analyzed, which include the choosing of wide-area protections, as well as the topological structures of power grid and fault threshold.

• Journal of Sensor Science and Technology
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• v.29 no.5
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• pp.303-311
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• 2020

Nondispersive infrared CO₂ gas sensor was developed after the simulation of optical cavity structure and assembling the optical components: IR source, concave reflectors, Fresnel lens, a hollow disk, and IR detectors. By placing a hollow disk in front of reference IR detector, the output voltages are almost constant value, near to 70.2 mV. The absorbance of IR light, F_a, shows the second order of polynomial according to ambient temperatures at 1,500 ppm. The differential output voltages and the absorbance of IR light give a higher accuracy in estimations of CO₂ concentrations with less than ± 1.5 % errors. After implementing the parameters that are dependent upon the ambient temperatures in microcontroller unit (MCU), the measured CO₂ concentrations show high accuracies (less than ± 1.0 %) from 281 K to 308 K and the time constant of developed sensor is about 58 sec at 301 K. Even though the estimation errors are relatively high at low concentration, the developed sensor is competitive to the commercial product with a high accuracy and the stability.

• Journal of Korea Game Society
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• v.13 no.5
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• pp.73-80
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• 2013

NUI(Natural User Interface) allows users to directly interact with surrounding digital devices using their voices or body motions without additional input/output interface devices. Our study has been carried out on human users who play a tangible game with body motions in the NUI-applied smart space. ECG was measured for 60 seconds duration before and after playing the game to determine user stress levels, and the measured signals were analyzed through an improved Random Forest algorithm. In order to experiment by a supervised learning, users additionally input whether or not the user felt stress. Moreover, the improved algorithm showed 1.04% higher accuracy than existing algorithm.

• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.421-431
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• 2020

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis. Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naïve Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study. Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms. Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.

• Journal of the Korean GEO-environmental Society
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• v.10 no.5
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• pp.49-57
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• 2009

In this study, one of the complicated geotechnical problem, compression index was predicted by a artificial neural network method of Levenberg-Marquardt (LM) algorithm. Predicted values were compared and evaluated by the results of the Back Propagation (BP) method, which is used extensively in geotechnical engineering. Also two different results were compared with experimental values estimated by verified experimental methods in order to evaluate the accuracy of each method. The results from experimental method generally showed higher error than the results of both artificial neural network method. The predicted compression index by LM algorithm showed better comprehensive results than BP algorithm in terms of convergence, but accuracy was similar each other.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.852-858
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• 2010

Recently, the high-tech industries, such as aerospace industry, auto industry, and electronics industry, are growing up considerably. Because of that, high machining accuracy and productivity of precision parts have been required. The tooling system is important part in the machining center. HSK tooling system is more suitable than BT tooling system for that of high speed machining center. It is because static stiffness and machining accuracy of HSK tooling system are higher than those of BT tooling system. In this paper, stress distribution characteristics of the HSK tooling System is analyzed according to the spindle speed. In order that, the mechanism and the force amplification principle of HSK tooling system are analyzed. The HSK tooling system is modelled by using a 3D modeling/design program. Then stress distribution characteristics of HSK tooling system are analyzed according to spindle speed by using the finite element analysis.

• Agribusiness and Information Management
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• v.1 no.2
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• pp.43-60
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• 2009

To improve the performance of wide-issue superscalar processors, it is essential to increase the width of instruction fetch and the issue rate. Removal of control hazard has been put forward as a significant new source of instruction-level parallelism for superscalar processors and the conditional branch prediction is an important technique for improving processor performance. Branch mispredictions, however, waste a large number of cycles, inhibit out-of-order execution, and waste electric power on mis-speculated instructions. Hence, the branch predictor with higher accuracy is necessary for good processor performance. In global-history-based predictors like gshare and GAg, many mispredictions come from commit update of the branch history. Some works on this subject have discussed the need for speculative update of the history and recovery mechanisms for branch mispredictions. In this paper, we present a new mechanism for recovering the branch history after a misprediction. The proposed mechanism adds an age_counter to the original predictor and doubles the size of the branch history register. The age_counter counts the number of outstanding branches and uses it to recover the branch history register. Simulation results on the SimpleScalar 3.0/PISA tool set and the SPECINT95 benchmarks show that gshare and GAg with the proposed recovery mechanism improved the average prediction accuracy by 2.14% and 9.21%, respectively and the average IPC by 8.75% and 18.08%, respectively over the original predictor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6028-6042
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• 2019

Since Sparse Nonnegative Matrix Factorization (SNMF) method can control the sparsity of the decomposed matrix, and then it can be adopted to control the sparsity of facial feature extraction and recognition. In order to improve the accuracy of SNMF method for facial feature recognition, new additive iterative rules based on the improved iterative step sizes are proposed to improve the SNMF method, and then the traditional multiplicative iterative rules of SNMF are transformed to additive iterative rules. Meanwhile, to further increase the sparsity of the basis matrix decomposed by the improved SNMF method, a threshold-sparse constraint is adopted to make the basis matrix to a zero-one matrix, which can further improve the accuracy of facial feature recognition. The improved SNMF method based on the additive iterative rules and threshold-sparse constraint is abbreviated as ASNMF, which is adopted to recognize the ORL and CK+ facial datasets, and achieved recognition rate of 96% and 100%, respectively. Meanwhile, from the results of the contrast experiments, it can be found that the recognition rate achieved by the ASNMF method is obviously higher than the basic NMF, traditional SNMF, convex nonnegative matrix factorization (CNMF) and Deep NMF.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.3
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• pp.232-248
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• 2009

Numerical methodology to solve ship springing problem, which is basically fluid-structure interaction problem, was explored in this study. Solution of this hydroelasticity problem was sought by coupling higher order B-spline Rankine panel method and finite element method in time domain, each of which is introduced for fluid and structure domain respectively. Even though varieties of different combinations in terms of numerical scheme are possible and have been tried by many researchers to solve the problem, no systematic study regarding the characteristics of each scheme has been done so far. Here, extensive case studies have been done on the numerical schemes especially focusing on the iteration method, FE analysis of beam-like structure, handling of forward speed problem and so on. Two different iteration scheme, Newton style one and fixed point iteration, were tried in this study and results were compared between the two. For the solution of the FE-based equation of motion, direct integration and modal superposition method were compared with each other from the viewpoint of its efficiency and accuracy. Finally, calculation of second derivative of basis potential, which is difficult to obtain with accuracy within grid-based method like BEM was discussed.