• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.24-32
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• 2010

In this study, we have proposed a pre-processing algorithm newly developed for improving the tracking efficiency of the optical flow method. The developed pre-processing algorithm consists of a median filter, binarization, morphology, canny edge, contour detecting and an approximation method. In order to evaluate whether the optical flow tracking capacity increases, this study applied the pre-processing algorithm to the Lucas-Kanade(LK) optical flow algorithm, and comparatively analyzed its images and tracking results with those of optical flow without the pre-processing algorithm and with the existing pre-processing algorithm(composed of median filter and histogram equalization). As a result, it was observed that the tracking performance derived from the LK optical flow algorithm with the pre-processing algorithm, shows better tracking accuracy, compared to the one without the pre-processing algorithm and the one with the existing pre-processing algorithm. It seems to have resulted by successful segmentation for characteristic areas and subdivision into inner and outer contour lines.

• International Journal of Contents
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• v.8 no.2
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Korean Journal of Veterinary Service
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• v.38 no.1
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• pp.25-30
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• 2015

Bee venom has been used as to prevent and treat bovine mastitis as natural antimicrobial compounds in some dairy cattle farms in Korea. It is needed to determine the residual of bee venom in milks of dairy cattle treated with bee venom. Since bee venom is not approved as a raw material for animal drugs, the preprocessing method to detect bee venom residual in milk and the tolerance limit for its residue has not been established yet in Korea. Therefore, the purpose of this study was to develop pre-processing method not affecting major component of bee venom for detection of its residue in milks using ultra-high performance liauid chromatography (UHPLC). In addition, bee venom residue was also analyzed in milk samples of dairy cattle treated for mastitis with bee venom using UHPLC with the developed pre-processing method in this study. As a result, melittin, histamin and phospolipase A2, the major components of bee venom, were all detected by UHPLC with the pre-processing method developed in this study. The results of this study suggest that the pre-processing method developed in this study can be useful to detect bee venom residue in dairy cattle milk. We also found that no bee venom residues were detected in milk samples collected from dairy cattle treated with bee venom after 1 and 3 days, respectively.

• The Journal of Information Technology
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• v.7 no.2
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• pp.43-54
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• 2004

Since Dr. John J. Hopfield has proposed the HOpfield network, it has been widely applied to the pattern recognition and the routing optimization. The method of Jian-Hua Li improved efficiency of Hopfield network which input pattern's weights are regenerated by SVD(singluar value decomposition). This paper deals with Li's Hopfield Network by linear pre-processing. Linear pre-processing is used for increasing orthogonality of input pattern set. Two methods of pre-processing are used, Hadamard method and random method. In manner of success rate, radom method improves maximum 30 percent than the original and hadamard method improves maximum 15 percent. In manner of success time, random method decreases maximum 5 iterations and hadamard method decreases maximum 2.5 iterations.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.264-273
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• 2002

Adaptive filters cannot exactly estimate the echo path of the receiving room because of the cross-correlation of stereo signals. In this paper, a new pre-processing method reducing the cross-correlation without degradation of stereophony is proposed to enhance the performance of stereophonic acoustic echo canceller. To reduce the cross-correlation, absolutes of two orthogonal signals derived from each channel signals are added to original channel signals. Assuming that the power of each channel signal is larger than that of the cross-correlation, the computation of pre-processing can be reduced. As results of simulations, it is shown that the performance of stereo acoustic echo canceller with the proposed pre-processing method is better than that of conventional ones.

• Agricultural and Biosystems Engineering
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• v.5 no.2
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• pp.60-63
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• 2004

Demand for development of processing systems for pre-washed rice and propagation of the systems has recently been on the rise, because rice-cooking requires watering 15 times the rice quantity to cook, as in case of the regular rice currently being circulated in Korean market, in addition to paying the trouble of washing it for cooking, and besides the milky turbid water coming from the rice-washing contributes to water contamination. In this study, therefore, a processing system for pre-washed rice was designed and built with rice surface polishing devices that adopted abrading and airing methods, an electrostatic method and a method using a fine watering, to conduct its performance test. The result showed that turbidity of the wash water, which is the base to determine the pre-washed rice standard, turned out 47.33 ppm and 48.00 ppm respectively for 800 kg/hr and 1,000 kg/hr supplies, which meets the standard for the processing system free from rice-washing for cooking. The quantity of watering at this experiment was only 0.43 times the rice, thus resulting in curtailment of process-watering by approximately $69\%$ compared with the existing wet-type pre-washed rice processing system popular in Korean market.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1278-1285
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• 2008

An optimization-based real-time joint angle extraction method of human elbow is proposed by processing the biomedical signal of surface EMG (electromyogram) measured at the center point of biceps brachii. The EMG signal is known as non-stationary (time-varying) signal, but we assume that it is quasi-stationary because a physical or physiological system has limitations in the rate at which it can change its characteristics. Based on the assumption, a pre-processing method to obtain pre-angle values from raw EMG signal is firstly suggested, and then an optimization method to minimize the error between the pre-angle and real joint angle is proposed in this paper. Finally, we suggest the experimental results showing the effectiveness of the proposed algorithm.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.1031-1041
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• 2018

ANFIS-based methodology for improving GloSea5 ensemble weather forecast is developed and evaluated in this study. The proposed method consists of two steps: pre & post processing. For ensemble prediction of GloSea5, weights are assigned to the ensemble members based on Optimal Weighting Method (OWM) in the pre-processing. Then, the bias of the results of pre-processed is corrected based on Model Output Statistics (MOS) method in the post-processing. The watershed of the Chungju multi-purpose dam in South Korea is selected as a study area. The results of evaluation indicated that the pre-processing step (CASE1), the post-processing step (CASE2), pre & post processing step (CASE3) results were significantly improved than the original GloSea5 bias correction (BC_GS5). Correction performance is better the order of CASE3, CASE1, CASE2. Also, the accuracy of pre-processing was improved during the season with high variability of precipitation. The post-processing step reduced the error that could not be smoothed by pre-processing step. It could be concluded that this methodology improved the ability of GloSea5 ensemble weather forecast by using ANFIS, especially, for the summer season with high variability of precipitation when applied both pre- and post-processing steps.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.86-91
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• 1991

A capability of performing the optical logic and arithmetic operations is followed by an effective encoding technique. In this paper, we proposed the color light encoding technique. By using this encoding technique, the space bandwidth product(SBP) is minimized in the output plane. In addition, we proposed the pre-operation pro-carry processing method that performs faster than the same time operation and carry processing method in optical computing. We proposed that the color liquid crystal device(CLCD) is used as the encoded color light input source.