• Journal of Food Hygiene and Safety
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• v.14 no.1
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• pp.67-75
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• 1999

The new approach using the Peak Pattern Method was conducted for the analysis of polychlorinated biphenyls (PCBs) from the papers or paper products to provide certain hygienic data for the recycled papers as well as white papers. The height of the each peak, obtained from the PCBs standards, was transformed to the spectrum to compare with that of the samples. In addition to the results of the single PCBs standards, the pattern of mixed PCBs standards with the adequate concentration and ratios were obtained prior to the analysis of the paper samples. The test showed excellent repetition within 5% variation, and the recoveries of PCBs ranged from 92% to 97%. The PCBs, considered as a hazard material, were analyzed using the Peak Pattern Method from six different types of paper samples including the roll tissue. It could not find the same pattern of the PCBs peaks from all of the paper samples.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.98-103
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• 2002

In case of power demand forecasting, the most important problems are to deal with the load of special-days. Accordingly, this paper presents the method that forecasting long (the Lunar New Year, the Full Moon Festival) and short(the Planting Trees Day, the Memorial Day, etc) special-days peak load using neural networks and regression models. long and short special-days peak load forecast by neural networks models uses pattern conversion ratio and four-order orthogonal polynomials regression models. There are using that special-days peak load data during ten years(1985∼1994). In the result of special-days peak load forecasting, forecasting % error shows good results as about 1 ∼2[%] both neural networks models and four-order orthogonal polynomials regression models. Besides, from the result of analysis of adjusted coefficient of determination and F-test, the significance of the are convinced four-order orthogonal polynomials regression models. When the neural networks models are compared with the four-order orthogonal polynomials regression models at a view of the results of special-days peak load forecasting, the neural networks models which uses pattern conversion ratio are more effective on forecasting long special-days peak load. On the other hand, in case of forecasting short special-days peak load, both are valid.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.233-242
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• 2018

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.

• Archives of Pharmacal Research
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• v.15 no.4
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• pp.376-378
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• 1992

A chemical tool was developed for the analysis of complex mixtures such as crude drugs by the method of pattern recognition. Pattern recognition was accomplished by a multiple reference peak identification method and three kinds of outlier statistics. This tool was tested on the analysis of synthetic mixtures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.213-219
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• 2004

This paper proposes speaker recognition algorithm which includes the pitch parameter for the peak and valley. The time-frequency hybrid method for pitch extraction is valuable in that it can improve resolution in the time domain and accuracy in the frequency domain at the same time. It makes reference pattern using membership function and performs vocal track recognition of common character using fuzzy pattern matching in order to include time variation width for non-linear utterance for proposed method, speaker recognition experiments are carried out using vowels and number sounds.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.833-839
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• 2019

Bus Information System (BIS) collects information related to the operation of buses and provides information to users through predictive algorithms. Method of predicting through recent information in same section reflects the traffic situation of the section, but cannot reflect the characteristics of the target line. The method of predicting the historical data at the same time zone is limited in forecasting peak time with high volatility of traffic flow. Therefore, we developed a pattern recognition bus arrival time prediction algorithm which could be overcome previous limitation. This method recognize the traffic pattern of target flow and select the most similar past traffic pattern. The results of this study were compared with the BIS arrival forecast information history of Seoul. RMSE of travel time between estimated and observed was approximately 35 seconds (40 seconds in BIS) at the off-peak time and 40 seconds (60 seconds in BIS) at the peak time. This means that there is data that can represent the current traffic situation in other time zones except for the same past time zone.

• Journal of ILASS-Korea
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• v.21 no.3
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• pp.155-161
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• 2016

Injected fuel pressure waves of a common rail injector with various current profiles supplied to the injecor were measured using Bosch method. In order to drive the common rail injector, the current in the solenoid should be controlled using what is known as a peak and hold pattern, which consists of a high current level with a short time duration (peak) in the first step and a low current level with a long time duration (hold) in the subsequent step. The current profile can be shaped by swithcing an injector driving power source with the peak and hold waves. The capture, compare and PWM (CCP) pin in the microprocessor was used to generate the combined peak and hold waves. The PWM square wave generated from the CCP pin has a duty ratio of 100% for the peak current and 10% or 30% for the hold pattern. Five patterns of the current profile were generated by combining the peak and hold wave. The common rail pressure is controlled at 75, 100, and 130 MPa. As the fuel rail pressure increases, the variations of the measured fuel injection pressure wave according to the current profiles decrease.

• Journal of Korea Water Resources Association
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• v.34 no.1
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• pp.67-80
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• 2001

This study is to propose temporal pattern of design rainfall which causes maximum peak discharge and to analyze the variation in peak discharge according to design rainfall durations. In this study, the Mononobe, the Yen and Chow triangular, the Huff's 4th quartiles and the Keifer and Chu methods are applied to estimate the proper temporal pattern of design rainfall and three rainfall-runoff models such as SCS, Nakayasu, and Clark methods are used to estimate the runoff hydrograph. And to examine the variability of peak discharge, the hydrologic characteristics from the rainfall-runoff models to which uniform rainfall intensity is applied are used as the standard values. The type of temporal pattern of design rainfall which causes maximum peak discharge in both of the watersheds and the rainfall-runoff models has resulted in Yen and Chow distribution method with the dimensionless vague of 0.75. On the basis of determined temporal pattern, the examination of the variability of peak discharge according to design rainfall durations shows that design rainfall duration varies greatly with the types of probable intensity formula, and the variation of peak discharge is more affected by the types of probable intensity formula and I-D-F currie than rainfall-runoff models.

• Korean Journal of Applied Biomechanics
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• v.28 no.2
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• pp.109-117
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• 2018

Objective: To compare head and hand movement patterns during squash forehand motions between experts and less-skilled squash players. Method: Four experts and four less-skilled squash players participated in this study. They performed squash forehand swings and a VICON motion analysis system was used to obtain displacement and velocity data of the head and right hand during the movement. Mann-Whitney U-tests were performed to compare head and hand range of motion and peak velocity, and cross-correlation was performed to analyze the head-hand coordination pattern between groups in three movement directions. Results: In terms of head and hand kinematic data, experts had greater head range of motion during down swings than less-skilled squash players. Experts seemed to reach peak hand velocity at impact by reaching peak head velocity followed by hand peak velocity within a given temporal sequence. In terms of head-hand coordination patterns, both groups revealed high positive correlations in the medial-lateral direction, indicating a dominant allocentric coordination pattern. However, experts had uncoupled coordination patterns in the vertical direction and less-skilled squash players had high positive correlations. These results indicate that the head-hand movement pattern likely an important factor squash forehand movement. Conclusion: Analysis of head and hand movement patterns could be a key variable in squash training to reach expert-level performance.