• PNF and Movement
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• v.17 no.3
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• pp.421-429
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• 2019

Purpose: This study evaluated the relationship between pelvic pressure and irradiation of the proprioceptive neuromuscular facilitation (PNF) upper arm pattern exercises with an elastic band while in a sitting position. Methods: Fourteen subjects with asymptomatic pelvic pressure participated in this study. Pelvic pressure was measured using a Gaitview® system while sitting and performing PNF bilateral upper arm patterns. Resistance strength was provided by the blue elastic band. The statistical significance of the results was evaluated using a repeated one-way ANOVA and the independent t-test. The Bonferroni method was used for the post-hoc test. Results: The results revealed a significant change in the pelvic pressure when performing the PNF arm pattern. The average resistance pressure on the pelvis, with the elastic band, significantly increased after the initial sitting position (F=3.91, 3.92; p<0.05). No significant pelvic pressure changes were noted for each PNF upper arm pattern (p>0.05). Conclusion: The results of this study showed a positive relationship between pelvic pressure and the irradiation of PNF upper arm pattern exercises with resistance in the sitting position.

• Transactions of Materials Processing
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• v.31 no.5
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• pp.281-289
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• 2022

Presently, 3D printers manufactured by material extrusion are economical and easy to use, so they are being used in various fields. However, this study conducted a tensile test on the infill pattern and density of the PLA+ material, due to the limitations of long printing time as well as low mechanical strength. The infill area for the infill density change was measured, using a vision-measuring machine for four infill patterns (concentric, zigzag, honeycomb, and cross) in which the nozzle path was the same for each layer. The tensile strength/weight[MPa/g] and tensile strength/printing time[MPa/min] of the tensile specimens were analyzed. In this study, efficient infill density and patterns are suggested, for cost reduction and productivity improvement. Consequently, it was confirmed that the infill area and infill percentage of the four patterns, were not constant according to the infill pattern. And the tensile strength of the infill density 40% of the honeycomb pattern and infill density 20% of the cross pattern, tended to highly consider the weight and printing time. Honeycomb and cross patterns could reduce the weight of the tensile specimen by 19.11%, 28.07%, as well as the printing time by 29.56%, 52.25%. Tensile strength was high in the order of concentric, zigzag, honeycomb, and cross patterns, considering the weight and printing time.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.76-85
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• 2023

The present study aims to review the wave pattern resistance analysis method suggested by the International Towing Tank Conference. From the experimental database of a container carrier ship model, the wave pattern measurement and resistance test results are utilized. The wave pattern resistance at the design Froude number is obtained to be compared with the wave making resistance of experiments. Wave pattern resistance is lower than wave making resistance by 1978 ITTC and uniform regardless of transverse location of wave cut. The method is also applied to the wave height field by Computational Fluid Dynamics (CFD) analyses with Froude number variation. Although numerical damping suppressed waves in downstream, waves around the hull and wave pattern resistance are properly predicted.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.2
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• pp.23-31
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• 1990

Acoustic Emission(AE) signals obtained during fracture toughness test and fatigue test for nuclear pressure vessel material(SA 508 cl.3) and artificial AE signals from pencil break and ultrasonic pulser were classified using pattern recognition methods. Three different classifiers ; namely Minimum Distance Classifier, Linear Discriminant Classifier and Maximum Likelihood Classifier were used for pattern recognition. In this study, the performance of each classifier was compared. The discrimination of AE signals from cracking and crack surface rubbing was possible and the analysis for crack propagation was applicable by pattern recognition methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.345-350
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• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Journal of Family Resource Management and Policy Review
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• v.1 no.1
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• pp.57-70
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• 1997

The purpose of this study is to analyze housewives’ cognition, usage, and management behavior of credit cards according to home management behavior pattern. The sample of this study was selected from the housewives’ living in Seoul who has credit cards. After class-analyzation of the sample, 523 out of 612 repondents were finally selected data. The data were analyzed by the statical methods such as frequency, mean, percentile, Factor Analysis, t-test, ANOVA, Duncan’s multiple range test, Person’s Correlation, and Multiple Regression Analysis through the SAS program package. The major findings of this study are as followers : First, housewives’ usage and management behavior of credit cards was a positive relationship between home management behavior pattern. Second, housewives’ cognition of credit cards showed a significant positive relationship with usage and management behavior of credit card. Third, the most influencial variables on housewives’usage and management behavior of credit cards were their value and home management behavior pattern.

• Journal of the Korea Fashion and Costume Design Association
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• v.2 no.2
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• pp.155-171
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• 2000

This study is to measure and evaluate the sensibility image for textile pattern design based on the generation. Ten patterns classified by a practical survey on the market are presented. A questionnaire has 14 sensibility related words scaled by 7 point semantic differential method. The practical research is performed for 200 women screened by sensibility test for individual character analysis based on the generation. Each subject is answered by a face-to-face interview method to improve survey's accuracy, For statistical test about differences in treatment means, SAS package is used and analyzed through ANOVA, significance probability and mean, In summary, this paper has proposed the sensibility image scale for apparel pattern design to satisfy individual sensibility, The results of this study can be effectively applied to develop textile pattern design based on human sensibility.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.721-726
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• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Journal of Mechanical Science and Technology
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• v.19 no.12
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• pp.2289-2295
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• 2005

Two-Phase flow pattern and pressure drop data were obtained for pure refrigerants R134a and R123 and their mixtures as test fluids in a horizontal tube. The flow pattern is observed through tubular sight glasses located at inlet and outlet of the test section. The flow map of Baker developed for air-water two-phase flow at atmospheric pressure failed to predict the observed flow patterns at the higher value of the mass velocity used in the present study. The map of Kattan et al. predicted the data well over the entire region of mass velocity selected in the present study. The measured pressure drop increased with an increase in vapor quality and mass velocity. A new two-phase multiplier was developed from a dimensional analysis of the frictional pressure drop data measured in the present experiment. This new multiplier was found successfully to correlate the frictional pressure drop.

• Journal of Institute of Convergence Technology
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• v.6 no.1
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• pp.13-16
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• 2016

The purpose of this study is analysing the characteristics of vehicle fuel economy and greenhouse gase emissions according to driving pattern of drivers. Current fuel economy has not established on official test methods. The difference between actual fuel efficiency and specification fuel efficiency bring up consumer complaints and misunderstandings about fuel economy. Against this background, The country is progressing the study on influence of the fuel efficiency according to variety test conditions. This study analyze the driving pattern of the different drivers and influence of the fuel efficiency according to driving pattern of different drivers.