• The Journal of the Society of Korean Medicine Diagnostics
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• v.9 no.1
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• pp.112-124
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• 2005

Background: PWV is determined by dividing the distance by the time taken for the pulses traveling between two measuring sites, used as a marker of arterial stiffness and an important indicator for cardiovascular disease. Methods: A PWV measurement system, which offers a non-invasive, simple method of measurement, and simultaneous recording of six signlas(ECG, PCG and four pulse waves from carotid, femoral, radial and dorsalis pedis arteries) was developed. Seventeen healthy subjects with a mean age of 33 years(22 to 52) without any cardiovascular disease were participated for the experiment. Two observers(A and B) performed two consecutive measurements from the same subject in a random order. For the evaluation of stability and accuracy of the PWV measurement system, reproducibility of PWV from between-observer were also evaluated. Results: PWV $values(Mean{\pm}SD)$ measured by A were $7.07{\pm}1.48m/s$, $8.43{\pm}1.14m/s$ , $8.09{\pm}0.98m/s$ for aorta, arm, and leg, respectively. The values obtained from B were $6.76{\pm}1.00m/s$, $7.97{\pm}0.80m/s$, and $7.97{\pm}0.72m/s$ for aorta, arm, and leg, respectively. Between-observer $differences(mean{\pm}SEM)$ from the aorta, arm and leg were $0.14{\pm}0.15m/s$, $0.18{\pm}0.10m/s$ and $0.07{\pm}0.10m/s$. Reproducibility coefficients(2SD) from the aorta, arm, and leg were 0.62m/s, 0.84m/s and 0.86m/s. Correlation coefficients were significantly higher in aortic PWV, 0.93, compared to the coefficients for arm and leg. Coefficient of variance which reflects the reproducibility of the system ranged from $4.4{\sim}5.8%$ in all regional PWV. , Conclusion: Reproducibility of PWV in the study shows that the developed system has reliable and reproducible characteristics. The PWV measurement system used for the study offers comfortable and simple operation and provides accurate analysis and results with high reproducibility. Results of the PWV measurement system could contribute to various clinical applications in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.32-36
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• 2018

A tensile test evaluation of a polypropylene plate was carried out using an Instron tester with a capacity of 500 kgf. To evaluate the strain rate sensitivity of the polypropylene plate with a thickness of 0.8 mm, a tensile test was performed at room temperature through strain rate variations from $5{\times}10^{-4}/sec$ to $5{\times}10^{-2}/sec$. From these, the changes in strength due to the strain rate change and temperature change were compared. As a result of the experiment, the strength increased with increasing initial strain rate. Polypropylene was found to be a material with a positive strain rate sensitivity. In addition, the high temperature tensile properties of the polypropylene plate were evaluated using high temperature tensile tests at 80, 120, and $160^{\circ}C$. The strength decreased with increasing temperature. In particular at $160^{\circ}C$, the tensile strength decreased to zero. The increase in yield strength and the tensile strength at room temperature, $80^{\circ}C$ and $120^{\circ}C$ were similar. At $160^{\circ}C$, however, there was almost no increase in strength because the stress approached zero. In the high temperature tensile test, the tensile strength increased more than the increase in yield strength with increasing strain rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.4
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• pp.193-205
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• 2003

There has been so many research activities about robot soccer system in the many research fields, for example, intelligent control, communication, computer technology, sensor technology, image processing, mechatronics. Especially researchers research strategy for attacking in the field of strategy, and develop intelligent strategy. Then, soccer robots cannot defense completely and efficiently by using simple defense strategy. Therefore, intention extraction of attacker is needed for efficient defense. In this thesis, intention extractor of soccer robots is designed and developed based on FMMNN(Fuzzy Min-Max Neural networks ). First, intention for soccer robot system is defined, and intention extraction for soccer robot system is explained.. Next, FMMNN based intention extractor for soccer robot system is determined. FMMNN is one of the pattern classification method and have several advantages: on-line adaptation, short training time, soft decision. Therefore, FMMNN is suitable for soccer robot system having dynamic environment. Observer extracts attack intention of opponents by using this intention exactor, and this intention extractor is also used for analyzing strategy of opponent team. The capability of developed intention extractor is verified by simulation of 3 vs. 3 robot succor simulator. It was confirmed that the rates of intention extraction each experiment increase.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.2
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• pp.79-85
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• 2018

A two-way pump can reduce costs by draining and circulating water out and into the drum of the washing machine using a single motor whereas a conventional one-way pump uses two motors for doing the same function. However, when the water is drained through the drainage outlet in the two-way pump casing, a backward or inhalation flow occurs and the water flows to the circulation outlet. Likewise, when the water is circulated, the backward flow or inhalation makes the water flow to the drainage outlet. In this study, design optimization of the two-way pump casing is performed to maximize its performance while improving backward flow and inhalation occurring inside of the pump casing. For this, design variables of the pump casing that mainly affect the performance of the pump such as flow rate and torque of the motor were selected through the analysis of mean. Using response surface models for the performances, the ratio of the flow rate to the torque was maximized with satisfying the constraints for the back flow and inhalation through design optimization.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.