• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.5
• /
• pp.369-374
• /
• 2021

Apartment recently built in kitchen is made is made because the gas hose with built-in ways invisible inside the sink. In this case, if the gas leaks, it is a dangerous method that can accumulate inside the sink and lead to an explosion. In this study, since the hose connected between the gas range and the intermediate valve is inside the sink, it is not possible to test for gas leaks, so a valve system that can easily check for gas leaks using a pressure sensor was studied. As for the pressure measurement method, the pressure of the hose connecting the intermediate valve and the gas range was measured so that data could be collected and analyzed using the I2C communication method. In addition, the calculation of the gas pressure supplied to the home was investigated for the atmospheric pressure error for the value calculated by adding the average value of the gas gauge pressure of 22.46 mbar at the inlet of the gas meter to the atmospheric pressure. A valve system was developed to detect minute gas leaks.

• Korean Journal of Applied Biomechanics
• /
• v.19 no.4
• /
• pp.771-778
• /
• 2009

We aimed to determine the effect of heel height on foot pressure by comparing and analyzing data on foot pressure in shoes with different heel heights. Qn the basis of a previous study, we selected 3cm and 7cm as the shoe heel heights preferred by female college students. We divided 10 female students into forefoot and hindfoot to measure vertical force, maximum pressure, and average pressure. The average pressure on the forefoot was higher and that on the hindfoot was lower in the case of 7cm high-heeled shoes. The maximum pressure on the forefoot was significantly higher in the case of the 7cm heel height (p<.05). The vertical force, maximum pressure, and average pressure on the hindfoot were also significantly higher in the case of the 7cm heel height (p<.05). The results showed that wearing 7cm high-heeled shoes exerted greater maximum pressure on the forefoot and greater vertical force, maximum pressure, and average pressure on the Hndfoot. This leads to increase in confining pressure caused by high pressure distribution over the forefoot and increase in the pressure on the hindfoot, which may cause deformation of toes and heel pain over a long period. Therefore, female college students who wish to wear high heels are recommended to wear 3cm high-heeled shoes rather than 7cm high-heeled shoes.

• Plant Journal
• /
• v.10 no.3
• /
• pp.39-44
• /
• 2014

The gas turbine thermal efficiency has been predicted when the compressor pressure ratio increases from the previously set 13.5. Thermal efficiency has been predicted from 14.2 up to 18.2 at which the turbine work reaches its maximum value on the assumption that isentropic efficiency of the compressor and the turbine are constant using the operating data at the pressure ratio of 13.5. 35.11% of thermal efficiency has been acquired by the performance test when the pressure ratio increased to 16.2 since replacing the compressor low pressure stages. It's been approved that predicting thermal efficiency using the operating data at the pressure ratio of 13.5 is useful within 7.86% of tolerance as the figure measured by the performance test.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.11
• /
• pp.672-682
• /
• 2020

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.8 no.2
• /
• pp.32-38
• /
• 2004

A comprehensive understanding is given for the hot-firing test results, which were obtained throughout the verification program of mono-propellant hydrazine rocket engines (thrusters) producing 0.95 lbf (4.2 N) of nominal steady-state thrust at an inlet pressure of 350 psia (2.41 Mpa). A scrutiny for the engine performance is made in terms of thrust and temperature behavior of steady state firing mode at the given propellant injection pressures: Pinj = 400, 250, 100, and 50 psi. The thrust and specific impulse are compared with a reference performance of 1-lbf standard rocket engines and their normalization procedure is introduced. A practical engineering approach to the data measurement and reduction is addressed, too.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.25 no.6
• /
• pp.29-35
• /
• 2021

Bell mouth inlet is applied in various industries due to the advantage of little pressure loss and accurate flow measurement. In this study, the configuration of the bell mouth intake is designed in a long radius shape, and a suitable grid size was selected to minimize the pressure drop and to prevent the engine damage by foreign objects at outdoor operating conditions. It was able to present a modified pressure drop coefficient equation from two data obtained from the computational simulation and experimental results for the total pressure loss by inlet screen installation.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.7
• /
• pp.963-971
• /
• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.19 no.1
• /
• pp.27-35
• /
• 2023

Pressure tubes are the main components of PHWR core and serve as the pressure boundary of the primary heat transport system. However, because pressure tubes have changed their geometrical dimensions under the severe operating conditions of high temperature, high pressure and neutron irradiation according to the increase of operation time, all dimensional changes should be predicted to ensure that dimensions remain within the allowable design ranges during the operation. Among the deformations, the diameter expansion due to creep leads to the increase of bypass flow which may not contribute to the fuel cooling, the decrease of critical channel power and finally the deration of the power to maintain the operational safety margin. This study is focused on the modeling of the expansion of the pressure tube diameter based on the operating conditions and measured diameter data. The pressure tube diameter expansion was modeled using the neutron flux and temperature distributions of each fuel channel and each fuel bundle as well as the measured diameter data. Although the basic concept of the current modeling approach is simple, the diameter prediction results using the developed methodology showed very good agreement with the real data, compared to the existing methodology.

• Proceedings of the Optical Society of Korea Conference
• /
• 1991.06a
• /
• pp.144-149
• /
• 1991

코헤런트 라만분광학 실험에 사용할 다중채널 데이터 수집과 제어장치를 제작하고 프로그램을 개발하였다. CARS 신호를 규격화하기 위해 두 레이저의 세기를 측정하기 위해 최대값 검출기를 제작하였고, 제작한 로직제어기를 프로그램으로 제어하여 측정을 동기시켰다. 또한 우라늄 음극전구의 optogalvanic 신호를 측정하여 섹소레이저의 파장을 교정할 수 있었다. 프로그램은 매뉴선택방식으로 작성하여 수정과 사용이 용이하도록 하였다. 실제로 압력이 100 Torr 인 질소의 CARS 신호를 측정하여 이 장치의 유용성을 확인하였다.

• Journal of Korea Water Resources Association
• /
• v.55 no.spc1
• /
• pp.1295-1303
• /
• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.