• Journal of computational fluids engineering
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• v.14 no.4
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• pp.93-100
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• 2009

The gridless (or meshfree) methods, such as MPS, SPH, FPM an so forth, are feasible and robust for the problems with moving boundary and/or complicated boundary shapes, because these methods do not need to generate a grid system. In this study, a gridless solver, which is based on the combination of moving least square interpolations on a cloud of points with point collocation for evaluating the derivatives of governing equations, is presented for two-dimensional unsteady incompressible Navier-Stokes problem in the low Reynolds number. A MAC-type algorithm was adopted and the Poission equation for the pressure was solved successively in the moving least square sense. Some typical problems were solved by the presented solver for the validation and the results obtained were compared with analytic solutions and the numerical results by conventional CFD methods, such as a FVM.

• Journal of Ocean Engineering and Technology
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• v.22 no.1
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• pp.1-5
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• 2008

A 2-D Fluent-based numerical wave tank(FBNWT) capable of simulating wave propagating and overtopping is presented. The FBNWT model is based on the Reynolds averaged Naiver-Stokes equations and VOF free surface tracking method. The piston wave maker system is realized by dynamic mesh technology(DMT) and user defined function(UDF). The non-iteration time advancement(NITA) PISO algorithm is employed for the velocity and pressure coupling. The FBNWT numerical solutions of linear wave propagation have been validated by analytical solutions. Several overtopping problems are simulated and the prediction results show good agreements with the experimental data, which demonstrates that the present model can be utilized in the corresponding analysis.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.131-135
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• 2023

Neural network-based time series models called time series neural networks (TSNNs) are trained by the error backpropagation algorithm and used to predict process shifts of parameters such as gas flow, RF power, and chamber pressure in reactive ion etching (RIE). The training data consists of process conditions, as well as principal components (PCs) of optical emission spectroscopy (OES) data collected in-situ. Data are generated during the etching of benzocyclobutene (BCB) in a SF₆/O₂ plasma. Combinations of baseline and faulty responses for each process parameter are simulated, and a moving average of TSNN predictions successfully identifies process shifts in the recipe parameters for various degrees of faults.

• Journal of ILASS-Korea
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• v.29 no.2
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• pp.91-97
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• 2024

When investigating the droplet, spray, and impact of liquid on a solid plate, backlight imaging has been widely used to understand these phenomena. However, some previous studies have suffered from poor image quality. In this study, various combinations of image processing algorithms, such as white image correction, histogram equalization, CLAHE, Otsu's binarization, and multi-Otsu's binarization, have been applied to flash boiling spray images to enhance image quality for qualitative observation and semi-quantitative spray angle evaluation. To acquire images with high contrast for qualitative observation, applying CLAHE was effective, making small droplets and detailed shapes of the jet noticeable. However, when images were averaged to determine spray angle or penetration length based on intensity, this method induced artifact unphysical patterns, thus requiring careful consideration. Based on the algorithm proposed in this study, the spray angle variation according to injection pressure and temperature has been calculated, showing a reasonable trend.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.161-167
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• 2018

The amount of CNG was measured using a pressure sensor in the case of CNG vehicles. However, the current measurement method causes anxiety to the driver because it is difficult to measure the detailed amount of CNG according to various environmental conditions. This study was performed to measure the amount of CNG in CNG fuel system, and presented the method of measurement by simulating the detection system of CNG. In this experiment, a detection simulator with an ultrasonic sensor in CNG tank of Type-3 was designed, and the reception signal of the ultrasonic sensor was verified by reducing the pressure from 100 bars to 0 bars (increment=5 bars) using compressed air. As a result, the output signal voltage of the ultrasonic sensor decreased as the pressure in the tank decreased, and the it was verified that the shape of the graph was linearity.

• Journal of Drive and Control
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• v.16 no.4
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• pp.1-8
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• 2019

The HPM (High-speed Power Matching) system is an electro-hydraulic control system. It directly controls the swash plate of the pump by selecting four-loop logic based on joystick signals, pump flow, and pressure signal to improve the efficiency and controllability of construction machines. In the NFC (Negative Flow Control) system, a typical pump control system using conventional open center type MCV, the loss is continuously generated by flow through the center bypass line even when the excavator is not in operation. Also, due to the slow response of the pump that indirectly controls the flow rate using the pressure regulator, peak pressure occurs at the start or stop of the operation. Conversely, the HPM system uses an MCV without center-by-pass flow path and the swash plate of a pump for the HPM is controlled by a high-speed proportional flow control valve. As a result, the HPM system minimizes energy loss in standby state of the excavator and enables peak pressure control through rapid electro-hydraulic control of a pump. In this paper, the concept of the HPM system algorithm is introduced and the hydraulic system efficiency is compared with the NFC system using the excavator SAT (System Analysis Tool).

• Journal of the Korean Society for Precision Engineering
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• v.33 no.1
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• pp.23-29
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• 2016

In this paper, a real-time condition diagnosis system for the lens injection molding process is developed through the use of LabVIEW. The built-in-sensor (BIS) mold, which has pressure and temperature sensors in their cavities, is used to capture real-time signals. The measured pressure and temperature signals are processed to obtain features such as maximum cavity pressure, holding pressure and maximum temperature by the feature extraction algorithm. Using those features, an injection molding condition diagnosis model is established based on a response surface methodology (RSM). In the real-time system using LabVIEW, the front panels of the data loading and setting, feature extraction and condition diagnosis are realized. The developed system is applied in a real industrial site, and a series of injection molding experiments are conducted. Experimental results show that the average real-time condition diagnosis rate is 96%, and applicability and validity of the developed real-time system are verified.

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• Korean Circulation Journal
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• v.52 no.10
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• pp.785-794
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• 2022

Background and Objectives: Self-monitoring of blood pressure (SMBP) is a reliable method used to assess BP accurately. However, patients do not often know how to respond to the measured BP value. We developed a mobile application-based feed-back algorithm (SMBP-App) for tailored recommendations. In this study, we aim to evaluate whether SMBP-App is superior to SMBP alone in terms of BP reduction and drug adherence improvement in patients with hypertension. Methods: Self-Monitoring of blood pressure and Feed-back using APP in Treatment of UnconTrolled Hypertension (SMART-BP) is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App compared with SMBP alone. Patients with uncomplicated essential hypertension will be randomly assigned to the SMBP-App (90 patients) and SMBP alone (90 patients) groups. In the SMBP group, the patients will perform home BP measurement and receive the standard care, whereas in the SMBP-App group, the patients will receive additional recommendations from the application in response to the obtained BP value. Follow-up visits will be scheduled at 12 and 24 weeks after randomization. The primary endpoint of the study is the mean home systolic BP. The secondary endpoints include the drug adherence, the home diastolic BP, home and office BP. Conclusions: SMART-BP is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App. If we can confirm its efficacy, SMBP-App may be scaled-up to improve the treatment of hypertension.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.5
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• pp.330-338
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• 2024

With the growing interest in rehabilitation therapy and exercise programs, there is an increasing need for smart content that simultaneously addresses both health and engagement. Particularly, exercises performed in a state of physical imbalance carry a high risk of injury, making it essential to detect and integrate balance into the training process. This paper proposes Rehabilitation Training program that combines a pressure platform with virtual reality (VR) technology to address this issue. The program enables users to perform exercises such as squats, stationary walking, and forward-backward walking in a VR environment, utilizing real-time foot pressure data captured through a pressure mat. Additionally, an algorithm based on YOLOv8-pose extracted skeletal coordinates is proposed to assess body balance and automatically count squat repetitions. The experimental results showed an average accuracy of 87.9% for each posture, confirming that users can be provided with a safer, more efficient, and immersive training experience through this approach.