• Journal of the Korea Concrete Institute
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• v.28 no.6
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• pp.665-672
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• 2016

This study is conducted to investigate the effect of phase change materials (PCM) on hydration heat and strength characteristics of cement mortar. Two types of Barium and Strontium-based PCMs were used in this study and the addition ratio of each PCM to the cement mortar ranged from 1% to 5% by cement weight. Flow test, semi-adiabatic temperature rise test, compressive strength and flexural strength test were carried out to examine the PCM effect on heat and mechanical properties of cement mortar. Test results indicated that PCMs used in this study were effective to control hydration heat of cement mortar, and Barium-based PCM slightly reduce flow value. The compressive and flexural strength of cement mortar with PCM decreased with increasing the adding mount of PCM. The prediction model for compressive strength of cement mortar with different addition levels of PCMs are suggested in this study.

• Smart Structures and Systems
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• v.25 no.1
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• pp.81-96
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• 2020

Wind measurements were made on the Canton Tower at a height of 461 m above ground during the Typhoon Vincente, the wind-induced accelerations and displacements of the tower were recorded as well. Comparisons of measured wind parameters at upper level of atmospheric boundary layer with those adopted in wind tunnel testing were presented. The measured turbulence intensity can be smaller than the design value, indicating that the wind tunnel testing may underestimate the crosswind structural responses for certain lock-in velocity range of vortex shedding. Analyses of peak factors and power spectral density for acceleration response shows that the crosswind responses are a combination of gust-induced buffeting and vortex-induced vibrations in the certain range of wind directions. The identified modal frequencies and mode shapes from acceleration data are found to be in good agreement with existing experimental results and the prediction from the finite element model. The damping ratios increase with amplitude of vibration or equivalently wind velocity which may be attributed to aerodynamic damping. In addition, the natural frequencies determined from the measured displacement are very close to those determined from the acceleration data for the first two modes. Finally, the relation between displacement responses and wind speed/direction was investigated.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.464-464
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• 2010

A few years ago, for maintaining high stability and production yield of production equipment in a semiconductor fab, on-line monitoring of wafers is required, so that semiconductor manufacturers are investigating a software based process controlling scheme known as virtual metrology (VM). As semiconductor technology develops, the cost of fabrication tool/facility has reached its budget limit, and reducing metrology cost can obviously help to keep semiconductor manufacturing cost. By virtue of prediction, VM enables wafer-level control (or even down to site level), reduces within-lot variability, and increases process capability, $C_{pk}$. In this research, we have practiced VM on $SiO_2$ etch rate with optical emission spectroscopy(OES) data acquired in-situ while the process parameters are simultaneously correlated. To build process model of $SiO_2$ via, we first performed a series of etch runs according to the statistically designed experiment, called design of experiments (DOE). OES data are automatically logged with etch rate, and some OES spectra that correlated with $SiO_2$ etch rate is selected. Once the feature of OES data is selected, the preprocessed OES spectra is then used for in-situ sensor based VM modeling. ICP-RIE using 葰.56MHz, manufactured by Plasmart, Ltd. is employed in this experiment, and single fiber-optic attached for in-situ OES data acquisition. Before applying statistical feature selection, empirical feature selection of OES data is initially performed in order not to fall in a statistical misleading, which causes from random noise or large variation of insignificantly correlated responses with process itself. The accuracy of the proposed VM is still need to be developed in order to successfully replace the existing metrology, but it is no doubt that VM can support engineering decision of "go or not go" in the consecutive processing step.

• Journal of the Korean Society of Propulsion Engineers
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• v.14 no.4
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• pp.1-9
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• 2010

Fuel shutoff valve of a gas generator controls propellant mass flowrate of a rocket engine, by using pilot pressure and spring force. The developing fuel shutoff valve can be self sustained even though pilot pressure is removed in an actuator. Therefore, it is necessary to analyze the characteristics of the forces with respect to the opening and closing of the valve in order to evaluate its performance. In light of this, the valve has been designed to adjust the control pressure for the opening of the poppet and to determine the working fluid pressure at which the valve starts to close. This paper also has been designed dynamic model using the AMESim and predicted flow coefficient of the valve by Fluent CFD analysis. Various results from the prediction and the analysis have been compared with experiments. Finally, dynamic characteristics of the valve have been verified with experimental results.

• Design & Manufacturing
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• v.13 no.2
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• pp.54-59
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• 2019

CNC(Computer Numerical Control) machine tools are being used in various industrial fields such as aircraft and automobiles. The machining conditions used in the mold industry are used, and the simulation and the experiment are compared. The tool used in the experiment was carried out to increase the reliability of the simulation of the cutting machining. The program used in the 3D-FEM (finite element method) was the AdvantEdge and predicted by down-milling. The tool model is used 3D-FEM simulation by using the cutting force, temperature prediction. In this study, we carried out the verification of cutting force by using a 3-axis tool dynamometer (Kistler 9257B) system when machining the plastic mold Steel machining of NAK-80. The cutting force experiment data using on the charge amplifier (5070A) is amplified, and the 3-axis cutting force data are saved as a TDMS file using the Lab-View based program using on NI-PXIe-1062Q. The machining condition 7 was the most similar to the simulation and the experimental results. The material properties of the NAK-80 material and the simulation trends reflected in the reverse design of the tool were derived similarly to the experimental results.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

• Atmosphere
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• v.32 no.4
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• pp.323-340
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• 2022

We analyzed the high-resolution wind data of Aircraft-Based Observation from the Mode-Selective Enhanced Surveillance (Mode-S EHS) data in Korea. For assessment of its quality, the Mode-S wind data was compared with the ECMWF ReAnalysis 5 (ERA5) reanalysis and Aircraft Meteorological Data Relay (AMDAR) data for more than 3-months from 7 May 2021 to 24 August 2021 near Incheon International Airport, Korea. Considering that the AMDAR reports are not provided by all commercial aircraft, total number of the Mode-S derived wind data with a second sampling rate was about twice larger than that of available AMDAR wind data. After the quality control procedures by removing erroneous samples, it was found that the root mean square errors (RMSEs) of the Mode-S retrieved winds are similar to that from the AMDAR winds. In particular, between 550 and 650 hPa levels, RMSE of the Mode-S (AMDAR) zonal wind against ERA5 data was about 2.3 m s^-1 (1.9 m s^-1), and those increased to 3.3 m s^-1 (2.4 m s^-1) in 200~500 hPa levels. A similar trend was found in the meridional wind, but a distinct positive mean bias of 2.16 m s^-1 was observed between 875 and 1,000 hPa levels. Winds retrieved from the Mode-S also showed a good agreement directly with AMDAR data. As the Mode-S provides a large amount of data with a reliable quality, it can be useful for both data assimilation in the numerical weather prediction model and situational awareness of wind and turbulence for aviation safety in Korea.

• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.313-320
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• 2022

The KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD) was launched in 2011 with the support of the Korea Disease Control and Prevention Agency. The study was designed with the aim of exploring the various clinical features and characteristics of chronic kidney disease (CKD) in Koreans, and elucidating the risk factors for CKD progression and adverse outcomes of CKD. For the cohort study, nephrologists at 9 tertiary university-affiliated hospitals participated in patient recruitment and follow-up. Biostatisticians and epidemiologists also participated in the basic design and structuring of the study. From 2011 until 2016, the KNOW-CKD Phase I recruited 2238 adult patients with CKD from stages G1 to G5, who were not receiving renal replacement therapy. The KNOW-CKD Phase II recruitment was started in 2019, with an enrollment target of 1500 subjects, focused on diabetic nephropathy and hypertensive kidney diseases in patients with reduced kidney function who are presumed to be at a higher risk of adverse outcomes. As of 2021, the KNOW-CKD investigators have published articles in the fields of socioeconomics, quality of life, nutrition, physical activity, renal progression, cardiovascular disease and outcomes, anemia, mineral bone disease, serum and urine biomarkers, and international and inter-ethnic comparisons. The KNOW-CKD researchers will elaborate a prediction model for various outcomes of CKD such as the development of end-stage kidney disease, major adverse cardiovascular events, and death.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.1-13
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• 2022

Household refrigerator cabinets must undergo cyclic testing at -20 ℃ and 65 ℃ for quality control (QC) after their production is complete. These cabinets were assembled from different materials, including acrylonitrile butadiene styrene (ABS), polyurethane (PU) foam, and steel plates. However, different thermal expansion values could be observed owing to differences in the mechanical properties of the materials. In this study, a technique to predict delamination on a refrigerator wall caused by thermal deformation was developed. The mechanical properties of ABS and PU foams were tested, theload factors causing delamination were analyzed, delamination was observed using a high-speed camera, and comparison and verification in terms of stress and strain were performed using a finite element model (FEM). The results indicated that the delamination phenomenon of a refrigerator wall can be defined in two cases. A method for predicting and evaluating delamination was established and applied in an actual refrigerator. To determine the effect of temperature changes on the refrigerator, strain measurements were performed at the weak point and the stress was calculated. The results showed that the proposed FEM prediction technique can be used as a basis for virtual testing to replace future QC testing, thus saving time and cost.