• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2565-2571
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• 2020

Background: Understanding the behaviour of nuclear fuel claddings by conducting burst test on single cladding tube under simulated loss-of-coolant accident conditions and developing theoretical cum empirical predictive computer codes have been the focus of several investigations. The developed burst criterion (a) assumes symmetrical deformation of cladding tube in contrast to experimental observation (b) interpolates the properties of Zircaloy-4 cladding in mixed α+β phase (c) does not account for azimuthal temperature variations. In order to overcome all these drawbacks of burst criterion, it is reasoned that artificial intelligence technique may be a better option to predict the burst parameters. Methods: Artificial neural network models based on feedforward backpropagation algorithm with logsig transfer function are developed. Results: Neural network architecture of 2-4-4-3, that is model with two hidden layers having four nodes in each layer is found to be the most suitable. The mean, maximum, and minimum prediction errors for this optimised model are 0.82%, 19.62%, and 0.004%, respectively. Conclusion: The burst stress, burst temperature, and burst strain obtained from burst criterion have average deviation of 19%, 12%, and 53% respectively whereas the developed neural network model predicted these parameters with average deviation of 6%, 2%, and 8%, respectively.

• Food Science of Animal Resources
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• v.36 no.5
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• pp.650-655
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• 2016

This study investigated the effects of artificial supercooling followed by still air freezing (SSF) on the qualities of pork loin. The qualities of pork frozen by SSF were compared with the fresh control (CT, stored at 4℃ for 24 h), slow freezing (SAF, still air freezing) and rapid freezing (EIF, ethanol immersion freezing) treatments. Compared with no supercooling phenomena of SAF and EIF, the extent of supercooling obtained by SSF treatment was 1.4℃. Despite that SSF was conducted with the same method with SAF, application of artificial supercooling accelerated the phase transition (traverse from -0.6℃ to -5℃) from 3.07 h (SAF) to 2.23 h (SSF). The observation of a microstructure indicated that the SSF prevented tissue damage caused by ice crystallization and maintained the structural integrity. The estimated quality parameters reflected that SSF exhibited superior meat quality compared with slow freezing (SAF). SSF showed better water-holding capacity (lower thawing loss, cooking loss and expressible moisture) and tenderness than SAF, and these quality parameters of SSF were not significantly different with ultra-fast freezing treatment (EIF). Consequently, the results demonstrated that the generation of supercooling followed by conventional freezing potentially had the advantage of minimizing the quality deterioration caused by the slow freezing of meat.

• 한국지구물리탐사학회:학술대회논문집
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• 2010.10a
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• pp.27-27
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• 2010

There have been several studies about empirical relation between seismic source parameters(e.g., focal mechanisms, depths, magnitudes, etc.) and T-wave observation. In order to delineate the relation, numerical and theoretical approaches to figure out T-wave excitation mechanism are required. In an attempt to investigate source radiation and wave scattering effects in the oceanic crust on T-wave envelopes, we perform three-dimensional numerical modeling to synthesize T-wave envelopes. We first calculate seismic P- and SV-wave energy on the seafloor using the Direct Simulation Monte Carlo based on the Radiative Transfer Theory, which enables us to take into account both realistic seismic source parameters and wave scattering in heterogeneous media, and then estimate excited T-wave energy by normal mode computation. The numerical simulation has been carried out considering the following different conditions: source types (strike and normal faults), source depths (shallow and deep), and wave propagation through homogeneous and heterogeneous Earth media. From the results of numerical modeling, we confirmed that T-wave envelopes vary according to spatial seismic energy distributions on the seafloor for the various input parameters. Furthermore, the synthesized T-wave envelopes show directional patterns due to anisotropic source radiation, and the slope change of T-wave envelopes caused by focal depth. Seismic wave scattering in the oceanic crust is likely to control the shape of envelopes.

• The Korea Journal of Herbology
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• v.27 no.1
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• pp.1-10
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• 2012

Objective : To investigate effects of herbal preparations formulated(Gihyeolgwanjeolbang-A, CWS-A) on complete Freund's adjuvant (CFA)-induced arthritis in rats. Method : Arthritis was induced by injecting CFA subcutaneously into the left knee joint and paw, and the herbal preparations formulated(CWS-A I, 82.3 mg/kg ; CWS-A II, 164.6 mg/kg) was administered orally (i.g.) for 10 consecutive days beginning on day 10 after the injection. External shape, paw edema, inflammatory cytokines tumor necrosis factor alpha (TNF-alpha) and interleukin-1 beta (IL-1beta), and histological observation were assessed. Result : In swelling of the paw, CWS-A I and CWS-A II group in 15 days and 20 days were significantly reduced compared to controls. In serum ALT, CWS-A I and CWS-A II group were significantly reduced compared to controls. In TNF-${\alpha}$, CWS-A I and CWS-A II group were a tendency reduced compared to controls. In HGB, HCT, MCHC of erythrocytes, CWS-A II group was increased compared to controls. In histological observations, CWS-A II group was observed synoviocytes more than control group and was observed proteoglycans in the deep layers. Conclusion : The data suggest that CWS-A significant anti-arthritic effects that may be mediated by suppressing inflammatory parameters.

• Atmosphere
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• v.23 no.3
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• pp.265-273
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• 2013

To investigate properties of cloud and rainfall occurred at Boseong on 10 July 2012, Raindrop Size distributions (RSDs) and other parameters were analyzed using observation data collected by Micro Rain Radar (MRR) and PARticle SIze and VELocity (PARSIVEL) disdrometer located in the National center for intensive observation of severe weather at Boseong in the southwest of the Korean peninsula. In addition, time series of RSD parameters, relationship between reflectivity-rain rate, and vertical variation of rain rates-fall velocities below melting layer were examined. As a result, good agreements were found in the reflectivity-rain rate time series as well as their power relationships between MRR and PARSIVEL disdrometer. The rain rate was proportional to reflectivity, mean diameter, and inversely proportional to shape (${\mu}$), slope (${\Lambda}$), intercept ($N_0$) parameter of RSD. In comparison of the RSD, as rain rate was increased, the slope of RSD became less steep and the mean diameter became larger. Also, it was verified that reflectivities are classified in three categories (Category 1: Z (reflectivity) > 40 dBZ, Category 2: 30 dBZ < Z < 40 dBZ, Category 3: Z < 30 dBZ). As reflectivity was increased, rain rate was intensified and larger raindrops were existed, while reflectivity was decreased, shape (${\mu}$), slope (${\Lambda}$), intercept ($N_0$) parameter of RSD were increased. We expected that these results will lead to better understanding of microphysical process in convective rainfall system occurred during short-term period over Korean peninsula.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.119-124
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• 2007

The few observed data related snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) building for 5 major watersheds in South Korea. Especially SDC is important parameter of snowmelt model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.194-204
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• 2020

Cultivar parameters, which are key inputs to a crop growth model, have been estimated using observation data in good quality. Observation data with high quality often require considerable labor and cost, which makes it challenging to gather a large quantity of data for calibration of cultivar parameters. Alternatively, data in sufficient quantity can be collected from the reports on the evaluation of cultivars by region although these data are of questionable quality. The objective of our study was to assess the quality of crop and management data available from the reports on the regional adaptation trials for rice cultivars. We also aimed to propose the measures for improvement of the data quality, which would aid reliable estimation of cultivar parameters. DatasetRanker, which is the tool designed for quantitative assessment of the data for parameter calibration, was used to evaluate the quality of the data available from the regional adaptation trials. It was found that these data for rice cultivars were classified into the Silver class, which could be used for validation or calibration of key cultivar parameters. However, those regional adaptation trial data would fall short of the quality for model improvement. Additional information on management, e.g., harvest and irrigation management, can increase the quantitative quality by 10% with the minimum effort and cost. The quality of the data can also be improved through measurements of initial conditions for crop growth simulations such as soil moisture and nutrients. In addition, crop model improvement can be facilitated using crop growth data in time series, which merits further studies on development of approaches for non-destructive methods to monitor the crop growth.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.3
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• pp.19-29
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• 2024

Total organic carbon (TOC) represents the total amount of organic carbon contained in water and is a key water quality parameter used, along with biochemical oxygen demand (BOD) and chemical oxygen demand (COD), to quantify the amount of organic matter in water. In this study, a model to predict TOC was developed using XGBoost (XGB), a representative ensemble machine learning algorithm. Independent variables for model construction included water temperature, pH, electrical conductivity, dissolved oxygen concentration, BOD, COD, suspended solids, total nitrogen, total phosphorus, and discharge. To quantitatively analyze the impact of various water quality parameters used in model construction, the feature importance of input variables was calculated. Based on the results of feature importance analysis, items with low importance were sequentially excluded to observe changes in model performance. When built by sequentially excluding items with low importance, the performance of the model showed a root mean squared error-observation standard deviation ratio (RSR) range of 0.53 to 0.55. The model that applied all input variables showed the best performance with an RSR value of 0.53. To enhance the model's field applicability, models using relatively easily measurable parameters were also built, and the performance changes were analyzed. The results showed that a model constructed using only the relatively easily measurable parameters of water temperature, electrical conductivity, pH, dissolved oxygen concentration, and suspended solids had an RSR of 0.72. This indicates that stable performance can be achieved using relatively easily measurable field water quality parameters.

• Computers and Concrete
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• v.10 no.3
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• pp.277-294
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• 2012

The aim of the study is to determine the modal parameters of a prototype damaged arch dam by operational modal analysis (OMA) method for some damage scenarios. For this purpose, a prototype arch dam-reservoir-foundation model is constructed under laboratory conditions. Ambient vibration tests on the arch dam model are performed to identify the modal parameters such as natural frequency, mode shape and damping ratio. The tests are conducted for four test-case scenarios: an undamaged dam with empty reservoir, two different damaged dams with empty reservoirs, and a damaged dam with full reservoir. Loading simulating random impact effects is applied on the dam to crack. Cracks and fractures occurred at the middle of the upper part of the dams and distributed through the abutments. Sensitivity accelerometers are placed on the dams' crests to collect signals for measurements. Operational modal analysis software processes the signals collected from the ambient vibration tests, and enhanced frequency domain decomposition and stochastic subspace identification techniques are used to estimate modal parameters of the dams. The modal parameters are obtained to establish a basis for comparison of the results of two techniques for each damage case. Results show that approximately 35-40% difference exists between the natural frequencies obtained from Case 1 and Case 4. The natural frequencies of the dam considerably decrease with increasing cracks. However, observation shows that the filled reservoir slightly affected modal parameters of the dam after severe cracking. The mode shapes obtained are symmetrical and anti-symmetrical. Apparently, mode shapes in Case 1 represent the probable responses of arch dams more accurately. Also, damping ratio show an increase when cracking increases.

• Journal of Astronomy and Space Sciences
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• v.30 no.3
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• pp.179-185
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• 2013

Photometric observation is one of the most effective techniques for determining the physical characteristics of unknown space objects and space debris. In this research, we examine the change in brightness of the Communication, Ocean, Meteorological Satellite-1 (COMS-1) Geostationary Orbit Satellite (GEO), and compare it to our estimate model. First, we calculate the maximum brightness time using our calculation method and then derive the light curve shape using our rendering model. The maximum brightness is then calculated using the induced equation from Pogson's formula. For a comparison with our estimation, we carried out photometric observation using an optical telescope. The variation in brightness and the shape of the light curve are similar to the calculations achieved using our model, but the maximum brightness shows a slightly different value from our calculation result depending on the input parameters. This paper examines the photometric phenomenon of the variation in brightness of a GEO satellite, and the implementation of our approach to understanding the characteristics of space objects.