• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.454-458
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• 2007

Nonpoint source (NPS) pollution is a serious problem causing the degradation of soil and water quality. Concentrated overland flow is the primary transport mechanism for a large amount of NPS pollutants from hillslope areas to downslope areas in a watershed. In this study, a soil erosion model, nLS model, to identify transitional overland flow regions (i.e., ephemeral gully head areas) was developed using the kinematic wave overland flow theory. Spatial data, including digital elevation models (DEMs), soil, and landcover, were used in the GIS-based model algorithm. The model was calibrated and validated using gully head locations in a large agricultural watershed, which were identified using 1-m aerial photography. The model performance was better than two previous approaches; the overall accuracy of the nLS model was 72 % to 87 % in one calibration subwatershed and the mean overall accuracy was 75 to 89 % in four validation subwatersheds, showing that the model well predicted potential transitional erosion areas at different watersheds. However, the user accuracy in calibration and validation was still low. To improve the user accuracy and study the effects of DEM resolution, finer resolution DEMs may be preferred because DEM grid is strongly sensitive to estimating model parameters. Information gained from this study can improve assessing soil erosion process due to concentrated overland flow as well as analyze the effect of microtopographic landscapes, such as riparian buffer areas, in NPS control.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.51-58
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• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.61-68
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• 2023

The phenomenon of fretting wear due to the flow-induced vibration in steam generator (SG) tube is a significant degradation mechanism in nuclear power plants. Fretting wear in SG tube is primarily attributed to the friction and impact forces between the SG tube and the tube support structures, experienced during nuclear power plants operation. While the Archard model has generally been used for the prediction of fretting wear in SG tube, it is limited by its linear nature. In this study, we introduced an "Impact Shear Work-rate" (ISW) model, which takes into account the combined effects of impact and sliding. The ISW model was evaluated using existing experimental data on fretting wear in SG tube and was compared against the Archard model. The prediction results using the ISW model were more accurate than those using the Archard model, particularly for impact forces.

• Steel and Composite Structures
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• v.23 no.3
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• pp.303-315
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• 2017

This paper describes the finite element model for predicting the fundamental performance of embedded steel column base connections under monotonic and cyclic loading. Geometric and material nonlinearities were included in the proposed finite element model. Bauschinger and pinching effects were considered in the simulation of embedded column base connections under cyclic loading. The degradation of steel yield strength and accumulation of plastic damage can be well simulated. The accuracy of the finite element model is examined by comparing the predicted results with independent experimental dataset. It is demonstrated that the finite element model accurately predicts the behaviour and failure models of the embedded steel column base connections. The finite element model is extended to carry out evaluations and parametric studies. The investigated parameters include column embedded length, concrete strength, axial load and base plate thickness. Moreover, analytical models for predicting the initial stiffness and bending moment strength of the embedded column base connection were developed. The comparison between results from analytical models and those from experiments and finite element analysis proved the developed analytical model was accurate and conservative for design purposes.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3100-3111
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• 2021

Numerical modeling for the safety-related equipment used in a nuclear power plant (i.e., cabinet facilities) plays an essential role in seismic risk assessment. A full finite element model is often time-consuming for nonlinear time history analysis due to its computational modeling complexity. Thus, this study aims to generate a simplified model that can capture the nonlinear behavior of the electrical cabinet. Accordingly, the distributed plasticity approach was utilized to examine the stiffness-degradation effect caused by the local buckling of the structure. The inherent dynamic characteristics of the numerical model were validated against the experimental test. The outcomes indicate that the proposed model can adequately represent the significant behavior of the structure, and it is preferred in practice to perform the nonlinear analysis of the cabinet. Further investigations were carried out to evaluate the seismic behavior of the cabinet under the influence of the constitutive law of material models. Three available models in OpenSees (i.e., linear, bilinear, and Giuffre-Menegotto-Pinto (GMP) model) were considered to provide an enhanced understating of the seismic responses of the cabinet. It was found that the material nonlinearity, which is the function of its smoothness, is the most effective parameter for the structural analysis of the cabinet. Also, it showed that implementing nonlinear models reduces the seismic response of the cabinet considerably in comparison with the linear model.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.10
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• pp.1090-1098
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• 2005

Benzoyl peroxide is very toxic to aquatic organisms but environmental concentration or exposure effects were not studied. Distribution of the chemical among multimedia environment was estimated using EQC(Equilibrium Criterion) model based on the physical-chemical properties to evaluate the risk of benzoyl peroxide in environment. Level I describes a situation that 100,000 kg of benzoyl peroxide is emitted into the environment which is equilibrium and steady-state without degradation and advection condition. Level II describes a situation that a constant rate of 1,000kg/h of benzoyl peroxide is continuously discharged into the environment which is equilibrium and steady-state with degradation and advection condition. Level III describes a situation that 1,000 kg/h of benzoyl peroxide is continuously introduced in each air, water, soil, and sediment compartment which are non-equilibrium and steady-state with degradation, advection, and inter-media transfer condition. In Level I and II calculations the chemical was distributed to soil(68.3%) and water(28.7%). In Level III calculation it was primarily distributed to soil(99.9%) and overall residence time was estimated to be 3.4 years. Benzoyl peroxide can be persistent in environment.

• Korean Journal of Food Science and Technology
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• v.21 no.3
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• pp.419-424
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• 1989

The effects of browning Products (BP) from Kanjang(soy sauce) and charcoal on the degradation of aflatoxin $B_1(AFB_1)$ in Kanjang and its model system were studied. Approximately 60% of $AFB_1$ was degraded in the presence of 0.05% BP at pH 7 of phosphate buffer after 2 days of incubation at $30^{\circ}C$. The mutagenicity of the $AFB_1$ which reacted with the BP was decreased to about 50% and 70% in Salmonella typhimurium TA98 and TA100 strains, respectively (p<0.05). When a few pieces of charcoal were added to home made Kanjang, $AFB_1$ was quite stable for 5days at $30^{\circ}C$, however, about 80% of $AFB_1$ was removed when the charcoal was either in distilled water or in 20% of NaCl solution after 2 days of incubation. Activated carbon instead of the charcoal removed $AFB_1$ completely in the all samples under the same conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.

• Transactions on Electrical and Electronic Materials
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• v.18 no.2
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• pp.111-118
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• 2017

The increased diversity of different types of energy sources requires moving towards smart distribution networks. This paper proposes a probabilistic DG (distributed generation) units planning model to determine technology type, capacity and location of DG units while simultaneously allocating ESS (energy storage systems) based on pre-determined capacities. This problem is studied in a wind integrated power system considering loads, prices and wind power generation uncertainties. A suitable method for DG unit planning will reduce costs and improve reliability concerns. Objective function is a cost function that minimizes DG investment and operational cost, purchased energy costs from upstream networks, the defined cost to reliability index, energy losses and the investment and degradation costs of ESS. Electrical load is a time variable and the model simulates a typical radial network successfully. The proposed model was solved using the DICOPT solver under GAMS optimization software.