• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.11
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• pp.1161-1170
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• 2009

In this paper, dielectric, drude model and ideal metamaterial are applied to core of A-Sandwich radome and each radome is analyzed using recursive method in Ku band. The main parameters of radome performance are insertion loss, insertion phase delay and depolarization. In case of ideal metamaterial, the radome using ideal metamaterial dose not generate depolarization because insertion loss, insertion phase delay and loss for incidence angle of wave do not happened. If circular polarization wave is incident on radome with meta material, transmitted wave also keeps circular polarization. In case of the dispersive metamaterial, the performance of radome using dispersive metamaterial is better than it of radome using dielectric in a part of frequency band. From these results, it is showed that metamaterial can be applied to various radome structure.

• Culinary science and hospitality research
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• v.19 no.2
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• pp.28-39
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• 2013

The purpose of this study is to examine the causality among customer orientation, job satisfaction, and organizational commitment of food service employees using a non-recursive model. To do this, literary and empirical research was carried out. In the literary research, this study examined previous studies related to the concepts such as customer orientation, job satisfaction, and organizational commitment. The empirical research was analyzed based on the questionnaire answered by 203 food service employees. Main results of this study are indicated below. First, customer orientation had a positive effect on job satisfaction(t=2.404, p=0.016). Second, job satisfaction had a positive effect on organizational commitment(t=8.555, p=0.000). Third, organizational commitment had a positive effect on customer orientation(t=6.071, p=0.000). Our main theoretical and practical implication includes the significance of extending the range of research by verifying the non-recursive causality among customer orientation, job satisfaction, and organizational commitment. The limitations of this study are possible problems with generalization by convenience sampling in limited areas.

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.867-880
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• 2006

Hydrological models with many parameters and complex model structures require a powerful and detailed model calibration/validation scheme. In this study, we proposed a multi-variable and multi-site calibration and validation framework for the Soil Water Assessment Tool (SWAT) model applied in the Gap-cheon catchment located downstream of the Geum river basin. The sensitivity analysis conducted before main calibration helped understand various hydrological processes and the characteristics of subcatchments by identifying sensitive parameters in the model. In addition, the model's parameters were estimated based on existing data prior to calibration in order to increase the validity of model. The Nash-Sutcliffe coefficients and correlation coefficient were used to estimate compare model output with the observed streamflow data: $R_{eff}\;and\;R^2$ ranged 0.41-0.84 and 0.5-0.86, respectively, at the Heuduck station. Model reproduced baseflow estimated using recursive digital filter except for 2-5% overestimation at the Sindae and Boksu stations. Model also reproduced the temporal variability and fluctuation magnitude of observed groundwater levels with $R^2$ of 0.71 except for certain periods. Therefore, it was concluded that the use of multi-variable and multi-site method provided high confidence for the structure and estimated parameter values of the model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.5
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• pp.455-463
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• 2012

In this study, we calculate dynamic constrained force of tower top and blade root of a floating offshore wind turbine. The floating offshore wind turbine is multibody system which consists of a floating platform, a tower, a nacelle, and a hub and three blades. All of these parts are regarded as a rigid body with six degree-of-freedom(DOF). The platform and the tower are connected with fixed joint, and the tower, the nacelle, and the hub are successively connected with revolute joint. The hub and three blades are connected with fixed joint. The recursive formulation is adopted for constructing the equations of motion for the floating wind turbine. The non-linear hydrostatic force, the linear hydrodynamic force, the aerodynamic force, the mooring force, and gravitational forces are considered as external forces. The dynamic load at the tower top, rotor shaft, and blade root of the floating wind turbine are simulated in time domain by solving the equations of motion numerically. From the simulation results, the mutual effects of the dynamic response between the each part of the floating wind turbine are discussed and can be used as input data for the structural analysis of the floating offshore wind turbine.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.127-132
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• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• Korean Journal of Social Welfare Studies
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• v.42 no.2
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• pp.179-203
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• 2011

The purpose of the study is 1) to investigate the reciprocal relationship between cognitive functioning and depressive symptoms and 2) to examine whether there is gender difference in this mutual relationship. The uniqueness of the current study is in its investigation on the simultaneous reciprocal relationship between cognitive functioning and depressive symptoms, which is different from previous studies examining unidirectional relationship. Subjects were 3,511 individuals aged 65 and over who participated in the first and second wave of Korean Longitudinal Study of Ageing. Non-recursive structural equation modeling identified the reciprocal relationship between cognitive functioning and depressive symptoms, with poorer cognitive functioning leading to higher depressive symptoms and higher depressive symptoms resulting in poorer cognitive functioning. Multi-group analysis showed the gender difference in the relationship between cognitive functioning and depressive symptoms. Specifically, cognitive functioning was the significant predictor of depressive symptoms for females, whereas the depressive symptoms was the significant predictor of cognitive functioning for males. These findings indicate that the reciprocal relationship and gender difference should be considered when we development practice implications for prevention and treatment related to depression and cognitive functioning. Based on these findings, implications for theory and practice were discussed.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.277-281
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• 2006

This paper presents a method for the automatic recognition of pitch accents over syllables. The method that we propose is based on the time-delay recursive neural network (TDRNN). which is a neural network classifier with two different representation of dynamic context: the delayed input nodes allow the representation of an explicit trajectory F0(t) along time. while the recursive nodes provide long-term context information that reflects the characteristics of pitch accentuation in spoken English. We apply the TDRNN to pitch accent recognition in two forms: in the normal TDRNN. all of the prosodic features (pitch. energy, duration) are used as an entire set in a single TDRNN. while in the distributed TDRNN. the network consists of several TDRNNs each taking a single prosodic feature as the input. The final output of the distributed TDRNN is weighted sum of the output of individual TDRNN. We used the Boston Radio News Corpus (BRNC) for the experiments on the speaker-independent pitch accent recognition. π 1e experimental results show that the distributed TDRNN exhibits an average recognition accuracy of 83.64% over both pitch events and non-events.