• Korean Journal of Family Social Work
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• no.55
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• pp.31-58
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• 2017

This study aims to explore the longitudinal reciprocal relationship between family conflict and depression level of household head. Using the Korean Welfare Panel study (KOWEPS) of 2006-2009, the study applied multiple regression analysis and autoregressive cross-lagged model to test the hypothesis. Results of multiple regression analysis indicate that single direction of the impact of family conflict on a head of household's levels of depression and the vise versa were statistically significant. That is, higher level of family conflict in 2006 caused an increased levels of depression of household head in 2009, controlling gender, age and depression level in 2006. Also, the higher level of depression of household head in 2006 increased the level of family conflict in 2009 fixed with same control variables. The autoregressive and cross-lagged coefficients of family conflict and a head of household's levels of depression were statistically significant during the 4 years. The findings support the family system theory, indicating that there are reciprocal causal relationships between the whole family conflict and individual depression level. The strategies of social welfare practice and policy should thus aim to decrease individual's levels of depression and improve positive family function simultaneously to break the vicious circle.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.599-605
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• 2019

Rice yield (kg 10a^-1) in South Korea was estimated by meteorological variables that are influential factors in crop growth. This study investigated the possibility of anticipating the rice yield variability using a simple but an efficient statistical method, a multiple linear regression analysis, on the basis of the annual variation of meteorological variables. Due to heterogeneous environmental conditions by region, the yearly rice yield was assessed and validated for each province in South Korea. The monthly mean meteorological data for the period 1986-2018 (33 years) from 61 weather stations provided by Korean Meteorological Administration was used as the independent variable in the regression analysis. An 11-fold (leave-three-out) cross-validation was performed to check the accuracy of this method estimating rice yield at each province. This result demonstrated that temporal variation of rice yield by province in South Korea can be properly estimated using such concise procedure in terms of correlation coefficient (0.7, not significant). Furthermore, the estimated rice yield well captured spatial features of observation with mean bias of 0.7 kg 10a^-1 (0.15%). This method may offer useful information on rice yield by province in advance as long as accurate agro-meteorological forecasts are timely obtained from climate models.

• Korean Journal of Social Welfare Studies
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• v.47 no.1
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• pp.151-178
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• 2016

The primary goal of the current study was to investigate the longitudinal relationship between household income, main caregiver depression, and youth aggression from the interactionist perspective. The data were derived by combining the 2006, 2009 and 2012 survey waves from the Korean Welfare Panel Study. This data set covered the full span of adolescence from elementary to high school. The study utilized 561 families as the final sample and conducted autoregressive cross-lagged analysis. As a result, the early income status, main caregiver depression and youth aggression were likely maintained over time. Second, the results provided support for a reciprocal relationship between income and main caregiver depression. On the other hand, the reciprocal relationship between main caregiver depression and youth aggression was not found in the current study. Finally, the mediating effect of main caregiver depression between income and youth aggression was not found in the present study. In conclusion, the results of this study support the interactionist model in that the association between family income and main caregiver depression involves reciprocity and mutual influence across time. These findings have major implications for policy and interventions in regards to low-income families.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1718-1724
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• 2017

Recently, Smart plugs for real time monitoring of household appliances based on IoT(Internet of Things) have been activated. Through this, consumers are able to save energy by monitoring real-time energy consumption at all times, and reduce power consumption through alarm function based on consumer setting. In this paper, we measure the alternating current from a wall power outlet for real-time monitoring. At this time, the current pattern for each household appliance was classified and it was experimented with deep learning to determine which product works. As a result, we used a cross validation method and a bootstrap verification method in order to the classification performance according to the type of appliances. Also, it is confirmed that the cost function and the learning success rate are the same as the train data and test data.

• Analytical Science and Technology
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• v.24 no.6
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• pp.544-555
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• 2011

The acute toxicity in the rainbow trout (Oncorhynchus mykiss) was analyzed and predicted using quantitative structure-activity relationships (QSAR). The aquatic toxicity, 96h $LC_{50}$ (median lethal concentration) of 275 organic pesticides, was obtained from EU-funded project DEMETRA. Prediction models were derived from 558 2D molecular descriptors, calculated in PreADMET. The linear (multiple linear regression) and nonlinear (support vector machine and artificial neural network) learning methods were optimized by taking into account the statistical parameters between the experimental and predicted p$LC_{50}$. After preprocessing, population based forward selection were used to select the best subsets of descriptors in the learning methods including 5-fold cross-validation procedure. The support vector machine model was used as the best model ($R^2_{CV}$=0.677, RMSECV=0.887, MSECV=0.674) and also correctly classified 87% for the training set according to EU regulation criteria. The MLR model could describe the structural characteristics of toxic chemicals and interaction with lipid membrane of fish. All the developed models were validated by 5 fold cross-validation and Y-scrambling test.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.65-76
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• 2004

This study aims to estimate daily maximum air temperature estimated using satellite-derived surface temperature and Elevation Derivative Database (EDD). The analysis is focused on the establishment of a semi-empirical estimation technique of daily maximum air temperature through the multiple regression analysis. This tests the contribution of EDD in the air temperature estimation when it is added into regression model as an independent variable. The better correlation is shown with the EDD data as compared with the correlation without this data set. In order to provide a progressive estimation technique, we propose and compare three approaches: 1) seasonal estimation non-considering landcover, 2) seasonal estimation considering landcover, and 3) estimation according to landcover type and non-considering season. The last method shows the best fit with the root-mean-square error between 0.56$^{\circ}C$ and 3.14$^{\circ}C$. A cross-validation procedure is performed for third method to valid the estimated values for two major landcover types (cropland and forest). For both landcover types, the validation results show reasonable agreement with estimation results. Therefore it is considered that the estimation technique proposed may be applicable to most parts of South Korea.

• Korean Journal of Optics and Photonics
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• v.12 no.2
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• pp.129-134
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• 2001

Novel supervisory system for WDM transmission link using conventional optical time domain reflectometry was presented. By modifying the structure of erbuim doped fiber amplifier to support bi-directional transmission at arDR pulse wavelength and launching the optical pulse into transmission link in the opposite direction of data signal propagation to avoid the distortion by cross-gain modulation, it is possible to monitor the WDM link in service. To prove the validity of proposed scheme, the supervision result of 2.5 Gbps $\times$ 8 channel WDM 320 km transmission system in service by arDR was presented. And power penalty due to monitoring was measured as smaller than 0.3 dB. .3 dB.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1086-1096
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• 2011

In this paper, we present the trisection band-pass filter with a transmission zero at 2.63 GHz, which has a center frequency of 2.44 GHz, relative bandwidth of 5 %, and return loss of 18 dB, based on a multi-port ElectroMagnetic simulation. The coupling matrix for the trisection filter is calculated and this filter is transformed into band-pass filter prototype through a lossless 2-port circuit transformation. The J-inverter values and slope parameters of each individual resonator are computed using an EM simulation Y-parameters of the filter with multi port. The dimensions of desired filter are determined by matching the computed J-inverter and susceptance slope parameters to those of the prototype band-pass filter. Undesired cross-couplings are found to occur which does not appear in the prototype trisection filter. To overcome the problem of undesired couplings, the filter was optimized to satisfy the same frequency response of prototype filter. The validity of the proposed design method was verified through the implementation of the designed and optimized filter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.942-948
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• 2003

In this paper, we analyze the performance of multiple trellis coded modulation applied to continuous phase frequency shift keying (MTCM/CPFSK) for the noncoherent receiver without channel state information (CSI) on the interleaved Rician fading channel. In this system, the squared cross-correlation between the received signal and a candidate signal is used as the branch metric of the Viterbi decoder. To obtain the bit error performance of this system, we analyze the approximated pairwise error probability (PEP) and the exact PEP. We also derive the equivalent normalized squared distance (ENSD) and compare it with the ENSD of the noncoherent receiver with perfect CSI. Simulation results are also provided to verify the theoretical performance analysis.