• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.467-480
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• 2011

Spatial and temporal variabilities of NPP(Net Primary Production) retrieved from two satellite instruments, AVHRR(Advanced Very High Resolution Radiometer, 1981-2000) and MODIS(MODerate-resolution Imaging Spectroradiometer, 2000-2006), were investigated. The range of mean NPP from A VHRR and MODIS were estimated to be 894-1068 $g{\cdot}C/m^2$/yr and 610-694.90 $g{\cdot}C/m^2$/yr, respectively. The discrepancy of NPP between the two instruments is about 325 $g{\cdot}C/m^2$/yr, and MODIS product is generally closer to the ground measurement than AVHRR despite the limitation in direct comparison such as spatial resolution and vegetation classification. The higher NPP values over South Korea are related to the regions with higher biomass (e.g., mountains) and higher annual temperature. The interannual NPP trends from the two satellite products were computed, and both mean annual trends show continuous NPP increase; 2.14 $g{\cdot}C/m^2$/yr from AVHRR(1981-2000) and 6.08 $g{\cdot}C/m^2$/yr from MODIS (2000-2006) over South Korea. Specifically, the higher increasing trends over the Southwestern region are likely due to the increasing productivity of crop fields from sufficient irrigation and fertilizer use. The retrieved NPP shows a closer relationship between monthly temperature and precipitation, which results in maximum correlation during summer monsoons. The difference in the detection wavelength and model schemes during the retrieval can make a significant difference in the satellite products, and a better accuracy in the meterological and land use data and modeling applications will be necessary to improve the satellite-based NPP data.

• Korean Journal of Social Welfare
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• v.61 no.2
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• pp.325-348
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• 2009

This study intends to investigate the main and mediating effects which caregiving appraisal and positive reappraisal exert on quality of life (QOL) of primary family caregivers of cancer patient considering the relationship with social support. The processes of this study areas follows. First, the variables which research model were chosen on the basis of stress-appraisal-coping theory through reviews of the previous studies. Second, a survey was conducted upon 295 primary caregiver of patient with cancer at National Cancer Center. Collected data were analyzed by SPSS 12.0 and SEM (Structural Equation Modeling) method using AMOS 5.0. The summary of the result is as follows. First, the entire model including measurement and structural model shows sufficient fit index of CFI(.951), TLI(.940) and RMSEA(.062). Second, the results of analysis of direct effects among variables are as follows. The 'Social support' has statistically significant direct effect on the 'feeling of burden' and 'growth'. The 'feeling of burden' has statistically significant direct effect on the 'growth' and 'QOL-mental and physical'. The 'growth' has statistically significant direct effect on the 'QOL-mental'. Third, the results of analysis of mediating effects of the 'social support and QOL' and 'feeling of burden and QOL' are as follows. The effects of 'social support' on 'QOL-mental' are significantly mediated by the 'feeling of burden' and 'growth'. The effects of 'social support' on 'QOL-physical' are significantly mediated by the 'feeling of burden'. The effects of 'feeling of burden' on 'QOL-mental' are significantly mediated by 'growth'. Through this research, these implications in social work study and practice are found: (1) this study extended the scope of study in the caregiver's health area from negative sides into positive ones by using growth variables as positive reappraisalof caregiving in research model, which has not been tried on the Korean family caregivers of the cancer patient. (2) The effects of positive reappraisal on QOL-mental can provide a foundational necessity for social workers to help family caregivers find positive meaning in their caregiving experience. This approach of social work practice will improve QOL of family caregivers. (3) This study present a framework including social support, negative appraisal, positive reappraisal, and QOL variables available to social work practice and explaining affective relationships among these variables in various aspects.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• 한국노년학
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• v.37 no.1
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• pp.173-200
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• 2017

This study is designed to verify the actual effect on the prevention of the elderly suicide of the integrated crisis intervention service which has been widely provided across all local communities in Gyeonggi-province focusing on the integrated crisis intervention model developed for the prevention of elderly suicide. The integrated crisis intervention model for the local communities and its manual were developed for the prevention of elderly suicide by integrating the crisis intervention theory which contains local community's integrated system approach and the stress vulnerability theory. For the analysis of the effect, the geriatric depression and suicidal ideation scale was adopted and the data was collected as follows; The data was collected from 258 people in the first preliminary test. Then, it was collected from the secondary test of 184 people after the integrated crisis intervention service was performed for 6 months. The third collection of data was made from 124 people after 2 or 3 years later using the backward tracing method. As for the analysis, the researcher used the R Statistics computing to conduct the test equating, and the vertical scaling between measuring points. Then, the researcher conducted descriptive statistics analysis and univariate analysis of variance, and performed multi-level modeling analysis using Bayesian estimation. As a result of the study, it was found out that the integrated crisis intervention model which has been developed for the elderly suicide prevention has a statistically significant effect on the reduction of elderly suicide in terms of elderly depression and suicide ideation in the follow-up measurement after the implementation of crisis intervention rather than in the first preliminary scores. The integrated crisis intervention model for the prevention of elderly suicide was found to be effective to the extent of 0.56 for the reduction of depression and 0.39 for the reduction of suicidal ideation. However, it was found out in the backward tracing test conducted 2-3 years after the first crisis intervention that the improved values returned to its original state, thus showing that the effect of the intervention is not maintained for long. Multilevel analysis was conducted to find out the factors such as the service type(professional counseling, medication, peer counseling), characteristics of the client (sex, age), the characteristics of the counselor(age, career, major) and the interaction between the characteristics of the counselor and intervention which affect depression and suicidal ideation. It was found that only medication can significantly reduce suicidal ideation and that if the counselor's major is counseling, it significantly further reduces suicidal ideation by interacting with professional counseling. Furthermore, as the characteristics of the suicide prevention experts are found to regulate the intervention effect on elderly suicide prevention in applying integrated crisis intervention model, the primary consideration should be given to the counseling ability of these experts.

• Korean Journal of Optics and Photonics
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• v.27 no.1
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• pp.18-24
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• 2016

To effectively utilize a flash and predict its effects on an infrared device, it is essential to know the infrared characteristics of the flash source. In this paper, a study of the IR characteristics of flash light sources is carried out. The IR characteristics of three flash sources, of which two are combustive and the other is explosive, are measured with an IR characteristic measurement system over the middle- and long-wavelength infrared ranges. From the measurements, the radiances over the two IR ranges and the radiative temperatures of the flashes are extracted. The IR radiance of flash A is found to be the strongest among the three, followed by those of sources C and B. It is also shown that the IR radiance of flash A is about 10 times stronger than that of flash B, even though these two sources are the same type of flash with the same powder. This means that the IR radiance intensity of a combustive flash source depends only on the amount of powder, not on the characteristics of the powder. From the measured radiance over MWIR and LWIR ranges for each flashes, the radiative temperatures of the flashes are extracted by fitting the measured data to blackbody radiance. The best-fit radiative temperatures (equivalent to black-body temperatures) of the three flash sources A, B, and C are 3300, 1120, and 1640 K respectively. From the radiance measurements and radiative temperatures of the three flash sources, it is shown that a combustive source radiates more IR energy than an explosive one; this mean, in turn, that the effects of a combustive flash on an IR device are more profound than those of an explosive flash source. The measured IR radiances and radiative temperatures of the flash sources in this study can be used to estimate the effects of flashes on various IR devices, and play a critical role for the modeling and simulation of the effects of a flash source on various IR devices.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.223-231
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• 2018

Recently, due to various environmental problems, blast tracks in tunnel are replaced with concrete tracks, but they have more adverse effects on noise than blast tracks so that additional noise measures are needed. Among these measures, sound-absorbing blocks start to be used due to its easy and quick installation. However, the performance of sound absorption blocks need to be verified under real environmental and operational conditions. In this paper, interior noise levels in KTX train cruising in Dalseong tunnel are measured before and after the installation of sound-absorbing blocks and the measured data are analyzed and compared. Additionally, noise reduction are estimated by modeling the high speed train, the tunnel and absorption blocks. Measurement devices and methods are used according to ISO 3381 and the equivalent sound pressure levels during the cruising time inside the tunnel are computed. In addition to overall SPLs(Sound Pressure Levels), 1/3-octave-band levels are also analyzed to account for the frequency characteristics of sound absorption and equipment noise in a cabin. In addition, to consider the effects of train cruising speeds and environmental conditions on the measurements, the measured data are corrected by using those measured during the train-passing through the tunnels located before and behind the Dalseong tunnel. Analysis of measured results showed that the maximum noise reduction of 6.8 dB (A) can be achieved for the local region where the sound-absorbing blocks are installed. Finally, through the comparison of predicted 1/3-octave band SPLs for the KTX interior noise with the measurements, the understanding of noise reduction mechanism due to sound-absorbing blocks is enhanced.

• The Journal of Information Systems
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• v.25 no.1
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• pp.1-23
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• 2016

Purpose Self-congruity deals with the effect of symbolic value-expressive attributes on consumer decision and behavior, which is the theoretical foundation of the "non-utilitarian destination positioning". Functional congruity refers to utilitarian evaluation of a product or service by consumers. In addition, recent years, social network services, especially mobile social network services have created many opportunities for e-WOM communication that enables consumers to share personal consumption related information anywhere at any time. Moreover, self-construal is a hot and popular topic that has been discussed in the field of modem psychology as well as in marketing area. This study aims to examine the moderating effect of self-construal on the relationship between self-congruity, functional congruity and tourists' positive electronic word of mouth (e-WOM). Design/methodology/approach In order to verify the hypotheses, we developed a questionnaire with 32 survey items. We measured all the items on a five-point Likert-type scale. We used Sojump.com to collect questionnaire and gathered 218 responses from whom have visited Korea before. After a pilot test, we analyzed the main survey data by using SPSS 20.0 and AMOS 18.0, and employed structural equation modeling to test the hypotheses. We first estimated the measurement model for its overall fit, reliability and validity through a confirmatory factor analysis and used common method bias test to make sure that whether measures are affected by common-method variance. Then we tested the hypotheses through the structural model and used regression analysis to measure moderating effect of self-construal. Findings The results reveal that the effect of self-congruity on tourists' positive e-WOM is stronger for tourists with an independent self-construal compared with those with interdependent self-construal. Moreover, it shows that the effect of functional congruity on tourists' positive e-WOM becomes salient when tourists' self-construal is primed to be interdependent rather than independent. We expect that the results of this study can provide important implications for academic and practical perspective.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.848-858
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• 2007

Sontag and Lee (2004) recently developed an objectively measurable instrument, the Proximity of Clothing to Self(PCS) Scale, which measured the psychological closeness of clothing to self. They validated a 4-factor, 24-item PCS Scale for use with adolescents and identified the need for confirmation of the factor structure with other age groups. This paper extends the work of Sontag and Lee by employing the PCS Scale with older persons, age 65 and over, and reports the validation of a 3-factor, 19-item PCS Scale for older persons. A mail survey was sent to a national random sample of 1,700 older Persons by means of a list purchased from a U.S. survey sampling company in late November 2004. Total usuable number of respondents was 250 with an adjusted response rate of 15.6 percent. Three analytical rounds of confirmatory factor analysis(CFA) to test the construct validity of the PCS Scale were conducted by using AMOS 5.0(Analysis of Moment Structures), one of several structural equation modeling(SEM) programs. Completion of three rounds of the CFA resulted in a 3-factor, 19-item PCS Scale with demonstrated construct validity and reliability for older persons. The three PCS dimensions are clothing in relation to 1) self as structure-process(PCS Dimension 1-2-3 combined), 2) self-esteem-evaluative and affective processes(PCS Dimension 4-5 combined), and 3) body image and body cathexis(PCS Dimension 6). The initially hypothesized 6-factor scale(Sontag & Lee, 2004) was not confirmed for adolescents in their study nor with older persons in this study. In addition, the 4-factor solution for the adolescent group did not hold for older persons. It appears that the self-system of older persons is more integrated than may be true for younger individuals. Recommendations for future testing of construct validity of the PCS Scale are made.

• Geophysics and Geophysical Exploration
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• v.26 no.1
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• pp.18-30
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• 2023

Submarine mud volcanos are topographic features that resemble volcanoes, and are formed due to eruptions of fluidized or gasified sediment material. They have gained attention as a source of subsurface heat, sediment, or hydrocarbons supplied to the surface. In the continental slope of the Canadian Beaufort Sea, mud volcano exists at various water depths. The MV420, is an active mud volcano erupting at a water depth of 420 meters, and it has been the subject of extensive study. The Korea Polar Research Institute(KOPRI) collected high-resolution seismic data and heat flow data around the caldera of the mud volcano. By analyzing the multi-channel seismic data, we confirmed the reverse-polarity reflector assumed by a gas hydrate-related bottom simulating reflector(BSR). To further elucidate the relationship between the BSR and gas hydrates, as well as the thermal structure of the mud volcano, a numerical geothermal model was developed based on the steady-state heat equation. Using this model, we estimated the base of the gas hydrate stability zone and found that the BSR depth estimated by multi-channel seismic data and the bottom of the gas hydrate stability zone were in good agreement., This suggests the presence of gas hydrates, and it was determined that the depth of the gas hydrate was likely up to 50 m, depending on the distance from the mud conduit. Thus, this depth estimate slightly differs from previous studies.