• 한국노년학
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• v.38 no.2
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• pp.351-364
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• 2018

The purpose of this study is to learn about the decision factors affecting utilization of elderly welfare center of the elderly living in Gimpo city. The reason of the study is that the elderly welfare center as a provider of general welfare services could not only thinking about the state policy but also need to consider about the inherent role and function of the elderly. Especially for these elders living in rural areas, although the number of elderly welfare centers of the whole country has greatly increased in last 10 years, the effect and function of the facility are almost the same and they are still lack of leisure activities. This issue become a serious problem nowadays. For the above reasons, this article conducts a social survey of 360 elderly people over the age of 65 who lives in the Gimpo city which is a rural-urban type city. The research method is to examine the relationship between the predisposing factors, enabling factors and need factors of Andersen's behavior model with binary logistic regression analysis and the decision tree analysis. The result of binary logistic regression shows the most of factors of Andersen's model is significant. The factors of age, gender, education level in predisposing factors; monthly income in enabling factors and the reserve for old life, the preparation of economic activity for old life in need factors are significant. Then the result of decision tree analysis shows the interaction between factors; when the education level in predisposing factors is higher, the possibility of using of elderly welfare center becomes bigger. Also as the level of healthy promoting preparation in the need factors gets lower, the possibility of using of elderly welfare center still becomes bigger. Although differences were found in the interpretation of the results of regression analysis and decision tree analysis, the results of this study can still provide support for the necessity of elderly welfare centers providing integrated welfare services.

• Korean Journal of Social Welfare Studies
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• v.42 no.2
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• pp.493-523
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• 2011

This study set out to investigate the effects of individual motivation on turnover intention among social workers and examine their turnover intentions in details by focusing on the mediation effects of multiple commitment. To be specific, it aimed to propose and test a prediction model for social workers' turnover intentions based on the Self-determination Theory and Theory of Planned Behavior. For those purposes, a mail survey was taken among social workers working for use facilities, residential facilities, public health centers, social welfare foundations and associations, and all kinds of centers and institutions in 15 cities and provinces across the nation. Total 1,918 questionnaires were distributed, and 1,671 ones were returned, and 979 whose respondents expressed a turnover intention were used in final analysis. The analysis results indicate that psychological motivation of social workers had direct impacts on their turnover intention. However, their role stress had no direct impacts on their turnover intention, which suggests that the impulsive routes model for turnover intention is supported only in psychological motivation and job characteristics. Secondly, their psychological and job motivation turned out to have indirect impacts on turnover intention through the multiple commitment, which suggests that the reflective routes model for turnover intention is supported in all career, job, and organizational commitment. Career commitment had the most significant impacts on turnover intention, being followed by job commitment and organizational commitment in the order, which suggests that the social welfare academy should increase their interest in career commitment. Based on the findings, the study proposed implication for the career management plans, plans for human resources

• Korean Journal of Culture and Social Issue
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• v.20 no.2
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• pp.133-154
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• 2014

The purpose of this study was to investigate psychological processes based on the Korean college students' development of social justice interest and commitment. For this study, we replicated Miller and colleagues'(2009) study, which explained the development of social justice interest and commitment by social-cognitive career theory(SCCT). Social desirability was controlled, and then self-reports data from 343 college students were analyzed using Structural Estimate Modeling(SEM). As a result, the final research model that social justice self-efficacy and outcome expectations affect social justice interest and commitment was proven valid for Korean college students. Also, in comparison with the direct effects model(social supports and barriers affect directly on commitment), the indirect effects model(social supports and barriers affect indirectly on commitment through self-efficacy) was supported. As an unique path of social-justice domain, the indirect effect by social support on commitment through outcome expectation was proved, as well. This study covers measurement limitations, future directions for research, and some lessons points with regards to how Korean college students to have social justice interest and commitment.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.240-245
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• 2016

This study was conducted to predict the changes of potential distribution for invasive alien plant, Amaranthus viridis in Korea. The habitats of A. viridis were roadside, bare ground, farm area, and pasture, where the interference by human was severe. We used maximum entropy modeling (MaxEnt) for analyzing the environmental influences on A. viridis distribution and projecting on two different representative concentration pathways (RCP) scenarios, RCP 4.5 and RCP 8.5. The results of our study indicated annual mean temperature, elevation and precipitation of coldest month had higher contribution for A. viridis potential distribution. Projected potential distribution of A. viridis will be increased by 110% on RCP 4.5, 470% on RCP 8.5.

• Economic and Environmental Geology
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• v.44 no.2
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• pp.135-152
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• 2011

Nitrate concentrations in springs at the southern slope of Jeju Island were predicted using multiple linear regression (MLR) of spatial variables including hydrogeological parameters and land use characteristics. Springs showed wide range of nitrate concentrations from <0.02 to 86 mg/L with a mean of 20 mg/L. Spatial variables were generated for the circular buffer when the optimal buffer radius was assigned as 400 m. Selected regression models were tested using the p values and Durbin-Watson statistics. Explanatory variables were selected using the adjusted $R^2$, Cp (total squared error) and AIC (Akaike's Information Criterion), and significance. In addition, mutual linear relations between variables were also considered. Small portion of springs, usually <10% of total samples, were identified as outliers indicating limitations of MLR using circular buffers. Adjusted $R^2$ of the proposed models was improved from 0.75 to 0.87 when outliers were eliminated. In particular, the areal proportion of natural area had the greatest influence on the nitrate concentrations in springs. Among anthropogenic land uses, the influence of nitrate contamination is diminishing in the following order of orchard, residential area, and dry farmland. It is apparent quality of springs in the study area is likely to be controlled by land uses instead of hydrogeological parameters. Most of all, it is worth highlighting that the contamination susceptibility of springs is highly sensitive to nearby land uses, in particular, orchard.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.723-737
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• 2017

In order to decide the national technical standards policy for national policy/market economy activities, the people in charge commonly make policy decisions based on the current technology level/concentration/utilization by means of major common technology field analysis using patent data. One possible source of such patent data is the domestic mobile carriers through the Korea Intellectual Property Rights Information System (KIPRIS) of the Korean Intellectual Property Office (KIPO). Using this system, we collected 20,294 patents and 152 International Patent Classification (IPC) types and confirmed KTs (9,738 cases / 47.98%), which perform relatively high technology retention activities compared to other mobile carriers through the KIPRIS of KIPO. Based on these data, we performed three analyses (SNA, PCA, ARIMA) and extracted 30 IPC types from the SNA and 4 IPC types from the PCA. Based on the above analysis results, we confirmed that 4 IPC (H04W, H04B, G06Q, H04L) types are the major common technology field of the domestic mobile carriers. Finally, the number of 4 IPC (H04W, H04B, G06Q, H04L) forecast averages of the ARIMA forecast result is lower than the number of existing time series patent data averages.

• Journal of the Korean Geotechnical Society
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• v.33 no.8
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• pp.5-16
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• 2017

When a lateral load is applied to a short pile whose embedded depth is relatively smaller than its diameter, an overturning failure occurs. To investigate the behavior of laterally loaded short piles, several model tests in laboratory scales had been carried out, however the behavior of large moment carrying piles for electric poles, traffic sign and road lamp, etc. have not been revealed yet. This paper deals with the real-scale load tests for 750 mm diameter short piles. To simulate the actual loading condition, very large moment was mobilized by applying lateral loads to the location 8 m away from the pile head. Three load tests changing the pile embedded lengths to 2.0 m, 2.5 m, and 3.0 m were carried out. The test piles overturned abruptly with very small displacement and rotation before the failures. These brittle failures are in contrast with the ductile failures shown in the former model tests with the relatively smaller moment to lateral load ratio. Comparisons of the test results with three existing methods for the estimation of the ultimate lateral capacity show that the method assuming the rotation point at pile tip matches well when the embedded depth is small, however, as the embedded depth increases the other two methods assuming the inversion of soil pressure with respect to rotation points in pile length match better.