• Journal of Korean Academy of Nursing
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• v.49 no.3
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• pp.241-253
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• 2019

Purpose: This study aimed to develop and test a model of the happiness of mothers with young children based on the stress-coping-adaptation model of Lazarus and Folkman. Methods: The data collection period was from May to July 2016. A self-report questionnaire was used to collect data from 210 mothers with children under 5 years of age living in Seoul, Gyeonggi, and Gangwon provinces. The exogenous variable was parenting stress, and the endogenous variables were parenting alliance, depression, optimism, ways of coping, and happiness. Data from 201 questionnaires were analyzed using the SPSS 22.0 and AMOS 20.0 programs. Data analyses included descriptive statistics, factor analysis, and structural equation modeling. Results: The final modified model showed a reasonable fit to the data, and out of 25 paths, 13 were statistically significant. This model explained 78.4% of the variance in the happiness of mothers with young children and confirmed that depression, optimism, parenting alliance, and social support-focused coping have a direct effect on the subject's happiness. Parenting stress also influenced happiness through parenting alliance, depression, and optimism. Conclusion: In order to bolster the happiness of mothers with young children, positive psychological interventions that can minimize psychological vulnerabilities, such as depression, and that can enhance their strengths, such as optimism, may serve as effective ways of coping with and adapting to stress.

• Journal of Distribution Science
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• v.12 no.5
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• pp.93-107
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• 2014

Purpose - The pool of entrepreneurs with progressive qualities such as creativity and innovation was considered concurrently with such factors as work and capital that stimulate economic development and growth. This study aims to present a model to support the development of a strategic approach for achieving an overall academic entrepreneurship system in Iran. Research design, data, and methodology - The research design of this study is based on applied research because of its objectives, using principles and techniques formulated for basic research to solve operational and real organizational issues. This design also drives the method used, describing and interpreting the findings. Secondary data (library research) was used for this study's data collection. Because of this research's essential characteristics, no hypothesis is launched, and no research setting, questionnaire design, population or population sampling, validity or reliability tests, or statistical analysis are needed. Results and Conclusions - The model is created using a strategic approach acting in an octal setting comprising social, cultural, legal, economic, political, technological, competitive, and natural environments to present a conceptual framework for future studies.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.427-433
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• 2024

This study aims to develop a personalized music digital therapeutic based on MBTI personality types and apply it to depression treatment. In the data collection stage, participants' MBTI personality types and music preferences were surveyed to build a database, which was then preprocessed as input data for the KNN model. The KNN model calculates the distance between personality types using Euclidean distance and recommends music suitable for the user's MBTI type based on the nearest K neighbors' data. The developed system was tested with new participants, and the system and algorithm were improved based on user feedback. In the final validation stage, the system's effectiveness in alleviating depression was evaluated. The results showed that the MBTI personality type-based music recommendation system provides a personalized music therapy experience, positively impacting emotional stability and stress reduction. This study suggests the potential of nonpharmacological treatments and demonstrates that a personalized treatment experience can offer more effective and safer methods for treating depression.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.4
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• pp.18-29
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• 1998

This study was carried out to test the theory of planned behavior in recreational choices behavior. Lotte World was chosen as study area, and college students were selected by nonprobability sampling for two waves of data collection. The first wave of data were collected one weeks into the spring semester : intention, attitude, subjective norm, and perceived behavioral control were measured. To collect the data of the second wave, the same resondents were asked their behavior, one week data of the second wave, the same respondents were asked their behavior , one week prior to the final examination : whether they visited the Lotte World or not. Polychoric correlation among variables were calculated by the PRELIS because behavior was nominal variable. Then, weighted least square method was utilized to calibrate structural equation model by the LISREL version 7.2. Structural link effect on intention among three determinants : the direct effect on intention was 0.421 and the indirect effect via intention on behavior was 0.145, respectively. However, its effect on behavior was insignificant because actual control over 'visiting of Lotte World' was relatively high. A few comments were sugested on data collection, and inclusion of new variables was discussed for the sufficiency f the theory of planned behavior.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.537-542
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• 2021

This paper improves the possibility of recognizing fast-moving objects through the YOLO (You Only Look Once) deep learning recognition model in an application environment for object recognition in images. The purpose was to study the method of collecting semantic data through processing. In the recognition model, the moving object recognition error was identified as unrecognized because of the difference between the frame rate of the camera and the moving speed of the object and a misrecognition due to the existence of a similar object in an environment adjacent to the object. To minimize the recognition errors by compensating for errors, such as unrecognized and misrecognized objects through the proposed data collection method, and applying vision processing technology for the causes of errors that may occur in images acquired for sports (tennis games) that can represent real similar environments. The effectiveness of effective secondary data collection was improved by research on methods and processing structures. Therefore, by applying the data collection method proposed in this study, ordinary people can collect and manage data to improve their health and athletic performance in the sports and health industry through the simple shooting of a smart-phone camera.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.181-186
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• 2014

Web data mining aims at discovering useful knowledge from various Web resources. There is a growing trend among companies, organizations, and individuals alike of gathering information through Web data mining to utilize that information in their best interest. In science, cloud computing is a synonym for distributed computing over a network; cloud computing relies on the sharing of resources to achieve coherence and economies of scale, similar to a utility over a network, and means the ability to run a program or application on many connected computers at the same time. In this paper, we propose a new system framework based on the Hadoop platform to realize the collection of useful information of Web resources. The system framework is based on the Map/Reduce programming model of cloud computing. We propose a new data mining algorithm to be used in this system framework. Finally, we prove the feasibility of this approach by simulation experiment.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.253-263
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• 2021

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.171-172
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• 2008

We propose the sensor networks middleware based on publish/subscribe model for adaptive service to client. Publish/subscribe middleware make capability of sensor to data through the advertisement message. Based on capability of sensor, Publish/subscribe middleware adaptively service to client. And client make a collection of information that it require. Therefore middleware service more effectively.