• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.217-223
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• 2024

The current study aimed to evaluate the effectiveness of using Support Vector Machine (SVM) for political affiliation classification. The system was designed to analyze the political tweets collected from Twitter and classify them as positive, negative, and neutral. The performance analysis of the SVM classifier was based on the calculation of metrics such as accuracy, precision, recall, and f1-score. The results showed that the classifier had high accuracy and f1-score, indicating its effectiveness in classifying the political tweets. The implementation of SVM in this study is based on the principle of Structural Risk Minimization (SRM), which endeavors to identify the maximum margin hyperplane between two classes of data. The results indicate that SVM can be a reliable classification approach for the analysis of political affiliations, possessing the capability to accurately categorize both linear and non-linear information using linear, polynomial or radial basis kernels. This paper provides a comprehensive overview of using SVM for political affiliation analysis and highlights the importance of using accurate classification methods in the field of political analysis.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.1
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• pp.123-131
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• 2015

A CCTV inspection method has been widely used to assess sewer condition and performance, but Korea lacks a proper decision support system for prioritizing sewer repair and rehabilitation (R&R). The objective of this paper is to introduce the results that we have developed in the Sewer Condition Assessment and Rehabilitation Decision-making (SCARD) Program using MS-EXCEL. The SCARD-Program is based on a standardized defect score for sewer structural and hydraulic assessment. Priorities are ranked based on risk scores, which are calculated by multiplying the sewer severity scores by the environmental impacts. This program is composed of three parts, which are decision-making for sewer condition and performance assessment, decision-making for sewer R&R priority assessment, and decision-making for optimal budget allocation. The SCARD-Program is useful for decision-makers, as it enables them to assess the sewer condition and to prioritize sewer R&R within the limited annual budget. In the future, this program logic will applied to the GIS-based sewer asset management system in local governments.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.1
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• pp.97-106
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• 2015

A CCTV inspection method has been widely used to assess sewer condition and performance, but Korea lacks a proper decision support system for prioritizing sewer repair and rehabilitation (R&R). The objective of this paper is to introduce the results that we have developed in the Sewer Condition Assessment and Rehabilitation Decision-making (SCARD) Program using MS-EXCEL. The SCARD-Program is based on a standardized defect score for sewer structural and hydraulic assessment. Priorities are ranked based on risk scores, which are calculated by multiplying the sewer severity scores by the environmental impacts. This program is composed of three parts, which are decision-making for sewer condition and performance assessment, decision-making for sewer R&R priority assessment, and decision-making for optimal budget allocation. The SCARD-Program is useful for decision-makers, as it enables them to assess the sewer condition and to prioritize sewer R&R within the limited annual budget. In the future, this program logic will applied to the GIS-based sewer asset management system in local governments.

• Health Policy and Management
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• v.17 no.1
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• pp.50-74
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• 2007

The purpose of this study was to identify a determent of mastery approach goal and performance approach goal using a basic concept of goal orientations and goal setting theory, and to evaluate a preference of goal achievement index as a balance score card (BSC). The study model proposed had a adoptable level of goodness of fit index(.94) and root mean square residual(.08). The meditating variable, goal contribution, totally mediated the impact of goal commitment, Y-theory human behavior, and self-efficacy but organizational resource contribution for pursuing goal orientation. Moreover, goal contribution significantly determined mastery approach goal(p<.01) and performance approach goal(.05). In standardized effects, the most powerful antecedent of mastery approach goal and performance approach goal were in order of organizational resource contribution(.27/.28), goal contribution(.21/.17), self-efficacy(.07/.06), and Y -theory human behavior and goal commitment(.05/.05), respectively. Moreover, goal contribution had a more powerful impact on mastery approach goal(.21) rather than performance approach goal(.17). In the preference of BSC, all job types preferred learning and growth index in first. In the second preference, medical doctors and pharmacists chose financial results, nurses customer service, and office managers internal processes. Each job type reflected its' own preferred BSC index to that of the other job types. In comparing a preference of four BSC index of each own job type, it was statistically different at p<.001. In conclusion, one who emphasize organizational goal contribution in pursuing goal orientation has a more strong orientation toward mastery approach goal rather than performance approach goal. A hospital should overcome and harmonize the different preferences of four BSC index since the differences might cause organizational conflicts among job types with having each unique professional norm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.68-76
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• 2016

The present study proposes the definition of service life and the end-of-life criterion for bridge maintenance. Bridges begin to deteriorate as soon as they are put into service. Effective bridge maintenance requires sound understanding of the deterioration mechanism as well as the expected service life. In order to determine the expected service life of a bridge for effective bridge maintenance, it is necessary to have a clear definition of service life and end-of-life. However, service life can be viewed from several perspectives based on literature review. The end of a bridge's life can be also defined by more than one perspective or performance measure. This study presents definition of service life which can be used for bridge maintenance and the end-of life criterion using the performance measure such as a damage score. The regression model can predict an average service life of bridges using the proposed end-of-life criterion.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.193-201
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• 2020

This study is to evaluate the effectiveness of problem-solving ability by applying a team-based learning model to the classes of humanities and social science students, and to conduct a structural model analysis on the relationship between sub-factors. Team-based learning was conducted six times in six teams with 30 students in the second and third grades of the humanities and social sciences. The problem solving ability score of the target students was significantly higher after team-based learning and was statistically significant. There was no problem in normality with the latent variables, which are the sub-factors of problem solving ability, and the factor load value was statistically significant at the .001 level in the confirmatory factor analysis of the observed variables for the latent variables, which was a valid model. A good level of fitness was also shown in the verification of the fitness of the research model. As a result, it was analyzed that latent variables of cause analysis, problem clarification, planning execution, performance evaluation, and alternative development had an indirect or direct influence on each other.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

• The Korean Journal of Emergency Medical Services
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• v.9 no.1
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• pp.43-53
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• 2005

The purpose of this study was to survey a perception of frequency & importance level of job performance, from 249 paramedics who were working at fire station. The job related activities of 4 duties, 18 tasks and 145 task elements were checked by 4 rating scale. The data were analyzed using a SPSS program for descriptive statistics. The results of the study were as follows ; 1. Some of the most frequent tasks were medical tx, managing people & organization, trauma care, pt assessment, general coping skill 2. Some of the most frequent task elements were preparation of written reports, operation of pt lifting supplies, transportation of patients on stretchers, administration of cervical collar and utilization of scoop stretcher. 3. Some of the highest level of importance in task were resuscitation of circulation, surgical tx, safety & infection control, environmental emergency care, trauma care. 4. Some of the highest level of importance in task elements were administration of cervical collar, adult CPR, infant CPR, child CPR, and AED. 5. The highest level of task elements in perception of frequency & importance were administration of cervical collar, infection control after pt care, utilization of long back board, disinfection of ambulance after ride a long, care of chest pain pt, care of unconscious pt, tx of asthma. 6. A difference between frequent & importance score were due to lack of supplies(41%), structural problems(30%) and medical control system(16%), lack of skills(10%), Suggestion; 1. This paper would be more reliable and confirm through wilder range of survey. 2. It would be necessary of more depth survey through dacom study from paramedic. 3. Development of field oriented protocol & curriculum that based on task elements which have high score of both frequency & importance level is required.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.12
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• pp.1880-1892
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• 2010

The purposes of this study were to: a) provide evidence concerning the effects of emotional intelligence on job outcomes, b) examine the impacts of emotional intelligence on employee-related variables such as 'job satisfaction', 'organizational commitment', 'organizational performance', and 'turnover intention' c) identify the conceptual framework underlying emotional intelligence. A survey was conducted to collect data from foodservice managers (N=231). Statistical analyses were completed using SPSS Win (16.0) for descriptive analysis, reliability analysis, factor analysis, t-test, correlation analysis, cluster analysis and AMOS (16.0) for confirmatory factor analysis and structural equation modeling. The concept of emotional intelligence (EI) has been on the radar screens of many leaders and managers over the last several decades. The emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thinking and interaction with others. The main results of this study were as follows. The four EI (Emotional Intelligence) dimensions correlated significantly with age. The means of job satisfaction score were above the midpoint (3.04 point) scale. The organizational commitment score was above the midpoint (3.41 point) scale and was higher at 'loyalty' factor than 'commitment' factor. The means of organizational performance score were above the midpoint (3.34) scale. The correlations among the four EI (emotional intelligence) factors were significant with job satisfaction; organizational commitment, organizational performance and turnover intention. The test of hypothesis using structural equation modeling found that emotional intelligence produced positive effects on job attitude and job performance. Emotional intelligence enhanced organizational commitment, and in turn, managers' attitude produced positive effects on organizational performance; emotional intelligence also had a direct impact on organizational performance. This study has identified the effect of emotional intelligence on organizational performance and attitudes toward one's job.