• The Journal of the Korea Contents Association
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• v.12 no.1
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• pp.103-111
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• 2012

This paper is a basic study for making a system that can predict the success and failure of entertainment contents at the initial stage of production. It proposes the user's emotion modeling of dynamic narrative on entertainment contents. To make this possible, 1) dynamic narrative emotion model is proposed based on theoretical research of narrative structure and cognitive emotion model. 2) configuring the emotion types and emotion value, proposed model of three emotion parameter(desire, expectation, emotion type) are derived. 3)To measure user's emotion in each story event of dynamic narrative, cognitive behavior and description of user(film, game) is established. The earlier studies on the user research of conceptual, analytic approach is aimed of predicting on review of the media and user's attitude, and consequently these results is delineated purely descriptive. In contrast, this paper is proposed the method of user's emotion modeling on dynamic narrative. It would be able to contributed to the emotional evaluation of entertainment contents using specific information.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.11-21
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• 2015

If the cloud computing environment is not sufficiently provide the required resources due to the number of virtual server to process the request, may cause a problem that the load applied to the specific server. Migration administrator receive the resources of each physical server for improving the efficiency of the virtual server that exists in the physical servers, and determines the migration destination based on the simulation results. But, there is more overhead predicting the future resource consumption of all the physical server to decide the migration destination through the simulation process in large and complex cloud computing environments. To solve this problem, we propose an improved prediction method with the simulation-based approach. The proposed method is a fuzzy-logic based priority model for VM migration. We design a proposed model with the DEVS formalism. And we also measure and compare a performance and migration count with existing simulation-based migration method. FPLM shows high utilization.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.239-249
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• 2013

The role of software process is getting more important to make good quality softwares. One of the measures to improve the software process is Defect Removal Efficiency(DRE). DRE gives a measure of the development team ability to remove defects prior to release. It is calculated as a ratio of defects resolved to total number of defects found. Software Engineering Levels are usually decided by CMMI Model. The model is designed to help organizations improve their software product and service development, acquisition, and maintenance processes. The score of software engineering levels can be calculated by CMMI model. The levels are composed of the three groups(absent, average, and advanced). This study is to find if there is any difference among the three categories in term of the result of software engineering levels on DRE. We propose One way ANOVA to analyze influence of software engineering levels on DRE. Bootstrap method is also used to estimate the sampling distribution of the original sample because the data are not sampled randomly. The method is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample. The data were collected in 106 software development projects by the survey. The result of this study tells that there is some difference of DRE among the groups. The higher the software engineering level of a specific company becomes, the better its DRE gets, which means that the companies trying to improve software process can increase their good management performance.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.77-86
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• 2019

This paper proposes using machine-learning technology to analyze and classify historical collected documents based on them. Data is collected based on keywords associated with a specific domain and the non-conceptuals such as special characters are removed. Then, tag each word of the document collected using a Korean-language morpheme analyzer with its nouns, verbs, and sentences. Embedded documents using Doc2Vec model that converts documents into vectors. Measure the similarity between documents through the embedded model and learn the document classifier using the machine running algorithm. The highest performance support vector machine measured 0.83 of F1-score as a result of comparing the classification model learned.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.257-262
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• 2024

Today, high-speed rail is gradually increasing in importance as an eco-friendly means of transportation responsible for the movement of people between regions. In the past, problems of inefficiency continued due to monopolistic operation of the railway industry, and with the introduction of a public enterprise competition system, KTX and SRT operating organizations are making efforts to expand service quantity and improve quality. However, the high-speed rail service quality evaluation was limited to modifying and supplementing indicators from the operator's perspective suitable for past quality evaluations, and the evaluation target or method was not specific. Therefore, in this study, we developed a hierarchical model to measure high-speed rail customer service quality based on the model of Brady and Cronin(2001) and applied the analytical hierarchical decision-making method(AHP) to derive the priorities of Korea's high-speed rail competitiveness factors. Based on the results, it is believed that introducing reasonable and standardized service quality indicators will contribute to establishing a marketing strategy to improve the customer service competitiveness of high-speed rail operators.

• Journal of Korean Academy of Nursing
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• v.45 no.1
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• pp.14-24
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• 2015

Purpose: The purpose of this study was to measure the level of expectations regarding aging (ERA) and identify relationship between ERA and physical activity of middle aged adults. Methods: Participants were middle aged adults who resided in the community in three cities in Korea. Data were collected using questionnaires that contained items on individual characteristic, International Physical Activity Questionnaires (IPAQ), and behavior-specific cognitive factors including ERA-12. Hierarchical multiple regression was conducted to examine whether ERA would predict physical activity by controlling other factors. Results: The mean age of the participants was $51.1{\pm}6.9$ years. The mean score for ERA (possible range=0 to 100) was $40.04{\pm}14.31$. More than half of the participants (62.6%) were not engaged in health promoting physical activity. Gender, employment status and exercise confidence were associated with level of physical activity (F=7.14, p<.001, $R^2=.36$). After controlling for individual factors and behavior-specific cognitive factors, ERA was independently related to physical activity (F=7.19, p<.001, $R^2=.38$). Conclusion: The results demonstrate that individuals' belief about aging has effects on physical activity in Korean middle aged adults. Thus, nursing interventions which focused on ERA could help enhance physical activity in middle aged adults.

• The Journal of Industrial Distribution & Business
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• v.10 no.12
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• pp.35-42
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• 2019

Purpose : This study examines The impact of human resource investment in internal control on stock price crash risk. Effective internal control ensures that information provided is complete and accurate, financial statements are reliable. By overseeing management, internal control systems can reduce agency costs between management and outside parties. In Korea, firms have to disclose information about internal control systems. The working experience of human resources in internal control systems is also provided for interested parties. If a firm hires more experienced internal control personnel, it can better facilitate the disclosure of information. Prior studies reported that information asymmetry between managers and investors increases future stock price crash risk. Therefore, the longer working experience internal control personnel have, the lower probability stock crashes have. Research design, data and methodology : This study analyzed the association between the working experience of internal control personnel and crash risk using regression analysis on KOSPI listed companies for fiscal years 2016 through 2017. The sample consists of 1,034 firm-years of non-financial firms whose fiscal year end on December 31. Career spanning data of internal control personnel was collected from internal control reports. The professionalism(IC_EXP) was measured as the logarithm of the average working experience of internal control personnel in months. Negative conditional skewness(NSKEW) and down-to-up volatility (DUVOL) are used to measure firm-specific crash risk. Both measures are based on firm-specific weekly returns derived from the expanded market model. Results : We find that work experience in internal control environment is negatively related to stock price crashes. Specifically, skewness(NSKEW) and volatility (DUVOL) are reduced when firms have longer tenure of human resources in internal control division. The results imply that firms with experienced internal control personnel are less likely to experience stock price crashes. Conclusions : Stock price crashes occur when investors realize that stock prices have been inflated due to information asymmetry. There is a learning effect when internal control processes are done repetitively. Thus, firms with more experienced internal control personnel could manage their internal control more effectively. The results of this study suggest that firms could decrease information asymmetry by investing in human resources for their internal control system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.349-354
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• 2020

The preliminary investigation to expand the green space requires a lot of cost and time. In this paper, we solve the problem by measuring the ratio of green space in a specific region through a convolutional neural network based the green space classification using Google Earth images. First, the proposed method collects various region images in Google Earth and learns them by using the convolutional neural network. The proposed method divides the image recursively to measure the green space ratio of the specific region, and it determines whether the divided image is green space using a trained convolutional neural network model, and then the green space ratio is calculated using the regions determined as the green space. Experimental results show that the proposed method shows high performance in measuring green space ratios in various regions.

• Journal of Veterinary Science
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• v.23 no.1
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• pp.7.1-7.14
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• 2022

Background: Enterotoxigenic Escherichia coli (ETEC) infection is a primary cause of livestock diarrhea. Therefore, effective vaccines are needed to reduce the incidence of ETEC infection. Objectives: Our study aimed to develop a multivalent ETEC vaccine targeting major virulence factors of ETEC, including enterotoxins and fimbriae. Methods: SLS (STa-LTB-STb) recombinant enterotoxin and fimbriae proteins (F4, F5, F6, F18, and F41) were prepared to develop a multivalent vaccine. A total of 65 mice were immunized subcutaneously by vaccines and phosphate-buffered saline (PBS). The levels of specific immunoglobulin G (IgG) and pro-inflammatory cytokines were determined at 0, 7, 14 and 21 days post-vaccination (dpv). A challenge test with a lethal dose of ETEC was performed, and the survival rate of the mice in each group was recorded. Feces and intestine washes were collected to measure the concentrations of secretory immunoglobulin A (sIgA). Results: Anti-SLS and anti-fimbriae-specific IgG in serums of antigen-vaccinated mice were significantly higher than those of the control group. Immunization with the SLS enterotoxin and multivalent vaccine increased interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) concentrations. Compared to diarrheal symptoms and 100% death of mice in the control group, mice inoculated with the multivalent vaccine showed an 80% survival rate without any symptom of diarrhea, while SLS and fimbriae vaccinated groups showed 60 and 70% survival rates, respectively. Conclusions: Both SLS and fimbriae proteins can serve as vaccine antigens, and the combination of these two antigens can elicit stronger immune responses. The results suggest that the multivalent vaccine can be successfully used for preventing ETEC in important livestock.