• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.153-172
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• 2022

In this study, the authors intend to analyze factors contributing to research performance using Backpropagation Neural Network and Support Vector Machine. The analyzing factors contributing to lecturer research performance start from defining the features. The next stage is to collect datasets based on defining features. Then transform the raw dataset into data ready to be processed. After the data is transformed, the next stage is the selection of features. Before the selection of features, the target feature is determined, namely research performance. The selection of features consists of Chi-Square selection (U), and Pearson correlation coefficient (CM). The selection of features produces eight factors contributing to lecturer research performance are Scientific Papers (U: 154.38, CM: 0.79), Number of Citation (U: 95.86, CM: 0.70), Conference (U: 68.67, CM: 0.57), Grade (U: 10.13, CM: 0.29), Grant (U: 35.40, CM: 0.36), IPR (U: 19.81, CM: 0.27), Qualification (U: 2.57, CM: 0.26), and Grant Awardee (U: 2.66, CM: 0.26). To analyze the factors, two data mining classifiers were involved, Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). Evaluation of the data mining classifier with an accuracy score for BPNN of 95 percent, and SVM of 92 percent. The essence of this analysis is not to find the highest accuracy score, but rather whether the factors can pass the test phase with the expected results. The findings of this study reveal the factors that have a significant impact on research performance and vice versa.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.532-538
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• 2002

This study was performed to classify the fatigue crack opening and closure for three kinds of aluminum alloy using principal component analysis (PCA). Fatigue cycle loading test was conducted to acquire AE signals which come from different source mechanisms such as crack opening and closure, rubbing, fretting etc. To extract the significant feature from AE signal, correlation analysis was performed. Over 94% of the variance of AE parameters could accounted for the first two principal components. The results of the PCA on AE parameters showed that the first principal component was associated with the size of AE signals and the second principal component was associated with the shape of AE signals. An artificial neural network (ANN) an analysis was successfully used to classify AE signals into six classes. The ANN classifier based on PCA appeared to be a promising tool to classify AE signals for fatigue crack opening and closure.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.157-165
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• 2006

The change of external/internal factors of the cell rquires specific biological functions to maintain life. Such functions encourage particular genes to jnteract/regulate each other in multiple ways. Accordingly, we applied a linear decomposition model IFSA, which derives hidden variables, called the 'expression mode' that corresponds to the functions. To interpret gene interaction/regulation, we used a cross-correlation method given an expression mode. Linear decomposition models such as principal component analysis (PCA) and independent component analysis (ICA) were shown to be useful in analyzing high dimensional DNA microarray data, compared to clustering methods. These methods assume that gene expression is controlled by a linear combination of uncorrelated/indepdendent latent variables. However these methods have some difficulty in grouping similar patterns which are slightly time-delayed or asymmetric since only exactly matched Patterns are considered. In order to overcome this, we employ the (IFSA) method of [1] to locate phase- and shut-invariant features. Membership scoring functions play an important role to classify genes since linear decomposition models basically aim at data reduction not but at grouping data. We address a new function essential to the IFSA method. In this paper we stress that IFSA is useful in grouping functionally-related genes in the presence of time-shift and expression phase variance. Ultimately, we propose a new approach to investigate the multiple interaction information of genes.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.77-96
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• 2011

An exhibition is defined as market events for specific duration to present exhibitors' main products to business or private visitors, and it plays a key role as effective marketing channels. As the importance of exhibition is getting more and more, domestic exhibition industry has achieved such a great quantitative growth. But, In contrast to the quantitative growth of domestic exhibition industry, the qualitative growth of Exhibition has not achieved competent growth. In order to improve the quality of exhibition, we need to understand the preference or behavior characteristics of visitors and to increase the level of visitors' attention and satisfaction through the understanding of visitors. So, in this paper, we used the observation survey method which is a kind of field research to understand visitors and collect the real data for the analysis of behavior pattern. And this research proposed the following methodology framework consisting of three steps. First step is to select a suitable exhibition to apply for our method. Second step is to implement the observation survey method. And we collect the real data for further analysis. In this paper, we conducted the observation survey method to obtain the real data of the KIDS & EDU EXPO for Children in SETEC. Our methodology was conducted on 160 visitors and 78 booths from November 4th to 6th in 2010. And, the last step is to analyze the record data through observation. In this step, we analyze the feature of exhibition using Demographic Characteristics collected by observation survey method at first. And then we analyze the individual booth features by the records of visited booth. Through the analysis of individual booth features, we can figure out what kind of events attract the attention of visitors and what kind of marketing activities affect the behavior pattern of visitors. But, since previous research considered only individual features influenced by exhibition, the research about the correlation among features is not performed much. So, in this research, additional analysis is carried out to supplement the existing research with data mining techniques. And we analyze the relation among booths using data mining techniques to know behavior patterns of visitors. Among data mining techniques, we make use of two data mining techniques, such as clustering analysis and ARM(Association Rule Mining) analysis. In clustering analysis, we use K-means algorithm to figure out the correlation among booths. Through data mining techniques, we figure out that there are two important features to affect visitors' behavior patterns in exhibition. One is the geographical features of booths. The other is the exhibit contents of booths. Those features are considered when the organizer of exhibition plans next exhibition. Therefore, the results of our analysis are expected to provide guideline to understanding visitors and some valuable insights for the exhibition from the earlier phases of exhibition planning. Also, this research would be a good way to increase the quality of visitor satisfaction. Visitors' movement paths, booth location, and distances between each booth are considered to plan next exhibition in advance. This research was conducted at the KIDS & EDU EXPO for Children in SETEC(Seoul Trade Exhibition & Convention), but it has some constraints to be applied directly to other exhibitions. Also, the results were derived from a limited number of data samples. In order to obtain more accurate and reliable results, it is necessary to conduct more experiments based on larger data samples and exhibitions on a variety of genres.

• Journal of the korean academy of Pediatric Dentistry
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• v.26 no.2
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• pp.275-283
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• 1999

Authors evaluated 152 patients at the department of Pediatric Dentistry in Seoul National University Hospital through clinical records and radiographs. And the following features were studied ; age, sex distribution, number of mesiodens per patients, location, status of eruption, shape and orientation of crown, and complication. From the above results, the relationship between features of mesiodens and complications were evaluated using chi-square analysis. 1. Complications due to the presence of mesiodens did not occur in 31.6%, delayed eruption of adjacent teeth was observed in 33.6%, midline diastema in 22.4%, rotation in 8.6%, displacement in 3.3%, and crowding in 0.7% of all evaluated patients. 2. As compared with the above 8.5 year group, in the under 8.5 year group, the frequency of complications was significantly higher(P<0.05). As compared with those positioned lingually, in mesiodens labially or within the arch the frequency of complications was significantly higher(P<0.01). Also, the frequency of complications was significantly higher when the mesiodens was tuberculate in form(P<0.05). 3. Of the 104 patients with complications, the frequency of delayed eruption was significant higher in the under 8.5 year group, and in above 8.5 year group, the frequency of malocclusion was significantly higher(P<0.05). When mesiodens were located in the midline region, the frequency of malocclusion was significant higher, while in case with laterally positioned mesiodens the frequency of delayed eruption was significantly higher(P<0.01).

• The Journal of Korean society of community based occupational therapy
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• v.3 no.1
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• pp.11-20
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• 2013

Objective : The purpose of this study is to identify the impact of the social support and social network on the quality of life of the elderly residing in a local community. Method : The subjects of this study were 75 healthy old men and women of 13 sites of welfare centers for the disabled and public health centers and senior welfare centers in Busan and Gyeongju. A survey was conducted with a questionnaire that include general characteristics, cognitive ability, social support, social network and quality of life. The analysis was made on 63 replies except 12 subjects who had been excluded by the subject selection criteria. Result : As a result of analyzing correlation of variables affecting life quality, there was positive correlation in contact frequency(p<.05), intimacy(p<.001), and social support(p<.001). Finally, it was analyzed that the variable of intimacy (p<.001) affected life quality of general aged people living in regional community. Conclusion : It was found that intimacy of general aged people living in regional community was a major variable to affect life quality. It could be identified that intimacy which is qualitative feature of social, relational network for the aged who live passive life was important.

• Science of Emotion and Sensibility
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• v.10 no.1
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• pp.87-98
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• 2007

The aim of this study is to grasp the features of the object which reveals its own specific purposes, and to apply them to the product concept and design forms when designers develop products. For this study, the subjects of the experiment were chosen to fill out a basic questionnaire, and an image analysis of them was performed. After the analysis, the functional design elements of the subjects were extracted and coded. They preyed the correlation between the results of the image analysis and the characteristics of the subjects. The questionnaire was carried out to determine the characteristics of the subjects. As the features of specific products were extracted through this experiment, they can be used as basic data to analyze consumer needs and to better understand the products when we design for them. This can be useful fundamental data enabling designers to understand products easily and to establish concepts for their designs. In the case of the MP3 player in this study, the results of the image analysis of it are turned out to be sound quality, compatibility, portability, employment, interface, and personality. Their respective related features were investigated as well. The important features of designing the MP3 player were presented. Through this fundamental study, it will be possible to understand consumer's needs more effectively, which will bring about the development of the fundamental basis of various fields in design.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.3
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• pp.12-24
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• 2002

The purpose of this paper is to decompose the effect of resources of natural parks(NP) on user's satisfaction to answer the research question: What are the causal effects of resources of natural parks on user\ulcorner After reviewing the literature, classification of resources of NP, various approaches and analysis methods employed, we constructed the conceptual framework and have formulated the hypothesis of this research. We had obtained data through a questionnaire, which surveyed 414 visitors at 6 of the 73 NP in Korea in 2001, based on a stratified sampling method. We have analyzed the data using descriptive statistical methods, Pearson's correlation analysis, and a path analysis method. We found that 1) While the indirect effect of topographical feature and valley(TFV), socio-cultural resources(SCR), and climate, sound, and scent(CSS) turned out to be 2.75, 1.20, and 2.00 times higher than that of wild animal and plant(WAP), the direct effect of TFV, SCR, and landscape turned out to be 2.95, 2.88, and 2.64 times higher than that of CSS, 2) The magnitude of causal effects of the three exogeneous variables of TFV, WAP, and SCR and two intervening variables of CSS and landscape on User's satisfaction turned out to be 0.403, 0.048, 0.323, 0.188, and 0.243, respectively, 3) Total direct effect of the exogeneous and intervening variables on user's satisfaction is 0.871, while that of indirect effect is 0.334, and 4) Causal effect of tangible resources is 1.80 times higher than that of intangible while total effect of tangible resources are 1.36 times higher than that of intangible. The research results suggest that 1) Criteria for designation and maintenances of NP and results of previous studies on resources turned out to be unreliable and distorted, 2) In the criteria of planning and maintenance of NP, intangible resources must be included, 3) Remedial directions to increase user's satisfaction should be focused on maintenance of TFV and landscape in NP, and 4) The approach and path analysis adopted by this research is valid and highly useful for other resource based recreation area. It is recommended that more empirical study on seasonal variation of resources in NP based user's preference be performed in the future.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.156-168
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• 2024

Purpose: The trend of highway traffic accidents shows a repeating pattern of increase and decrease, with the fatality rate being highest on highways among all road types. Therefore, there is a need to establish improvement measures that reflect the situation within the country. Method: We conducted accident severity analysis using Random Forest on data from accidents occurring on 10 specific routes with high accident rates among national highways from 2019 to 2021. Factors influencing accident severity were identified. Result: The analysis, conducted using the SHAP package to determine the top 10 variable importance, revealed that among highway traffic accidents, the variables with a significant impact on accident severity are the age of the perpetrator being between 20 and less than 39 years, the time period being daytime (06:00-18:00), occurrence on weekends (Sat-Sun), seasons being summer and winter, violation of traffic regulations (failure to comply with safe driving), road type being a tunnel, geometric structure having a high number of lanes and a high speed limit. We identified a total of 10 independent variables that showed a positive correlation with highway traffic accident severity. Conclusion: As accidents on highways occur due to the complex interaction of various factors, predicting accidents poses significant challenges. However, utilizing the results obtained from this study, there is a need for in-depth analysis of the factors influencing the severity of highway traffic accidents. Efforts should be made to establish efficient and rational response measures based on the findings of this research.