• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.83-92
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• 2009

In this paper, an extended snake algorithm using color variance energy is proposed for segmenting an interest object in color image. General snake algorithm makes use of energy in image to segment images into a interesting area and background. There are many kinds of energy that can be used by the snake algorithm. The efficiency of the snake algorithm is depend on what kind of energy is used. A general snake algorithm based on active contour model uses the intensity value as an image energy that can be implemented and analyzed easily. But it is sensitive to noises because the image gradient uses a differential operator to get its image energy. And it is difficult for the general snake algorithm to be applied on the complex image background. Therefore, the proposed snake algorithm efficiently segment an interest object on the color image by adding a color variance of the segmented area to the image energy. This paper executed various experiments to segment an interest object on color images with simple or complex background for verifying the performance of the proposed extended snake algorithm. It shows improved accuracy performance about 12.42 %.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1130-1137
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• 2022

The Industry Foundation Classes (IFC) provides standardized product models for the building construction domain. However, the current IFC schema has limited representation for infrastructure. Several studies have examined the data schema for road and highway modeling, but not in a sufficiently comprehensive and robust manner to facilitate the overall integrated project delivery of road projects. Several discussions have focused on slope engineering for road projects, but no solution has been provided regarding the formalized parametric modeling up to now. Iterative design, analysis, and modification are observed during the process of slope design for road projects. The practitioners need to carry out the stability analysis to consider different road design alternatives, including horizontal, vertical, and cross-section designs. The procedure is neither formalized nor automated. Thus, there is a need to develop the formal representation of the product and process of slope analysis for road design. The objective of this research is to develop a formal representation (i.e., an IFC extension data schema) for slope analysis. It consists of comprehensive information required for slope analysis in a structured manner. The deliverable of this study contributes to both the formal representation of infrastructure development and, further, the automated process of slope design for road projects.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Journal of the Korea Institute of Building Construction
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• v.24 no.4
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• pp.507-515
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• 2024

Recent advancements in Building Information Modeling(BIM) have significantly impacted the construction industry, driving competitiveness and innovation. However, rebar construction, a critical component influencing project quality and cost, has lagged behind in BIM adoption. Traditional methods relying heavily on 2D drawings for rebar detailing have hindered efficiency and introduced potential errors. This paper presents a novel system designed to automate the detailed modeling of rebar, thereby promoting BIM integration within rebar construction and optimizing construction management processes. The system leverages confirmed structural drawings from the post-structural design phase to automatically generate intricate rebar models for columns and beams. To ensure adherence to domestic structural design standards, the system is developed using C# programming language and the Revit API. By automating rebar modeling, this system aims to minimize human error, reduce labor-intensive tasks, and enhance overall rebar construction efficiency through the effective utilization of generated rebar model data.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.111-122
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• 2023

The purpose of this study is to verify the factors affecting survival time by estimating survival rate and survival time using non-financial information of social enterprises using credit guarantee in credit guarantee institutions, and provide information to stakeholders to improve survival rate and employ to contribute to maintaining and expanding the As a research method, survival analysis was performed using a non-parametric analysis method, Kaplan-Meier Analysis. As a sample, 621 companies (577 normal companies, 44 insolvent companies) established between 2009 and 2018 were selected as the target companies. As a result of examining the factors affecting survival time by classifying social enterprise representative information and corporate information, representative credit rating, representative home ownership, credit transaction period, and corporate credit rating were derived as significant variables affecting survival time. In the future, financial institutions will be able to induce corporate soundness by reflecting factors that affect survival when examining loans for social enterprises, contributing to job retention and reduction of social costs. Supporting organizations such as the government and private organizations will be able to use it in various ways, such as policy establishment and education and training for the growth and sustainability of social enterprises. With this study as an opportunity, I hope that research will continue with more interest in the factors influencing social enterprise performance as well as corporate insolvency.

• The Journal of Korean society of community based occupational therapy
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• v.6 no.2
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• pp.51-59
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• 2016

Objective : The purpose of study is to provide basic information about the effects of leisure time use and leisure activity performance for intellectual disabilities residing in a residential care facility by participating a regular rehabilitation sports program. Methods : Participants were recruited 8 individual with intellectual disability in a residential care facility in Yong-in city and the study period lasted 12 weeks, from september 1 to November 30 in 2015. As a program, participants participated a muscle strengthening exercise using a Gym-ball and a elastic band. In order to analyze leisure time-use, time questionnaire was used every month to analyze total time and exercise frequency. Also, analyze the effects of leisure activity performance, Canadian Occupational Performance Measure(COPM) was used to performance and satisfaction of dynamic leisure activity. Collected data was encoded by item and analyzed with SPSS ver18.0. Descriptive statistics were used for the participants' general information. A non-parametric test (the Friedman test) was used to analyze leisure time-use. A non-parametric test (the Wilcoxon's signed ranked test) was used to analyze to the effects of leisure activity performance. Statistical significance was accepted outside the 95% confidence interval. Results : The results of the total time and the exercise frequency showed significant increase. Also, the results of the performance and the satisfaction showed significant increase. Conclusion : Thus, the participation of the rehabilitation sports program is a vital element to lead to change leisure time use and leisure activity performance for intellectual disabilities residing in a residential care facility. Also, through the providing and the developing a regular rehabilitation sports program systematically, intellectual disabilities residing in a residential care facility have a higher quality of life and satisfaction of the daily routine and life in a residential care facility.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• Fashion & Textile Research Journal
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• v.16 no.6
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• pp.961-970
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• 2014

Several retailers such as Target and Kohle's and their vendors have piloting the 3D clothing simulation programs to produce garment samples. However, few studies have verified the virtual fit information and 3d visualization process for pants, and no study compared the commercial 3D virtual programs. This study is designed to analyze similarity of fit and appearance between real pants with 3D virtual pants based on three 3D virtual programs (Optitex, CLO 3D, and i-Designer), three lower body types (slim, normal, and thick waist type), and fit status. We selected a representative model for each lower body type, produced their custom pants according to Lee and Nam's method(2007), and took photos of front, side and back view for visual analysis. Then, we virtually tried each model's custom pants on her parametric avatar developed by manually inputting their body measurements using the three 3D virtual program. Thirty fit experts compared the real fit to virtual fit. This study found that 'Optitex' and 'i-Designer' can visualize more effectively than 'CLO 3D' in many fit locations. Regarding the body types, 3D virtual program can visualize pants fit for 'thick waist body type' more preciously than the other body types. With respect to fit status, it does not affect much on the similarity overall.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.147-153
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• 2004

This paper proposes an efficient edge detection using Van der Waerden statistic in original and noisy images. An edge is where the intensity of an image moves from a low value to a high value or vice versa. We describe a nonparametric Wilcoxon test and a parametric T test based on statistical hypothesis testing for the detection of edges. We use the threshold determined by specifying significance level $\alpha$, while Bovik, Huang and Munson consider the range of possible values of test statistics for the threshold. From the experimental results of edge detection, the T and Wilcoxon method perform sensitively to the noisy image, while the proposed Waerden method is robust over both noisy and noise-free images under $\alpha$=0.0005. Comparison with our statistical test and Sobel, LoG, Canny operators shows that Waerden method perform more effectively in both noisy and noise-free images.