• Journal of the Korean Society of Groundwater Environment
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• v.6 no.3
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• pp.111-119
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• 1999

The purpose of this study is the simulation of discontinuous rockmass and identification of characteristics of discontinuity network as a branch of the study on characteristics of groundwater system in discontinuous rockmass for evaluation of safety on disposal site of radioactive waste. In this study the site for LPG underground storage was selected for the similarities of the conditions which were required for disposal site of radioactive waste. Through the identification of hydraulic properties. characteristics of discontinuities and selection of discontinuity model around LPG underground storage facility. the applications of discrete fracture network model were evaluated for the analysis of pathway. The orientation and spatial density of discontinuities are primarily important elements for the simulation of groundwater and solute transportation in discrete fracture network model. In this study three fracture sets identified and the spatial intensity (P$_{32}$) of discontinuities is revealed as 0.85 $m^2$/㎥. The conductive fracture intensity (P$_{32c}$) estimated for the simulation area around propane cavern (200${\times}$200${\times}$200) is 0.536 $m^2$/㎥. Truncated conductive fracture intensity (T-P$_{32c}$) is calculated as 0.26 $m^2$/㎥ by eliminating the fracture with the iowest transmissivity and based on this value the pathway from the water curtain to PC 2. PC 3 analyzed.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2519-2521
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• 2003

In this paper, we propose Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SOFPNN are not predetermined (as this is the case in a popular topology of a multilayer perceptron). As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership function are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SOFPNN architectures, that is, the basic and modified one with both the generic and the advanced type. The superiority and effectiveness of the proposed SOFPNN architecture is demonstrated through nonlinear function numerical example.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.14-20
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• 2024

Traffic sign recognition is an essential feature of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS), which are necessary for improving road safety and advancing the development of autonomous cars. This research investigates the incorporation of the YOLOv9 model into traffic sign recognition systems, utilizing its sophisticated functionalities such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to tackle enduring difficulties in object detection. We employed a publically accessible dataset obtained from Roboflow, which consisted of 3130 images classified into five distinct categories: speed_40, speed_60, stop, green, and red. The dataset was separated into training (68%), validation (21%), and testing (12%) subsets in a methodical manner to ensure a thorough examination. Our comprehensive trials have shown that YOLOv9 obtains a mean Average Precision (mAP@0.5) of 0.959, suggesting exceptional precision and recall for the majority of traffic sign classes. However, there is still potential for improvement specifically in the red traffic sign class. An analysis was conducted on the distribution of instances among different traffic sign categories and the differences in size within the dataset. This analysis aimed to guarantee that the model would perform well in real-world circumstances. The findings validate that YOLOv9 substantially improves the precision and dependability of traffic sign identification, establishing it as a dependable option for implementation in intelligent transportation systems and ADAS. The incorporation of YOLOv9 in real-world traffic sign recognition and classification tasks demonstrates its promise in making roadways safer and more efficient.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.471-480
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• 2015

The market of public-private partnership (PPP) projects has reduced from 9.4 trillion won in 2007 to 4.5 trillion won in 2012. However, the need of PPP projects is brought up by a massive down scale of government financial business. Previous studies regarding PPP projects mostly evaluate profitability from the financial perspective or analyze risk factors as a whole. Although PPP projects generally have complex structure involving diverse stakeholders, such as contractor, financial investor, and special purpose company (SPC) operators, existing studies have rarely considered the different viewpoints of PPP project stakeholders. Therefore, purpose of this study is to develop a structural equation model (SEM) considering the diverse stakeholders of PPP projects. To this end, the authors first reviewed the organizational structure of PPP projects. Next, the identification of the factors affecting project profitability are done via comprehensive literature reviews. After that, we conducted in-depth interviews and questionnaire surveys to reflect stakeholders' perspectives (contractors, financial investors, and SPC operators). As a result, a SEM model is developed to analyze direct and indirect effect on the PPP project performances. Finally, using the analysis results, relevant implications and directions for improvements are discussed. The prediction of the business performance of contractor, financial investor, and SPC operator is expect to be possible through the model developed and supports the strategy deduction that is appropriate for the participants.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.167-174
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• 2021

Purpose: Sex determination can be done by morphological analysis of different parts of the body. The mastoid region, with its anatomical location at the skull base, is ideal for sex identification. Statistical shape analysis provides a simultaneous comparison of geometric information on different shapes in terms of size and shape features. This study aimed to investigate the geometric morphometry of the inter-mastoid triangle as a tool for sex determination in the Iranian population. Materials and Methods: The coordinates of 5 landmarks on the mastoid process on the 80 cone-beam computed tomographic images(from individuals aged 17-70 years, 52.5% female) were registered and digitalized. The Cartesian x-y coordinates were acquired for all landmarks, and the shape information was extracted from the principal component scores of generalized Procrustes fit. The t-test was used to compare centroid size. Cross-validated discriminant analysis was used for sex determination. The significance level for all tests was set at 0.05. Results: There was a significant difference in the mastoid size and shape between males and females(P<0.05). The first 2 components of the Procrustes shape coordinates explained 91.3% of the shape variation between the sexes. The accuracy of the discriminant model for sex determination was 88.8%. Conclusion: The application of morphometric geometric techniques will significantly impact forensic studies by providing a comprehensive analysis of differences in biological forms. The results demonstrated that statistical shape analysis can be used as a powerful tool for sex determination based on a morphometric analysis of the inter-mastoid triangle.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.2329-2336
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• 2016

Background: Despite the existence of established guidelines advocating the use and value of chemotherapy order templates, chemotherapy orders are still handwritten in many hospitals in Lebanon. This manuscript describes the implementation of standardized chemotherapy order templates (COT) in a Lebanese tertiary teaching hospital through multiple steps. Initial Assessment: An initial assessment was conducted through a retrospective appraisal of completeness of handwritten chemotherapy orders for 100 adult patients to serve as a baseline for the project and identify parameters that might afford improvement. Choice of solution: Development of over 300 standardized pre-printed COTs based on the National Comprehensive Cancer Network templates and adapted to the practice culture and patient population. Implementation: The COTs were implemented, using Kotter's 8-step model for leading change, by engaging health care providers, and identifying and removing barriers. Evaluation: Assessment of physicians' compliance with the new practice (122 orders assessed) was completed through two phases and allowed for the identification of areas of improvement. Lessons Learned: Overall, COT implementation showed an average improvement in order completion from 49.5% (handwritten orders) to 77.6% (phase 1-COT) to 87.6% (phase 2-COT) reflecting an increase of 38.1% between baseline and phase 2 and demonstrating that chemotherapy orders completeness was improved by pre-printed COT. As many of the hospitals in Lebanon are moving towards standardized COTs and computerized physician order entry (CPOE) in the next few years, this study provides a prototype for the successful implementation of COT and demonstrates their role in promoting quality improvement of cancer care.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.7
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• pp.1049-1064
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• 2013

This study aims to seek effective measures centering on developing countries that can export Korea's e-Government ranked the first place two times in a row (2010 and 2012) in the United Nations' e-Government Survey. Up to the present, individual ministries or agencies have made efforts to export systems or services related to e-Government, but it is judged that comprehensive and systematic approaches have not been sufficient. This study, therefore, has been intended to find realistic mid- and long-term strategies and supporting methods that can promote Korean-type e-Government exports by analyzing problems identified in the strategies implemented by the current ministries or agencies for assisting enterprises engaged in e-Government exports. In addition, this study as an empirical one has been intended to diagnose improvement points by analyzing e-Government exporting strategies executed by existing government ministries and agencies, suggest methods for filling in gaps between as-is and to-be models, and seek timely and effective measures in the future.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.159-172
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• 2023

In general, social problem-solving research aims to create important social value by offering meaningful answers to various social pending issues using scientific technologies. Not surprisingly, however, although numerous and extensive research attempts have been made to alleviate the social problems and issues in nation-wide, we still have many important social challenges and works to be done. In order to facilitate the entire process of the social problem-solving research and maximize its efficacy, it is vital to clearly identify and grasp the important and pressing problems to be focused upon. It is understandable for the problem discovery step to be drastically improved if current social issues can be automatically identified from existing R&D resources such as technical reports and articles. This paper introduces a comprehensive dataset which is essential to build a machine learning model for automatically detecting the social problems and solutions in various national research reports. Initially, we collected a total of 700 research reports regarding social problems and issues. Through intensive annotation process, we built totally 24,022 sentences each of which possesses its own category or label closely related to social problem-solving such as problems, purposes, solutions, effects and so on. Furthermore, we implemented four sentence classification models based on various neural language models and conducted a series of performance experiments using our dataset. As a result of the experiment, the model fine-tuned to the KLUE-BERT pre-trained language model showed the best performance with an accuracy of 75.853% and an F1 score of 63.503%.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.