• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.100-107
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• 2024

Currently, in Korea, due to the rapid aging and deterioration of facilities, the minimum Maintenance Level and Performance Level' of facilities are required by the 'Facility Safety Act' or 'Infrastructure Management Act'. Since infrastructure assets have a long lifespan and the pattern of deterioration over time is complex, it is very difficult to maintain infrastructure as 'minimum maintenance state' or 'minimum performance state' by the current way of management. 'Asset Management' shall be performed not only by a technical perspective, but also by an accounting perspective such as cost and asset value. However, due to lack of awareness of 'asset management' among stakeholder, only technical perspective management is being carried out in practice. In order to effectively manage infrastructure assets, complex consideration of various asset value factors such as budget and service as well as safety and durability are required. In this paper, we presented a theory to evaluate and quantify the road network value for efficient asset management of the road network. We also presented a method of simulation to apply the theory presented in this paper. Through simulation and the results derived from this study, it is possible to specify the budget for the future national asset management, and to optimize the strategy for the management of old road facilities.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.12
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• pp.885-893
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• 2006

The Bayesian network(BN) has emerged in recent years as a powerful technique for handling uncertainty iii complex domains. Parameter learning of BN to find the most proper network from given data set has been investigated to decrease the time and effort for designing BN. Off-line learning needs much time and effort to gather the enough data and since there are uncertainties in real world, it is hard to get the complete data. In this paper, we propose an online learning method of Bayesian network parameters from incomplete data. It provides higher flexibility through learning from incomplete data and higher adaptability on environments through online learning. The results of comparison with Voting EM algorithm proposed by Cohen at el. confirm that the proposed method has the same performance in complete data set and higher performance in incomplete data set, comparing with Voting EM algorithm.

• Advances in nano research
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• v.12 no.2
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• pp.223-239
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• 2022

Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.3
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• pp.157-168
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• 2014

From a complex systems perspective, ecohydrological systems in forests may be characterized with (1) large networks of components which give rise to complex collective behaviors, (2) sophisticated information processing, and (3) adaptation through self-organization and learning processes. In order to demonstrate such characteristics, we applied the recently proposed 'process networks' approach to a temperate deciduous forest in Gwangneung National Arboretum in Korea. The process network analysis clearly delineated the forest ecohydrological systems as the hierarchical networks of information flows and feedback loops with various time scales among different variables. Several subsystems were identified such as synoptic subsystem (SS), atmospheric boundary layer subsystem (ABLS), biophysical subsystem (BPS), and biophysicochemical subsystem (BPCS). These subsystems were assembled/disassembled through the couplings/decouplings of feedback loops to form/deform newly aggregated subsystems (e.g., regional subsystem) - an evidence for self-organizing processes of a complex system. Our results imply that, despite natural and human disturbances, ecosystems grow and develop through self-organization while maintaining dynamic equilibrium, thereby continuously adapting to environmental changes. Ecosystem integrity is preserved when the system's self-organizing processes are preserved, something that happens naturally if we maintain the context for self-organization. From this perspective, the process networks approach makes sense.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.12
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• pp.93-102
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• 2004

This paper presents a general theory for the analysis of an aperture-coupled cavity-fed microstrip patch antenna to develop a simple but accurate equivalent circuit model. The developed equivalent circuit consists of ideal transformers, admittance elements, and transmission lines. These circuit element values are computed by applying the complex power concept, the Fourier transform and series representation, and the spectral-domain immittance approach. The input impedance of the antenna is calculated and compared with the published data. Good agreements validate the simplicity and accuracy of the developed equivalent circuit model.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.180-184
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• 2024

Since COVID-19, many foreign tourists have visited Korea for medical tourism. When statistical data were checked from 2022, after COVID-19, the number of foreign patients visiting Korea for two years was 24.8 million, an increase of 70.1% from 2020. It was confirmed that it has achieved a 50% level compared to 2019 (Statistics Office, 2023). Therefore, to create a development plan by linking medical tourism and wellness tourism, the purpose of this study is to find the link between medical tourism and wellness tourism as big data and present a development plan. In this research method, medical tourism, and wellness tourism for two years from 2022 to 2023 from the post-COVID period as big data are set as central keywords to compare text data to find common points. When analyzing wellness tourism and medical tourism, it was confirmed that most wellness tourism had a greater frequency than medical tourism. This confirmed that wellness tourism occupies a larger pie than medical tourism. As a result, when checking the word frequency, it was confirmed that wellness tourism and medical tourism share a lot as complex tourism products, and when checking 2-gram, to attract many medical tourists, it is necessary to combine medical tourism clusters and wellness tourism according to each other's characteristics among local governments.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.381-386
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• 2002

In this paper, we have proposed a new approach to the design of robot vision system to develop the technology for the automatic test and assembling of precision mechanical and electronic parts for the factory automation. In order to perform real time implementation of the automatic assembling tasks in the complex processes, we have developed an intelligent control algorithm based-on neural networks control theory to enhance the precise motion control. Implementing of the automatic test tasks has been performed by the real-time vision algorithm based-on TMS320C31 DSPs. It distinguishes correctly the difference between the acceptable and unacceptable defective item through pattern recognition of parts by the developed vision algorithm. Finally, the performance of proposed robot vision system has been illustrated by experiment for the similar model of fifth cell among the twelve cell for automatic test and assembling in S company.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.147-150
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• 2004

인터넷산업은 기술, 자본, 마케팅 등의 이유로 활발한 전략적 제휴가 이루어지고 있다. 본 논문은 최근 7년간 인터넷 산업의 기업간 전략적 제휴 사례를 조사하여 이를 바탕으로 전략제휴 네트워크화 하였다. 이 네트워크에 대해 연결정도, 밀도, 컴퍼넌트 분석을 수행하였으며, 연결정도의 분포를 분석하여 자연계, 사회시스템 등에서 많이 발견되고 있는 스케일 프리 네트워크의 성질을 갖는 지를 분석하였다. 또한 인터넷 붕괴 이전과 이후의 2기간의 전략적 제휴 네트워크의 변화 정도도 파악 하였다. 이와 같은 네트워크 모델분석은 국내 인터넷산업의 제휴 관계와 그 변화 추세 등을 거시적으로 살펴보는데 도움이 될 것이다.