• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.367-373
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• 2021

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.117-136
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• 2004

As the time-to-market gets more important for competitiveness of an enterprise in manufacturing industry, it becomes important to shorten the development cycle of a product. Reuse of existing design models and e-Catalog for components are required for faster product development. To achieve this goal, an electric catalog must provide parametric CAD models since parametric information is indispensable for configuration design. There are difficulties in building up a parametric library of all the necessary combination using a CAD system, since we have too many combinations of components for a product. For example, there are at least 80 million combinations of components on one page of paper catalog of a mold base. To solve this problem, we propose the method of table parametric for the automatic generation of parametric CAD models. Any combination of mold base can be generated by mapping between a classification system of an electric catalog and the design parameters set of the table parametric. We propose how to select parametric models and to construct the design parameters set.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.247-248
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• 2008

본 논문에서는 TYPE-2 퍼지 추론 기반의 RBF 뉴럴 네트워크(TYPE-2 Radial Basis Function Neural Network, T2RBFNN)를 설계하고 PSO(Particle Swarm Optimization) 알고리즘을 이용하여 모델의 파라미터를 동정한다. 제안된 모델의 은닉층은 TYPE-2 가우시안 활성 함수로 구성되며, 출력층은 Interval set 형태의 연결가중치를 갖는다. 여기에서 규칙 전반부 활성함수의 중심 선택은 C-means 클러스터링 알고리즘을 이용하고, 규칙 후반부 Interval set 형태의 연결가중치 결정에는 경사 하강법(Gradient descent method)을 이용한 오류 역전파 알고리즘을 사용하여 학습한다. 또한, 최적의 모델을 설계하기 위한 학습율 및 활성함수의 활성화 영역 결정에는 입자 군집 최적화(PSO; Particle Swarm Optimization) 알고리즘으로 동조한다. 마지막으로, 제안된 모델의 평가를 위하여 모의 데이터 집합(Synthetic dadaset)을 적용하고 근사화 및 일반화 능력에 대하여 토의한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.149-155
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• 2013

In these days, Entity-Relationship Model is the most popular modeling tool for designing databases, and XML is a de facto standard language for representing and exchanging data. But, because of many commercial products supporting Entity-Relationship Model use their's own representation formats, and thus it gives rise to difficulties the inter-operability between these products. In this paper, we propose an ERX, a generation tool of XML Schema from Entity-Relationship Model. ERX receives an Entity-Relationship Diagram as an input, transforms it based on transformation rules, and generates a XML Schema Definition as an output. Transformation rules contain entity set, relationship set, mapping cardinalities, and generalization.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.440-444
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• 2009

In order to improve the effectiveness of software process improvement, more than two models can be use to compensate the weakness of each other. One of integrated models is the one in which CMMI and software six sigma. However, it is very difficult for a small software development organization to select and apply an appropriate set of six sigma tools since there are a lot of six sigma tools and statistical knowledge is required. In this paper, we suggest a six sigma tool selection process to help small organizations select six sigma tools effectively based on organization business value. Thereby, small organizations can efficiently implement CMMI by adopting an appropriate set of six sigma toolkits.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.13 no.1
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• pp.1-8
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• 2001

Wave-induced currents drive nearshore transport processes, and hence an accurate understanding of wave-current interaction is required for proper management of coastal zone. This paper presents details of an adaptive quadtree grid based numerical model of the coupled wave climate and depth-averaged current field. The model accounts for wave breaking, shoaling, refraction, diffraction, wave-current interaction, set-up and set-down, mixing processes, bottom friction effects, and movement of land-water interface at the shoreline. The wave period- and depth-averaged governing equations arc discrctized explicitly by means of an Adarns¬Bashforth second-order finite difference technique on adaptive hierarchical staggered quadtree grids. Results from the numerical model are in reasonable agreement with the laboratory data of longshore current generated by oblique waves on a plane beach (Visser 1980, 1991).

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.188-193
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• 2000

This paper presents an approach to obtain a reduced neuro-fuzzy model for a plant. The Neuro-Fuzzy Network are compose of the Radial Basis Function Networks with Gausis membership and learned by using temporal back propagation. The dependency in rough set theory is used to eliminate rules. Dependency between the condition membership value of each rule in a model and the output of the plant can allow us to see how much contribution the rule is to identify the plant. While the reduced model maintains the same performance as the original one, the selection algorithm can minimize its complexity and redundancy of the structure.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.241-242
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• 2008

본 논문에서는 fuzzy granular computing 방법 중의 하나인 context-based FCM을 이용하여 granular-based radial basis function neural network에 대한 기본적인 개면과 그들의 포괄적인 설계 구조에 대해서 자세히 기술한다. 제안된 모델에 기본이 되는 설계 도구는 context-based fuzzy c-means (C-FCM)로 알려진 fuzzy clustering에 초점이 맞춰져 있으며, 이는 주어진 데이터의 특징에 맞게 공간을 분할함으로써 효율적으로 모델을 구축할 수가 있다. 제안된 모델의 설계 공정은 1) Context fuzzy set에 대한 정의와 설계, 2) Context-based fuzzy clustering에 대한 모델의 적용과 이에 따른 모델 구축의 효율성, 3) 입력과 출력공간에서의 연결된 information granule에 대한 parameter(다항식의 계수들)에 대한 최적화와 같은 단계로 구성되어 있다. Information granule에 대한 parameter들은 성능지수를 최소화하기 위해 Least square method에 의해서 보정된다. 본 논문에서는 모델을 설계함에 있어서 체계적인 설계 알고리즘을 포괄적으로 설명하고 있으며 더 나아가 제안된 모델의 성능을 다른 표준적인 모델들과 대조함으로써 제안된 모델의 우수성을 나타내고자 한다.

• Tunnel and Underground Space
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• v.13 no.2
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• pp.153-168
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• 2003

The objective of this present study is to understand long term(500 years) thermohydromechanical interaction behavior in the vicinity of a repository cavern on the joint location and repository depth variations. The model includes a saturated discontinuous granitic rock mass, PWR spent nuclear fuel in a disposal canister surrounded with compacted bentonite inside a deposition hole, and mixed bentonite backfilled in the rest of the space within a repository cavern. It is assumed that two joint sets exist within the model. Joint set 1 includes joints of 56$^{\circ}$ dip angle, spaced at 20 m, and joint set 2 is in the perpendicular direction to joint set 1 and includes joints of 34$^{\circ}$ dip angle, spaced at 20 m. In order to understand the behavior change on the joint location variations, 5 different models of 500m in depth are analyzed, and additional 3 different models of 1000 m in depth are analyzed to understand the effect of depth variation.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.83-94
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• 2011

Main researching issue in affective computing is to give a machine the ability to recognize the emotion of a person and to react it properly. Efforts in that direction have mainly focused on facial and oral cues to get emotions. Postures have been recently considered as well. This paper aims to discriminate emotions posture by identifying and measuring the saliency of posture features that play a role in affective expression. To do so, affective postures from human subjects are first collected using a motion capture system, then emotional features in posture are described with spatial ones. Through standard statistical techniques, we verified that there is a statistically significant correlation between the emotion intended by the acting subjects, and the emotion perceived by the observers. Discriminant Analysis are used to build affective posture predictive models and to measure the saliency of the proposed set of posture features in discriminating between 6 basic emotional states. The evaluation of proposed features and models are performed using a correlation between actor-observer's postures set. Quantitative experimental results show that proposed set of features discriminates well between emotions, and also that built predictive models perform well.