• Korean Journal of Computational Design and Engineering
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• v.4 no.4
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• pp.381-390
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• 1999

In this study, a pseudo ship structure data model for the :.hip cargo hold structure based on STEP is proposed. The proposed data model is based on Application Reference Model of AP218 Ship Structure which is the model that specifies conceptual structures and constraints used to describe the information requirements of an application. And the proposeddata model refers the Ship Common Model framework for the model architecture which is the basis for ongoing ship AP development within the ISO ship-building group and the ship product definition information model of CSDP research project for analyzing the relationship between ship structure model entities. The proposed data model includes Space, Compartment. Ship Structural System, Structural Part and Structural Feature of cargo hold. To generate this data model schema in EXPRESS format, ‘GX-Converter’was used which enables user to edit a model in EXPRESS format and convert schema file in EXPRESS format. Using this model schema, STEP physical file containing design data for ship cargo hold data structure was generated through SDAI programming. The another STEP physical file was also generated containing geometry data of ship cargo hold which was extracted and calculated by SDAI and external surface/surface intersection program. The geometry information of ship cargo hold can be then transferred to commercial CAD system, for example, Pro/Engineer. Examples of the modification of the design information are also Presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.200-207
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• 2001

In this paper, we propose an HMM(Hidden Markov modeJ)-MLP(Multi-layer perceptron) hybrid model for recognizing legal words on the English bank check. We adopt an explicit segmentation-based word level architecture to implement an HMM engine with nonscaled and non-normalized symbol vectors. We also introduce an MLP for implicit segmentation-based word recognition. The final recognition model consists of a hybrid combination of the HMM and MLP with a new hybrid probability measure. The main contributions of this model are a novel design of the segmentation-based variable length HMMs and an efficient method of combining two heterogeneous recognition engines. ExperimenLs have been conducted using the legal word database of CENPARMI with encouraging results.

• Korean Journal of Computational Design and Engineering
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• v.9 no.2
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• pp.122-132
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• 2004

Progressive mesh representation and generation have become one of the most important issues in network-based computer graphics. However, current researches are mostly focused on triangular mesh models. On the other hand, solid models are widely used in industry and are applied to advanced applications such as product design and virtual assembly. Moreover, as the demand to share and transmit these solid models over the network is emerging, the generation and the transmission of progressive solid models depending on specific engineering needs and purpose are essential. In this paper, we present a Cellular Topology-based approach to generating and transmitting progressive solid models from a feature-based solid model for internet-based design and collaboration. The proposed approach introduces a new scheme for storing and transmitting solid models over the network. The Cellular Topology (CT) approach makes it possible to effectively generate progressive solid models and to efficiently transmit the models over the network with compact model size. Thus, an arbitrary solid model SM designed by a set of design features is stored as a much coarser solid model SM/sup 0/ together with a sequence of n detail records that indicate how to incrementally refine SM/sup 0/ exactly back into the original solid model SM = SM/sup 0/.

• Journal of Korea Technology Innovation Society
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• v.18 no.1
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• pp.204-231
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• 2015

This study aims to present a conceptual model for technology valuation based on market approach. We first propose a transition model in which technology itself is endowed with its intrinsic value based on its technology- and market-specific features, which subsequently goes through a transition to the market perceived value. We also discuss the model's logical validity. Based upon the transition model, we develop market approach based technology valuation models by identifying factors comprising each component of the transition model for each case of fixed royalty and running royalty. We also show that the proposed model can be used to describe the relationship between cost, income and market approaches for technology valuation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11b
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• pp.140-143
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• 2005

The hybrid CAE/CAT methods are widely applied to product development in various fields because this method can predict the response of the whole system when a part of the system is changed. Especially, the hybrid CAE/CAT method is very useful to predict tile vehicle NVH characteristics after changing some parts of the vehicle. Target parts can be established on the basis of test models and FE models of the prototype constructed in the planning stage of car development. In this study, the topic was focused on the proper test-based FBS application process to predict vehicle NVH characteristic. First, the test-based FBS method was apply to vehicle substructure and car-body. And then the test-based model was replaced with FE model to apply hybrid CAE/CAT method. The replaced FE model was modified through the optimization process. The interior noise in vehicle during the drive was predicted with Modified FE model, then the predicted results were verified by experimenting with actual modified model.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.437-455
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• 2012

The purpose of the study is to design a space-based locality documentation model. It begins with analysing the changes of directions and strategies of locality documentation through literature surveys. Based on the analysis, a new paradigm of locality documentation is suggested in digital environment. It then suggests space-based documentation model, i.e., 'spanDoc Model (SPAace-based Networked Documentation Model)' which represent the new paradigm. The model focuses on planning the framework of use and accumulation of locality documentation.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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• pp.335-342
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• 2015

The increasing interests on patents have led many individuals and companies to apply for many patents in various areas. Applied patents are stored in the forms of electronic documents. The search and categorization for these documents are issues of major fields in data mining. Especially, the keyword extraction by which we retrieve the representative keywords is important. Most of techniques for it is based on vector space model. But this model is simply based on frequency of terms in documents, gives them weights based on their frequency and selects the keywords according to the order of weights. However, this model has the limit that it cannot reflect the relations between keywords. This paper proposes the advanced way to extract the more representative keywords by overcoming this limit. In this way, the proposed model firstly prepares the candidate set using the vector model, then makes the graph which represents the relation in the pair of candidate keywords in the set and selects the keywords based on this relationship graph.

• Journal of Drive and Control
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• v.16 no.2
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• pp.22-29
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• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• Journal of Korean Port Research
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• v.13 no.1
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• pp.101-112
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• 1999

Traffic accidents increase with the increase of the vehicles in operation on the street. Especially big traffic accidents composed of over 3 killed or 20 injured accidents with the property damage become one of the serious problems to be solved in most of the cities. The purpose of this study is to build the discrimination model on big traffic accidents using the Quantification II theory for establishing the countermeasures to reduce the big traffic accidents. The results are summarized as follows. 1)The existing traffic accident related model could not explain the phenomena of the current traffic accident appropriately. 2) Based on the big traffic accident types vehicle-vehicle, vehicle-alone, vehicle-pedestrian and vehicle-train accident rates 73%, 20.5% 5.6% and two cases respectively. Based on the law violation types safety driving non-fulfillment center line invasion excess speed and signal disobedience were 48.8%, 38.1% 2.8% and 2.8% respectively. 3) Based on the law violation types major factors in big traffic accidents were road and environment, human, and vehicle in order. Those factors were vehicle, road and environment, and human in order based on types of injured driver’s death. 4) Based on the law violation types total hitting and correlation rates of the model were 53.57% and 0.97853. Based on the types of injured driver’s death total hitting and correlation rates of the model were also 71.4% and 0.59583.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.