• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.595-604
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• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.114-129
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• 2006

Ontology mapping is the task of finding semantic correspondences between two ontologies. In order to improve the effectiveness of ontology mapping, we need to consider the characteristics and constraints of data models used for implementing ontologies. Earlier research on ontology mapping, however, has proven to be inefficient because the approach should transform input ontologies into graphs and take into account all the nodes and edges of the graphs, which ended up requiring a great amount of processing time. In this paper, we propose a multi-strategic mapping approach to find correspondences between ontologies based on the syntactic or semantic characteristics and constraints of the topic maps. Our multi-strategic mapping approach includes a topic name-based mapping, a topic property-based mapping, a hierarchy-based mapping, and an association-based mapping approach. And it also uses a hybrid method in which a combined similarity is derived from the results of individual mapping approaches. In addition, we don't need to generate a cross-pair of all topics from the ontologies because unmatched pairs of topics can be removed by characteristics and constraints of the topic maps. For our experiments, we used oriental philosophy ontologies, western philosophy ontologies, Yahoo western philosophy dictionary, and Yahoo german literature dictionary as input ontologies. Our experiments show that the automatically generated mapping results conform to the outputs generated manually by domain experts, which is very promising for further work.

• Journal of the Korean Mathematical Society
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• v.50 no.1
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• pp.17-39
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• 2013

The study of nonlinear discrete-time control dynamical systems with disturbance is an important topic in control theory. In this paper, we concentrate our efforts to multiple valued iterative dynamical systems, which model the nonlinear discrete-time control dynamical systems with disturbance. After establishing the validity of such modeling, we study the invariant set theory of the multiple valued iterative dynamical systems, including the controllability/reachablity problems of the maximal invariant sets.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.265-267
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• 2000

본 논문에서는 텍스트 문서의 주제어를 추출하고 문서를 주제별로 분류하기 위해 확률적 그래프 모델을 사용하는 방법을 제안하였다. 텍스트 문서 데이터를 문서와 단어의 쌍으로(dyadic)표현하여 확률적 생성 모델을 학습하였다. 확률적 그래프 모델의 학습에는 정의된 likelihood를 최대화하기 위한 EM(Expected Maximization)알고리즘을 사용하였다. TREC-8 AdHoc 텍스트 에이터에 대하여 학습된 확률 그래프 모델의 성능을 실험적으로 평가하였다. 이로부터 찾아 낸 문서에 대한 주제어가 사람이 제시한 주제어와 유사한 지와, 사람이 각 주제에 대해 분류한 문서가 이 확률모델로부터의 분류와 유사한 지를 실험적으로 검토하였다.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.141-152
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• 2012

This study is a comparatively analyzes innovation tools for developing appropriate business models according to the types of convergence. Firstly, it examines previous studies on the type of convergence, business models, and innovation tools. Based on the understanding of each topic through literature search, it introduces Convergence-Business-Innovation Tools Cube (CBI Cube) model with the concept of developing innovative business models by applying innovation tools under the condition of convergence. In order to quantify (concretize) the concept, we have compared the relative priority of innovation tools for developing business models to find component factors of CBI Cube model through the survey of an expert group by adopting DelPhi method and AHP method. From the result of this study, we expect to be able to make an easier approach to the development of innovative products, services and market as it allo ws to develop business models of value innovation beyond just benchmarking or simple imitation of existing business models.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10a
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• pp.547-552
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• 1998

The small-scale models have been utilized for the prediction of inelastic behavior of reinforced concrete structures for a long time. The parameters that affect the similitude between the model and the prototype are various. Among them, the effect of bond between the model reinforcement and the model concrete is one of the most important factors. The study reported herein is addressed to verifying this similitude in bond behavior. Another topic is the similitude in shear. The selected scales are 1/1, 1/5, 1/10 and 1/12. Two prototype specimens and three models were tested in addition to the associated material tests. The test results are compared from the viewpoint of similitude.

• 한국전산유체공학회:학술대회논문집
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• 2001.05a
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• pp.111-115
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• 2001

The study of nonlinear gas transport in rarefied condition or associated with the microscale length of the geometry has emerged as an interesting topic in recent years. Along with the DSMC method, several fluid dynamic models that come under the general category of the moment method or the Chapman-Enskog method have been used for this type of problem. In the present study, on the basis of Eu's generalized hydrodynamics, a computational model for diatomic gases is proposed. The preliminary result indicates that the bulk viscosity plays a considerable role in fundamental flow problems such as the shock structure and shear flow. The general properties of the constitutive equations are obtained through a simple mathematical analysis. With an iterative computational algorithm of the constitutive equations, numerical solutions for the multi-dimensional problem can be obtained.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.235-238
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• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.

• Journal of the Korean Society for Library and Information Science
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• v.35 no.1
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• pp.101-122
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• 2001

To take maximum advantage of computers without compromising the health of library staffs and users, it is important that the computer workstation be adapted to the needs of the users. While the topic of occupational safety and health is a major industry concern, it is not commonplace in libraries. Therefore, this study is to apply the ergonomic principles to library computer workstations and suggest the ergonomic models for computer table and chair, monitor and keyboard placement, posture and motion. lighting, and other environments.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.126-133
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• 2024

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.