• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.41-49
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• 2016

Counter Fire Operations can be characterized as having a system of systems that key features include situational awareness, command and control systems and highly responsive strike achieved by precision weapons. Current modeling methodology cannot provide an appropriate methodology for a system of systems and utilizes modeling and simulation tools to implement analytic options which can be time consuming and expensive. We explain developing methodology and tools for the effectiveness analysis of the counter fire operations under Network Centric Warfare Environment and suggest how to support a efficient decision making with the methodology and tools. Theater Counter Fire Operations tools consist of Enemy block, ISR block, C2 block and Shooter block. For the convenience of using by domain expert or non simulation expert, it is composed of the environments that each parameter and algorithm easily can be altered by user.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.475-481
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• 2019

The importance of performance-based design feedback is being emphasized when it comes to the potential impact that affects all the lifecycle of the building. However, the latency and disconnection of domain expert in the sector of AEC/FM remain current obstacles between design and performance feedback. It is hard to utilize performance feedback information for design exploration and support design decision making during the conceptual phase of design. Using parametric design, this paper proposes various design alternatives from a set of rules and constraints defined by algorithms for the geometric configurations of an Office Building. A Building Performance Analysis (BPA) was to developed using Autodesk® Revit® 2019 which integrates Autodesk® Green Building Studio® to predict its sufficient daylighting conditions of the LEED v4's Daylighting Autonomy (DA). The parametric-based performance feedback of this study outlines potential design improvements for further exploration in application to the early design process.

• Journal of KIISE
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• v.43 no.8
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• pp.902-909
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• 2016

Question answering system (QA system) is a system that finds an actual answer to the question posed by a user, whereas a typical search engine would only find the links to the relevant documents. Recent works related to the open domain QA systems are receiving much attention in the fields of natural language processing, artificial intelligence, and data mining. However, the prior works on QA systems simply replace all words that are not in the training data with a single token, even though such unseen words are likely to play crucial roles in differentiating the candidate answers from the actual answers. In this paper, we propose a method to compute vectors of such unseen words by taking into account the context in which the words have occurred. Next, we also propose a model which utilizes inverse document frequencies (IDF) to efficiently process unseen words by expanding the system's vocabulary. Finally, we validate that the proposed method and model improve the performance of a QA system through experiments.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.519-532
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• 2004

With the explosion of bioinformatics data such proteins and genes, biologists need a integrated system to analyze and organize large datasets that interact with heterogeneous types of biological data. In this paper, we propose a integration system based on a mediated data warehouse architecture using a XML model in order to combine protein related data at biology laboratories. A fact constellation model in this system is used at a common model for integration and an integrated schema it translated to a XML schema. In addition, to track source changes and provenance of data in an integrated database employ incremental update and management of sequence version. This paper shows modeling of integration for protein structures, sequences and classification of structures using the proposed system.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.329-338
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• 2011

Terminology recognition system which is a preceding research for text mining, information extraction, information retrieval, semantic web, and question-answering has been intensively studied in limited range of domains, especially in bio-medical domain. We propose a domain independent terminology recognition system based on machine learning method using dictionary, syntactic features, and Web search results, since the previous works revealed limitation on applying their approaches to general domain because their resources were domain specific. We achieved F-score 80.8 and 6.5% improvement after comparing the proposed approach with the related approach, C-value, which has been widely used and is based on local domain frequencies. In the second experiment with various combinations of unithood features, the method combined with NGD(Normalized Google Distance) showed the best performance of 81.8 on F-score. We applied three machine learning methods such as Logistic regression, C4.5, and SVMs, and got the best score from the decision tree method, C4.5.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.95-107
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• 2014

In general, topographical phenomena are represented graphically by data in the spatial domain, while attributes of the non-spatial domain are expressed by alpha-numeric texts. GIS functions for analysis of attributes in the non-spatial domain remain quite simple, such as search methods and simple statistical analysis. Recently, graph modeling and network analysis of social phenomena are commonly used for understanding various social events and phenomena. In this study, we applied the network analysis functions to the non-spatial domain data of GIS to enhance the overall spatial analysis. For this purpose, a novel design was presented to integrate the spatial database and the graph database, and this design was then implemented into a WebGIS system for better decision makings. The developed WebGIS with underlying synchronized databases, was tested in a simulated application about the selection of water supply households during an epidemic of the foot-and-mouse disease. The results of this test indicate that the developed WebGIS can contribute to improved decisions by taking into account the social proximity factors as well as geospatial factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.716-729
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• 2006

The information systems in the most enterprise environments are distributed locally and are comprised with various heterogeneous data sources, so that it is difficult to obtain necessary and integrated information for supporting user decision. For solving 'this problems efficiently, it provides uniform interface to users and constructed database systems between heterogeneous systems make a consistence each independence and need to provide transparency like one interface. This paper presents XMDR that consists of category, standard ontology, location ontology and knowledge base. Standard ontology solves heterogeneous problem about naming, attributes, relations in data expression. Location ontology is a mediator that connects each legacy systems. Knowledge base defines the relation for sharing glossary. Adaptive retrieve proposes integrated retrieve system through reflecting site weight by location ontology, information sharing of various forms of knowledge base and integration and propose conceptual domain model about how to share unstructured knowledge.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.133-138
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• 2006

Learning with Attribute Value Taxonomies (AVT) has shown that it is possible to construct accurate, compact and robust classifiers from a partially missing dataset (dataset that contains attribute values specified with different level of precision). Yet, in many cases AVTs are generated from experts or people with specialized knowledge in their domain. Unfortunately these user-provided AVTs can be time-consuming to construct and misguided during the AVT building process. Moreover experts are occasionally unavailable to provide an AVT for a particular domain. Against these backgrounds, this paper introduces an AVT generating method called GA-AVT-Learner, which finds a near optimal AVT with a given training dataset using a genetic algorithm. This paper conducted experiments generating AVTs through GA-AVT-Learner with a variety of real world datasets. We compared these AVTs with other types of AVTs such as HAC-AVTs and user-provided AVTs. Through the experiments we have proved that GA-AVT-Learner provides AVTs that yield more accurate and compact classifiers and improve performance in learning missing data.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.581-591
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• 2010

In this paper, we proposed a new wavelet-based image fusion algorithm, which has advantages in both frequency and spatial domains for signal analysis. The developed algorithm compares the ratio of SAR image signal to optical image signal and assigns the SAR image signal to the fused image if the ratio is larger than a predefined threshold value. If the ratio is smaller than the threshold value, the fused image signal is determined by a weighted sum of optical and SAR image signal. The fusion rules consider the ratio of SAR image signal to optical image signal, image gradient and local variance of each image signal. We evaluated the proposed algorithm using Ikonos and TerraSAR-X satellite images. The proposed method showed better performance than the conventional methods which take only relatively strong SAR image signals in the fused image, in terms of entropy, image clarity, spatial frequency and speckle index.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.87-96
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• 2006

Most conventional database systems support specific queries that are concerned only with data that match a query qualification precisely. A cooperative query answering supports query analysis, query relaxation and provides approximate answers as well as exact answers. The key problem in the cooperative answering is how to provide an approximate functionality for alphanumeric as well as categorical queries. In this paper, we propose a metricized knowledge abstraction hierarchy that supports multi-level data abstraction hierarchy and distance metric among data values. In order to facilitate the query relaxation, a knowledge representation framework has been adopted, which accommodates semantic relationships or distance metrics to represent similarities among data values. The numeric domains also compatibly incorporated in the knowledge abstraction hierarchy by calculating the distance between target record and neighbor records.