• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.435-442
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• 2017

The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.875-878
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• 2007

Up until now, when a user search product information, the keyword-based search that mainly uses frequency of words or vocabulary information has been utilized in large. In the keyword-based research, the user should have to bear additional burden in order to search the displayed results manually once again because it shows those files that have no connection at all with the inquiries made by the user. To resolve such a problem, ontology has been emerged. In this paper, product search system using ontology was constructed directly and also tested how accurate search it does perform through the searching according to classification. To test this, about 40,000 product data of A discount store, which was operating on/off line discount stores, were constructed as database, and developmental environment for User Interface was tested by having developed the search system using JSP and PowerBuilder 9.0. Results from the test proved that the search method using Domain Ontology for product presented and designed in this paper was superior to the existing keyword-based search method.

• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.219-228
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• 2003

This paper presents an efficient construction method of determination rules of thematic roles from syntactic relations in Korean language processing. This process is one of the main core of semantic analysis and an important issue to be solved in natural language processing. It is problematic to describe rules for determining thematic roles by only using general linguistic knowledge and experience, since the final result may be different according to the subjective views of researchers, and it is impossible to construct rules to cover all cases. However, our method is objective and efficient by considering large corpora, which contain practical osages of Korean language, and case frames in the Sejong Electronic Lexicon of Korean, which is being developed by dozens of Korean linguistic researchers. To determine thematic roles more correctly, our system uses syntactic relations, semantic classes, morpheme information, position of double subject. Especially by using semantic classes, we can increase the applicability of the rules.

• Journal of KIISE
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• v.42 no.11
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• pp.1397-1403
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• 2015

A named entity(NE) dictionary is an important resource for the performance of NE recognition. However, it is not easy to construct a NE dictionary manually since human annotation is time consuming and labor-intensive. To save construction time and reduce human labor, we propose a semi-automatic system for the construction of a NE dictionary. The proposed system constructs a pseudo-document with Wiki-categories per NE class by using an active learning technique. Then, it calculates similarities between Wiki entries and pseudo-documents using the BM25 model, a well-known information retrieval model. Finally, it classifies each Wiki entry into NE classes based on similarities. In experiments with three different types of NE class sets, the proposed system showed high performance(macro-average F1-score of 0.9028 and micro-average F1-score 0.9554).

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.255-262
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• 2014

Our previous research[1] suggested object extraction and modeling method based on Fillmore's case grammar. This approach had not considered of use case extraction and method. To solve this problem, we adopt Fillmore's semantic method as linguistic approach into requirement engineering, which refine fillmore's case grammar for extracting and modeling use cases from customer requirements. This Refined mechanism includes the definition of a structured procedure and the representation of visual notations for 'case' modeling. This paper also proposes the use case decision matrix to identify use case size from extracted use cases based on goal oriented requirement engineering(GoRE), which related with the complexity of use case, and also prioritizes the use cases with this matrix. It demonstrates our proposal with the bank ATM system.

• Journal of KIISE:Software and Applications
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• v.34 no.8
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• pp.721-730
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• 2007

UML (Unified Modeling Language) is widely used to analyze and design target software. Developers also implement the target software based on the UML artifacts. However, it is difficult to validate whether the artifacts are generated to correspond to the modeling guidelines because the guidelines for UML modeling are described in natural language. This paper discusses rule based model checker focused on whether models are designed according to modeling methodology. We propose rules and their own checker, named GARDIAN, for UML model validation. The checkers are designed for COMET method for the real-time embedded system. We illustrate our checkers using Intelligent Robot system to validate our approach.

• Journal of KIISE
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• v.44 no.11
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• pp.1138-1148
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• 2017

Topic extraction is a technology that automatically extracts a set of topics from a set of documents, and this has been a major research topic in the area of natural language processing. Representative topic extraction methods include Latent Dirichlet Allocation (LDA) and word clustering-based methods. However, there are problems with these methods, such as repeated topics and mixed topics. The problem of repeated topics is one in which a specific topic is extracted as several topics, while the problem of mixed topic is one in which several topics are mixed in a single extracted topic. To solve these problems, this study proposes a method to extract topics using an LDA that is robust against the problem of repeated topic, going through the steps of separating and merging the topics using the similarity between words to correct the extracted topics. As a result of the experiment, the proposed method showed better performance than the conventional LDA method.

• Journal of KIISE:Software and Applications
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• v.35 no.2
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• pp.128-136
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• 2008

Recognition of parallel structure at early stage of sentence parsing can reduce the complexity of parsing. In this paper, we propose an unsupervised language-independent probabilistic model for recongition of parallel noun structures. The proposed model is based on the idea of swapping constituents, which replies the properties of symmetry (two or more identical constituents are repeated) and of reversibility (the order of constituents is inter-changeable) in parallel structures. The non-symmetric patterns that cannot be captured by the general symmetry rule are resolved additionally by the modifier information. In particular this paper shows how the proposed model is applied to recognize Korean parallel noun phrases connected by "wa/kwa" particle. Our model is compared with other models including supervised models and performs better on recongition of parallel noun phrases.

• Journal of Internet Computing and Services
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• v.14 no.3
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• pp.81-90
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• 2013

Mobile device can get useful user information, because users have always this device. In this paper, we propose automatically story generation method and user topic extraction using user information in mobile environment. Proposed method is follows: (1) We collect user action information in mobile device. Then, (2) we extract topics from collected information. (3) For the results of (2), we determine episodes for one day. Then, (4) we generate sentences using sentence templates and we compose stories which have theme-based or time-based. Because proposed method is simpler than previous method, proposed method can work only in mobile device. There's no room to leak user information. And proposed method is expressed more informative than previous method, because proposed method is provided sentence-based result. Extracted user-topic, a result of our method, can use to analyze user action and user preference.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.1
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• pp.33-42
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• 2003

This paper presents an efficient determination method of thematic roles from syntactic relations using rules and statistical model in Korean language processing. This process is one of the main core of semantic analysis and an important issue to be solved in natural language processing. It is problematic to describe rules for determining thematic roles by only using general linguistic knowledge and experience, since the final result may be different according to the subjective views of researchers, and it is impossible to construct rules to cover all cases. However, our hybrid method is objective and efficient by considering large corpora, which contain practical usages of Korean language, and case frames in the Sejong Electronic Lexicon of Korean, which is being developed by dozens of Korean linguistic researchers. To determine thematic roles more correctly, our system uses syntactic relations, semantic classes, morpheme information, position of double subject. Especially by using semantic classes, we can increase the applicability of our system.