• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.414-424
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• 2012

Finding or automatically generating problem instance is useful for algorithm analysis/test. The topic has been of interest in the field of hardware/software engineering and theory of computation. We apply objective value-distance correlation analysis to problem spaces, as previous researchers applied it to solution spaces. According to problems, we define the objective function by (1) execution time of tested algorithm or (2) its optimality; this definition is interpreted as difficulty of the problem instance being solved. Our correlation analysis is based on the following aspects: (1) change of correlation when we use different algorithms or different distance functions for the same problem, (2) change of that when we improve the tested algorithm, (3) relation between a problem instance space and the solution space for the same problem. Our research demonstrates the way of problem instance space analysis and will accelerate the problem instance space analysis as an initiative research.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.193-195
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• 2003

본 논문에서는 k-Nearest Neighbor(k-NN) 알고리즘을 최적화하기 위해 지역적으로 다른 k(고려할 neighbor의 개수)를 사용하는 새로운 방법을 제안한다. 인스턴스 공간(instance space)에서 노이즈(noise)의 분포가 지역적(local)으로 다를 경우, 각 지점에서 고려해야 할 최적의 이웃 인스턴스(neighbor)의 수는 해당 지점에서의 국부적인 노이즈 분포에 따라 다르다. 그러나 기존의 방법은 전체 인스턴스 공간에 대해 동일한 k를 사용하기 때문에 이러한 인스턴스 공간의 지역적인 특성을 고려하지 못한다. 따라서 본 논문에서는 지역적으로 분포가 다른 노이즈 문제를 해결하기 위해 인스턴스 공간을 여러 개의 부분으로 나누고, 각 부분에 최적화된 k의 값을 사용하여 kNN을 수행하는 새로운 방법인 Local-k Nearest Neighbor 알고리즘(LkNN Algorithm)을 제안한다. LkNN을 통해 생성된 k의 집합은 인스턴스 공간의 각 부분을 대표하는 값으로, 해당 지역의 인스턴스가 고려해야 할 이웃(neighbor)의 수를 결정지어준다. 제안한 알고리즘에 적합한 데이터의 도메인(domain)과 그것의 향상된 성능은 UCI ML Data Repository 데이터를 사용한 실험을 통해 검증하였다.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.585-592
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• 2003

In the spatial database systems, it is necessary to manage spatial objects that have two or more aspatial information with different security levels on the same layer. If we adapt the polyinstantiation concept of relational database system for these spatial objects, it is difficult to process the representation problem of spatial objects and to solve the security problem that is service denial and information flow by access of subject that has a different security level. To address these problems, we propose a polyinstantiation method for security management of spatial objects in this paper. The proposed method manages secure spatial database system efficiently by creating spatial objects according to user's security level through security-level-conversion-step and polyinstantiation-generation-step with multi-level security policy. Also, in case of user who has a different security level requires secure operations, we create polyinstance for spatial object to solve problems of service denial and information flow.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.439-441
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• 2023

전통적인 텍스트 분류 방법은 상당량의 라벨링된 데이터와 미리 정의된 클래스가 필요해서 그 적용성과 확장성이 제한된다. 그래서 이런 한계를 극복하기 위해 제로샷 러닝(Zero-shot Learning)이 등장했다. 텍스트 분류 분야에서 제로샷 텍스트 분류는 모델이 대상 클래스의 샘플을 미리 접하지 않고도 인스턴스를 분류할 수 있도록 하는 중요한 주제이다. 이 문제를 해결하기 위해 정책 네트워크를 활용한 심층 강화 학습(DRL) 기반 접근법을 제안한다. 이러한 방법을 통해 모델이 새로운 의미 공간에 효과적으로 적응하면서, 다른 모델들과 비교하여 제로샷 텍스트 분류의 정확도를 향상시킬 수 있었다. XLM-R 과 비교하면 최대 15.9%의 정확도 향상이 나타났다.

• Journal of Korea Spatial Information System Society
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• v.7 no.2 s.14
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• pp.57-66
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• 2005

The Interconnecting Highways problem is an abstract of many practical Layout Design problems in the areas of VLSI design, the optical and wired network design, and the planning for the road constructions. For the road constructions, the shortest-length road layouts that interconnect existing positions will provide many more economic benefits than others. That is, finding new road layouts to interconnect existing roads and cities over a wide area is an important issue. This paper addresses an approximation scheme that finds near optimal road layouts for the Interconnecting Highways problem which is NP-hard. As long as computational resources are provided, the near optimality can be acquired asymptotically. This implies that the result of the scheme can be regarded as the optimal solution for the problem in practice. While other approximation schemes can be made for the problem, this proposed scheme provides a big merit that the algorithm designed by this scheme fits well to given problem instances.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.406-409
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• 2008

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2357-2364
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• 2008

In this paper, we consider the problem of exploiting a taxonomy of propositionalized attributes in order to generate compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data set, show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Spatial Information Research
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• v.22 no.1
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• pp.65-73
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• 2014

Recently indoor navigation with indoor map such as Indoor Google Maps is served. For the services, constructing indoor data are required. CityGML and IFC are widely used as standards for representing indoor data. The data models contains spatial information for the indoor visualization and analysis, but indoor navigation requires semantic and topological information like graph as well as geometry. For this reason, IndoorGML, which is a GML3 application schema and data model for representation, storage and exchange of indoor geoinformation, is under standardization of OGC. IndoorGML can directly describe geometric property and refer elements in external documents. Because a lot of data in CityGML or IFC have been constructed, a huge amount of construction time and cost for IndoorGML data will be reduced if CityGML can help generate data in IndoorGML. Thus, this paper suggest practical use of CityGML including deriving from and link to CityGML. We analyze relationships between IndoorGML and CityGML. In this paper, issues and solutions for linkage of IndoorGML and CityGML are addressed.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.