• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.161-163
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• 2001

This paper presents a Case-Sort method to solve the unit commitment problem using database in electric power systems. The formulation of the unit commitment may be described as nonlinear mixed integer programming. However, it is hard to optimize a problem with discrete and continuous variables in a large-scale system at the same time. The Case-Sort method is based on the unit [MW] generation cost considered drive hour. Then, this paper shows effectiveness and economical efficiency of the proposed algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.130-142
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• 2008

Different from other ontology languages, TopicMap is capable of integrating numerous amount of heterogenous information resources using the locational information without any information transformation. Although many conventional editors have been developed for topic maps, they are standalone-type only for writing XTM documents. As a result, these tools request too much time for handling large-scale data and provoke practical problems to integrate with legacy systems which are mostly based on relational database. In this paper, we model a large-scale topic map structure based on XTM 1.0 into RDB structure to minimize the processing time and build up the ontology in legacy systems. We implement a topic map platform called X-TOP that can enhance the efficiency of ontology construction and provide interoperability between XTM documents and database. Moreover, we can use conventional SQL tools and other application development tools for topic map construction in X-TOP. The X-TOP is implemented to have 3-tier architecture to support flexible user interfaces and diverse DBMS. This paper shows the usability of X-TOP by means of the comparison with conventional tools and the application to healthcare cancer ontology management.

• Journal of Environmental Impact Assessment
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• v.2 no.2
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• pp.73-83
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• 1993

Increasing concern for the environment in Korea has led to the demand that major policies and large-scale development projects be subjected to detailed impact assessment. This paper reports on the state of data related to the prediction of the environmental impact (EIA) to emphasize the importance of data quality. Environmental impact statements (EIS) consulted with the Ministry of Environment of Korea were analyzed from 1981 through 1992. Many of assessors used existing data and collected supplementary data from field survey. Most of the results of EIA are presented directly or summarized on maps and as graphics. For the national purpose, large source of quality-controlled data such as atmospheric data have been developed, However, there are the deficiency in data to analyze the impact of human activity, and data gaps and incompatibilities among systems. Consequently, the development of data bank systems including computer database and remotely-sensed satellite data is required to improve the quality of data which are relevant to EIA. The data bank system should be organized meaningfully in minimum time with a least cost, and measurement standards must be made explicit. Geographical information systems (GIS) are applicable to the graphic presentation or to the impact prediction model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• Earthquakes and Structures
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• v.16 no.5
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• pp.513-522
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• 2019

A long-span transmission tower-line system is indispensable for long-distance electricity transmission across a large river or valley; hence, the failure of this system, especially the collapse of the supporting towers, has serious impacts on power grids. To ensure the safety and reliability of transmission systems, this study experimentally and numerically investigates the collapse failure of a 220 kV long-span transmission tower-line system subjected to severe earthquakes. A 1:20 scale model of a transmission tower-line system is constructed in this research, and shaking table tests are carried out. Furthermore, numerical studies are conducted in ABAQUS by using the Tian-Ma-Qu material model, the results of which are compared with the experimental findings. Good agreement is found between the experimental and numerical results, showing that the numerical simulation based on the Tian-Ma-Qu material model is able to predict the weak points and collapse process of the long-span transmission tower-line system. The failure of diagonal members at weak points constitutes the collapse-inducing factor, and the ultimate capacity and weakest segment vary with different seismic wave excitations. This research can further enrich the database for the seismic performance of long-span transmission tower-line systems.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.643-654
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• 2015

Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine SIFT is an extension of SIFT algorithm to detect affine invariant local descriptors. Affine SIFT generates a series of different viewpoints using affine transformation. In this way, it allows for a viewpoint difference between the gallery face and probe face. However, the human face is not planar as it contains significant 3D depth. Affine SIFT does not work well for significant change in pose. To complement this, we combined it with probabilistic similarity, which gets the log likelihood between the probe and gallery face based on sum of squared difference (SSD) distribution in an offline learning process. Our experiment results show that our framework achieves impressive better recognition accuracy than other algorithms compared on the FERET database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2359-2376
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• 2022

With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4835-4855
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• 2015

TV white space (TVWS) is showing promise to become the first widespread practical application of cognitive technology. In fact, regulators worldwide are beginning to allow access to the TV band for secondary users, on the provision that they access the geolocation database. Device-to-device (D2D) can improve the spectrum efficiency, but large-scale D2D communications that underlie TVWS may generate undesirable interference to TV receivers and cause severe mutual interference. In this paper, we use an established geolocation database to investigate the power allocation problem, in order to maximize the total sum throughput of D2D links in TVWS while guaranteeing the quality-of-service (QoS) requirement for both D2D links and TV receivers. Firstly, we formulate an optimization problem based on the system model, which is nonconvex and intractable. Secondly, we use an effective approach to convert the original problem into a series of convex problems and we solve these problems using interior point methods that have polynomial computational complexity. Additionally, we propose an iterative algorithm based on the barrier method to locate the optimal solution. Simulation results show that the proposed algorithm has strong performance with high approximation accuracy for both small and large dimensional problems, and it is superior to both the active set algorithm and genetic algorithm.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.3-17
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• 2004

With the rapid growth of the Internet geographic information services through the WWW such as a location-based service and so on. Web GISs (Geographic Information Systems) have also come to be a cluster-based architecture like most other information systems. That is, in order to guarntee high quality of geographic information service without regard to the rapid growth of the number of users, web GISs need cluster-based architecture that will be cost-effective and have high availability and scalability. This paper proposes the design of the cluster-based web GIS with high availability and scalability. For this, each node within a cluster-based web GIS consists of main memory spatial databases which accomplish role of caching by using data declustering and the locality of spatial query. Not only simple region queries but also the proposed system processed spatial join queries effectively. Compare to the existing method. Parallel R-tree spatial join for a shared-Nothing architecture, the result of simulation experiments represents that the proposed spatial join method achieves improvement of performance respectively 23% and 30% as data quantity and nodes of cluster become large.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.1
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• pp.105-118
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• 2010

In this paper, we proposed the effectively method for constructing of name authority data in korean academic papers and designed the authority database system that is applied the method. For these, we analyze the requisite for identifying the author name and suggest the author identification method. Because construction of name authority record costs time and effort, and considering frequently period of large-scale acquisitions of academic papers, our suggestion includes the system that be able to manage and construct the name authority database, and that is tightly connected with the academic paper management and service systems.