• Tuberculosis and Respiratory Diseases
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• v.48 no.5
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• pp.794-801
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• 2000

BOOP(Bronchiolitis Obliterans Organizing Pneumonia) is an inflammatory reaction that follows damage to the bronchiolar epithelium of the small conducting airways. BOOP is characterized by the pathologic finding of excessive proliferation of granulation tissue within the respiratory bronchioles, alveolar duct and spaces, accompanied by organizing pneumonia in the more distal parenchyma BOOP may result from diverse causes such as toxic fumes, connective tissue disorders, infections, organ transplantation and drugs or appear idiopathically. Drug induced BOOP has been described in association with acebutolol, amiodarone, cephalosporin, bleomycine, tryptophan, gold salts, barbiturates, sulfasalazine, and carbamazepine. Carbamazepine is an iminostilbene derivative that is used as both an anticonvulsant and pain reliever for pains associated with trigeminal neuralgia. It is structually related to the tricyclic antidepressants. To our knowledge, there have been no previously reported case that has described development of BOOP during carbamazepine treatment in Korea, and only two cases have been reported in the world. We report a case carbamazepine-induced BOOP with a brief review of literature.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1505-1514
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• 2004

Sequence matching in time-series databases is an operation that finds the data sequences whose changing patterns are similar to that of a query sequence. Typically, sequence matching hires a multi-dimensional index for its efficient processing. In order to alleviate the dimensionality curse problem of the multi-dimensional index in high-dimensional cases, the previous methods for sequence matching apply the Discrete Fourier Transform(DFT) to data sequences, and take only the first two or three DFT coefficients as organizing attributes of the multi-dimensional index. This paper first points out the problems in such simple methods taking the firs two or three coefficients, and proposes a novel solution to construct the optimal multi -dimensional index. The proposed method analyzes the characteristics of a target database, and identifies the organizing attributes having the best discrimination power based on the analysis. It also determines the optimal number of organizing attributes for efficient sequence matching by using a cost model. To show the effectiveness of the proposed method, we perform a series of experiments. The results show that the Proposed method outperforms the previous ones significantly.

• The KIPS Transactions:PartC
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• v.11C no.7 s.96
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• pp.905-916
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• 2004

Self-organizing hierarchical ad hoc network (SOHAN) is a new ad-hoc network architecture designed to improve the scalability properties of conventional 'flat' ad hoc networks. This network architecture consists of three tiers of ad-hoc nodes, i.e.. access points, forwarding nodes and mobile nodes. This paper presents a topology discovery and routing protocol for the self-organization of SOHAN. We propose a cross-layer path metric based on link quality and MAC delay which plays a key role in producing an optimal cluster-based hierarchical topology with high throughput capacity. The topology discovery protocol provides the basis for routing which takes place in layer 2.5 using MAC addresses. The routing protocol is based on AODV with appropriate modifications to take advantage of the hierarchical topology and interact with the discovery protocol. Simulation results are presented which show the improved performance as well as scalability properties of SOHAN in terms of through-put capacity, end-to-end delay, packet delivery ratio and control overhead.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.83-98
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• 2005

Exploring distributional patterns of multivariate data is very essential in understanding the characteristics of given data set, as well as in building plausible models for the data. For that purpose, low-dimensional visualization methods have been developed by many researchers along various directions. As one of methods, Kohonen's SOM (Self-Organizing Map) is prominent. SOM compresses the volume of the data, yields abstraction from the data and offers visual display on low-dimensional grids. Although it is proven quite effective, it has one undesirable property: SOM's display is discrete. In this study, we propose two techniques for enhancing quality of SOM's display, so that SOM's display becomes continuous. The proposed methods are demonstrated in two numerical examples.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.321-333
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• 2003

Self-organizing map (SOM), a unsupervised learning neural network, has been developed by T. Kohonen since 1980's. Main application areas were pattern recognition and text retrieval. Because of that, it has not been spread to statisticians until late. Recently, SOM's are frequently drawn in data mining fields. Kohonen's SOM, however, needs improvements to become a statistician's standard tool. First, there should be a good guideline as for the size of map. Second, an enhanced visualization mode is wanted. In this study, principal components self-organizing map (PC-SOM), a modification of Kohonen's SOM, is proposed to meet such needs. PC-SOM performs one-dimensional SOM during the first stage to decompose input units into node weights and residuals. At the second stage, another one-dimensional SOM is applied to the residuals of the first stage. Finally, by putting together two stages, one obtains two-dimensional SOM. Such procedure can be easily expanded to construct three or more dimensional maps. The number of grid lines along the second axis is determined automatically, once that of the first axis is given by the data analyst. Furthermore, PC-SOM provides easily interpretable map axes. Such merits of PC-SOM are demonstrated with well-known Fisher's iris data and a simulated data set.

• Tuberculosis and Respiratory Diseases
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• v.50 no.4
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• pp.504-509
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• 2001

There are numerous agents with potential toxic effects on the lung. In particular, cytotoxic drugs constitute the largest and most important group of agents associated with lung toxicity. Bleomycin is commonly used, either alone or in combination with other chemotherapeutic agents, in the treatment of squamous cell carcinoma(head and neck, esophagus, and genitourinary tract), lymphoma, and germ cell tumor. One of the therapeutic advantages of bleomycin is its minimal bone marrow toxicity. However, pulmonary toxicity is one of the most serious adverse side effects. Classically, pulmonary toxicity manifests as a diffuse interstitial process or less commonly as a hypersensitivity reaction. This pulmonary toxicity is generally considered to be dose related and can progress to a fatal fibrosis. It is also possible that bronchiolitis obliterans organizing pneumonia(BOOP) is another manifestation of bleomycin induced toxicity. Bleomycin induced BOOP is less common and has a favorable response to steroid therapy. Here we present a case that demonstrates a BOOP, secondary to a relatively small cumulative dose of bleomycin($225mg/m^2$), may be reversible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.5
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• pp.260-270
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• 2014

In this paper, we first analyze the delay performance of Dynamic Space-time Subframe (DSTS)-based frame structure which has been proposed to support the real-time service as well as non real-time service, using Ad hoc Self-Organizing Time Division Multiple Access (ASO-TDMA) MAC protocol, especially when transmitting a MAC SDU with two or more MAC PDUs, in a multi-hop ad-hoc maritime communication network. We propose two key transmission schemes: contiguous DSTS reservation which guarantees the end-to-end delay for the multiple PDUs, and adaptive transmission probability control schemes to maximize the system throughput. Our simulation results show that the proposed schemes outperform the system throughput of the existing transmission schemes, while supporting the real-time requirement.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.29-38
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• 2005

Although people frequently rely on intuition in managing activities, they rarely use it in developing effective decision-making support systems. In this paper, we investigate and compare the correlations between such characteristics as cognition and emotion characteristics and judgmental time-series forecasting accuracy by using a self-organizing neural network, and eventually aim to help build efficient decision-making atmosphere. The neural network used in this paper employs a self-supervised adaptive algorithm, and the feature of which is that it inherently can use correlation between input vectors by exchanging information between neuron clusters in the self-organizing layer during the training. Our experiments showed that both cognition and emotion characteristics had correlations with judgmental time-series forecasting, and that cognition characteristics had larger correlation than emotion characteristics. We also found that conceptual style had larger correlation than behavioral and analytical styles, and displeasure-sleepiness style had larger correlation than pleasure-arousal style with the forecasting.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.145-170
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• 2011

The purpose of this paper is to suggest a pilot model of knowledge organizing system which reflects the knowledge structure of research papers, using a case analysis on the "Korean Research Memory" of the National Research Foundation of Korea. In this paper, knowledge structure of the research papers in humanities and social science is described and the function of the "Korean Research Memory" for scholarly sense-making is analysed. In order to suggest the pilot model of the knowledge organizing system, the study also analysed the relation between indexed keyword and knowledge structure of research papers in the Korean Research Memory. As a result, this paper suggests 24 axioms and 11 inference rules for an ontology based on semantic relation of the knowledge structure.

• Korean Journal of Environmental Agriculture
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• v.34 no.3
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• pp.168-177
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• 2015

BACKGROUND: In recent year, there has been great concern regarding agricultural land uses and their importance for the conservation of biodiversity. Rice fields are managed unique wetland for wildlife, especially waterbirds. A comprehensive monitoring of the waterbird assemblage to understand patterning changes was attempted for rice ecosystem in South Korea. This rice ecosystem has been recognized as one of the most important for waterbirds conservation. METHODS AND RESULTS: Biweekly monitoring was implemented for the 4 years from April 2009 to March 2010, from April 2011 to March 2014. 32 species of waterbirds were observed. Self-organizing map (SOM) and random forest were applied to the waterbirds dataset to identify the characteristics in waterbirds distribution. SOM and random forest analysis clearly classified into four clusters and extract ecological information from waterbird dataset. Waterbird assemblages represented strong seasonality and habitat use according to waterbird group such as shorebirds, herons and waterfowl. CONCLUSION: Our results showed that the combination of SOM and random forest analysis could be useful for ecosystem assessment and management. Furthermore, we strongly suggested that a strict management strategy for the rice fields to conserve the waterbirds. The strategy could be seasonally and species specific.