• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.112-120
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• 2010

In this paper, we propose semi-ML adaptive parallel QRDM (APQRDM) and iterative QRDM (AIQRDM) algorithms based on set grouping. Using the set grouping, the tree-search stage of QRDM algorithm is divided into partial detection phases (PDP). Therefore, when the treesearch stage of QRDM is divided into 4 PDPs, the APQRDM latency is one fourth of that of the QRDM, and the hardware requirements of AIQRDM is approximately one fourth of that of QRDM. Moreover, simulation results show that in $4{\times}4$ system and at Eb/N0 of 12 dB, APQRDM decreases the average computational complexity to approximately 43% of that of the conventional QRDM. Also, at Eb/N0 of 0dB, AIQRDM reduces the computational complexity to about 54% and the average number of metric comparisons to approximately 10% of those required by the conventional QRDM and AQRDM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6C
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• pp.487-497
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• 2008

This paper proposes a new adaptive QRD-M algorithm for MIMO systems. The proposed scheme controls the number of survivor paths,0 based on the channel condition at each layer. The original QRD-M algorithm used fixed M at each layer and it needs large M to achieve near-MLD (maximum-likelihood detection) performance. However, using the large M increases the computation complexity. In this paper, we further effectively control M by employing the channel indicator which includes not only the channel gain, but also instantaneous noise information without necessity of SNR measurement. We found that the ratio of the minimum path metric to the second minimum is good reliability indicator for the channel condition. By adaptively changing M based on this ratio, the proposed scheme effectively achieves near MLD performance and computation complexity of the proposed scheme is significantly smaller than the conventional QRD-M algorithms.

• Mycobiology
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• v.29 no.3
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• pp.121-131
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• 2001

This study was carried out to identify the phylogenetic relationships among several caterpillar fungi by comparing the sequences of internal transcribed spacer regions(ITS1 and ITS2) and 5.8S ribosomal DNA(rDNA) repeat unit. The sequences of ITS1, ITS2, and the 5.8S rDNA from 10 strains of Cordyceps species, 12 strains of Paecilomyces, 3 strains of Beauveria, 2 strains of Metarhizium and 1 strains of Hirsutella were amplified, determined and compared with the previously known Cordyceps species. The sequences of 5.8S rDNA were more conserved in length and variation than those of ITS regions. Although the variable ITS sequences were often ambiguously aligned, the conserved sites could be found. In the phylogenetic tree, the species generally divided into three clusters, supported by their morphology and/or host ranges. The 5.8S rDNA and TTS1 sequences among 10 species of Cordyceps militaris were identical and only one base pair in ITS2 sequence was different. Cordyceps sinensis and Cordyceps ophioglossoides were also clearly different, although they belonged to the same cluster. The Geniank database search of species revealed sister taxa of an entomogenous fungus. Metarhizium was used as an putgroup in all taxa.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.575-580
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• 2011

With the increasing expectation on personalized medicine, it is getting importance to analyze medical information in molecular biology perspective. Gene expression data are one of representative ones to show the microscopic phenomena of biological activities. In gene expression data analysis, one of major concerns is to identify markers which can be used to predict disease occurrence, progression or recurrence in the molecular level. Existing markers candidate identification methods mainly depend on statistical hypothesis test methods. This paper proposes a search method based decision tree induction to identify candidate markers which consist of multiple genes. The propose method discretizes numeric expression level into three categorical values and allows candidate markers' genes to be expressed by their negation as well as categorical values. It is desirable to have some number of genes to be included in markers. Hence the method is devised to try to find candidate markers with restricted number of genes.

• 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.

• Korean journal of applied entomology
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• v.44 no.3 s.140
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• pp.251-255
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• 2005

The occurrence and the host plants of Arboridia kakogawana and A. maculifrons in the province of Chungcheonbuk-Do were observed. They started to infest grapevine in a vineyard in early May and reached peak population two times once in late June and once in mid August in general. In preparation for overwinter, A. kakogawana moved to the nearby forest in search of a tree with bark from early October. A. maculifrons also moved to the weeds on the ridge of vineyard circumferences from the end of September. Population density of the two species were found to be the highest in Okcheon county among the five counties of Chungbuk province. Developmental period of A. kakogawana was shorter than that of A. maculifrons.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11A
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• pp.862-869
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• 2009

We propose a new adaptive K-best Sphere Decoding (SD) algorithm for Multiple Input Multiple Output (MIMO) systems where the number of survivor paths, K is changed based on the characteristics of path metrics which contain the instantaneous channel condition. In order to overcome a major drawback of Maximum Likelihood Detection (MLD) which exponentially increases the computational complexity with the number of transmit antennas, the conventional adaptive K-best SD algorithms which achieve near to MLD performance have been proposed. However, they still have redundant computation complexity since they only employ the channel fading gain as a channel condition indicator without instantaneous Signal to Noise Ratio (SNR) information. hi order to complement this drawback, the proposed algorithm use the characteristics of path metrics as a simple channel indicator. It is found that the ratio of the minimum path metric to the other path metrics reflects SNR information as well as channel fading gain. By adaptively changing K based on this ratio, the proposed algorithm more effectively reduce the computation complexity compared to the conventional K-best algorithms which achieve same performance.

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.193-203
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• 2005

XML (extensible Markup Language) is recognized as a standard of data representation and transmission on Internet. XPath is a standard for specifying parts of XML documents anda suitable language for both query processing and access control of XML. In this paper, we use the XPath expression for representing user queries and access control for XML. And we propose an access control method for XML, where we control accesses to XML documents by filtering query XPath expressions through access control XPath expressions. In the proposed method, we directly search XACT (XML Access Control Tree) for a query XPath expression and extract the access-granted parts. The XACT is our proposedstructure, where the edges are structural summary of XML elements and the nodes contain access-control information. We show the query XPath expressions are successfully filtered through the XACT by our proposed method, and also show the performance improvement by comparing the proposed method with the previous work.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.266-273
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilize Holland career search test results, TOEIC score, course work list, and GPA score as the input for data mining and generation the student advisory information. Factor analysis, AHP(Analytic Hierarchy Process), artificial neural net, and CART(Classification And Regression Tree) techniques are deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained with the human student advice experts.