• Genomics & Informatics
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• v.9 no.4
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• pp.181-188
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• 2011

In planning a model-based phylogenic study for highly related ethnic data, the SNP marker number is an important factor to determine for relationship inferences. Genotype frequency data, utilizing a sub sampling method, from 63 Pan Asian ethnic groups was used for determining the minimum SNP number required to establish such relationships. Bootstrap random sub-samplings were done from 5.6K PASNPi SNP data. DA distance was calculated and neighbour-joining trees were drawn with every re-sampling data set. Consensus trees were made with the same 100 sub-samples and bootstrap proportions were calculated. The tree consistency to the one obtained from the whole marker set, improved with increasing marker numbers. The bootstrap proportions became reliable when more than 7,000 SNPs were used at a time. Within highly related ethnic groups, the minimum SNPs number for a robust neighbor-joining tree inference was about 7,000 for a 95% bootstrap support.

• Korean Journal of Medicinal Crop Science
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• v.23 no.1
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• pp.43-48
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• 2015

This study was carried out in order to investigate the photosynthesis response and leaf characteristics of Peucedanum japonicum growing in forest farming. The experiment was performed by leaf mold (pine tree and chestnut tree) and shading levels (0%, 35%, 50% and 75% shading). Light relative intensity was 100% (full sunlight), 60.3% (35% shading), 35.1% (50% shading), and 17.4% (75% shading) respectively. Light response curves of pine-leaf mold and chestnut-leaf mold were the highest in control (full sunlight) and these were getting lower in the higher shading level. Photosynthesis capacity and light saturation point were indicated higher in chestnut-leaf mold within the same shading level. As the shading level increased, maximum photosynthesis rate decreased. And apparent quantum yield was not indicated statistically significant difference from all treatment. Leaf area, leaf length and leaf width were significant higher in 35% shading and control under chestnut-leaf mold in all treatment. As the shading level increased, LAR (leaf area ratio), SLA (specific leaf area) and SPAD value decreased in pine-leaf mold and chestnut-leaf mold. As a result of surveying the whole experiment, P. japonicum is judged better growth and higher yield by maintaining 35% shading (relative light intensity 60%) under chestnut-leaf mold in forest farming.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.135-147
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• 2003

This study used the multistage hierarchical clustering image classification to analyze the satellite images for the land-cover types of an area in the Korean peninsula. The multistage algorithm consists of two stages. The first stage performs region-growing segmentation by employing a hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous, and finally the whole image space is segmented into sub-regions where adjacent regions have different physical properties. Without spatial constraints for merging, the second stage clusters the segments resulting from the previous stage. The image classification of hierarchical clustering, which merges step-by step two small groups into one large one based on the hierarchical structure of digital imagery, generates a hierarchical tree of the relation between the classified regions. The experimental results show that the hierarchical tree has the detailed information on the hierarchical structure of land-use and more detailed spectral information is required for the correct analysis of land-cover types.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.27-33
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• 2021

The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.

• Journal of the Korean Wood Science and Technology
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• v.31 no.2
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• pp.31-37
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• 2003

Among the nondestructive evaluation (NDE) methods for wood defect detection, ultrasonic wave has been considered as competitive technique in terms of economics and workability. Until now, researches on application of NDE methods for wood have focused mainly on standing tree and logs. Recently, some attempts have been conducted with NDE technique, for evaluation of wooden structural members. However, wooden structural members are different from others (standing tree or log) in various aspects. Expecially when some parts or whole member are covered with other materials, they can't be evaluated appropriately on general NDE methods. For the purpose of development of proper NDE technique for the wooden structural members, the ultrasonic wave transmission process investigated on artificial defect in wood. First, various types of transmission process were assumed, and then the transmission times were predicted respectively. Predicted times were compared with the measured time of ultrasonic wave and then a suitable type of transmission process is determined. In case of the ultrasonic wave doesn't transmit directly due to defect, it is reflected once only at the opposite surface of member, and the path is accord with the line of shortest length.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.562-569
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• 2019

Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.

• The KIPS Transactions:PartA
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• v.18A no.3
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• pp.115-122
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• 2011

It is very hard to obtain a general algorithm for solution of all the constraint satisfaction optimization problems. However, if the whole problem is separated into subproblems by characteristics of decision variables, we can assume that an algorithm to obtain solutions of these subproblems is easier. Under the assumption, we propose a problem classifying rule which subdivide the whole problem, and develop backtracking algorithms fit for these subproblems. One of the methods of finding a quick solution is efficiently arrange for any order of the search tree nodes. We choose the cluster head positioning problem in wireless sensor networks in which static characteristics is dominant and interference minimization problem of RFID readers that has hybrid mixture of static and dynamic characteristics. For these problems, we develop optimal variable ordering algorithms, and compare with the conventional methods. As a result of classifying the problem into subproblems, we can realize a backtracking framework for systematic search. We also have shown that developed backtracking algorithms have good performance in their quality.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.741-747
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• 2010

These days many meeting minutes of various organizations are publicly available and the interest in these documents by people is increasing. However, it is time-consuming and tedious to read and understand whole documents even if the documents can be accessed easily. In addition, what most people want from meeting minutes is to catch the main issues of the meeting and understand its contexts rather than to know whole discussions of the meetings. This paper proposes a novel method for summarizing documents considering the characteristics of the meeting minutes. It first extracts the sentences which are addressing the main issues. For each issues expressed in the extracted sentences, the sentences related with the issue are then extracted in the next step. Then, by transforming the extracted sentences into a tree-structure form, the results of the proposed method can be understood better than existing methods. In the experiments, the proposed method shows remarkable improvement in performance and this result implies that the proposed method is plausible for summarizing meeting minutes.

• Journal of Veterinary Science
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• v.23 no.5
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• pp.67.1-67.11
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• 2022

Background: Budgerigar fledgling disease polyomavirus (BFDV) is the pathogen that causes budgerigar fledgling disease in psittacine species. The clinical signs of PBFV infection include ascites, hepatitis, and crop stasis. BFDV is associated with a high mortality rate in nestling birds. In contrast, adult birds only have mild symptoms such as feather dystrophy. Objectives: This study aimed to determine the prevalence, genetic characteristics, and phylogenetic analysis of BFDV in pet parrots in Korea. Methods: Fecal and tissue samples were collected from 217 pet parrots from 10 veterinary hospitals including Chungbuk National University Veterinary Hospital. The molecular screening was performed using polymerase chain reaction (PCR) analysis of the small t/large T antigen gene segment. Full-length genome sequencing with the Sanger and phylogenetic analysis were performed on BFDV-positive samples. Results: The PCR results based on the small t/large T antigen gene marker indicated that BFDV DNA was present in 10 out of 217 screened samples. A whole-genome sequence was obtained from six strains and phylogenetic analysis revealed no significant relationship existed between the species and geographical locations amongst them. Conclusions: The prevalence of BFDV infection in South Korea is not high when compared to the prevalence of BFDV in other parts of the world, however, it has been reported sporadically in various species and geographic locations. The whole-genome analysis revealed 0.2%-0.3% variation in intragenomic homogeneity among the six strains analyzed. Korean strains are separately on the phylogenetic tree from their counterparts from China and Japan which might reflect the substantial genetic variation.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.29-35
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• 2010

Soil organic carbon (SOC), being a help to forest formation and control of carbon dioxide in the air, is found to be an important factor by which global warming is influenced. Excavating the samples by whole area is very inefficient method to discovering the distribution of SOC. So, the development of suitable model for expecting the relative amount of the SOC makes better use of expecting the SOC. In the present study, a model based on a decision tree algorithm is introduced to estimate the amount of SOC along with accessing influencing factors such as altitude, aspect, slope and type of trees. The model was applied to a real site and validated by 10-fold cross validation using two softwares, See 5 and Weka. From the results given by See 5, it can be concluded that the amount of SOC in surface layers is highly related to the type of trees, while it is, in middle depth layers, dominated by both type of trees and altitude. The estimation accuracy was rated as 70.8% in surface layers and 64.7% in middle depth layers. A similar result was, in surface layers, given by Weka, but aspect was, in middle depth layers, found to be a meaningful factor along with types of trees and altitude. The estimation accuracy was rated as 68.87% and 60.65% in surface and middle depth layers. The introduced model is, from the tests, conceived to be useful to estimation of SOC amount and its application to SOC map production for wide areas.