• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.152-157
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• 2012

Sarawak is the largest state in Malaysia that covers 37.5% of the total land area. Multitemporal satellite images of Landsat and SPOT were used to examine deforestation and forest fragmentation in Sarawak between 1990 and 2009. Supervised classification with maximum likelihood classifier was used to classify the land cover types in Sarawak. The overall accuracies of all classifications were more than 80%. Our results showed that forests were reduced at 0.62% annually during the two decades. The peat swamp forest suffered a tremendous loss of almost 50% between 1990 and 2009 especially at coastal divisions due to intensified oil palm plantation development. Fragmentation analysis revealed the loss of about 65% of the core area of intact forest during the change period. The core area of peat swamp forest had almost completely disappeared during the two decades.

• Journal of Korean Society of Rural Planning
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• v.12 no.1 s.30
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• pp.75-83
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• 2006

본 연구에서는 최대우도법과 인공신경망 모형에 의해 카테고리 분류를 수행하고 각각의 분류 성능을 비교 평가하였다. 인공신경망 모형은 오류역전파 알고리즘을 이용한 것으로서 학습을 통한 은닉층의 최적노드수를 결정하여 카테고리 분류를 수행하도록 하였다. 인공신경망 최적 모형은 입력층의 노드수가 7개, 은닉층의 최적노드수가 18개, 그리고 출력층의 노드수가 5개인 것으로 구성하였다. 위성영상은 1996년에 촬영된 Landsat TM-5 영상을 사용하였고, 최대우도법과 인공신경망 모형에 의한 카테고리 분류를 위하여 각각의 카테고리에 대한 분광특성을 대표하는 지역을 절취하였다. 분류 정확도는 인공신경망 모형에 의한 방법이 90%, 최대우도법이 83%로서, 인공신경망 모형의 분류 성능이 뛰어난 것으로 나타났다. 카테고리 분류 항목인 토지 피복 상태에 따른 분류는 두 가지 방법에서 밭과 주거지의 분류오차가 큰 것으로 나타났다. 특히, 최대우도법에 의한 밭에서의 태만오차는 62.6%로서 매우 큰 값을 보였다. 이는 밭이나 주거지의 특성이 위성영상 촬영시기에 따라 나지의 형태로 분류되거나 산림, 또는 논으로도 분류되는 경향이 있기 때문인 것으로 보인다. 차후에 카테고리 분류를 위한 각각의 클래스의 보조적인 정보를 추가한다면, 카테고리 분류 향상이 이루어질 것으로 기대된다.

• Proceedings of the KSRS Conference
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• v.1
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• pp.118-121
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• 2006

Variations in the extent and dispersal of river plume are important in the study of coastal environment. The objectives of this study are to examine relationship between satellite detected plume area and river discharge and to clarify the temporal and spatial dynamic of plume from Tokachi River, Hokaido, Japan. We used 1.1 km spatial resolution of SeaWiFS normalized water-leaving radiance (nLw) images from 1998 to 2002. Supervised maximum likelihood classification was implemented to define classes of surface water optical properties. Satellite observed plume area was correlated to the amount of river discharge from April to October. First mode (44% of variance) of EOF analysis shows the turbid plume distribution resulting from re-suspension by strong wind mixing along the coast during winter. This mode also shows plume distribution along-shelf direction in spring and late summer. Second mode (17% of variance) shows spring pattern across-shelf direction due to strong discharge of snow melting water.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• ALGAE
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• v.28 no.4
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• pp.307-330
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• 2013

The genus Cryptomonas is easily recognized by having two flagella, green brownish color, and a swaying behavior. They have relatively simple morphology, and limited diagnostic characters, which present a major difficulty in differentiating between species of the genus. To understand species delineation and phylogenetic relationships among Cryptomonas species, the nuclear-encoded internal transcribed spacer 2 (ITS2), partial large subunit (LSU) and small subunit ribosomal DNA (rDNA), and chloroplast-encoded psbA and LSU rDNA sequences were determined and used for phylogenetic analyses, using Bayesian and maximum likelihood methods. In addition, nuclear-encoded ITS2 sequences were predicted to secondary structures, and were used to determine nine species and four unidentified species from 47 strains. Sequences of helix I, II, and IIIb in ITS2 secondary structure were very useful for the identification of Cryptomonas species. However, the helix IV was the most variable region across species in alignment. The phylogenetic tree showed that fourteen species were monophyletic. However, some strains of C. obovata had chloroplasts with pyrenoid while others were without pyrenoid, which used as a key character in few species. Therefore, classification systems depending solely on morphological characters are inadequate, and require the use of molecular data.

• The Korean Journal of Malacology
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• v.27 no.4
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• pp.395-400
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• 2011

Phylogenetic analyses on the Phylum Mollusks has so far been conducted by many researchers in the world. However, there was no report on taxonomic analysis on Acila divaricata vigila which is belonging to Class Bivalvia, Subclass Protobranchia. In this study, we performed molecular phylogenetic analysis on Acila divaricata vigila using 16S rRNA sequence through maximum likelihood method. As a result, it is clearly divided into the legion of mollusk classification unit (when you zoom in order) and represented to support the current classification in the Phylum Mollusca belong to Class Bivalvia, Subclass Protobranchia, Subclass Pteriomorphia, Subclass Paleoheterodonta, Subclass Heterodonta and Subclass Anomalodesmacea. To our knowledge, this is the first report of molecular phylogenetic analysis on Acila divaricata vigila using 16S rRNA gene and these data suggests that 16S rRNA gene will be useful for analyzing the phylogenetic relationship of Subclass Protobranchia.

• Korean Journal of Microbiology
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• v.43 no.4
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• pp.285-291
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• 2007

According to the Baltimore Scheme, viruses are classified into 6 main classes based on their replication and coding strategies. Except for some small DNA viruses, most viruses code for their own polymerases: DNA-dependent DNA, RNA-dependent RNA and RNA-dependent DNA polymerases, all of which contain 4 common motifs. We undertook a phylogenetic study to establish the relationship between the Baltimore Scheme and viral polymerases. Amino acid sequence data sets of viral polymerases were taken from NCBI GenBank, and a multiple alignment was performed with CLUSTAL X program. Phylogenetic trees of viral polymerases constructed from the distance matrices were generally consistent with Baltimore Scheme with some minor exceptions. Interestingly, negative RNA viruses (Class V) could be further divided into 2 subgroups with segmented and non-segmented genomes. Thus, Baltimore Scheme for viral taxonomy could be supported by phylogenetic analysis based on the amino acid sequences of viral polymerases.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.4
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• pp.476-484
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• 2015

The objective of this study was to estimate genetic and phenotypic parameters for linear type traits, as well as milk yield (MY), fat yield (FY) and protein yield (PY) in 18,831 Holstein cows reared in 495 herds in Brazil. Restricted maximum likelihood with a bivariate model was used for estimation genetic parameters, including fixed effects of herd-year of classification, period of classification, classifier and stage of lactation for linear type traits and herd-year of calving, season of calving and lactation order effects for production traits. The age of cow at calving was fitted as a covariate (with linear and quadratic terms), common to both models. Heritability estimates varied from 0.09 to 0.38 for linear type traits and from 0.17 to 0.24 for production traits, indicating sufficient genetic variability to achieve genetic gain through selection. In general, estimates of genetic correlations between type and production traits were low, except for udder texture and angularity that showed positive genetic correlations (>0.29) with MY, FY, and PY. Udder depth had the highest negative genetic correlation (-0.30) with production traits. Selection for final score, commonly used by farmers as a practical selection tool to improve type traits, does not lead to significant improvements in production traits, thus the use of selection indices that consider both sets of traits (production and type) seems to be the most adequate to carry out genetic selection of animals in the Brazilian herd.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1305-1315
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• 2015

Consider the risk score which is a function of a linear score for the classification models. The AUC optimization method can be applied to estimate the coefficients of linear score. These estimates obtained by this AUC approach method are shown to be better than the maximum likelihood estimators using logistic models under the general situation which does not fit the logistic assumptions. In this work, the VUS and HUM approach methods are suggested by extending AUC approach method for more realistic discrimination and prediction worlds. Some simulation results are obtained with both various distributions of thresholds and three kinds of link functions such as logit, complementary log-log and modified logit functions. It is found that coefficient prediction results by using the VUS and HUM approach methods for multiple categorical classification are equivalent to or better than those by using logistic models with some link functions.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.423-423
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• 2019

RGB나 다중분광영상은 높은 공간 해상도로 인해 크기가 작은 물질의 클래스를 부여하는데 있어서는 효과적이지만 분광해상도가 낮아 다양한 종류의 지표물 분류 및 분광적으로 미세한 차이를 보이는 대상 체간의 분류에는 한계를 가지고 있다. 그러나 초분광 영상(Hyperspectral Image)은 대상 객체의 분광 반사곡선을 수백개의 연속적인 분광 파장대 영역으로 상세하게 해당 물체의 정보를 취득할 수 있는 기능을 가지고 있다. 최근 국내에서도 초분광 영상을 이용한 토지피복도 작성 및 환경 모니터링 등 다양한 분야에 적용하기 위한 연구가 시도되고 있다. 최근에는 드론과 같은 소형 UAV를 활용하여 경제적인 비용으로 시공간해상도가 높은 영상을 획득하는 것이 가능하게 되었으며 분광정보를 수집하는 영상 장비의 발전으로 드론에 탑재가 가능한 경량의 소형 초분광센서가 개발됨으로써 보다 높은 분광해상도의 영상을 취득할 수 있게 되었다. 본 연구에서는 효율적인 하천환경조사를 위해 UAV를 활용하여 고해상도 초분광 영상을 취득하였으며, 차원축소법과 분류기 적용에 따른 공간 분류 정확도 분석을 통해 하천환경에 대한 분류 및 평가를 실시하였다. 연구지역에서 획득한 초분광 영상은 노이즈로 인한 영향을 줄이고자 MNF와 PCA 기법으로 차원축소를 수행하였으며, MLC(Maximum Likelihood Classification)와 SVM(Support Vector Machine), SAM(Spectral Angle Mapping) 감독분류기법을 적용하여 하천환경특성에 따른 공간분류를 수행하였다. 연구 결과 MNF기법으로 차원 축소한 영상을 적용하여 MLC 감독분류를 수행하였을 때 가장 높은 분류정확도를 얻을 수 있었으나, 일부 클래스 및 수역의 경계와 그림자 공간에서 주로 오분류가 나타나는 것을 확인할 수 있었다.