• The Korean Journal of Mycology
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• v.40 no.2
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• pp.73-77
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• 2012

Nomenclatural code for fungi was dramatically modified in the 18th International Botanical Congress (IBC) held in Melbourne, Australia in July 2011. Its name was changed into International Code of Nomenclature for Algae, Fungi and Plants (ICN), which was formerly called as International Code of Botanical Nomenclature (ICBN) of the Vienna Code of 2005. The most important change for fungi is abandoning dual nomenclature and introducing one fungus/one name system (2013. 1). Since more than 10,000 species of fungal names should be renamed based on this new classification system (one fungus/one name system), it is challenging to both mycologists and taxonomic users such as plant pathologists and food scientists. Here, we introduced background, progress and future plan for its transition into one fungus/one name system. The new code is allowing electronic-only publication of names of new taxa (2102. 1) and the requirement for a Latin validating diagnosis was changed to allow either English or Latin for the publication of a new name (2011. 1). Furthermore, pre-publication deposit of key nomenclatural information in a recognized repository is mandatory in ICN (2013. 1). The aims of this manuscript are to introduce new code of fungal nomenclature and recent trends in one fungus/one name system to Korean mycological society.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• Journal of the Korea Institute of Building Construction
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• v.10 no.3
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• pp.99-111
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• 2010

In recent years, the number of high-rise building construction projects has grown, and the number of construction safety accidents has also been increasing. Therefore, the objective of this study is to propose plans to prevent accidents by systematically organizing accident principles and developing a tree diagram for the process of safety accidents that occur in the construction industry. This study aims to show the diverse characteristics of construction accidents based on KOSHA's annual reports on safety accidents(burial, conflagration, explosion, burn) from 1993 to 2009. To achieve these objectives, in this study we first examined the risk factors for burial, conflagration, explosion, and burn. We then systematically organized the classification viewpoint of accident causes, and suggested a methodology for the rationalization of safety management through an analysis of the primary causes of accidents by work type. The results of this study based on this methodology can be divided into three areas: 1)the types of facilities were divided into 43 categories by analyzing the information of KOSHA's annual reports; 2)the causes of burial, conflagration, explosion, and burn were divided into 63types; 3)the types of work were divided into 29 categories.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1724-1733
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• 2014

This paper presents a pattern recognition analysis of two spirals problem and optimization of Cascade Correlation learning algorithm using in combination with a non-monotone function as CosExp(cosine-modulated symmetric exponential function) and a monotone function as sigmoid function. In addition, the algorithm's optimization is attempted. By using genetic algorithms the optimization of the algorithm will attempt. In the first experiment, by using CosExp activation function for candidate neurons of the learning algorithm is analyzed the recognized pattern in input space of the two spirals problem. In the second experiment, CosExp function for output neurons is used. In the third experiment, the sigmoid activation functions with various parameters for candidate neurons in 8 pools and CosExp function for output neurons are used. In the fourth experiment, the parameters are composed of 8 pools and displacement of the sigmoid function to determine the value of the three parameters is obtained using genetic algorithms. The parameter values applied to the sigmoid activation functions for candidate neurons are used. To evaluate the performance of these algorithms, each step of the training input pattern classification shows the shape of the two spirals. In the optimizing process, the number of hidden neurons was reduced from 28 to15, and finally the learning algorithm with 12 hidden neurons was optimized.

• Journal of KIISE
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• v.43 no.10
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• pp.1085-1093
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• 2016

Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of the Korean association of regional geographers
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• v.13 no.3
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• pp.301-320
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• 2007

In Korean academic societies, old maps has not yet been properly investigated in terms of their genealogy, classification, detailed place names, historical backgrounds and the other aspects. With publication of the bibliographies and papers on old maps reserved in museum and library, the scope of research enlarged gradually its scope from 1970s. In 1980s, with the development of theoretical geography, scientific analysis were applied to investigate the projection method of Daedongyeo-jido. The 1990s proved a prominent decade for researches. The photo-copies of old maps enabled researchers to investigate the in-depth comparative study. The more important thing is that old maps became to be powerful instrument in the research of historical geography, such as territorial disputes and marine name(東海). And county old maps compiled by region became to be regional-cultural contents of local areas. Important issues in old map research in Korean academic societies are about Cheonha-do which is unique old world map in Korea, grid-system projection in old county maps and the genealogy of Daedongyeo-jido(manuscript and block print edition). This study shows that bibliography of all old maps preserved in each library and museum should be standardized. This could enable the exchange of information of old maps between institutes. The more important thing is that conciliation of human, social and natural sciences should be applied in the research of old maps.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.41 no.3
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• pp.266-273
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• 1996

To obtain basic information on forage sorghum F$_1$ hybrid breeding a total of 16 lines were selected from 311 introduced sorghum germplasm accessions, assessed and classified by the taxonomic distance and principal component analysis. The lines of which plant height and morphological characters were diverse and the 50% flowering date was similar to each other, were selected for parental lines in sorghum $\times$ sweet sorghum and sorghum $\times$ sudangrass crossing groups. Three varietal groups were classified by the average linkage cluster analysis based on the D$^2$ computed in eleven characters. Group I, II and III included 6 lines of sudangrass, 4 lines of sweet sorghum and 6 lines of grain sorghum, respectively. In the result of principal component analysis for eleven characters, about 82% of total variation could be appreciated by the first four principal components, the first principal component was highly loaded with head compactness and shape, l00-seed weight, plant color and grain covering, the second principal component with flowering date, plant height and awnness.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.6
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• pp.348-355
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• 2017

Human activities have resulted in soil pollution problems to us. Human and ecological risk assessment have been suggested as an efficient environmental management strategy for protecting human and ecosystems from soil pollution. However, Korean environmental policy is currently focused on human protection, and fundamental researches for ecology protection are required for institutional frameworks. In this study, we developed a schematic frame of Korean soil ecological risk assessment, and suggested the basic information for its application. This study suggested a soil ecological risk assessment scheme consisting of 4 steps for derivation of Predicted-No-Effect-Concentration (PNEC): 1) ecotoxicity data collection and reliability determination, 2) data standardization, 3) evaluation of data completeness for PNEC calculation, and 4) determination of ecological-risk. The reliability determination of ecotoxicity data was performed using Reliability Index (RI), and the classification of domestic species, acute/chronic, toxicity endpoint, and soil properties was used for data cataloging. The PNEC calculation methodology was determined as low-reliability, middle-reliability, and high-reliability according to their quantitative and qualitative levels of ecotoxicity data. This study would be the introductory plan research for establishment of Korean soil ecological risk assessment, and it can be a fundamental framework to further develop guidelines of Korean environmental regulation.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.27 no.3
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• pp.187-192
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• 2017

Objectives: It is difficult to identify exposure factors in the semiconductor industry due to low exposure levels to hazardous substances and because various processes take place in fabrication (FAB). Furthermore, a single worker often experiences a variety of job histories, so it is difficult to classify similar exposure groups (SEG) in the semiconductor industry. Therefore, we intend to develop a new exposure index, the period of working in FAB, that is applicable to the semiconductor industry. Methods: First, in specifying the classification of jobs, we clearly distinguished whether they were FAB workers or non-FAB workers. We checked FAB working hours per week through questionnaires administered to FAB workers. We derived an exposure index called FAB-Year that can represent the period of working in FAB. FAB-Year is an index that can quantitatively indicate the period of working in FAB, and one FAB-Year is defined as working in FAB for 40 hours per week for one year. Results: A total of 8,453 persons were surveyed, and male engineers and female operators occupied 90% of the total. The average total years of service of the subjects was 9.7 years, and the average FAB-Year value was 6.8. This means that the FAB-working ratio occupies 70% of total years of service. The average FAB-Year value for female operators was 8.4, for male facility engineers it was 7.7, and for male process engineers it was 3.5. A FAB-Year standardization value according to personal information (gender, job group, entry year, retirement year) for the survey subjects can be calculated, and standardized estimation values can be applied to workers who are not participating in the survey, such as retirees and workers on a leave of absence (LOA). Conclusions: This study suggests an alternative method for overcoming the limitations on epidemiological study of the semiconductor industry where it is difficult to classify exposure groups by developing a new exposure index called FAB-Year. Since FAB-Year is a quantitative index, we expect that various approaches will be possible in future epidemiological studies.