• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.619-629
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• 2023

Recent intensification of climate change has led to an increase in damages caused by droughts. Currently, in Korea, the Standardized Precipitation Index (SPI) is used as a criterion to classify the intensity of droughts. Based on the accumulated precipitation over the past six months (SPI-6), meteorological drought intensities are classified into four categories: concern, caution, alert, and severe. However, there is a limitation in classifying drought intensity solely based on precipitation. To overcome the limitations of the meteorological drought warning criteria based on SPI, this study collected emergency water supply damage data from the National Drought Information Portal (NDIP) to classify drought intensity. Factors of SPI, such as precipitation, and factors used to calculate evapotranspiration, such as temperature and humidity, were indexed using min-max normalization. Coefficients for each factor were determined based on the Genetic Algorithm (GA). The drought intensity based on emergency water supply was used as the dependent variable, and the coefficients of each meteorological factor determined by GA were used as coefficients to derive a new Drought Severity Classification Index (DSCI). After deriving the DSCI, cumulative distribution functions were used to present intensity stage classification boundaries. It is anticipated that using the proposed DSCI in this study will allow for more accurate drought intensity classification than the traditional SPI, supporting decision-making for disaster management personnel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.861-866
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• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

• The Korean Journal of Pain
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• v.31 no.3
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• pp.147-154
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• 2018

Fibromyalgia (FM) is a contested illness with ill-defined boundaries. There is no clearly defined cut-point that separates FM from non-FM. Diagnosis of FM has been faced with several challenges that occur, including patients' health care-seeking behavior, symptoms recognition, and FM labeling by physicians. This review focuses on important but less visible factors that have a profound influence on under- or over-diagnosis of FM. FM shows different phenotypes and disease expression in patients and even in one patient over time. Psychosocial and cultural factors seem to be a contemporary ferment in FM which play a major role in physician diagnosis even more than having severe symptom levels in FM patients. Although the FM criteria are the only current methods which can be used for classification of FM patients in surveys, research, and clinical settings, there are several key pieces missing in the fibromyalgia diagnostic puzzle, such as invalidation, psychosocial factors, and heterogeneous disease expression. Regarding the complex nature of FM, as well as the arbitrary and illusory constructs of the existing FM criteria, FM diagnosis frequently fails to provide a clinical diagnosis fit to reality. A physicians' judgment, obtained in real communicative environments with patients, beyond the existing constructional scores, seems the only reliable way for more valid diagnoses. It plays a pivotal role in the meaning and conceptualization of symptoms and psychosocial factors, making diagnoses and labeling of FM. It is better to see FM as a whole, not as a medical specialty or constructional scores.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.531-540
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• 2010

In this paper, we present a two-phase shallow semantic parsing method based on a maximum entropy model. The first phase is to recognize semantic arguments, i.e., argument identification. The second phase is to assign appropriate semantic roles to the recognized arguments, i.e., argument classification. Here, the performance of the first phase is crucial for the success of the entire system, because the second phase is performed on the regions recognized at the identification stage. In order to improve performances of the argument identification, we incorporate syntactic knowledge into its pre-processing step. More precisely, boundaries of the immediate clause and the upper clauses of a predicate obtained from clause identification are utilized for reducing the search space. Further, the distance on parse trees from the parent node of a predicate to the parent node of a parse constituent is exploited. Experimental results show that incorporation of syntactic knowledge and the separation of argument identification from the entire procedure enhance performances of the shallow semantic parsing system.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.358-371
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• 2003

Automatic word spacing is a process of deciding correct boundaries between words in a sentence including spacing errors. It is very important to increase the readability and to communicate the accurate meaning of text to the reader. The previous statistical approaches for automatic word spacing do not consider the previous spacing state, and thus can not help estimating inaccurate probabilities. In this paper, we propose two statistical word spacing models which can solve the problem of the previous statistical approaches. The proposed models are based on the observation that the automatic word spacing is regarded as a classification problem such as the POS tagging. The models can consider broader context and estimate more accurate probabilities by generalizing hidden Markov models. We have experimented the proposed models under a wide range of experimental conditions in order to compare them with the current state of the art, and also provided detailed error analysis of our models. The experimental results show that the proposed models have a syllable-unit accuracy of 98.33% and Eojeol-unit precision of 93.06% by the evaluation method considering compound nouns.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.367-376
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• 2009

In this paper, we propose a flame detection method using Haar wavelet and moving averages in outdoor infrared video sequences. Our proposed method is composed of three steps which are Haar wavelet decomposition, flame candidates detection, and their tracking and flame classification. In Haar wavelet decomposition, each frame is decomposed into 4 sub- images(LL, LH, HL, HH), and also computed high frequency energy components using LH, HL, and HH. In flame candidates detection, we compute a binary image by thresholding in LL sub-image and apply morphology operations to the binary image to remove noises. After finding initial boundaries, final candidate regions are extracted using expanding initial boundary regions to their neighborhoods. In tracking and flame classification, features of region size and high frequency energy are calculated from candidate regions and tracked using queues, and we classify whether the tracked regions are flames by temporal changes of moving averages.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.3 no.3
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• pp.20-28
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• 2000

The objective of this study is to establish a methodology of bioregional approach for coastal area management as a basis for planning and design. Focusing on the bioregional approach, this study reviewed currently prevailing approaches such as watershed approach and ecological unit approach for planning and management purposes. This research placed its geographical focus on the landward watershed of the Bay of Hampyong located in Chonnam Province, dealing efficiently with shortcomings of existing researches which mainly covered seaward tidal flats without considering outside effects. The main methods of the study are classified into indoor computerized map analysis and field work. For computer analysis, printed maps and digital maps have been analysed, and GIS techniques have been utilized for its synthesis and finalizations. Field work included on-site landscape analysis and verification of a tentative place unit boundary. As a practical step, criteria for classifying bioregion were presented and the selected criteria included : topography & water ways ; roads & administrative boundaries ; habitat types ; and visual enclosure. First, based on the data of topography and water ways, broad classification work was performed and corrections were made based on data drawn out from other criteria. A tentative place unit map was drawn and revised through field visits. This study encompassed an initial but integral part for bioregional approach in landward watershed management of a coastal area. As results of the study, the necessity and efficiency of bioregional approach which considers environmental and cultural components systematically have been presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.679-685
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• 2003

The cores and deltas of fingerprints designate the parts where the flow of the ridges change radically. Observations on the change of the orientation of the ridges around the cores and deltas enable us to guess the location of the cores and deltas. According]y clustering the orientation flowing to the same direction after doing research on the orientation of the ridges on the whole makes us see that the cores and deltas are shaping around the boundaries of the clustering area. It is also observed that The patterns of clustering of the orientation of the ridges classified as Arch, Tented Arch, Left loop, Right Loop and Whorl have its own characteristics respectively. In this paper the method of classifying the fingerprints effectively is proposed and proved its effectiveness by using the clustering of the orientation of the ridges, finding the cores of the fingerprints which don't secure the deltas.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.5 s.143
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• pp.479-486
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• 2005

The final goal of shift alignment design is that the bearing reaction forces or mean pressures are within design boundaries for various service conditions of a ship. However, it is found that calculated bearing load can be substantially variable according to the locations of the effective support points of after sterntube bearing which are determined by simple calculation or assumption suggested by classification societies. A new analysis method for shaft alignment calculation is introduced in order to resolve these problems. Key concept of the new method is featured by adopting both nonlinear elastic and multi-support elements to simulate a bearing support Hertz contact theory is basically applied for nonlinear elastic stiffness calculation instead of the projected area method suggested by most of classification societies. Three loading conditions according to the bearing offset and the hydrodynamic moment and twelve models according to the locations of the effective support points of sterntube bearings are prepared to carry out quantitative verifications for an actual shafting system of 8000 TEU class container vessel. It is found that there is relatively large difference between assumed and calculated effective support points.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.131-140
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• 2020

In training a classification model using machine learning, the acquisition of training data is a very important stage, because the amount and quality of the training data greatly influence the model performance. However, when the cost of obtaining data is so high that it is difficult to build ideal training data, the number of samples for each class may be acquired very differently, and a serious data-imbalance problem can occur. If such a problem occurs in the training data, all classes are not trained equally, and classes containing relatively few data will have significantly lower recall values. Additionally, the reliability of evaluation indices such as accuracy and precision will be reduced. Therefore, this study sought to overcome the problem of data imbalance in two stages. First, we introduced weighted accuracy and weighted precision as new evaluation indices that can take into account a data-imbalance ratio by modifying conventional measures of accuracy and precision. Next, oversampling was performed to balance weighted precision and recall among classes. We verified the algorithm by applying it to the problem of facies classification. As a result, the imbalance between majority and minority classes was greatly mitigated, and the boundaries between classes could be more clearly identified.