• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.51-63
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• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.3
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• pp.53-72
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• 2013

For the purpose of improving the Korean Decimal Classification (KDC) in the design field, this study investigated the classification systems of design research suggested in previous studies and compared KDC, DDC, LCC, and NDC. The problems identified from the current KDC include lack of subdivisions regarding basic design theories and major design application fields and the absence of notes for explaining the scope of each design field. To solve these problems, this study suggested improvements for design theories, graphic design, industrial design, and environmental design.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.69-74
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• 2013

In this paper, we propose the pattern classification algorithm of recognizing wrist movements based on electromyogram(EMG) to raise the recognition rate. We consider 30 characteristics of EMG signals wirh the root mean square(RMS) and the difference absolute standard deviation value(DASDV) for the extraction of precise features from EMG signals. To get the groups of each wrist movement, we estimated 2-dimension features. On this basis, we divide each group into two parts with mean to compare and promote the recognition rate of pattern classification effectively. For the motion classification based on EMG, the k-nearest neighbor(k-NN) is used. In this paper, the recognition rate is 92.59% and 0.84% higher than the study before.

• Journal of Platform Technology
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• v.6 no.4
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• pp.26-33
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• 2018

In this paper, we propose an image retrieval algorithm for real-time processing and design it as hardware. The proposed method is based on the classification of BoWs(Bag of Words) algorithm and proposes an image search algorithm using bit stream. K-fold cross validation is used for the verification of the algorithm. Data is classified into seven classes, each class has seven images and a total of 49 images are tested. The test has two kinds of accuracy measurement and speed measurement. The accuracy of the image classification was 86.2% for the BoWs algorithm and 83.7% the proposed hardware-accelerated software implementation algorithm, and the BoWs algorithm was 2.5% higher. The image retrieval processing speed of BoWs is 7.89s and our algorithm is 1.55s. Our algorithm is 5.09 times faster than BoWs algorithm. The algorithm is largely divided into software and hardware parts. In the software structure, C-language is used. The Scale Invariant Feature Transform algorithm is used to extract feature points that are invariant to size and rotation from the image. Bit streams are generated from the extracted feature point. In the hardware architecture, the proposed image retrieval algorithm is written in Verilog HDL and designed and verified by FPGA and Design Compiler. The generated bit streams are stored, the clustering step is performed, and a searcher image databases or an input image databases are generated and matched. Using the proposed algorithm, we can improve convenience and satisfaction of the user in terms of speed if we search using database matching method which represents each object.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.35-43
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• 2024

This study aimed to objectively classify the lateral torso posture types and functions of older women. We used 3D body scan data of 119 women aged 70-85 years from the 6th SizeKorea project. First, we defined three torso axes to represent the lateral torso posture types: posterior waist-back, back-cervical, and whole torso axes. Next, we asked experts to select one of four lateral torso posture types-stooped, straight, leaning back, and swayback postures-by looking at the lateral photographic data of 119 older women. To identify the axis that best represented each lateral torso posture type, a discriminant analysis was conducted using the angle of each of the three torso axes as an independent variable and an expert's visual classification as a dependent variable. Based on the analysis, the whole torso and backcervical axis angles were selected as variables for judging lateral torso posture types. Subsequently, we developed a classification function to determine which of the four lateral torso posture types of a particular participant was applicable for a new individual. The method developed in this study is significant in that it enables the objective classification of the lateral torso postures types of older women.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.1
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• pp.57-71
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• 2007

While a concept on biotop or the urgency of its classification systems have been under discussion recently, this study aims to examine outdoor biotop classification systems for environment-friendly community. To this end, the feasibility of creating a biotop in the community and application elements were generated and biotops were classified and categorized. Then, elements that can be applied in consideration of traditional Korean techniques were generated and biotop classification systems and specific components in residential areas were reviewed. As for the result of this study, based on a preliminary draft prepared through literature review, considerations for biotop classification systems were taken into account. Then, based on classification criteria for biotop formats, biotop functions and biotop types, a second-tier classification system was developed. Criteria for biotop formats included surfaces, lines and points while criteria for biotop functions were large cores, small bases, corridors, stepping stones and ecological islands. Criteria for habitat types were divided to include natural forest, developed green areas, lacustrine wetland, palustrine wetland, shrubs, grasslands, linear habitats, vacant plots and practical green areas, which were sub-categorized. As for the biotop classification system, macro-classification divided biotops into three types-space, line and point-based on biotop formats. Meso-classification had five groups and micro-classification had 21 groups based on habitat types. Future studies should focus on the ecological features of each biotop categories generated in this study and their creation and management techniques to find many practical methods to create, protect and manage outdoor biotop for environment-friendly community.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.215-220
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• 2010

In this paper, we propose a new texture-based page segmentation and classification method in which table region, background region, image region and text region in a given document image are automatically identified. The proposed method for document images consists of two stages, document segmentation and contents classification. In the first stage, we segment the document image, and then, we classify contents of document in the second stage. The proposed classification method is based on a texture analysis. Each contents in the document are considered as regions with different textures. Thus the problem of classification contents of document can be posed as a texture segmentation and analysis problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. Our method does not assume any a priori knowledge about content or language of the document. As we can see experiment results, our method gives good performance in document segmentation and contents classification. The proposed system is expected to apply such as multimedia data searching, real-time image processing.

• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.37-62
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• 2018

This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

• Health Policy and Management
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• v.20 no.2
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• pp.40-52
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• 2010

Health insurance fees are set by relative value scales and conversion factors. Since 2008 the conversion factor has been classified into 7 according to the provider type, and a separate contract has been made respectively. As such classification of the conversion factor reflects only the different characteristics of providers, however, further classification to reflect the different cost structures of providers is proposed. Cost varies according to the type of not only providers but also services each provider supply. In fact different cost structures of providers are the result of their different services. This study analyzed the cost structure of medical services to propose a new approach to the classification of the conversion factor. This study analyzed the cost structure of medical services using cost data constructed in the revision study of relative value scales. The cost data consist of doctor's fee, support staff's fee, cost of medical equipments, cost of medical supplies and indirect cost. The proportion of each cost component to the total cost was analyzed in terms of service department and service type. 72 service groups are defined in terms of the combination of service department and service type. Through cluster analysis, 72 service groups were reduced into 7 clusters each of which has a similar cost structure. Conversion factor is contracted annually to reflect the change in the cost of providing medical services. So the classification of conversion factor has to be based on the cost structures of medical services, not the characteristics of providers. Service clusters derived in this study can be used as a new classification for health insurance fee contract.