• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.199-209
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• 2006

The Global Land Cover 2000 (GLC 200) project, as a most recent issue, is to provide for the year 2000 a harmonized land cover database over the whole globe. The classifications were performed according to continental or regional scales by corresponding organization using the data of VEGETATION sensor onboard the SPOT4 Satellite. Even if the global land cover classification for Asia provided by Chiba University showed a good accuracy in whole Asian area, some problems were detected in Korean region. Therefore, the construction of new land cover database over Korea is strongly required using more recent data set. The present study focuses on the development of a new upgraded land cover map at 1 km resolution over Korea considering the widely used K-means clustering, which is one of unsupervised classification technique using distance function for land surface pattern classification, and the principal components transformation. It is based on data sets from the Earth observing system SPOT4/VEGETATION. Newly classified land cover was compared with GLC 2000 for Korean peninsula to access how well classification performed using confusion matrix.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1777-1783
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• 2021

The use of drones is gradually increasing for the efficient maintenance of large-scale renewable energy power generation complexes. For a long time, photovoltaic panels have been photographed with drones to manage panel loss and contamination. Various approaches using artificial intelligence are being tried for efficient maintenance of large-scale photovoltaic complexes. Recently, semantic segmentation-based application techniques have been developed to solve the image classification problem. In this paper, we propose a classification model using semantic segmentation to determine the presence or absence of failures such as arcs, disconnections, and cracks in solar panel images obtained using a drone equipped with a thermal imaging camera. In addition, an efficient classification model was implemented by tuning several factors such as data size and type and loss function customization in U-Net, which shows robust classification performance even with a small dataset.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.693-701
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• 2017

In this paper, we propose a new method for music genre classification using spikegram and deep neural network. The human auditory system encodes the input sound in the time and frequency domain in order to maximize the amount of sound information delivered to the brain using minimum energy and resource. Spikegram is a method of analyzing waveform based on the encoding function of auditory system. In the proposed method, we analyze the signal using spikegram and extract a feature vector composed of key information for the genre classification, which is to be used as the input to the neural network. We measure the performance of music genre classification using the GTZAN dataset consisting of 10 music genres, and confirm that the proposed method provides good performance using a low-dimensional feature vector, compared to the current state-of-the-art methods.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.22-28
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• 2008

This paper aims to offer a solution based on semantic document classification to improve e-Government utilization and efficiency for people using their own information retrieval system and linguistic expression. Generally, semantic document classification method is an approach that classifies documents based on the diverse relationships between keywords in a document without fully describing hierarchial concepts between keywords. Our approach considers the deep meanings within the context of the document and radically enhances the information retrieval performance. Concept Weight Document Classification(CoWDC) method, which goes beyond using existing keyword and simple thesaurus/ontology methods by fully considering the concept hierarchy of various concepts is proposed, experimented, and evaluated. With the recognition that in order to verify the superiority of the semantic retrieval technology through test results of the CoWDC and efficiently integrate it into the e-Government, creation of a thesaurus, management of the operating system, expansion of the knowledge base and improvements in search service and accuracy at the national level were needed.

• Health Policy and Management
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• v.15 no.4
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• pp.46-64
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• 2005

This study was conducted to verify the current criteria and classification system used to determine specialized general hospitals status. In this study, we proposed a new classification system which Is simpler and more convenient than the current one. In the new classification system clinical procedure was chosen as the unit of analysis in order to reflect all the resource consumption and the complexities and degree of medical technologies in determining specialized general hospitals. We developed a statistical model and applied this model to 117 general hospitals which claim their national insurance through electronic data interchange(EDI). Analysis based on 984 clinical procedures and medical facilities' characteristic variable discriminated specialized general hospital in present without misclassification. It means that we can determine specialized general hospital's permission In new way without using the current complicated criteria. This study discriminated specialized general hospital by the new proposed model based on clinical procedures provided by each hospital. For clustering the same types of medical facilities using 984 clinical procedures, we executed multidimensional scale analysis and divided 117 hospitals into 4 groups by two axises : a variety of procedure and the Proportion of high technology Procedure. Therefore, we divided 117 hospitals into 4 groups and one of them was considered as specialized general hospital. In discriminating analysis, we abstracted proportion of 16 clinical procedures which effect on discriminating the specialized general hospital in statistical system also we identify discriminating function which include these variables. As a result, we identify 2 discriminating functions, one is for current discriminating system and the other two is for new discriminating system of specialized general hospital.

• Journal of the Korean Society of Physical Medicine
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• v.14 no.4
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• pp.45-53
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• 2019

PURPOSE: This study examined the differences in the trunk impairment scores according to the levels of the gross motor classification system by evaluating trunk control in children with spastic cerebral palsy using the index of trunk impairment. In addition, the characteristics of trunk control disabilities were investigated according to the cerebral palsy type. METHODS: The subjects were 49 children (mean age 8.57±1.83 years, 11 with hemiplegia, 26 with diplegia, and 12 with quadriplegia) with spastic cerebral palsy levels I to IV under the gross motor function classification system (GMFCS). The coordination and balance of the children with cerebral palsy were evaluated using the index for trunk impairment. Statistical analyses were performed using a Kruskal-Wallis test, and Bonferroni analyses were used as a post-hoc comparison for any significant results. RESULTS: The median of the total scores of trunk impairment was 13 (range, 9-17), which was 56% of the maximum score. The total score of trunk impairment and subscales differed significantly according to the disease severity and type of motor disability. The scores for children with quadriplegia were the lowest compared to children with hemiplegia and diplegia. CONCLUSION: Trunk control function in children with spastic cerebral palsy was reduced, and varied according to the disease severity and types of motor disabilities. The degree of trunk impairment differed from the trunk control ability according to the degree of motor disability of children with cerebral palsy.

• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• Journal of Korean Academy of Nursing
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• v.34 no.2
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• pp.278-289
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• 2004

Purpose: The purpose of this study was to classify the elderly in long-term care facilities using the Resource Utilization Group(RUG-III) and to examine the feasibility of a payment method based on the RUG-III classification system in Korea. Method: This study measured resident characteristics using a Resident Assessment Instrument-Minimum Data Set(RAI-MDS) and staff time. Data was collected from 530 elderly residents over sixty, residing in long-term care facilities. Resource use for individual patients was measured by a wage-weighted sum of staff time and the total time spent with the patient by nurses, aides, and physiotherapists. Result: The subjects were classified into 4 groups out of 7 major groups. The group of Clinically Complex was the largest (46.3%), and then Reduced Physical Function(27.2%), Behavior Problems (17.0%), and Impaired Cognition (9.4%) followed. Homogeneity of the RUG-III groups was examined by total coefficient of variation of resource use. The results showed homogeneity of resource use within RUG-III groups. Also, the difference in resource use among RUG major groups was statistically significant (p<0.001), and it also showed a hierarchy pattern as resource use increases in the same RUG group with an increase of severity levels(ADL). Conclusion: The results of this study showed that the RUG-Ill classification system differentiates resources provided to elderly in long-term care facilities in Korea.