• Smart Structures and Systems
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• v.24 no.6
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• pp.803-812
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• 2019

Triplex composite is an epoxy-bonded joint structure, which constitutes the secondary barrier in a liquefied natural gas (LNG) carrier. Defects in the triplex composite weaken its shear strength and may cause leakage of the LNG, thus compromising the structural integrity of the LNG carrier. This paper proposes an autonomous triplex composite inspection (ATCI) system for visualizing and classifying hidden defects in the triplex composite installed inside an LNG carrier. First, heat energy is generated on the surface of the triplex composite using halogen lamps, and the corresponding heat response is measured by an infrared (IR) camera. Next, the region of interest (ROI) is traced and noise components are removed to minimize false indications of defects. After a defect is identified, it is classified as internal void or uncured adhesive and its size and shape are quantified and visualized, respectively. The proposed ATCI system allows the fully automated and contactless detection, classification, and quantification of hidden defects inside the triplex composite. The effectiveness of the proposed ATCI system is validated using the data obtained from actual triplex composite installed in an LNG carrier membrane system.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.85-94
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• 2019

Warhead and debris show the different micro-Doppler frequency shape in the spectrogram because of the different micro motion. So we can classify them using the micro-Doppler features. In this paper, we classified warhead and debris in the separation phase using CNN(Convolutional Neural Networks). For the input image of CNN, we used micro-Doppler spectrogram. In addition, to improve classification performance of warhead and debris, we applied the preprocessing using CA-CFAR to the micro-Doppler spectrogram. As a result, when the preprocessing of micro-Doppler spectrogram was used, classification performance is improved in all signal-to-noise ratio(SNR).

• Journal of Biomedical Engineering Research
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• v.41 no.2
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• pp.101-106
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• 2020

Although benign gastric ulcers do not develop into gastric cancer, they are similar to early gastric cancer and difficult to distinguish. This may lead to misconsider early gastric cancer as gastric ulcer while diagnosing. Since gastric cancer does not have any special symptoms until discovered, it is important to detect gastric ulcers by early gastroscopy to prevent the gastric cancer. Therefore, we developed a Convolution Neural Network (CNN) model that can be helpful for endoscopy. 3,015 images of gastroscopy of patients undergoing endoscopy at Gachon University Gil Hospital were used in this study. Using ResNet-50, three models were developed to classify normal and gastric ulcers, normal and gastric cancer, and gastric ulcer and gastric cancer. We applied the data augmentation technique to increase the number of training data and examined the effect on accuracy by varying the multiples. The accuracy of each model with the highest performance are as follows. The accuracy of normal and gastric ulcer classification model was 95.11% when the data were increased 15 times, the accuracy of normal and gastric cancer classification model was 98.28% when 15 times increased likewise, and 5 times increased data in gastric ulcer and gastric cancer classification model yielded 87.89%. We will collect additional specific shape of gastric ulcer and cancer data and will apply various image processing techniques for visual enhancement. Models that classify normal and lesion, which showed relatively high accuracy, will be re-learned through optimal parameter search.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.878-881
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• 2010

For better diagnosis and prescription in Korean traditional medicine, Lee Je-Ma (1837-1900) created Sasang Constitution classification which was divided into four groups of Taeyangin, Soyangin, Taeumin and Soumin based on both body shape and natural disposition. The purpose of this study was to investigate the characteristics of facial skin parameters (hydration, lipid and pH) on forehead and cheek according to Sasang Constitution classifications of Taeumin, Soyangin and Soumin in Korean. Eighty-nine Korean female subjects were recruited for this study and the average age of them was 19.9${\pm}$0.84 years. The four groups by the Sasang Constitution were classified by questionnaire for the Sasang Constitution classification proposed by Kyung-Hee Oriental Medicine Hospital. Consequently, thirty-eight (42.7%) among the subjects were grouped into Soumin, twenty-nine (32.6%) into Taeumin, twenty (22.5%) into Soyangin and two (2%) into Taeyangin. Taeyangin group was excluded from statistical analysis due to small subjects. Hydration, lipid and pH parameters on forehead and cheek were measured by using non-invasive instruments of Corneometer (CM 825, Schwarzhaup, Germany), Sebumeter (SM 815, Schwarzhaup, Germany) and Skin-pH-meter (pH 905, Schwarzhaup, Germany), respectively. The measurements by the same investigator were performed under standardized condition with a room temperature of $21^{\circ}C$ and a humidity level of 40% to 50%. As a result, hydration (F=25.481, p=.000), lipid (F=5.753, p=.005) and pH (F=5.010, p=.009) of the forehead skin showed significant differences in the order of Taeumin, Soyangin and Soumin. Hydration (F=23.216, p=.000), lipid (F=6.898 p=.002) and pH (F=5.070, p=.008) of the cheek skin showed significant differences in the order of Taeumin, Soyangin and Soumin. In conclusion, facial skin surface seemed to be dependent on Sasang Constitution classification in Korean. These findings indicated that Sasang Constitution classification might be an useful esthetic treatment for caring facial skin in the future.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.703-712
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• 2003

With the explosive growth n the numbers and sizes of imaging technologies, Content-Based Image Retrieval (CBIR) has been attacked the interests of researchers in the fields of digital libraries, image processing, and database systems. In general, in the case of query-by-image, in user has to select an image from database to query, even though it is not his completely desired one. However, since query-by-sketch approach draws a query shape according to the user´s desire it can provide more high-level searching interface to the user compared to the query-b-image. As a result, query-by-sketch has been widely used. In this paper, we propose a Java-based image retrieval system that consists of sketch query and image classification. We use two features such as color histogram and Haar wavelets coefficients to search similar images. Then the Leave-One-Out method is used to classify database images. The categories of classification are photo & painting, city & nature, and sub-classification of nature image. By using the sketch query and image classification, w can offer convenient image retrieval interface to user and we can also reduce the searching time.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.77-85
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• 2006

In this paper, we propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet transformed images. We group the image classes into clusters which have similar texture features using Principal Component Analysis(PCA) and K-means. The hierarchical classifier has five layes which combine the clusters. The hierarchical classifier consists of 59 neural network classifiers learned with the back propagation algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 1000 training data and 1000 test data composed of 10 images from each of 100 classes shows classification rates of 81.5% and 75.1% correct, respectively.

• Journal of Biomedical Engineering Research
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• v.41 no.1
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.

• Journal of Sasang Constitutional Medicine
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• v.5 no.1
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• pp.87-104
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• 1993

This study was performed to analize the possibility of validation about Questionaire of Sasang constitution Classification which has been made through several stages for the purpose of promoting the objectivity of Sasang constitutional classification. The results of statistical research about the responses data of Sasang Questionaire are as follow: 1. In the case of mutual relation between the items of the QSCC(Questionaire of Sasang Constitution Classification) and 16PF, every item displaced singnificant result.(in male group) 2. In the investigation to classify the score distribution of Sasang items three groups and analize the gap of mean value in each of 16PF, the characters of each Sasang group were turn out as follows: (1) The Taee-Yang group has extrovert and narcissistic inclination. (2) The So-Yang group shows remarkable extrovert inclination. (3) The Tae-Eum group has hidden fear and introvert inclination. (4) The So-Eum group has revealed physical unstability and introvert inclination. 3. The statistical research of female group didn't display any significant result. It will be necessary to analize the respondent shape of female group and introduce new items to coincident with faminine psychology. 4. In the research using 16PF accompany with QSCC in order to classify the Sasang constitution, the accuracy rate of diagnosis showed inspiring elevation. For that reason, it seems to be desirable to introduce some items of 16PF into QSCC. According to above results, although QSCC contains several problems to be solved, its validity was proved, and the analysis of statistics suggests the possibility to step forward by introducing 16PF in some problems of promoting the accuracy of Sasang Constitutional diagnosis, assuring the objectibity of Sasang constitution classification and so on.

• The KIPS Transactions:PartC
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• v.10C no.4
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• pp.423-432
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• 2003

It is essential suity management and standardized asset analysis for telecommunication networks, however existing risk analysis methods and tools are not enough to give shape of the method to evaluate value and asset. they only support asset classification schemes. Moreover, since the existing asset classification schemes are to evaluate comprehensive general risk, they are not appropriate for being applied telecommunication networks and they can´t offer any solutions to an evaluator´s subjectivity problem. In this paper, to solve these problems, we introduce the standardized definition of asset evaluation model new asset classification scheme, two-dimensional asset process classification scheme to consider business process and asset, various evaluation standards for quantitative value and qualitative evaluation. To settle an valuator´s subjectivity problem, we proposed $\beta$-distribution Delphi method.

• Journal of Platform Technology
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• v.9 no.3
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• pp.63-69
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• 2021

Limited materials to make coins for countries and designs suitable for hand-carry make the shape, size, and color of coins similar. This similarity makes that it is difficult for visitors to identify each country's coins. To solve this problem, we propose the coin classification method using CNN effective to image processing. In our coin identification method, we collect the training data by using web crawling and use OpenCV for preprocessing. After preprocessing, we extract features from an image by using three CNN layers and classify coins by using two fully connected network layers. To show that our model designed in this paper is effective for coin classification, we evaluate our model using eight different coin types. From our experimental results, the accuracy for coin classification is about 99.5%.