• Journal of Aquaculture
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• v.19 no.1
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• pp.14-18
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• 2006

The objective of this study was to evaluate food value and profitability of black porgy (Acanthopagrus schlegeli) cultured in freshwater. In fish flesh, muscular hardness of black porgy reared in freshwater $(9210{\pm}1215g/cm^2)$ was slightly lower than those of fish in seawater $(9987{\pm}6549g/cm^2)$, but there was no significant difference. Also, there was no difference between muscular strength of fish reared in freshwater and seawater. When the flesh qualities of black porgy reared in freshwater was compared with those of fish reared in seawater through the questionnaire, there were no significant differences between fish reared in freshwater and seawater in appearance, texture, taste and flavor. For 10 months of black porgy culture in fresh water, the gross profit in culturing from juvenile (5.5 g) to adult size (100g), and from adult to marketable size (400 g) were 24,000,000 won (30.0%) and 53,870,000 won (36.9%), respectively.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.91-98
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• 2018

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.45-52
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• 2021

In this paper, we propose a prediction model for osteopenia and osteoporosis based on a convolutional neural network(CNN) using computed tomography(CT) images. In a single CT image, CNN had a limitation in utilizing important local features for diagnosis. So we propose a compound model which has two identical structures. As an input, two different texture images are used, which are converted from a single normalized CT image. The two networks train different information by using dissimilarity loss function. As a result, our model trains various features in a single CT image which includes important local features, then we ensemble them to improve the accuracy of predicting osteopenia and osteoporosis. In experiment results, our method shows an accuracy of 77.11% and the feature visualize of this model is confirmed by using Grad-CAM.

• Journal of the Korean Society of Floral Art and Design
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• no.43
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• pp.3-17
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• 2020

Through color analysis and survey of seasonal display cases using flower materials in department stores, hotels, and retailers, which are representative commercial spaces in Korea and abroad, it is designed to recognize the need for color planning that applies seasonal colors and emotional adjectives, away from the traditional method of relying on the season, shape and texture of materials, in the process of flower displays. The research method analyzed the colors used in the 48 domestic and foreign commercial space flower display cases collected. Based on this, the first expert questionnaire collected adjectives extraction and seasonal coordinates reminiscent of the case and examined the suitability of emotional adjectives extracted by the second public survey. The research results extracted typical colors and tones of spring, summer, fall, and winter, and recognized seasonal emotional adjectives. Based on these results, We could see that the color scheme should be advanced in the flower display, which used to depend solely on the shape or texture of the flower material, to produce the intended emotional design.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.10a
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• pp.162.2-163
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• 2003

두부는 특유의 질감으로 우리 민족의 주요부식이 되어온 전통식품으로서 국제적으로 잘 알려져있는 고단백식품이다. 두부의 원료인 대두는 전체단백질의 80∼90%를 차지하는 glycinin과 albumin등의 단백질 성분과 비단백 질소 화합물이 함유되어 있는데 가격이 저렴하면서 영양과 기능성이 우수하여 식육, 낙농제품, 계란단백질이 disulfide 결합, 수소결합 및 소수결합에 의해 응집되어 gel이 된 후 염농도 증가에 의해 침전되거나 산에 의해 등전점 (pH 4.2∼4.6)에서 침전되는 성질을 이용한다. 또한 두부는 80% 이상의 높은 수분함량 때문에 쉽게 변질되는 소지가 많으며 두부의 저장에 많은 한계성을 보이는 식품이기도 한다. 따라서 건조에 의한 식품의 저장은 식품내의 수분을 감소시킴으로써 용질의 상대적 농도를 높혀 식품내의 수분 활성도를 저하시켜 미생물 및 효소에 의한 부패나 변패 및 변질을 방지할 수 있다. 이에 본 연구에서는 두부의 안전저장과 유통을 위하여 열풍건조, 진공건조 및 동결건조에 따라 분말두부를 제조하고 수분흡습특성과 기존 모델식과의 적합성 및 평형수분함량의 예측모델을 구하여 분말두부의 활용성을 높이며 다른 분말식품에도 적용할 수 있는 기초자료를 제공하고자 하였다.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.735-742
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• 2023

Non-alcoholic fatty liver disease is an independent risk factor for the development of cardiovascular disease, diabetes, hypertension, and kidney disease, and the clinical importance of non-alcoholic fatty liver disease has recently been increasing. In this study, we aim to extract feature values by applying GLCM, a texture analysis method, to ultrasound images of patients with non-alcoholic fatty liver disease. By applying an artificial neural network model using extracted feature values, we would like to classify the degree of fat deposition in non-alcoholic fatty liver into normal liver, mild fatty liver, moderate fatty liver, and severe fatty liver. As a result of applying the GLCM algorithm, the parameters Autocorrelation, Sum of squares, Sum average, and sum variance showed a tendency for the average value of the feature values to increase as it progressed from mild fatty liver to moderate fatty liver to severe fatty liver. The four parameters of Autocorrelation, Sum of squares, Sum average, and sum variance extracted by applying the GLCM algorithm to ultrasound images of non-alcoholic fatty liver disease were applied as inputs to the artificial neural network model. The classification accuracy was evaluated by applying the GLCM algorithm to the ultrasound images of non-alcoholic fatty liver disease and applying the extracted images to an artificial neural network, showing a high accuracy of 92.5%. Through these results, we would like to present the results of this study as basic data when conducting a texture analysis GLCM study on ultrasound images of patients with non-alcoholic fatty liver disease.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.39-44
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• 2010

In this paper, we addressed a face transform scheme with age-progressing based on vector representation. Proposed approach utilized a vector modeling as well as morphing so as to improve not only a reliability but also a consistency. For the more, some elements of texture change owing to the face shape are defined and some parameters with respect to the internal and external environments are also considered. To testify the proposed approach, estimation of similarity is performed with qualitative manner by using experimental output, and finally resulted in satisfactory for face shape transformation aged from sixty to fourteen.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.10a
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• pp.210.1-210
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• 2003

8품종벼를 시료로 하여 이들 배유전분의 아밀로오스 함량, 당함량 및 아밀로그램 특성이 쌀빵의 가공성과 어떠한 상관성이 있는가에 대해서 검토하였다. 청가에 의한 아밀로오스 함량의 품종간 차이는 남풍벼, 화청벼 > 분질미 ＞ 남풍 CB243 ＞ 화청 du-1, 남풍 EM9O > 화청찰벼 ＞ shr 의 순이었다. 단백질 함량은 고당미인 shr이 가장 높아 8.2%였으며, 벼 품종간 단백질 함량은 거의 유사하나, 남풍벼 및 화청벼 변이체의 경우에는 아밀로오스 함량이 낮을수록 원품종보다 단백질의 함량이 증가하는 경향을 보이고 있었다. 품종별 쌀가루의 호화개시 온도는 분질미 및 shr이 낮았으며, 화청벼, 남풍벼들과 그것들의 변이체 품종들의 경우에는 아밀로오스 함량이 높은 품종일수록 쌀가루 풀의 점성 및 경도는 증가하며, 제조된 쌀빵의 비용적이 크며, 관능검사에 의한 부푼 정도, 질감 및 전반적인 기호도가 좋은 것으로 나타났다. 모든 품종에서 백미빵이 현미빵보다 제빵성이 좋았으며, 남풍 벼로 제조한 백미빵의 기호도가 높게 나타났으며, 저장에 따른 노화지표가 가장 낮았다.

• Archives of design research
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• v.18 no.4 s.62
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• pp.277-286
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• 2005

The purpose of this study is to ascertain the design element in traditional palaces of Korea, China and Japan. It takes threesteps to proceed this study. Firstly, it needs to be established the analysis framework from the documents. In second step, the design elements - the form, the material, the pattern and the color - should be collected and investigated through the observation of the actual traditional palaces the Changduckung, the Forbidden City, the Nijo castle. The third step is the analysis of the results of the investigation of the design elements from step two. To sum up similarities and dissimilarities among the design element in traditional palaces of Korea, China and Japan is as the following It is to be noticed that the mainly common characteristics of the artistic design are 'naturalism', 'harmonious ideas' and 'confucianism'. But the representation style of the design element is differed from the country. : The typical features of China are symmetry, glassy surface by artificial process, the meandered curve, the magnificent pattern and the constrable color. In Japan, the mathematical asymmetry, made-up rough surface by artificial skill, decorativepattern with abbreviation and achromatic color are important feature of the design element. While the major features of Korean design element are asymmetrical balance with nature, rough surface by natural process, moderate pattern and harmonious color.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.9-15
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• 2013

In this study, texture feature analysis (TFA) algorithm to automatic recognition of liver disease suggests by utilizing computed tomography (CT), by applying the algorithm computer-aided diagnosis (CAD) of hepatocellular carcinoma (HCC) design. Proposed the performance of each algorithm was to comparison and evaluation. In the HCC image, set up region of analysis (ROA, window size was $40{\times}40$ pixels) and by calculating the figures for TFA algorithm of the six parameters (average gray level, average contrast, measure of smoothness, skewness, measure of uniformity, entropy) HCC recognition rate were calculated. As a result, TFA was found to be significant as a measure of HCC recognition rate. Measure of uniformity was the most recognition. Average contrast, measure of smoothness, and skewness were relatively high, and average gray level, entropy showed a relatively low recognition rate of the parameters. In this regard, showed high recognition algorithms (a maximum of 97.14%, a minimum of 82.86%) use the determining HCC imaging lesions and assist early diagnosis of clinic. If this use to therapy, the diagnostic efficiency of clinical early diagnosis better than before. Later, after add the effective and quantitative analysis, criteria research for generalized of disease recognition is needed to be considered.