• Applied Biological Chemistry
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• v.38 no.2
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• pp.141-146
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• 1995

Changes of physicochemical properties of citron juice prepared by two different extraction methods, rotary-crushing and belt-pressing method, were investigated during the storage at $5^{\circ}C$ and $-20^{\circ}C$. Temperature drop of citron juice extracted by belt-pressing method was faster than that of citron juice prepared by rotary-crushing method and its freezing point was $0.8{\sim}0.9^{\circ}C$. During the storage, pH of stored citron juice with rotary-crushing method was increased up to 3.5 after 6 months storage while that of citron juice extracted by belt-pressing method was not changed significantly during the same storage time. Acidity of rotary-crushed citron juice was reduced a little more than that of belt-pressed citron juice during the storage. However, changes of soluble solid content were influenced largely by the storage temperature than by the extraction method. Contents of formol nitrogen and vitamin C were reduced remarkably in all of stored citron juice and $92{\sim}82%$ of farmol nitrogen and $72{\sim}43%$ of vitamin C were remained after 6 months of storage. Among the changes of color value, L values were reduced in the whole stored citron juice and a and b value had a different change pattern respectively according to the extraction and storage temperature. Changes in the content of both amino acid and fatty acid compositions was also observed after same storage period. Especially, in the case of change of fatty acid composition, content of linoleic acid and linolenic acid were reduced after 6 months storage, while those of palmitic acid, stearic acid and oleic acid were increased.

• Proceedings of the Korean Sanitation Conference
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• 2005.12a
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• pp.76-97
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• 2005

As the recent change of multiformity and taste in clination in eating habit culture is yearly in creasing foods used oil and fats. Because the frying food is especially important snack , it's safty is very essential. In order to know the safty and harmfulness of frying oil and fats. The 20 kinds samples were purchased chicken fried food shops around the north of seoul and kyunggi. The acid value, iodine value, peroxide value, TBA value, fatty acid, carbonyl value, and smoke point of deep fat fried oils were analyzed. Results of analyzed, A company of deep fat frying oil showed stability state and C company and B company of deep fat frying oil is acidification to turned. But D company of deep fat frying oil showed quite a bit acidification progressived of used hydrogenated oil.

• Korean Journal of Food Science and Technology
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• v.33 no.5
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• pp.507-513
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• 2001

An effective extraction methods of a garlic were investigated in order to improve the functional properties of the extracts. The solid yield, electron donating ability (EDA), nitrite-scavenging effects (NSE), peroxide value (POV) and total thiosulfinates contents of garlic extracts were determined. In order to improve the functional properties of extracts prepared with several organic solvents and acids, concentration and pH adjustment of the selected solvent and addition of acids and salts to solvents were also examined. Among the solvents tested, the methanol and ethanol extracts were found to be the most effective on the base of functionality and solid yields. The highest EDA, NSE and thiosulfinate value were obtained with 50% ethanol. The pH control of solvent and addition of citric acid, NaCl and phosphates to 50% ethanol did not affect on the functionality of the extracts. Therefore the optimal solvent for the best functional properties of garlic extract was 50% ethanol.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.742-757
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• 2001

Since database management systems(DBMSS) have limited lock resources, transactions requesting locks beyond the limit mutt be aborted. In the worst carte, if such transactions are aborted repeatedly, the DBMS can become paralyzed, i.e., transaction execute but cannot commit. Lock escalation is considered a solution to this problem. However, existing lock escalation methods do not provide a complete solution. In this paper, we prognose a new lock escalation method, adaptive lock escalation, that selves most of the problems. First, we propose a general model for lock escalation and present the concept of the unescalatable look, which is the major cause making the transactions to abort. Second, we propose the notions of semi lock escalation, lock blocking, and selective relief as the mechanisms to control the number of unescalatable locks. We then propose the adaptive lock escalation method using these notions. Adaptive lock escalation reduces needless aborts and guarantees that the DBMS is not paralyzed under excessive lock requests. It also allows graceful degradation of performance under those circumstances. Third, through extensive simulation, we show that adaptive lock escalation outperforms existing lock escalation methods. The results show that, compared to the existing methods, adaptive lock escalation reduces the number of aborts and the average response time, and increases the throughput to a great extent. Especially, it is shown that the number of concurrent transactions can be increased more than 16 ~256 fold. The contribution of this paper is significant in that it has formally analysed the role of lock escalation in lock resource management and identified the detailed underlying mechanisms. Existing lock escalation methods rely on users or system administrator to handle the problems of excessive lock requests. In contrast, adaptive lock escalation releases the users of this responsibility by providing graceful degradation and preventing system paralysis through automatic control of unescalatable locks Thus adaptive lock escalation can contribute to developing self-tuning: DBMSS that draw a lot of attention these days.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.836-848
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• 2019

This paper aims at providing a critical view over the cybernetics theory especially of first generation on which the artificial intelligence heavily depends nowadays. There has been a commonly accepted thought that the conception of artificial intelligence could not has been possible without being influenced by N. Wiener's cybernetic feedback based information system. Despite the founder of contemporary cybernetics' ethical concerns in order to avoid an increasing entropy phenomena(social violence, economic misery, wars) produced through a negative dynamics of the western modernity regarded as the most advanced form of humanism. In this civilizationally changing atmosphere, the newly born cybernetic technology was thus firmly believed as an antidote to these vices deeply rooted in humanism itself. But cybernetics has been turned out to be a self-organizing, self-controlling mechanical system that entails the possibility of telegraphing human brain (which are transformed into patterns) through the uploading of human brain neurons digitalized by the artificial intelligence embedded into computing technology. On this background emerges posthuman (or posthumanism) movement of which concepts have been theorized mainly by its ardent apostles like N. K. Hayles, Neil Bedington, Laurent Alexandre, Donna J. Haraway. The converging of NBIC Technologies leading to the opening of a much more digitalizing society has served as a catalyst to promote the posthuman representations and different narratives especially in the contemporary visual arts as well as in the study of humanities including philosophy and fictional literature. Once Bruno Latour wrote "Modernity is often defined in terms of humanism, either as a way of saluting the birth of 'man' or as a way of announcing his death. But this habit is itself modern, because it remains asymmetrical. It overlooks the simultaneous birth of 'nonhumaniy' - things, or objects, or beasts, - and the equally strange beginning of a crossed-out God, relegated to the sidelines."4) These highly suggestive ideas enable us to better understand what kind of human beings would emerge following the dazzlingly accelerating advancement of artificial intelligence technology. We wonder whether or not this newly born humankind would become essentially Homo Artificialis as a neuronal man stripping off his biological apparatus. However due to this unprecedented situation humans should deal with enormous challenges involving ethical, metaphysical, existential implications on their life.

• Tuberculosis and Respiratory Diseases
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• v.44 no.1
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• pp.44-51
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• 1997

Background : The changes of the composition in the T-lymphocyte are important as an immunological abnormality in the pathogenesis of tuberculosis. Previously, the second type of TCR dimer(${\gamma}{\delta}$ T lymphocyte) that did not express CD4 or CD8 molecules was found. In other reports the presence of this type of lymphocytes was increased in the initial stage of tuberculous infections. Method : To determine whether there are some differences in the T-lymphocyte subsets in the peripheral blood or pleural effusion between pleural tuberculosis and other pleurisy. Thirty patients with pleural effusion among the forty-nine patients were examined T-lymphocyte subset analysis(CD4+T-cell,CD8+ T-cell,${\gamma}{\delta}$ T-lymphocytes) with anti- Leu4, anti-Leu3a, anti-Lea2a, anti HLA-DR and anti-TCR-${\gamma}{\delta}$-1(Becton & Dickinson Co.). Results : The average age of the patients was 50 years old(17-81year). There were 33 males and 16 female patients. Patiensts with tuberculosis are 30cases(tuberculous pleurisy 15), lung cancer 12cases(malignant effusion 9) and pneumonia 7cases(parapneumonic effusion 6cases) In T lymphocyte subsets of pleural effusion, helper T lymphocyte(54.6 + 13.8 %) of tuberculous pleurisy was higher than that(36.2 + 25.3 %) of non-tuberculous pleurisy(p=0.04). The peripheral blood ${\gamma}{\delta}$ T-lymphocytes in tuberculousis was insignificantly higher than non-tuberculous patients(p= 0.24). The peripheral blood ${\gamma}{\delta}$ T-lymphocytes and pleural ${\gamma}{\delta}$ T-Iymphocytes in tuberculous pleurisy was insignificantly higher than in non-tuberculous pleurisy(p= 0.16, p= 0.12). Conclusion : The percentage of -${\gamma}{\delta}$ T lymphocytes among the total T-lymphocytes is not significantly increased in the peripheral blood or pleural effusion of the pleural tuberculosis. ${\gamma}{\delta}$ T lymphocytes is less useful as a diagnostic method of pleural tuberculosis.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.