• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.11
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• pp.1373-1385
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• 2017

The purpose of this study was to optimize the formulation for fish-meat noodles added with farmed olive flounder (Paralichthys olivaceus) using response surface methodology. Fish-meat (surimi) from P. olivaceus was prepared by a traditional washing process. Independent variables were Alaska pollack, fish-meat from P. olivaceus, and starch, whereas dependent variables were whiteness and texture. The results for whiteness and texture produced very significant values for whiteness (P<0.001), strength (P<0.001), hardness (P<0.05), breaking force (P<0.001), chewiness (P<0.001), brittleness (P<0.001), extensibility force (P<0.001), and extensibility distance (P<0.05). The optimal formula for fish-meat noodle was addition of 72.00 g Alaska pollack, 11.59 g P. olivaceus, and 15.86 g starch. Experimental values of whiteness, strength, hardness, breaking force, chewiness, brittleness, extensibility force, and extensibility distance under optimal conditions were $59.01{\pm}0.53$, $708.22{\pm}54.12g/cm^2$, $1,390.07{\pm}67.70g/cm^2$, $3,622.77{\pm}92.52g$, $2,686.94{\pm}103.22g$, $278,578.31{\pm}10,150.22g$, $52.22{\pm}2.97g$, $24.14{\pm}3.55mm$, respectively.

• Korean Journal of Heritage: History & Science
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• v.54 no.4
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• pp.152-173
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• 2021

The Large Banner was introduced during the Japanese Invasions of Korea with a new military system. It was a flag that controlled the movement of soldiers in military training. In addition, it was used in other ways, such as a symbol when receiving a king in a military camp, a flag raised on the front of a royal procession, at the reception and dispatch of envoys, and at a local official's procession. The Large Banner was recognized as a symbol of military dignity and training rites. The Large Banner was analyzed in the present study in the context of two different types of decorations. Type I includes chungdogi, gakgi and moongi. Type II includes grand, medium, and small obangi, geumgogi and pyomigi. Each type is decorated differently for each purpose. The size of the flag is estimated to be a square of over 4 ja long in length. Flame edges were attached to one side and run up and down The Large Banner used the Five Direction Colors based on the traditional principles of Yin-Yang and Five Elements. The pattern of the Large Banner is largely distinguished by four. The pattern of large obangi consists of divine beasts symbolizing the Five Directions and a Taoism amulet letter. The pattern of medium obangi features spiritual generals that escort the Five Directions. The pattern of small obangi has the Eight Trigrams. The pattern of moongi consists of a tiger with wings that keeps a tight watch on the army's doors. As for historical sources of coloring for Large Banner production, the color-written copy named Gije, from the collection of the Osaka Prefect Library, was confirmed as the style of the Yongho Camp in the mid to late 18th century, and it was also used for this essay and visualization work. We used Cloud-patterned Satin Damask as the background material for Large Banner production, to reveal the dignity of the military. The size of the 4 ja flag was determined to be 170 cm long and 145 cm wide, and the 5 ja flag was 200 cm long and 175 cm wide. The conversion formula used for this work was Youngjochuck (1 ja =30cm). In addition, the order of hierarchy in the Flag of the King was discovered within all flags of the late Joseon Dynasty. In the above historical study, the two types of Large Banner were visualized. The visualization considered the size of the flag, the decoration of the flagpole, and the patterns described in this essay to restore them to their original shape laid out the 18th century relics on the background. By presenting color, size, material patterns, and auxiliary items together, it was possible not only to produce 3D content, but also to produce real products.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.47
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• pp.15-32
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• 2002

The endeavors enhancing the grain quality of high-yielding japonica rice were steadily continued during 1980s-1990s along with the self-sufficiency of rice production and the increasing demands of high-quality rices. During this time, considerably great progress and success was obtained in development of high-quality japonica cultivars and quality evaluation techniques including the elucidation of interrelationship between the physicochemical properties of rice grain and the physical or palatability components of cooked rice. In 1990s, some high-quality japonica rice cultivars and special rices adaptable for food processing such as large kernel, chalky endosperm, aromatic and colored rices were developed and its objective preference and utility was also examined by a palatability meter, rapid-visco analyzer and texture analyzer, Recently, new special rices such as extremely low-amylose dull or opaque non-glutinous endosperm mutants were developed. Also, a high-lysine rice variety was developed for higher nutritional utility. The water uptake rate and the maximum water absorption ratio showed significantly negative correlations with the K/Mg ratio and alkali digestion value(ADV) of milled rice. The rice materials showing the higher amount of hot water absorption exhibited the larger volume expansion of cooked rice. The harder rices with lower moisture content revealed the higher rate of water uptake at twenty minutes after soaking and the higher ratio of maximum water uptake under the room temperature condition. These water uptake characteristics were not associated with the protein and amylose contents of milled rice and the palatability of cooked rice. The water/rice ratio (in w/w basis) for optimum cooking was averaged to 1.52 in dry milled rices (12% wet basis) with varietal range from 1.45 to 1.61 and the expansion ratio of milled rice after proper boiling was average to 2.63(in v/v basis). The major physicochemical components of rice grain associated with the palatability of cooked rice were examined using japonica rice materials showing narrow varietal variation in grain size and shape, alkali digestibility, gel consistency, amylose and protein contents, but considerable difference in appearance and texture of cooked rice. The glossiness or gross palatability score of cooked rice were closely associated with the peak, hot paste and consistency viscosities of viscosities with year difference. The high-quality rice variety "IIpumbyeo" showed less portion of amylose on the outer layer of milled rice grain and less and slower change in iodine blue value of extracted paste during twenty minutes of boiling. This highly palatable rice also exhibited very fine net structure in outer layer and fine-spongy and well-swollen shape of gelatinized starch granules in inner layer and core of cooked rice kernel compared with the poor palatable rice through image of scanning electronic microscope. Gross sensory score of cooked rice could be estimated by multiple linear regression formula, deduced from relationship between rice quality components mentioned above and eating quality of cooked rice, with high probability of determination. The $\alpha$-amylose-iodine method was adopted for checking the varietal difference in retrogradation of cooked rice. The rice cultivars revealing the relatively slow retrogradation in aged cooked rice were IIpumbyeo, Chucheongyeo, Sasanishiki, Jinbubyeo and Koshihikari. A Tonsil-type rice, Taebaegbyeo, and a japonica cultivar, Seomjinbyeo, showed the relatively fast deterioration of cooked rice. Generally, the better rice cultivars in eating quality of cooked rice showed less retrogradation and much sponginess in cooled cooked rice. Also, the rice varieties exhibiting less retrogradation in cooled cooked rice revealed higher hot viscosity and lower cool viscosity of rice flour in amylogram. The sponginess of cooled cooked rice was closely associated with magnesium content and volume expansion of cooked rice. The hardness-changed ratio of cooked rice by cooling was negatively correlated with solids amount extracted during boiling and volume expansion of cooked rice. The major physicochemical properties of rice grain closely related to the palatability of cooked rice may be directly or indirectly associated with the retrogradation characteristics of cooked rice. The softer gel consistency and lower amylose content in milled rice revealed the higher ratio of popped rice and larger bulk density of popping. The stronger hardness of rice grain showed relatively higher ratio of popping and the more chalky or less translucent rice exhibited the lower ratio of intact popped brown rice. The potassium and magnesium contents of milled rice were negatively associated with gross score of noodle making mixed with wheat flour in half and the better rice for noodle making revealed relatively less amount of solid extraction during boiling. The more volume expansion of batters for making brown rice bread resulted the better loaf formation and more springiness in rice breed. The higher protein rices produced relatively the more moist white rice bread. The springiness of rice bread was also significantly correlated with high amylose content and hard gel consistency. The completely chalky and large grain rices showed better suitability far fermentation and brewing. The glutinous rice were classified into nine different varietal groups based on various physicochemical and structural characteristics of endosperm. There was some close associations among these grain properties and large varietal difference in suitability to various traditional food processing. Our breeding efforts on improvement of rice quality for high palatability and processing utility or value-adding products in the future should focus on not only continuous enhancement of marketing and eating qualities but also the diversification in morphological, physicochemical and nutritional characteristics of rice grain suitable for processing various value-added rice foods.ice foods.