• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.193-216
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• 2020

Due to the recent mid- to long-term slump and falling growth rates in the global economy, interest in organizational structures that create new products or services as a new alternative to survive and develop in an opaque environment both internally and externally, and enhance organizational sustainability through changes in production methods and business innovation is increasing day by day. In this atmosphere, we agree that the growth of small and medium-sized venture companies has a significant impact on the national economy, and various efforts are being made to enhance the technological innovation capabilities of the members so that these small and medium-sized venture companies can enhance and sustain their performance. The purpose of this study is also to investigate how the technological innovation capabilities of small and medium-sized venture companies correlate with the performance of knowledge management and to analyze the role of network capabilities to organize the strategic activities of enterprise to obtain the resources and organizational capabilities to be used for value creation from external networks. In other words, research was conducted on the impact of technological innovation capabilities of small and medium venture companies on knowledge management performance by using network capabilities as parameters. Therefore, in this study, we would like to verify the hypothesis that innovation capabilities will have a positive impact on knowledge management performance by using network capabilities of small and medium venture companies. Economic activities based on technological innovation capabilities should respond quickly to new changes in an environment where uncertainty has increased, and lead to macro-economic growth and development as well as overcoming long-term economic downturns so that they can become the nation's new growth engine as well as sustainable growth and survival of the organization. In addition, this study was conducted by setting the most important knowledge management performance within the organization as a dependent variable. As a result, R&D and learning capabilities among technological innovation capabilities have no impact on financial performance. In contrast, it was shown that corporate innovation activities have a positive impact on both financial and non-financial performance. The fact that non-financial factors such as quality and productivity improvement are identified in the management of small and medium-sized venture companies utilizing their technological innovation capabilities is contrary to a number of studies by those corporate innovation activities affect financial performance during prior research. The reason for this result is that research companies have been out of start-up companies for more than seven years, but sales are less than 10 billion won, and unlike start-up companies, R&D and learning capabilities have more positive effects on intangible non-financial performance than financial performance. Corporate innovation activities have been shown to have a positive (+) impact on both financial and non-financial performance, while R&D and learning capabilities have a positive (+) impact on financial performance by parameters of network capability. Corporate innovation activities have been shown to have no impact on both financial and non-financial performance, and R&D and learning capabilities have no impact on non-financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance.

• 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.

• Tuberculosis and Respiratory Diseases
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• v.44 no.4
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• pp.822-835
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• 1997

Background : There have been many in vitro evidences that interleukin-4(IL-4) might be the most important cytokine inducing IgE synthesis from B-cells, and interferon-gamma(IFN-$\gamma$) might be a main cytokine antagonizing IL-4-mediated IgE synthesis. Recently some reports demonstrated that IFN-$\gamma$ might be used as a new therapeutic modality in some allergic diseases with high serum IgE level, such as atopic dermatitis or bronchial asthma. To evaluate the in vivo effect of IFN-$\gamma$ in bronchial asthma we tried a clinical study. Methods : Fifty bronchial asthmatics(serum IgE level over 200 IU/ml) who did not respond to inhaled or systemic corticosteroid treatment, and 17 healthy nonsmoking volunteers were included in this study. The CD 23 expressions of peripheral B-cells, the IL-4 activities of peripheral T-cells, the serum soluble CD23(sCD23) levels, and the superoxide anion(${O_2}^-$) generations by peripheral PMN were compared between bronchial asthmatics and normal subjects. The IL-4 activities of peripheral T-cells were analyzed by T-cell supernatant (T-sup)-induced CD23 expression from tonsil B-cells. In bronchial asthmatics the serum IgE levels and histamine $PC_{20}$ in addition to the above parameters were also compared before and after IFN-$\gamma$ treatment. IFN-$\gamma$ was administered subcutaneously with a weekly dose of 30,000 IU per kilogram of body weight for 4 weeks. Results : The ${O_2}^-$ generations by peripheral PMNs in bronchial asthmatics were higher than normal subjects($8.23{\pm}0.94$ vs $5.00{\pm}0.68\;nmol/1{\times}10^6$ cells, P<0.05), and significantly decreased after IFN-$\gamma$ treatment compared to initial values($3.69{\pm}0.88$ vs $8.61{\pm}1.53\;nmol/1{\times}10^6$ cells, P<0.05). CD23 expression of peripheral B-cells in bronchial asthmatics was higher than normal subjects($47.47{\pm}2.96%$, vs $31.62{\pm}1.92%$, P<0.05), but showed no significant change after IFN-$\gamma$ treatment. The serum sCD23 levels in bronchial asthmatics were slightly higher than normal subjects($191.04{\pm}23.3\;U/ml$ vs $162.85{\pm}4.85\;U/ml$), and 11(64.7%) of 17 patients showed a decreasing pattern in their serum sCD23 levels after IFN-$\gamma$ treatment. However the means of serum sCD23 levels were not different before and after IFN-$\gamma$ treatment. The IL-4 activities of peripheral T-cells in bronchial asthmatics were slightly higher than normal subjects($22.48{\pm}6.81%$ vs $18.90{\pm}2.43%$), and slightly increased after IFN-$\gamma$ treatment($27.90{\pm}2.56%$). Nine(60%) of 15 patients showed a decreasing pattern in their serum IgE levels after IFN-$\gamma$ treatment. And the levels of serum IgE were significantly decreased after IFN-$\gamma$ treatment compared to initial values ($658.67{\pm}120.84\;IU/ml$ vs $1394.32{\pm}314.42\;IU/ml$, P<0.05). Ten(83.3%) of 12 patients showed an improving pattern in bronchial hyperresponsiveness after IFN-$\gamma$ treatment, and the means of histamine $PC_{20}$ were significantly increased after IFN-$\gamma$ treatment compared to initial values ($1.22{\pm}0.29mg/ml$ vs $0.69{\pm}0.17mg/ml$, P<0.05). Conclusion : Our results suggest that IFN-$\gamma$ may be useful as well as safety in the treatment of bronchial asthmatics with high serum IgE level and that in vivo effects of IFN-$\gamma$ may be different from its in vitro effects on the regulations of IgE synthesis or the respiratory burst of PMN.

• Korean Journal of Agricultural Science
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• v.10 no.2
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• pp.221-234
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• 1983

A study was conducted to devise a method to estimate the edible meat weight in live broilers. White Cornish broiler chicks CC, Single Comb White Leghorn egg strain chicks LL, and two reciprocal cross breeds of these two parent stocks (CL and LC) were employed A total of 240 birds, 60 birds from each breed, were reared and sacrificed at 0, 2, 4, 6, 8 and 10 weeks of ages in order to measure various body parameters. Results obtained from this study were summarized as follows. 1) The average body weight of CC and LL were 1,820g and 668g, respectively, at 8 weeks of age. The feed to gain ratios for CC and LL were 2.24 and 3.28, respectively. 2) The weight percentages of edible meat to body weight were 34.7, 36.8 and 37.5% at 6, 8 and 10 weeks of ages, respectively, for CC. The values for LL were 30.7, 30.5 and 32.3%, respectively, The CL and LC were intermediate in this respect. No significant differences were found among four breeds employed. 3) The CC showed significantly smaller weight percentages than did the other breeds in neck, feather, and inedible viscera. In comparison, the LL showed the smaller weight percentages of leg and abdominal fat to body weight than did the others. No significant difference was found among breeds in terms of the weight percentages of blood to body weight. With regard to edible meat, the CC showed significantly heavier breast and drumstick, and the edible viscera was significantly heavier in LL. There was no consistent trend in neck, wing and back weights. 4) The CC showed significantly larger measurements body shape components than did the other breeds at all time. Moreover, significant difference was found in body shape measurements between CL and LC at 10 weeks of age. 5) All of the measurements of body shape components except breast angle were highly correlated with edible meat weight. Therefore, it appeared to be possible to estimate the edible meat wight of live chickens by the use of these values. 6) The optimum regression equations for the estimation of edible meat weight by body shape measurements at 10 weeks of age were as follows. $$Y_{cc}=-1,475.581 +5.054X_{26}+3.080X_{24}+3.772X_{25}+14.321X_{35}+1.922X_{27}(R^2=0.88)$$ $$Y_{LL}=-347.407+4.549X_{33}+3.003X_{31}(R^2=0.89)$$ $$Y_{CL}=-1,616.793+4.430X_{24}+8.566X_{32}(R^2=0.73)$$ $$Y_{LC}=-603.938+2.142X_{24}+3.039X_{27}+3.289X_{33}(R^2=0.96)$$ Where $X_{24}$=chest girth, $X_{25}$=breast width, $X_{26}$=breast length, $X_{27}$=keel length, $X_{31}$=drumstick girth, $X_{32}$=tibotarsus length, $X_{33}$=shank length, and $X_{35}$=shank diameter. 7) The breed and age factors caused considerable variations in assessing the edible meat weight in live chicken. It seems however that the edible meat weight in live chicken can be estimated fairly accurately with optimum regression equations derived from various body shape measurements.

• Korean Journal of Microbiology
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• v.49 no.2
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• pp.137-145
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• 2013

This study was performed at 2 sites of Nak-Dong River to investigate the changes of nitrifiers depending on the presence and absence of organic pollutants (due to the effluents of domestic wastewater treatment plant, WWTP). Conventional chemical parameters such as T-N, $NH_4$-N, $NO_2$-N, $NO_3$-N were measured and the quantitative nitrifiers at the 2 sites were analyzed comparatively by fluorescent in situ hybridization (FISH) with NSO190 and NIT3, after checking the presence of gene amoA of ammonia oxidizing bacteria (AOB) and 16S rDNA signature sequence for Nitrobacter sp. that belongs to nitrite oxidizing bacteria (NOB). Also ${\alpha}{\cdot}{\beta}{\cdot}{\gamma}$-Proteobacteria were detected using FISH to get a glimpse of the general bacterial community structure of the sites. Based on the distribution structure of the ${\alpha}{\cdot}{\beta}{\cdot}{\gamma}$-Proteobacteria and the measurement of nitrogen in different phases, it could be said that the site 2 was more polluted with organics than site 1. Corresponding to the above conclusion, the average numbers of AOB and NOB detected by NSO160 and NIT3, respectively, at site 2 [AOB, $9.3{\times}10^5$; NOB, $1.6{\times}10^6$ (cells/ml)] was more than those at site 1 [AOB, $7.8{\times}10^5$; NOB, $0.8{\times}10^6$ (cells/ml)] and also their ratios to total counts were higher at site 2 (AOB, 27%; NOB, 34%) than those at site 1 (AOB, 18%; NOB, 23%). Thus, it could be concluded that the nitrification at site 2 was more active due to continuous loading of organics from the effluents of domestic WWTP, compared to site 1 located closed to raw drinking water supply and subsequently less polluted with organics.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.15-23
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• 2014

A Case Study on Venture and Small-Business Executives' Use of Strategic Intuition in the Decision Making Process This paper is a case study on how Venture and Small-Business Executives managers can take advantage of their intuitions in situations where the business environment is increasingly uncertain, a novel situation occurs without any data to reflect on, when rational decision-making is not possible, and when the business environment changes. The case study is based on a literature review, in-depth interviews with 16 business managers, and an analysis of Klein, G's (1998) "Generic Mental Simulation Model." The "intuition" discussed in this analysis is classified into two types of intuition: the Expert Intuition which is based on one's own experiences, and Strategic Intuition which is based on the experience of others. Case study strategic management intuition and intuition, the experts were utilized differently. Features of professional intuition to work quickly without any effort by, while the strategic intuition, is time-consuming. Another feature that has already occurred, one expert intuition in decision-making about the widely used strategic intuition was used a lot in future decision-making. The case study results revealed that managers were using expert intuition and strategic intuition differentially. More specifically, Expert Intuition was activated effortlessly, while strategic intuition required more time. Also, expert intuition was used mainly for making judgments about events that have already happened, while strategic intuition was used more often for judgments regarding events in the future. The process of strategic intuition involved (1) Strategic concerns, (2) the discovery of medium, (3) Primary mental simulation, (4) The offsetting of key parameters, (5) secondary mental simulation, and (6) the decision making process. These steps were used to develop the "Strategic Intuition Decision-making Model" for Venture and Small-Business Executives. The case study results further showed that firstly, the success of decision-making was determined in the "secondary mental simulation' stage, and secondly, that more difficulty in management was encountered when expert intuition was used more than strategic intuition and lastly strategic intuition is possible to be educated.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.8 no.4
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• pp.599-604
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• 1998

We have investigated Pt and $RuO_2$ as a bottom electrode for ferroelectric capacitor applications. The bottom electrodes were prepared by using an RF magnetron sputtering method. Some of the investigated parameters were a substrate temperature, gas flow rate, RF power for the film growth, and post annealing effect. The substrate temperature strongly influenced the surface morphology and resistivity of the bottom electrodes as well as the film crystallographic structure. XRD results on Pt films showed a mixed phase of (111) and (200) peak for the substrate temperature ranged from RT to $200^{\circ}C$, and a preferred (111) orientation for $300^{\circ}C$. From the XRD and AFM results, we recommend the substrate temperature of $300^{\circ}C$ and RF power 80W for the Pt bottom electrode growth. With the variation of an oxygen partial pressure from 0 to 50%, we learned that only Ru metal was grown with 0~5% of $O_2$ gas, mixed phase of Ru and $RuO_2$ for $O_ 2$ partial pressure between 10~40%, and a pure $RuO_2$ phase with $O_2$ partial pressure of 50%. This result indicates that a double layer of $RuO_2/Ru$ can be grown in a process with the modulation of gas flow rate. Double layer structure is expected to reduce the fatigue problem while keeping a low electrical resistivity. As post anneal temperature was increased from RT to $700^{\circ}C$, the resistivity of Pt and $RuO_2$ was decreased linearly. This paper presents the optimized process conditions of the bottom electrodes for memory device applications.

• Journal of Bio-Environment Control
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• v.25 no.2
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• pp.106-110
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• 2016

In this study, a planting density-growth-harvest (PGH) chart was developed to easily read the growth and harvest factors such as crop growth rate, relative growth rate, shoot fresh weight, shoot dry weight, harvesting time, marketable rate, and marketable yield of common ice plant (Mesembryanthemum crystallinum L.). The plants were grown in a nutrient film technique (NFT) system in a closed-type plant factory using fluorescent lamps with three-band radiation under a light intensity of $140{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ and a photoperiod of 12 h. Growth and yield were analyzed under four planting densities ($15{\times}10cm$, $15{\times}15cm$, $15{\times}20cm$, and $15{\times}25cm$). Shoot fresh and dry weights per plant increased at a higher planting density until reached an upper limit and yield per area was also same tendency. Crop growth rate, relative growth rate and lost time were described using quadratic equation. A linear relationship between shoot dry weight and fresh weights was observed. PGH chart was constructed based on the growth data and making equations. For instance, with within row spacing (= 20 cm) and fresh weight per plant at harvest (= 100 g), we can estimate all the growth and harvest factors of common ice plant. The planting density, crop growth rate, relative growth rate, lost time, shoot dry weight per plant, harvesting time, and yield were $33plants/m^2$, $20g{\cdot}m^{-2}{\cdot}d^{-1}$, $0.27g{\cdot}g^{-1}{\cdot}d^{-1}$, 22 days, 2.5 g/plant, 26 days after transplanting, and $3.2kg{\cdot}m^{-2}$, respectively. With this chart, we could easily obtain the growth factors such as planting density, crop growth rate, relative growth rate, lost time and the harvest factors such as shoot fresh and dry weights, harvesting time, marketable rate, and marketable yield with at least two parameters, for instance, planting distance and one of harvest factors of plant. PGH charts will be useful tools to estimate the growth and yield of crops and to practical design of a closed-type plant production system.

• Journal of Animal Environmental Science
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• v.14 no.3
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• pp.201-210
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• 2008

Farmers directly spread the livestock manure compost on their arable land as an organic fertilizer. However, there are some difficult problems to solve. first, we are unsure of whether the livestock manure compost can meet the nutritional demand of plant. Second, application of the current powered livestock manure compost to crop land is very difficult work due to heavy weight of compost and its powdered shape. For this reason, this study was carried out to develope high quality pelletized livestock manure compost. In pelletizing process with composted manure, the optimal water content for pelletizing was around 30$\sim$40%. When rice bran was mixed with 5% as a bonding agent on volume basis, the pelletizing effect was remarkably improved. On a dry matter basis, the contents of N and P of manure compost were 1.31%, and 0.58%, respectively. After pelletizing, the contents of compost pelleted were 1.37% and 0.54%, respectively. The same parameters of pelletized compost made by screw type Instrument were 1.37% and 0.53%, respectively. The other hand, N and P content of pelletized compost made by pellet mill type instrument were 1.06% and 0.18%, respectively.