• 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.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.2
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• pp.15-24
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• 2021

Purpose SPECT/CT was noted for its excellent correction method and qualitative functions based on fusion images in the early stages of dissemination, and interest in and utilization of quantitative functions has been increasing with the recent introduction of companion diagnostic therapy(Theranostics). Unlike PET/CT, various conditions like the type of collimator and detector rotation are a challenging factor for image acquisition and reconstruction methods at absolute quantification of SPECT/CT. Therefore, in this study, We want to find out the effect on the radioactivity concentration estimate by the increase or decrease of the total acquisition time according to the number of projections and the acquisition time per projection among SPECT/CT imaging conditions. Materials and Methods After filling the 9,293 ml cylindrical phantom with sterile water and diluting 99mTc 91.76 MBq, the standard image was taken with a total acquisition time of 600 sec (10 sec/frame × 120 frames, matrix size 128 × 128) and also volume sensitivity and the calibration factor was verified. Based on the standard image, the comparative images were obtained by increasing or decreasing the total acquisition time. namely 60 (-90%), 150 (-75%), 300 (-50%), 450 (-25%), 900 (+50%), and 1200 (+100%) sec. For each image detail, the acquisition time(sec/frame) per projection was set to 1.0, 2.5, 5.0, 7.5, 15.0 and 20.0 sec (fixed number of projections: 120 frame) and the number of projection images was set to 12, 30, 60, 90, 180 and 240 frames(fixed time per projection:10 sec). Based on the coefficients measured through the volume of interest in each acquired image, the percentage of variation about the contrast to noise ratio (CNR) was determined as a qualitative assessment, and the quantitative assessment was conducted through the percentage of variation of the radioactivity concentration estimate. At this time, the relationship between the radioactivity concentration estimate (cps/ml) and the actual radioactivity concentration (Bq/ml) was compared and analyzed using the recovery coefficient (RC_Recovery Coefficients) as an indicator. Results The results [CNR, radioactivity Concentration, RC] by the change in the number of projections for each increase or decrease rate (-90%, -75%, -50%, -25%, +50%, +100%) of total acquisition time are as follows. [-89.5%, +3.90%, 1.04] at -90%, [-77.9%, +2.71%, 1.03] at -75%, [-55.6%, +1.85%, 1.02] at -50%, [-33.6%, +1.37%, 1.01] at -25%, [-33.7%, +0.71%, 1.01] at +50%, [+93.2%, +0.32%, 1.00] at +100%. and also The results [CNR, radioactivity Concentration, RC] by the acquisition time change for each increase or decrease rate (-90%, -75%, -50%, -25%, +50%, +100%) of total acquisition time are as follows. [-89.3%, -3.55%, 0.96] at - 90%, [-73.4%, -0.17%, 1.00] at -75%, [-49.6%, -0.34%, 1.00] at -50%, [-24.9%, 0.03%, 1.00] at -25%, [+49.3%, -0.04%, 1.00] at +50%, [+99.0%, +0.11%, 1.00] at +100%. Conclusion In SPECT/CT, the total coefficient obtained according to the increase or decrease of the total acquisition time and the resulting image quality (CNR) showed a pattern that changed proportionally. On the other hand, quantitative evaluations through absolute quantification showed a change of less than 5% (-3.55 to +3.90%) under all experimental conditions, maintaining quantitative accuracy (RC 0.96 to 1.04). Considering the reduction of the total acquisition time rather than the increasing of the image acquiring time, The reduction in total acquisition time is applicable to quantitative analysis without significant loss and is judged to be clinically effective. This study shows that when increasing or decreasing of total acquisition time, changes in acquisition time per projection have fewer fluctuations that occur in qualitative and quantitative condition changes than the change in the number of projections under the same scanning time conditions.

• Journal of Embryo Transfer
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• v.29 no.1
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• pp.73-81
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• 2014

The in vitro production of porcine embryos was essential to increase of blastocyst development rate and select of high quality blastocyst in early stage. There were a lot of reports about in vitro porcine embryo development, but there was no report about the selection of high quality embryos. Therefore, in this study, we investigated the effect of vitamin $K_1$ (vit $K_1$) on the development and survival rate of porcine in vitro fertilized embryos. When vit $K_1$ was treated for 24 hr at day 1 in vitro culture, blastocyst development rate in the control group ($35.5{\pm}3.2%$) was significantly lower compared to $1.0{\mu}M$, $3.0{\mu}M$, or $6.0{\mu}M$ groups ($14.5{\pm}4.3$, 0.0, or 0.0%; p<0.05). The survival rates of blastocysts at day 8 in $1.0{\mu}M$, $3.0{\mu}M$ or $6.0{\mu}M$ of vit $K_1$ treated groups ($22.2{\pm}2.9$, 0.0 or 0.0%) were significantly lower than that of the control group ($31.8{\pm}2.6%$; p<0.05). We were added at $1.0{\mu}M$, $3.0{\mu}M$ or $6.0{\mu}M$ vit $K_1$ for different durations of time at day 1 in vitro culture. The development rate and survival rate in the group of $1.0{\mu}M$ vit $K_1$ for 6 hr was $26.5{\pm}2.9%$ and $47.2{\pm}2.8%$, respectively, which were differed significantly in the group of 12 hr (p<0.05). In the group of $3.0{\mu}M$ vit $K_1$, the blastocyst development in control group was $36.4{\pm}3.1%$ but, the survival rate $41.7{\pm}3.2%$ in the group of 3.0 hr was significantly higher than that of the control group (p<0.05). In the group of $6.0{\mu}M$ vit $K_1$, the control group's the blastocyst development was $32.0{\pm}2.8%$ and the 0.5 hr supplement group's survival rates was $42.9{\pm}1.8%$ higher than other groups. We added vit $K_1$ at day 1, day 2, day 4 and day 6 of in vitro culture, on the based the results of supplemented concentration and duration. In the group of $1.0{\mu}M$ 6.0 hr addition, the blastocyst development rate of day 4 and the survival rate of day 2 were the highest in each group. In the groups of $3.0{\mu}M$ 3.0 hr addition or $6.0{\mu}M$ 0.5 hr addition, the blastocyst development ($59.5{\pm}4.1%$ and $50.0{\pm}3.6%$) and survival rates ($72.7{\pm}5.4%$ and $79.2{\pm}4.0%$) on day 4 were significantly higher than that of control and other experiment groups (p<0.05). Meanwhile, the number of cells in blastocysts that produced by vit $K_1$ supplementation was $53.4{\pm}5.8$, $49.4{\pm}3.8$ and $51.5{\pm}4.5$ respectively, which were significantly higher than that of $40.2{\pm}2.3$ in the control group (p<0.05). There was no difference of the number of apoptotic cells between control and experiment groups. In addition, gene expression of survival blastocyst, the Bax mRNA expression was similar between the control and the experiment groups. However, Bcl-xL mRNA expression's in the group of $6.0{\mu}M$ 0.5 hr on day 4 was highest among control and experiment groups (p<0.05). In this study suggested that the control of concentration, duration and time was effective on the survival and cell number of porcine blastocyst derived from in vitro. We are not know what the exact reasons of the effect of vit $K_1$ on embryo development and need to fur ther study. However, vit $K_1$ might be using the selection of high quality porcine blastocyst.

• Tuberculosis and Respiratory Diseases
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• v.56 no.1
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• pp.51-66
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• 2004

Objectives : The matrix metalloproteinases (MMPs) that participate in the extracellular matrix metabolism play a important role in the progression of pulmonary fibrosis. The effects of the MMPs are regulated by several factors including Th-1 cytokines, $interferon-{\gamma}$ ($IFN-{\gamma}$). Up to now, $IFN-{\gamma}$ is known to inhibit pulmonary fibrosis, but little is known regarding the exact effect of $IFN-{\gamma}$ on the regulation of the MMPs. This study investigated the effects of $interferon-{\gamma}$ on the pulmonary fibrosis and the expression of the lung MMP-2,-9, TIMP-1,-2, and Th-2 cytokines in aa rat model of bleomycin induced pulmonary fibrosis. Materials and methods : Male, specific pathogen-free Sprague-Dawley rats were subjected to an intratracheal bleomycin instillation. The rats were randomized to a saline control, a bleomycin treated, and a bleomycin+$IFN-{\gamma}$ treated group. The bleomycin+$IFN-{\gamma}$ treated group was subjected to an intramuscular injection of $IFN-{\gamma}$ for 14 days. At 3, 7, 14, and 28 days after the bleomycin instillation, the rats were sacrificed and the lungs were harvested. In order to evaluate the effects of the $IFN-{\gamma}$ on lung fibrosis and inflammation, the lung hydroxyproline content, inflammation and fibrosis score were measured. Western blotting, zymography and reverse zymography were performed at 3, 7, 14, 28 days after bleomycin instillation in order to evaluate the MMP-2,-9, and TIMP-1,-2 expression level. ELISA was performed to determine the IL-4 and IL-13 level in a lung homogenate. Results : 1. 7 days after bleomycin instillation, inflammatory changes were more severe in the bleomycin+$IFN-{\gamma}$ group than the bleomycin group (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$2.08{\pm}0.15:2.74{\pm}0.29$, P<0.05), but 28 days after bleomycin instillation, lung fibrosis was significantly reduced as a result of the $IFN-{\gamma}$ treatment (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$3.94{\pm}0.43:2.64{\pm}0.13$, P<0.05). 2. 28 days after bleomycin instillation, the lung hydroxyproline content was significantly reduced as a result of $IFN-{\gamma}$ treatment (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$294.04{\pm}31.73{\mu}g/g:194.92{\pm}15.51{\mu}g/g$, P<0.05). 3. Western blotting showed that the MMP-2 level was increased as a result of the bleomycin instillation and highest in the 14 days after bleomycin instillation. 4. In zymography, the active forms of MMP-2 were significantly increased as a result of the $IFN-{\gamma}$ treatment 3 days after the bleomycin instillation, bleomycin+$IFN-{\gamma}$ group (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$209.63{\pm}7.60%:407.66{\pm}85.34%$, P<0.05), but 14 days after the bleomycin instillation, the active forms of MMP-2 were significantly reduced as a result of the $IFN-{\gamma}$ treatment (bleomycin group : bleomycin+$IFN-{\gamma}$ group=$159.36{\pm}20.93%:97.23{\pm}12.50%$, P<0.05). 5. The IL-4 levels were lower in the bleomycin and bleomycin+$IFN-{\gamma}$ groups but this was not significant, and the IL-13 levels showed no difference between the experiment groups. Conclusion : The author found that lung inflammation was increased in the early period but the pulmonary fibrosis was inhibited in the late stage as a result of $IFN-{\gamma}$. The inhibition of pulmonary fibrosis by $IFN-{\gamma}$ appeared to be associated with the inhibition of MMP-2 activation by $IFN-{\gamma}$. Further studies on the mechanism of the regulation of MMP-2 activation and the effects of MMP-2 activation on pulmonary fibrosis is warranted in the future.

• Applied Biological Chemistry
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• v.7
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• pp.105-117
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• 1966

This experiment was conducted to investigate the effet of phthalimido-methyl-O,O-dimethyl-phosphorodithioate (Imidan) known as an acaricide and its possible metabolic products on the growth of plant, when sprayed on the leaves of rice plant. The results are summarized as follows. 1) Possible metabolic products of Imidan, the following compounds were synthesized or recrystallized for the present experiment a) N-Hydroxymethyl phthalimidem b) Phthalimide c) Phthalamidic acid d) Phthalic acid e) Anthranilic acid f) p-Amino benzoic acid g) p-Hydroxy benzoic acid h) Benzoic acid 2) Among the above materials, a), c), d), e), and Imidan were dissolved in a buffer solution respectively to be 10 and 20 p.p.m. and tested with the wheat coleoptile straight growth method. According to the results, Imidan inhibited the growth of coleoptile in both 10 and 20 p.p.m., whereas the others showed much better growth than the control, especially phthalamidic acid in 10 p.p.m. It appears that Imidan itself inhibits the coleoptile growth, whereas the metabolites derived from Imidan through various metabolisms, including hydrolysis in plant tissues show growth-regulating activity. (refer: Table 1, Fig. 1) 3) 20, 100 and 200 p.p.m. solutions of Imidall emulsion in xylene f·ere prepared. The lengths of shoot and root of rice seeds germinated on the re-respective media were measured after 12 days. The data showed that root was much more elongated in Imidan 20 p.p.m., whereas shoot in Imidan 100 p.p.m., respectively, than in the xylene control. An interesting finding was that xylene used as solvent had a tendency to inhibit seriously the root growth of rice seed. (refer: Table 2,5). 4) The emulsions of concentrations in 10, 25, 50 and 100 p.p.m's of control, Imidan, N-hydroxy methyl phthalimide, anthranilic acid, and phthalmide, respectively, were sprayed twice on the rice plant on pot. After a certain period of time lengths of rice culms were measured, showing that plots treated with Imidan and N-hydroxy methyl phthalimide exhibited much more growth than those of control and the others. 5) Loaves and stems of rice plant were sampled and extracted with dried acetone at the intervals of 3-, 5-, 7-, and 14 days after treated with Imidan 250 p.p.m. emulsion. This sample extracted with acetone was purified by means of prechromatographic purification method with acetonitrile and paperchromatographed to detect the following metabolic products. Imidan (Rf: 0.97-0,98), N-hydroxy-methyl phthalimide (Rf: 0.87) phthalimide (Rf: 0.86-0.87), phthalamidic acid (Rf: 0.13-0.14), phthalic acid (Rf: 0.02-0.03), benzoic acid (Rf: 0.42-0.43), p-amino benzoic acid or p-hydroxy benzoic acid (Rf: 0.08-0.09), and unidentified compounds (Rf: 0.73, 0.59, 0.33, 0.23. 0.07). In addition, in the early stages, such as 3- and 5 days nonhydrolyzed Imidan and its first hydrolytic product, N-hydroxymethyl phthalimide were detected in relatively large amounts, whereas in the last stages of 7- and 14 days due to further decomposition, the afore-mentioned two materials were reduced in the amount and p-hthalic, phthalamidic, benzoic, and p-Hydroxy benzoic, or p-Amino benzoic acids were detected in a considerably large amount. It is, therefore, believed that most of Imidan applied to the leaves of rice plant may be decomposed within almost 14 days. In the light of above observations it is considered that Imidan itself is not involved in plant growth regulating activity, whereas various phthaloyl derivatives produced in the course of metabolism (namelr, enzymic action) in plant tissues may have such effect.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Radiation Oncology Journal
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• v.15 no.1
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• pp.27-36
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• 1997

Purpose : This study was tried to evaluate the Potential benefits of concurrent chemoradiation therapy (low dose daily cisplatin combined with split course radiation therapy) compared with conventional radiation therapy alone in stage III non-small cell lung cancer. The end points of analyses were response rate. overall survival, survival without locoregional failure, survival without distant metastasis, prognostic factors affecting survival and treatment related toxicities. Materials and Methods : Between April 1992 and March 1994, 32 patients who had stage III non-small cell lung cancer were treated with concurrent chemoradiation therapy. Radiation therapy for 2 weeks (300 cGy given 10 times up to 3000 cGy) followed by a 3 weeks rest period and then radiation therapy for 2 more weeks (250 cGy given 10 times up to 2500 cGy) was combined with $6mg/m^2$ of cisplatin. Follow-up period ranged from 13 months to 48 months with median of 24 months. Historical control group consisted of 32 patients who had stage III non-small cell lung cancer were received conventionally fractionated (daily 170-200 cGy) radiation therapy alone. Total radiation dose ranged from 5580 cGy to 7000 cGy with median of 5940 cGy. Follow-up Period ranged from 36 months to 105 months with median of 62 months. Result : Complete reponse rate was higher in chemoradiation therapy (CRT) group than radiation therapy (RT) group (18.8% vs. 6.3%, CRT group showed lower in-field failure rate compared with RT group(25% vs. 47%. The overall survival rate had no significant differences in between CRT group and RT group (17.5% vs. 9.4% at 2 years). The survival without locoregional failure (16.5% vs. 5.3% at 2 years) and survival without distant metastasis (17% vs. 4.6% at 2 years) also had no significant differences. In subgroup analyses for Patients with good performance status (Karnofsky performance scale 80), CRT group showed significantly higher overall survival rate compared with RT group (62.5% vs. 15.6% at 2 years). The prognostic factors affecting survival rate were performance status and pathologic subtype (squamous cell cancer vs. nonsquamous cell cancer) in CRT group. In RT alone group, performance status and stage (IIIa vs IIIb) were identified as a Prognostic factors. RTOG/EORTC grade 2-3 nausea and vomiting(22% vs 6% and bone marrow toxicities (25% vs. 15.6% were significantly higher in CRT group compared with RT alone group. The incidence of RTOG/EORTC grade 3-4 pulmonary toxicity had no significant differences in between CRT group and RT group (16% vs. 6%. The incidence of WHO grade 3-4 pulmonary fibrosis also had no significant differences in both group (38% vs. 25%. In analyses for relationship of field size and Pulmonary toxicity, the Patients who treated with field size beyond 200cm2 had significantly higher rates of pulmonary toxicities. Conclusion : The CRT group showed significantly higher local control rate than RT group. There were no significant differences of survival rate in between two groups. The subgroup of patients who had good performance status showed higher overall survival rate in CRT group than RT group. In spite of higher incidence of acute toxicities with concurrent chemoradiation therapy, the survival gain in subgroup of patients with good performance status were encouraging. CRT group showed higher rate of early death within 1 year, higher 2 year survival rate compared with RT group Therefore, to evaluate the accurate effect on survival of concurrent chemoradiation therapy, systematic follow-up for long term survivors are needed.

• Journal of agricultural medicine and community health
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• v.25 no.2
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• pp.397-411
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• 2000

The present study was conducted to analyze the degree of changes in knowledge and attitude toward health, arid health promoting activities after providing health education intervention for a year to elementary school children, to examine the factors effecting knowledge, attitude and health promoting practices for obesity and diet, and to analyze whether changes are present in health level according to changes in knowledge on health and health promoting activities. After conducting a pre-survey rio 354 subjects of 3rd and 4th grade students and their mothers in the city area of Kyungju, in April, 1999, 301. responses with the responding rate of 85% were obtained. Final analysis was done with 231 pairs of a student and his/her mother who could be followed up after a year among 301 pairs of the respondents, excluding those students who transferred, those who were excused from school early, those who did not take abdominal fat measurements, and those students and mothers respondents whose survey was incomplete. Based on the changes before and after the intervention, the scores on knowledge about obesity and diet showed a significant difference in normal weight group, and the scores on the attitude toward obesity and diet increased significantly in obesity group but decreased significantly in normal weight group(p<0.01). The scores of practicing health promoting activities were significantly increased in both groups, and although the waist-hip ratio (WHR) did not change in obesity group, the rate increased significantly in normal weight group(p<0.01). As for changes on the knowledge of obesity and diet before and after the intervention while dividing the scores into 3 levels based on the scores of the pre-survey and compared to changes in the scores one year after, in the case of the changes in the scores in the 1st third, the score on the knowledge about obesity and diet changed from 1.3 in the pre survey to 3.7 after the intervention, showing significant increase(p<0.01) The scores of practicing health promoting activities for obesity and diet were significantly increase in all three levels(p<0.01), and the degree of changes in the scores was 7.0 points in the 1st third, 4.4 points for the and third and 1.8 points for the 3rd third, showing a significant difference among the three levels(p<0.01). It was shown that the increase in BMI in those students whose mothers have the education level higher than university was significantly higher than the increase in BMI in those students whose mothers have the education level under high school, and those students whose mothers are in their 30's showed higher changes in practicing health promoting activities for obesity and diet. When the scores of mothers' knowledge and attitude toward obesity and diet were compared by dividing the scores into tertile, the score of students' knowledge changed significantly according to the scores of mothers' attitude toward obesity and diet. In multiple regression analysis on changes in the scores of knowledge about obesity and diet, the student variables of the degree of awareness on the seriousness of obesity, and the scores of previous knowledge on diet and obesity were selected the significant variables, and among the mother variables, the degree of guiding the child on diet and the education level were the significant variables. In multiple regression analysis to analyze the factors effecting changes in the attitude toward obesity and diet, the student variables of the BMI, scores of previous knowledge on obesity and diet, and scores on the previous attitude toward obesity and diet were shown to be significant. In multiple regression analysis on the factors effecting changes in health promoting activities for obesity and diet, the student variables of the BMI, scores on the previous attitude toward obesity and diet, and changes in the scores of obesity and diet were selected the significant variables.