• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.21-28
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• 2024

In light of the rapid development of the space industry, there has been increased attention on small satellites. These satellites rely on components that are considered to have lower reliability compared to larger-scale satellites. As a result, predicting reliability becomes even more crucial in this context. Therefore, this study aims to compare three reliability prediction techniques: MIL-HDBK-217F, RiAC-HDBK-217Plus, and FIDES. The goal is to determine a suitable reliability standard specifically for nano-satellites. Furthermore, we have refined the quality assurance factors of the manufacturing company. These factors have been adjusted to be applicable across various industrial sectors, with a particular focus on the selected FIDES prediction standard. This approach ensures that the scoring system accurately reflects the suitability for the aerospace industry. Finally, by implementing this refined system, we confirm the impact of the manufacturer's quality assurance level on the total failure rate.

• Plant Journal
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• v.8 no.3
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• pp.49-54
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• 2012

In this study, the way how we can find the defects of motor windings in advance will be discussed. The magnetic flux spectrum in the high frequency region of the large motor was analyzed based on the actual fault practices related with motor windings. In case of defective motor relative amplitude ratio of the stator slot frequency to its sideband was very high compared to that of healthy motor. And the defective signal related with motor windings was indicated in advance in the magnetic flux spectrum prior to over 1 month before failure. Considering this aspect it can be estimated that magnetic flux spectrum in the high frequency region has the excellent predictive diagnostic capability.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Steel and Composite Structures
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• v.42 no.3
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• pp.407-426
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• 2022

Superior to traditional welded studs, high strength friction-grip bolted shear connectors facilitate the assembling and demounting of the composite members, which maximizes the potential for efficiency in the construction and retrofitting of new and old structures respectively. Hence, it is necessary to investigate the structural properties of high strength friction-grip bolts used in steel concrete composite beams. By means of push-out tests, an experimental study was conducted on post-installed high strength friction-grip bolts, considering the effects of different bolt size, concrete strength, bolt tensile strength and bolt pretension. The test results showed that bolt shear fracture was the dominant failure mode of all specimens. Based on the load-slip curves, uplifting curves and bolt tensile force curves between the precast concrete slab and steel beam obtained by push-out tests, the anti-slip performance of steel-concrete interface and shear behavior of bolt shank were studied, including the quantitative analysis of anti-slip load, and anti-slip stiffness, frictional coefficient, shear stiffness of bolt shank and ultimate shear capacity. Meanwhile, the interfacial anti-slip stiffness and shear stiffness of bolt shank were defined reasonably. In addition, a total of 56 push-out finite element models verified by the experimental results were also developed, and used to conduct parametric analyses for investigating the shear behavior of high-strength bolted shear connectors in steel-concrete composite beams. Finally, on ground of the test results and finite element simulation analysis, a new design formula for predicting shear capacity was proposed by nonlinear fitting, considering the bolt diameter, concrete strength and bolt tensile strength. Comparison of the calculated value from proposed formula and test results given in the relevant references indicated that the proposed formulas can give a reasonable prediction.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.369-376
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• 2013

Finance Information System is critical national infrastructure. Therefore it is important to select variables of defect causal factor for the system defect management effectively. We research and analyze detected errors in A Company's Finance Information System for three years. In the result of research and analysis, we have selected 9 variables of defect factor: the trading volume, the fluctuation of KOSDAQ index, and the number of public announcements, etc. Then we have assumed that these variables affect real system errors and analyzed correlation between the hypothesis and the detected system errors. After analyzing, we have extracted the trading volume, the number of orders and fills, changing tasks, and the fluctuations of NASDAQ index as valid variables of defect factor. These variables are proposed for failure prediction model as the variables to manage defects in the finance information system afterward.

• Journal of Technology Innovation
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• v.22 no.2
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• pp.185-220
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• 2014

The purpose of this study is to get a whole picture of financial conditions of the new and renewable energy sector which have been growing rapidly and predict bankruptcy risk quantitatively. There have been many researches on the methodologies for company failure prediction, such as financial ratios as predictors of failure, analysis of corporate governance, risk factors and survival analysis, and others. The research method for this study is Altman Z-score which has been widely used in the world. Data Set was composed of 121 companies with financial statements from KIS-Value. Covering period for the analysis of the data set is from the year 2006 to 2011. As a result of this study, we found that 38 percent of the data set belongs to "Distress" Zone (on alert) while 38% (on watch), summed into 76%, whose level could be interpreted to doubt about the sustainability. The average of the SMEs in wind energy sector was worse than that of SMEs in solar energy sector. And the average of the SMEs in the "Distress" Zone (on alert) was worse than that of the companies of large group in the "Distress" Zone (on alert). In conclusion, Altman Z-score was well proved to be effective for New & Renewable Energy Industry in Korea as a result of this study. The importance of this study lies on the result to demonstrate empirically that the majority of solar and wind enterprises are facing the risk of bankruptcy. And it is also meaningful to have studied the relationship between SMEs and large companies in addition to advancing research on new start-up companies.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.