• Journal of Distribution Science
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• v.11 no.3
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• pp.5-11
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• 2013

Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company's going concern status that is a tough process. Various going concern prediction models' based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature. Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model. Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data. Conclusions - The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1095-1100
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• 2011

In this paper, PHM (Prognostics and Health Management) techniques are briefly outlined. Prognostics, being a central step within the PHM, is explained in more detail, stating that there are three approaches - experience based, data-driven and model based approaches. Representative articles in the field of prognostics are also given in terms of the type of faults. Model based method is illustrated by introducing a case study that was conducted to the crack growth of the gear plate in UH-60A helicopter. The paper also addresses the comparison of the OBM (Overall Bayesian Method), which was developed by the authors with the PF (Particle Filtering) method, which draws great attention recently in prognostics, through the study on a simple crack growth problem. Their performances are examined by evaluating the metrics introduced by PHM society.

• Korean Institute of Interior Design Journal
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• v.14 no.2 s.49
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• pp.217-224
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• 2005

The lack of objective decisive criteria and the absence of analyzing tools accrued from the experiments on various types developed from space design process makes it difficult to select and execute alternatives for them. As an attempt of coping with these problems, the aims of this study is to establish space analysis' models and to propose possibility of analyzing models by utilizing the technology of image process. It is now under study in the field of artificial intelligence based on the accomplishment of digital images. This study focused on establishment an analysis model based on accomplished digital images and image processing framework. It helps utilize various processing technologies that are currently in use of image processes, and problems of the study can be supplemented through further follow-up studies. Finally, analysis model can be constructed gradually huge design data in the analogue data to the digital image database and be proposed with index in design or evaluation step.

• ETRI Journal
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• v.46 no.4
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• pp.633-647
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• 2024

In e-commerce platforms, sentiment analysis on an enormous number of user reviews efficiently enhances user satisfaction. In this article, an automated product recommendation system is developed based on machine and deep-learning models. In the initial step, the text data are acquired from the Amazon Product Reviews dataset, which includes 60 000 customer reviews with 14 806 neutral reviews, 19 567 negative reviews, and 25 627 positive reviews. Further, the text data denoising is carried out using techniques such as stop word removal, stemming, segregation, lemmatization, and tokenization. Removing stop-words (duplicate and inconsistent text) and other denoising techniques improves the classification performance and decreases the training time of the model. Next, vectorization is accomplished utilizing the term frequency-inverse document frequency technique, which converts denoised text to numerical vectors for faster code execution. The obtained feature vectors are given to the modified convolutional neural network model for sentiment analysis on e-commerce platforms. The empirical result shows that the proposed model obtained a mean accuracy of 97.40% on the APR dataset.

• The Journal of Society for e-Business Studies
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• v.1 no.1
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• pp.117-140
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• 1996

CALS is recognized as a national response to the new chapter of information society. It is essential that the standardization in Korea should be constructed compatible with not only domestic but also international standardization trend. This study aims to propose a proper direction of CALS standardization in Korea, based on the international CALS standardization movement. This paper classifies standard into five types and provides a proper direction and guidance far each standard. As a trend of CALS standard, all ten data files are converted using SGML standard far the interchangeability of data among heterogenous systems. CAD and Graphic data arc also moving toward to the STEP as their standard. In this regard, this paper discusses how to implement SGML and STEP Model. Finally, this paper proposes a method how to construct an EDI system with CALS standard and how to establish a standard authorization institute which will be responsible far the standard authorization. Furthermore, this paper also proposes the CALS Test Network (CTN) as its experimental method.

• The Journal of Society for e-Business Studies
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• v.4 no.2
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• pp.161-175
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• 1999

CALS is a strategy to share integrated product data through a set of standards to achieve efficiencies in business and operational mission areas. In this paper, we studied current status for CALS standard and then analyzed the case of US DoD. The results can be summarized as implementing for two major standard in defense CALS system. They are STEP and XML. Korea Defense can be used to set direction for CALS standard implementation and standard selection process based on this paper's recommendations.

• Honam Mathematical Journal
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• v.43 no.4
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• pp.717-739
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• 2021

We used blood data to predict obesity by complementing the BMI risk, because some blood factors are significantly associated with obesity. For the sampling method, a two-step stratified colony sampling method was used based on sixteen blood factors collected by the Korea National Health and Nutrition Examination Survey(KNHANES). We identify the number of effective blood data of obesity in the final model as 6 ~ 8 factors that differ somewhat depending on age and gender. Also, the coefficient of determination that represents the predictive power of obesity in the regression model is the highest for both men and women of aged 19 and in their 20s and 30s, and the predictive power decreases with increasing age.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.863-868
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• 2005

Hardware and software codesign framework called PeaCE(Ptolemy extension as a Codesign Environment) was developed. It allows to express both data flow and control flow which is described as fFSM which extends traditional finite state machine. While the fFSM model provides lots of syntactic constructs for describing control flow, it has a lack of their formality and then difficulties in verifying the specification. In order to define the formal semantics of the fFSM, in this paper, firstly the hierarchical structure in the model is flattened and then the step semantics is defined. As a result, some important bugs such as race condition, ambiguous transition, and circulartransition can be formally detected in the model.

• Preventive Nutrition and Food Science
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• v.1 no.1
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• pp.41-45
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• 1996

This study was conducted to develop the fermentation kinetic model for the prediction of acidity and pH changes in Kimchi as a function of fermentation temperatures. The fitness of the model was evaluated using traditional two-step method and an alternative non-linear regression method. The changes in acidity and pH during fermentation followed the pattern of the first order reaction of a two-step method. As the fermentation temperature increased from 4$^{\circ}C$ to 28, the reaction rates of acidity and pH were increased 8.4 and 7.6 times, respectively. The activation energies of acidity and pH were 16.125 and 16.003kcal/mole. The average activation energies of acidity and pH using a non-linear method were 16.006 by the first order and 15.813 kcal/mole by the zero order, respectively. The non-linear procedure had better fitting 개 experimental data of the acidity and pH than two-step method. The shelf-lives based on the time to reach the 1.0% of acidity were 33.1day at 4$^{\circ}C$ and 2.8 day 28$^{\circ}C$.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.