• Journal of Platform Technology
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• v.6 no.3
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• pp.47-54
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• 2018

Predictive analysis in the hardware industry can be performed at an appropriate point in time to prevent failure of production facilities and reduce management costs. This helps to perform more efficient and scientific maintenance through automation of failure analysis. Among them, predictive management aims to prevent the occurrence of anomalous state by identifying and improving the abnormal state based on the gathering, analysis, and scientific data management of facilities using information technology and constructing prediction model do. In this study, we made a fault tree through the Relex tool and analyzed the error code of the hardware to study the safety.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.846-854
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• 1996

In this paper, we propose a solution to the software maintenance problem that is a primary factor of software crisis. We surveyed and analyzed the current software maintenance problems through questionnaires and interviews. As a result, we defined the software maintenance management life cycle and established a fundamental strategies to solve the software maintenance problems efficiently. We also designed a software maintenance management support systems to construct an automated software maintenance management tool. Furthermore, tp improve the formalization and reliability of the software maintenance management procedure, we defined acost predictive model using a fixed-single parameter based on comprehensive program size for the source code and delivered effort(person/month). We elaborated the model by considering an experience level of maintainer, a skill- level defined by the manager, and a reliability level required by the model of maintenance management.

• Physical Therapy Korea
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• v.23 no.4
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• pp.63-70
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• 2016

Background: The Pediatric Balance Scale (PBS) and the Fullerton Advanced Balance (FAB) scale were used to assess balance function in patients with balance problem. These multidimensional clinical balance scales provide information about potential risk factors for falls. Objects: The purpose of this study was to investigate and compare the predictive properties of the PBS and FAB scales relative to fall risk in children with cerebral palsy (CP) using a receiver operating characteristic analysis. Methods: In total, 49 children with CP (boy=21, girl=28) who were diagnosed with level 1 or 2 according to the Gross Motor Function Classification System participated in this study. The PBS and FAB were performed, and verified cut-off score, sensitivity, specificity, and the area of under the curve (AUC). Results: In this study, the PBS scale was as a predictive measure of fall risk, but the FAB was not significant in children with CP. A cut-off score of 45.5 points provided optimal sensitivity of .90 and specificity of .69 on the PBS, and a cut-off score of 21.5 points provided optimal sensitivity of .90 and specificity of .62 on the FAB. Both scales showed moderately accurate of AUC with .79 and .76, respectively. Conclusion: The PBS is a useful screening tool for predicting fall risk in children with cerebral palsy, and those who score 45.5 or lower indicate a high risk for falls and are in need of balance intervention.

• Journal of Integrative Natural Science
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• v.11 no.3
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• pp.161-164
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• 2018

Over the past decade, mass spectrometry-based metabolomics, especially two dimensional gas chromatography mass spectrometry (GCxGC/TOF-MS), has become a key analytical tool for metabolomics data because of its sensitivity and ability to analyze complex biological or biochemical sample. However, the need to reduce variations within/between experiments has been reported and methodological developments to overcome such problem has long been a critical issue. Along with methodological developments, developing reasonable performance measure has also been studied. Following four numerical measures have been typically used for comparison: sensitivity, specificity, receiver operating characteristic (ROC) curves, and positive predictive value (PPV). However, more recently, such measures are replaced with F1 score in many fields including metabolomics area without any carefulness of its validity. Thus, we want to investigate the validity of F1 score on two examples, with the goal of raising the awareness in choosing appropriate performance comparison measure. We noticed that F1 score itself, as a performance measure, was not good enough. Accordingly, we suggest that F1 score be supplemented with other performance measure such as specificity to improve its validity.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.889-896
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• 2012

Recent progress in the computational fluid dynamics methods of two- and multiphase phase flows has already started opening up new exciting possibilities for using complete multidimensional models to simulate boiling systems. Combining this new theoretical and computational approach with novel experimental methods should dramatically improve both our understanding of the physics of boiling and the predictive capabilities of models at various scale levels. However, for the multidimensional modeling framework to become an effective predictive tool, it must be complemented with accurate mechanistic closure laws of local boiling mechanisms. Boiling heat transfer has been studied quite extensively before. However, it turns out that the prevailing approach to the analysis of experimental data for both pool boiling and forced-convection boiling has been associated with formulating correlations which normally included several adjustable coefficients rather than based on first principle models of the underlying physical phenomena. One reason for this has been the tendency (driven by practical applications and industrial needs) to formulate single expressions which encompass a broad range of conditions and fluids. This, in turn, makes it difficult to identify various specific factors which can be independently modeled for different situations. The objective of this paper is to present a mechanistic modeling concept for both pool boiling and forced-convection boiling. The proposed approach is based on theoretical first-principle concepts, and uses a minimal number of coefficients which require calibration against experimental data. The proposed models have been validated against experimental data for water and parametrically tested. Model predictions are shown for a broad range of conditions.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.4
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• pp.554-559
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• 2004

The objective of the present study was to investigate the accuracy of mastitis diagnostic indicators for different dairy goat breeds. Biweekly milk samples were collected from individual half mammary gland of seven Saanen and seven Alpine dairy goats in the period of 40 to 120 days in milk. With threshold value set at 2.8 and 3.1 for Alpine and Saanen dairy goats, respectively, log (SPC) offered good sensitivity (0.89, 0.93), specificity (0.88, 0.95), positive predictive value (0.75, 0.85) and negative predictive value (0.95, 0.98) as a mastitis diagnostic tool. The correlations of log (SPC) with milk yield, log (SCC), ALP, LDH, $Na^{+}$, $K^{+}$ and EC were significant in Saanen dairy goats (p<0.05), with the highest correlation coefficient (0.653) existing between log (SPC) and log (SCC). The correlations of log (SPC) with milk yield, milk fat, milk protein, log (SCC), $Na^{+}$, $K^{+}$, EC were significant in Alpine dairy goats (p<0.05), with the highest correlation coefficient (0.416) existing between log (SPC) and log (SCC). There were different best-fit regression equations with different multiple diagnostic indicators for Saanen and Alpine dairy goats. In conclusion, different breeds of dairy goats may have to adapt different mastitis diagnostic parameters for a better diagnosis.

• The Journal of the Acoustical Society of Korea
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• v.31 no.8
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• pp.551-560
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• 2012

Target detection by acoustic barrier method includes active and passive sonar technique and time reversal process whose theoretical background is already well defined. In this paper, the concept and theory of underwater detection by passive ocean time reversal is established. Also, the reason that this study was conducted was to investigate feasibility of complex mathematical modeling to provide some predictive capability for underwater acoustic barrier with passive time reversal. It may eventually lead to a useful predictive tool when designing underwater acoustic barrier detection system using the passive time reversal concept.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.837-851
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• 2013

In construction industry, strength is a primary criterion in selecting a concrete for a particular application. The concrete used for construction gains strength over a long period of time after pouring the concrete. The characteristic strength of concrete is defined as the compressive strength of a sample that has been aged for 28 days. Neither waiting for 28 days for such a test would serve the rapidity of construction, nor would neglecting it serve the quality control process on concrete in large construction sites. Therefore, rapid and reliable prediction of the strength of concrete would be of great significance. On this backdrop, the method is proposed to establish a predictive relationship between properties and proportions of ingredients of concrete, compaction factor, weight of concrete cubes and strength of concrete whereby the strength of concrete can be predicted at early age. Multiple regression analysis was carried out for predicting the compressive strength of concrete containing Portland Pozolana cement using statistical analysis for the concrete data obtained from the experimental work done in this study. The multiple linear regression models yielded fairly good correlation coefficient for the prediction of compressive strength for 7, 28 and 40 days curing. The results indicate that the proposed regression models are effectively capable of evaluating the compressive strength of the concrete containing Portaland Pozolana Cement. The derived formulas are very simple, straightforward and provide an effective analysis tool accessible to practicing engineers.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1215-1219
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• 2006

Scheduling plays an important role in the process management in terms of providing profit-maximizing operation sequence of multiple orders and estimating completion times of them. In order to takes its full potential, varying conditions should be properly reflected in computing the schedule. The adjustment of scheduling decisions has to be made frequently in response to the occurrence of variations. It is often challenging because their model has to be adjusted and their solutions have to be computed within short time period. This paper employs Model Predictive Control(MPC) principles for updating the process condition in the scheduling model. The solutions of the resulting problems considering variations are computed using parametric programming techniques. The key advantage of the proposed framework is that repetition of solving similar programming problems with decreasing dimensionis avoided and all potential schedules are obtained before the execution of the actual processes. Therefore, the proposed framework contributes to constructing a robust decision-support tool in the face of varying environment. An example is solved to illustrate the potential of the proposed framework with remarks on potential wide applications.

• Asia pacific journal of information systems
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• v.25 no.1
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• pp.83-97
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• 2015

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.