• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1739-1745
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• 2014

Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naive Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.8-18
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• 1993

A multi-font, multi-size and high speed character recognition system is designed. The design principles are simpilcity of algorithm, adaptibility, learnability, hierachical data processing and attention by feed back. For the multi-size character recognition, the extracted character images are normalized. A hierachical classifier classifies the feature vectors. Feature is extracted by applying the directional receptive field after the directional dege filter processing. The hierachical classifier is consist of two pre-classifiers and one decision making classifier. The effect of two pre-classifiers is prediction to the final decision making classifier. With the pre-classifiers, the time to compute the distance of the final classifier is reduced. Recognition rate is 95% for the three documents printed in three kinds of fonts, total 1,700 characters. For high speed implemention, a multiprocessor system with the ring structure of four transputers is implemented, and the recognition speed of 30 characters per second is aquired.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.5
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• pp.146-152
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• 1998

Recently, for the development of vehicle structures and components there is a tendency to increase using numerical simulation methods compared with practical tests for the estimation of the fatigue strength. In this study, an integrated powerful methodology is suggested for fatigue strength evaluation through development of the interface program to integrate dynamic analysis quasi-static stress analysis and fatigue analysis, which were so far used independently. To verify the presented evaluation method, a single and zigzag bump run test, 4-post road load simulation and driving durability test have been performed. The prediction results show a good agreement between analysis and test. This research indicates that the integrated life prediction methodology can be used as a reliable design tool in the pre-prototype and prototype development stage, to reduce the expense and time of design iteration.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.997-1002
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• 2002

Traditional prediction models have been developed with a fixed equation form based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network (ANN) does not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this paper is to develop the I-PreConS (Intelligent system for PREdiction of CONcrete Strength using ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.2
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• pp.81-88
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• 2014

Synthetic products such as casting tape and splints are rapidly replacing conventional plaster casts to treat orthopedic patients. Most synthetic products are produced through a polymerization process with related chemical agents. In this study, the effect of the process parameters on the residual NCO content within a prepolymer for casting tape and the hardening temperature for casting tape were experimentally evaluated. In order to verify the effects of the process parameters, an experimental method was adopted. From an S/N ratio analysis, optimal parameter combinations were determined to produce a pre-polymer with a suitable residual NCO content and alower hardening temperature. Prediction models for the NCO content and the hardening temperature were developed and confirmed.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.157-160
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• 1999

A prediction method of conditional event matching pre-diction (EMP) for a purpose of predicting nonlinear phenomena of insulator pollution was proposed in this paper. The EMP was used if the conditional probability for increase of insulator pollution exceeded a threshold value. A performance of the EMP was strongly related to selection of database of events and a closeness function. By use of the prediction of the insulator pollution based on the conditional EMP, reliable decision making for the washing timing of the polluted insulators was e-valuated based on actual data in Kasatsu substation, Japan.

• Health Policy and Management
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• v.12 no.3
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• pp.54-76
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• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.5
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• pp.234-240
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• 2001

Audible noise (AN) produced by corona discharges from high voltage transmission lines is one of the more important considerations in line design. Therefore, line designers must pre-determine the AN using prediction formulas. This paper presents the results of applying evolutionary computation techniques using AN data from lines throughout the world to develop new, highly accurate formulas for predicting a A-weighted AN during heavy rain and stable rain from overhead ac lines. Calculated ANs using these new formulas and existing formulas are compared with measured data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.554-558
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• 2004

In this paper, we introduce noise prediction program of HVAC system to assist low-noisy design of ship's cabin. The developed program calculates sound power levels at HVAC components considering primary and secondary noise generated by fan and duct element, duct element noise attenuation, and duct break-in noise based on the authentic empirical method suggested by NEBB and acoustic power balancing method. Sound pressure level at cabin with or without ceiling system is evaluated by the diffuse-field theory considering diffuser and duct break-out sound powers. Moreover, the program provides intuitive pre- and post-processors using modem GUI functions to help efficient modeling and evaluation of cabin and HVAC component noise. To validate the accuracy and convenience of the program, noise prediction for a HVAC system is demonstrated.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.14-19
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• 2019

In this paper, we propose a system for controlling the brightness of street lights by predicting pedestrian paths, identifying the position of pedestrians with motion sensing sensors and obtaining motion vectors based on past walking directions, then predicting pedestrian paths through the route prediction smart street lighting system. In addition, by using motion vector data, the pre-treatment process using linear interpolation method and the fuzzy system and neural network system were designed in parallel structure to increase efficiency and the rough set was used to correct errors. It is expected that the system proposed in this paper will be effective in securing the safety of pedestrians and reducing light pollution and energy by predicting the path of pedestrians in the detection of movement of pedestrians and in conjunction with smart street lightings.