• 한국경영과학회지
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• 제23권1호
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• pp.109-141
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• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• 음성과학
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• 제10권2호
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• pp.7-25
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• 2003

Predicting the right prosodic elements is a key factor in improving the quality of synthesized speech. Prosodic elements include break, pitch, duration and loudness. Pitch, which is realized by Fundamental Frequency (F0), is the most important element relating to the quality of the synthesized speech. However, the previous method for predicting the F0 appears to reveal some problems. If voiced and unvoiced sounds are not correctly classified, it results in wrong prediction of pitch, wrong unit of triphone in synthesizing the voiced and unvoiced sounds, and the sound of click or vibration. This kind of feature is usual in the case of the transformation from the voiced sound to the unvoiced sound or from the unvoiced sound to the voiced sound. Such problem is not resolved by the method of grammar, and it much influences the synthesized sound. Therefore, to steadily acquire the correct value of pitch, in this paper we propose a new model for predicting and classifying the voiced and unvoiced sounds using the CART tool.

• International Journal of Concrete Structures and Materials
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• 제3권2호
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• pp.81-90
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• 2009

This paper proposes a semi analytical model to describe the pore structure of concrete by a set of simple equations. The relationship between the porosity and the microstructure of concrete has been considered when constructing the analytical model. The microstructure includes the interface transition zone (ITZ) between aggregates and cement paste. The predicting model of porosity was developed with considering the ITZ for various mixing of mortar and concrete. The proposed model is validated by the rapid experimental programs. Although the proposed model is semi-analytical and relatively simple, this model could be reasonably utilized for the durability design and adapted for predicting the service life of concrete structures.

• Structural Engineering and Mechanics
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• 제61권5호
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• pp.605-615
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• 2017

Shear failure of reinforced concrete (RC) beams is a major concern for structural engineers. It has been shown through various studies that the shear strength and ductility of RC beams can be improved by adding steel fibers to the concrete. An accurate model predicting the shear strength of steel fiber reinforced concrete (SFRC) beams will help SFRC to become widely used. An artificial neural network (ANN) model consisting of an input layer, a hidden layer of six neurons and an output layer was developed to predict the shear strength of SFRC slender beams without stirrups, where the input parameters are concrete compressive strength, tensile reinforcement ratio, shear span-to-depth ratio, effective depth, volume fraction of fibers, aspect ratio of fibers and fiber bond factor, and the output is an estimate of shear strength. It is shown that the model is superior to fourteen equations proposed by various researchers in predicting the shear strength of SFRC beams considered in this study and it is verified through a parametric study that the model has a good generalization capability.

• 대한안전경영과학회지
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• 제16권3호
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• pp.145-154
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• 2014

Performance based design(PBD) is the method to make a fire safety design against them after predicting the factors of fire risk in a building. Therefore, predicting fire risk in a building is very important process in PBD. For predicting fire risk of a building, an engineer of PBD must consider various factors such as ignition location, ignition point, ignition source, first ignited item, second ignited item, flash over, the state of door and fire suppression system. But, it is difficult to trust fire safety capacity of the design because the process in Korea' PBD is unprofessional and unreasonable. This paper had surveyed some cases of PBD that had been made in Korea to find the problems of the process to predict fire risk. And it have proposed the improvements of process to predict fire risk of a building.

• 부모자녀건강학회지
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• 제20권1호
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• pp.39-47
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• 2017

Purpose: This study was conducted to investigate the factors predicting HPV (Human Papilloma Virus) vaccination practices among female college students. Methods: A convenience sample of 207 female students attending four universities in one metropolitan city participated. Self-report questionnaires consisted of general characteristics, characteristics related prevention of cervical cancer, knowledge of HPV, knowledge of cervical cancer vaccination, and health beliefs related to HPV vaccination. Data were analyzed by $x^2$ test, independent t-test, and bivariate logistic regression. Results: Factors predicting HPV vaccination practices were information about HPV (OR=3.37), experience of HPV test (OR=12.71), and health beliefs related to HPV vaccination (OR=1.13). Conclusion: In order to increase the practice rate of HPV vaccination, it is necessary to provide simple key information that is easy to understand, rather than expert knowledge about HPV. Therefore, it is necessary to provide a way for college students to get information about virus easily. It is necessary to intervene integrally with the facilitation factor and obstacle factor of vaccination practice.

• Tribology and Lubricants
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• 제25권3호
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• pp.150-156
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• 2009

Precise leakage prediction for annular type seals of turbomachinery is necessary for enhancing their efficiency and various prediction methods have been developed. As the seal passage is designed intricately, the analysis based on Bulk-flow concept which has been mainly used in predicting seal leakage is limited. In order to improve the seal leakage prediction, full Navier-Stokes Equations with turbulent model derived in the seal flow passage have to be solved. In this study, 3D CFD (Computational Fluid Dynamics) analysis has been performed for predicting leakage of various non-contact type anular seals using FLUENT. Compared to the results by Bulk-flow model analysis, experiment, and 2D CFD analysis, the result of 3D CFD analysis shows improvement in predicting seal leakage, especially for the parallel grooved pump seal.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.325-333
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• 1996

This study was conducted to develop neural networks of predicting the sugar content of fruits based on the optical densities obtained from a spectrophotometer. Pear, apple and peach were used in investigating the feasbility of the developed neural networks as a nondestructive measurement. A spectrophotometer was used to measure the optical densities of test fruits. The neural networks suggested in this study consisted of multi-layers having one hidden layer and one output layer. The correlation coefficients between the predicted and the measured sugar content for most fruits were high. The neural networks using 2nd derivatives of optical density spectrum produced a better results in predicting the sugar content of fruits. This study contributed to develop a method for nondestructively predicting the sugar content of fruits.

• 한국가금학회:학술대회논문집
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• 한국가금학회 2003년도 국제 심포지움
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• pp.9-20
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• 2003

미래를 예측한다는 것은 기회가 됨과 동시에 도전이 되기도 한다. 다음 시대에 양계산업에서 어떤 변화가 닥칠 것인가를 예상하는 것은 과거를 돌이켜보는 기회가 되며, 다시 이것은 미래를 예측할 수 있는 수단으로 사용할 수 있다. 지난 50∼60 년 동안 양계산업이 상업화된 것을 보면 차기 50 년 동안 일어날 수 있는 발전은 분명 굉장할 것이다. 영양, 육종, 건강, 생산, 사양과 같은 분야에서 기술의 진보는 양계산업의 발달에 이바지하였으며 또한 계속될 것이다. (중략)

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.