• 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.

• 아동학회지
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• 제4권
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• pp.1-10
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• 1983

This study examined the characteristics of the relationship of home environment variables and preschool children's intelligence, learning readiness and socio-emotional developments. The subjects of this study were 63 children at age five and their mothers. Instruments included the children's intelligence test, preschool inventory for learning readiness, the socio-emtional rating scale and the inventory of HOME. The data of the present study were analyzed by the statistical methods of Pearson's product-moment correlation coefficient and step-wise multiple regression analysis. The kinds of HOME variables that significantly predict children's intelligence were "need gratification and avoidance of restriction" "quality of language environment" "play materials" "aspects of physical environment" "organization of stable and predictable environment". The variables that significantly predict children's socio-emotional developments were "breath of experience" "fostering maturity and independence" "developmental stimulation". All of the HOME variables were not significantly predict children's learning readiness. The kinds of HOME factors that significantly predict children's intelligence were factor II and factor III. Factor I predicted children's socio-emotional developments significantly. All of the HOME factors were not significantly predicted children's learning readiness.

• 대한조선학회지
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• 제23권3호
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• pp.10-16
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• 1986

This paper deals with a simple, approximate formula to predict the natural frequencies of the sagged cable structures. Assuming that the propagation velocity of the lateral wave is dependent only on the local mass per unit length and local tension, the explicit simple formula to predict the fundamental period is newly derived. The modified form of these formula is also presented for the prediction of the fundamental period of general shaped cable structures. The results of comparison shows fairly good agreements with experimental results and with theoretical ones. This formula is also used to predict the natural frequencies of a long vertical cable and the derived approximate formula in that case, becomes identical to the exact solution.

• 한국자동차공학회논문집
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• 제8권2호
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• pp.33-40
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• 2000

In order to investigate the exhaust structure and secondary oxidation of unburned hydrocarbon (HC) in the exhaust port, a numerical simulation was performed with 3-dimensional flow model and oxidation mechanism optimized for port oxidation. To predict the exhaust and oxidation process with consideration of flow, mixing, and temperature, 3-dimensional flow model and HC oxidation model were used with a commercial computational program, STAR-CD. The flow model were with moving grid for valve motion, which could predict the change of flow field with respect to valve lift. Optimization was performed to predict the HC oxidation with temperature range of 1200~1500K, low HC and oxygen concentration, existence of intermediate species, as typical in port oxidation. The constructed model could predict the port oxidation process with oxidation degree of 14~48% according to the engine operation conditions.

• Journal of applied mathematics & informatics
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• 제39권1_2호
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• pp.239-249
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• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• Computers and Concrete
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• 제30권3호
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• pp.209-224
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• 2022

In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.

• 한국BIM학회 논문집
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• 제12권2호
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• pp.40-48
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• 2022

It is very important to accurately predict construction costs in the early stages of the construction project. However, it is difficult to accurately predict construction costs with limited information from the initial stage. In recent years, with the development of machine learning technology, it has become possible to predict construction costs more accurately than before only with schematic construction characteristics. Based on machine learning technology, this study aims to analyze plans to more accurately predict construction costs by using only the factors influencing construction costs. To the end of this study, the effect of the error rate according to the activation function and the node configuration of the hidden layer was analyzed.

• 대한인간공학회지
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• 제35권4호
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• pp.277-291
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• 2016

Objective: The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. Background: When estimating usability based on Model-based Evaluation (MBE) in interfaces, the GOMS model measured 'operators' to predict the execution time in the desktop environment. Therefore, this study used the concept of operator in GOMS for touch gestures. Since the touch gestures are comprised of possible unit touch gestures, these unit touch gestures can predict to performance time with unit touch gestures on mobile devices. Method: In order to extract unit touch gestures, manual movements of subjects were recorded in the 120 fps with pixel coordinates. Touch gestures are classified with 'out of range', 'registration', 'continuation' and 'termination' of gesture. Results: As a results, six unit touch gestures were extracted, which are hold down (H), Release (R), Slip (S), Curved-stroke (Cs), Path-stroke (Ps) and Out of range (Or). The movement time predicted by the unit touch gesture model is not significantly different from the participants' execution time. The measured six unit touch gestures can predict movement time of undefined touch gestures like user-defined gestures. Conclusion: In conclusion, touch gestures could be subdivided into six unit touch gestures. Six unit touch gestures can explain almost all the current touch gestures including user-defined gestures. So, this model provided in this study has a high predictive power. The model presented in the study could be utilized to predict the performance time of touch gestures. Application: The unit touch gestures could be simply added up to predict the performance time without measuring the performance time of a new gesture.

• 산업경영시스템학회지
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• 제43권3호
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• pp.1-9
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• 2020

Interest rate spreads indicate the conditions of the economy and serve as an indicator of the recession. The purpose of this study is to predict Korea's interest rate spreads using US data with long-term continuity. To this end, 27 US economic data were used, and the entire data was reduced to 5 dimensions through principal component analysis to build a dataset necessary for prediction. In the prediction model of this study, three RNN models (BasicRNN, LSTM, and GRU) predict the US interest rate spread and use the predicted results in the SVR ensemble model to predict the Korean interest rate spread. The SVR ensemble model predicted Korea's interest rate spread as RMSE 0.0658, which showed more accurate predictive power than the general ensemble model predicted as RMSE 0.0905, and showed excellent performance in terms of tendency to respond to fluctuations. In addition, improved prediction performance was confirmed through period division according to policy changes. This study presented a new way to predict interest rates and yielded better results. We predict that if you use refined data that represents the global economic situation through follow-up studies, you will be able to show higher interest rate predictions and predict economic conditions in Korea as well as other countries.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1189-1198
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• 2015

글로벌 금융 위기 발생으로 헤지펀드의 영향력이 증가하면서 헤지펀드의 위험도와 생존여부를 가늠할 새로운 접근법이 필요하게 되었다. 본 연구에서는 헤지펀드의 데이터를 입력값으로 하는 퍼지신경망 모형을 통해 헤지펀드의 생존여부를 예측한다. 헤지펀드의 데이터는 그 변수가 불명확하고 내재적인 불확실성을 가지고 있어 생존 여부의 경계를 설정하는데 어려움이 있다. 따라서 생존 여부를 소속정도로 평가하여 불확실성을 모사할 수 있는 퍼지신경망 모형을 적용하여 예측하고 정확도를 평가한다. 또한 다른 인공지능 방법론들을 이용하여 평가한 결과와 제시한 모형의 성과를 비교하여 그 차이점을 확인한다. 본 연구의 실험결과를 통해 퍼지신경망 모형의 예측력을 확인했으며, 향후 투자자들이 헤지펀드 투자에 대한 의사를 결정하는데 도움을 줄 것으로 기대한다.