• 대한토목학회논문집
• /
• 제32권6D호
• /
• pp.599-605
• /
• 2012

교통개방시점의 예측은 공사 계획을 위해 중요하며 이를 위해 에폭시 아스팔트 혼합물의 양생에 따른 강도를 예측하는 것이 필요하다. 본 연구에서는 에폭시 아스팔트 혼합물의 양생온도와 시간에 따른 마샬안정도를 측정하고 이를 이용해 강도 발현식을 구하였으며, 변화하는 온도와 강도에 따른 반응속도를 반영할 수 있도록 화학적 반응속도론을 이용하여 에폭시 아스팔트 강도 예측모델을 개발하였다. 예측모델을 사용하여 에폭시 아스팔트 포장이 적용된 국내 교량에 대해 교통개방시기를 예측하였다. 2009년~2011년의 기상조건에 따라 가정된 포장체 온도를 사용한 예측결과는 실제 교통개방일과 17일의 차이가 발생했으나 이는 2012년의 실제 기상상태와의 차이 때문이다. 실제 측정된 포장 온도를 예측모델에 대입할 경우 2일의 교통개방가능일 차이가 있었으며, 상관관계 분석 결과 R2가 0.95로 실제 강도값과 매우 유사한 결과를 얻을 수 있었다. 기상상태와 포장체의 온도에 대한 충분한 데이터를 확보한다면 에폭시 아스팔트 강도 예측모델을 사용하여 상당히 신뢰도 있는 교통개방 가능 시기의 예측이 가능한 것으로 나타났다.

• 한국정보통신학회논문지
• /
• 제6권7호
• /
• pp.1038-1043
• /
• 2002

The demand for multimedia service using Ka-band satellite communication are growing rapi이y. So, in this paper, we have analyzed rain attenuation with typical model, and proposed prediction model of rain attenuation in high frequency(over 20[GHz]). Path loss model by rain attenuation is based upon rain rate of representative region(6 cities). Proposed prediction model of rain attenuation and parameter of satellite link can be available for the Ka-band satellite communication.

• Journal of Information Processing Systems
• /
• 제15권6호
• /
• pp.1335-1349
• /
• 2019

Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.

• Structural Monitoring and Maintenance
• /
• 제10권2호
• /
• pp.175-190
• /
• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• Geomechanics and Engineering
• /
• 제18권4호
• /
• pp.373-389
• /
• 2019

It is important to study the problem of durability for tunnel structures. As a main influence on the durability of tunnel structures, carbonation-induced corrosion is studied. For the complicated environment of tunnel structures, based on the data samples from real engineering examples, the intelligent method (genetic programming) is used to construct the service life prediction model of tunnel structures. Based on the model, the prediction of service life for tunnel structures in carbonation environments is studied. Using the data samples from some tunnel engineering examples in China under carbonation environment, the proposed method is verified. In addition, the performance of the proposed prediction model is compared with that of the artificial neural network method. Finally, the effect of two main controlling parameters, the population size and sample size, on the performance of the prediction model by genetic programming is analyzed in detail.

• Computers and Concrete
• /
• 제12권4호
• /
• pp.377-392
• /
• 2013

This paper investigates prediction models estimating the hydration properties of concrete, such as the compressive strength, the splitting tensile strength, the elastic modulus,and the autogenous shrinkage. A prediction model is suggested on the basis of an equation that is formulated to predict the compressive strength. Based on the assumption that the apparent activation energy is a characteristic property of concrete, a prediction model for the compressive strength is applied to hydration-related properties. The hydration properties predicted by the model are compared with experimental results, and it is concluded that the prediction model properly estimates the splitting tensile strength, elastic modulus, and autogenous shrinkage as well as the compressive strength of concrete.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
• /
• pp.635-638
• /
• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• 한국안전학회지
• /
• 제32권1호
• /
• pp.115-120
• /
• 2017

The aim of this study is to develop construction safety and health management cost prediction model using support vector machine (SVM). To this end, theoretical concept of SVM is investigated to formulate the cost prediction model. Input and output variables have been selected by analyzing the balancing accounts for the completed construction project. In order to train and validate the proposed prediction model, 150 data sets have been gathered from field. Effects of SVM parameters on prediction accuracy are analyzed and from which the optimal parameter values have been determined. The prediction performance tests are conducted to confirm the applicability of the proposed model. Based on the results, it is concluded that the proposed SVM model can effectively be used to predict the construction safety and health management cost.

• Asia pacific journal of information systems
• /
• 제16권2호
• /
• pp.69-84
• /
• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• 한국통신학회논문지
• /
• 제38C권3호
• /
• pp.263-270
• /
• 2013

최근 세계적으로 바다가 자원의 보고로 주목 받으면서 해양 환경 분석 및 예측 기술에 대한 연구가 활발히 진행 되고 있다. 자동화된 해양 환경 자료의 수집과 수집된 자료를 분석하여서 해양재해를 예측하면 기름 유출에 의한 해양오염의 피해, 적조에 의한 수산업의 피해, 해양환경 이변에 의한 수산업 및 재해 피해를 최소화하는데 기여할 수 있다. 그러나 국내 해양 환경에 대한 조사 및 분석 연구는 제한적이다. 본 논문은 국내의 원해 및 근 해역에서 수집된 해양 환경 자료를 분석하여 해양재해를 예측할 수 있는 시스템 모델을 연구한다. 이를 위해서 본 논문에서는 해양재해 예측 시스템을 위해서 통신시스템 모델, 해양환경 자료 수집 시스템 모델, 예측분석 시스템 모델, 상황전파시스템에 대한 모델을 제시하였다. 또한 예측분석 시스템을 위한 적조 예측 모델과 요약분석 모델을 제시하였다.