• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10539-10542
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• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.942-946
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• 2010

As the major sources of interior noise of train at running condition are the wheel/rail contact noise, the traction motor's noise and the driving gear's noise and these noise sources are transmitted through the car body, the noises of HVAC and air duct can be ignored. But the interior noise of train at standstill condition is decided by HVAC's noise and noise from the diffuser through the air duct. the interior noise prediction of train at standstill condition should be performed considering the shape of air duct, the air velocity and noise reduction property inside the air duct. But it is hard to estimate the interior noise level by the numerical method. Therefore train maker predict the interior noise level using The commercial noise prediction program. This paper introduce the noise prediction method of the train at standstill condition using the commercial program appling the ray tracing method and statistical energy analysis.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.252-254
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.6
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• pp.154-161
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• 1995

There are several kinds of driving conditions according to the characteristic of each vehicle diver. Automaker produces vehicle strong enough to satisfy this several driving conditions at the point of vehicle durability. In order to develop the car in a short period, Automaker engineer tests vehicle at serveral accelerated durability test roads. Before testing the vehicle durability, test engineer must know how much this test road severe than general field road which is composed of high way, city road, paved road and unpaved road. This paper suggests two types of road severity test method that is using relative fatigue life prediction method and using absolute fatigue life prediction method, and also present the merits and demerits of two test methods.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.98-109
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• 2006

Fire control system(FCS) errors can be classified as hardware errors, filter prediction errors, effective ballistic function errors, and aiming errors. Among these errors, the filter prediction errors are the most significant error sources. To reduce them, a target future position calculation method using the acceleration estimate is suggested and it is compared with the constant velocity target prediction method. Simulation results show that the suggested method has better performance than the constant velocity prediction method. Target tracking algorithm is established with multiple target tracking filters based on IMM structure.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.1203-1208
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• 2005

This study is the evaluation of rating of railway prestressed concrete beam bridges by prediction of residual effective prestress force. Therefore, developed prediction method is based on the center camber of prestressed concrete beam, structural design report of various PSC beams, construction reference materials of PSC beams. Both rating evaluation and residual effective prestress force by developed method is compared with evaluation by structural design. This comparison results shows that this developed method is very effective method. Therefore evaluation of rating by prediction of residual effective prestress force will be used for evaluation of the rating of railway PSC beam bridges.

• Journal of the Korean GEO-environmental Society
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• v.16 no.11
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• pp.29-37
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• 2015

Non-excavation method is needed to secure the stability of existing structures during construction. Therefore, prediction of ground settlement is essential. Causes of settlement when using steel pipe indentation method are leading pipe-steel pipe gap, excessive excavation and soil-steel pipe friction etc. Also they are similar to the causes of settlement when using Shield TBM during construction. In this study, ground settlement during steel pipe indentation is predicted by the Gap Parameter Method and Volume Loss Method which are kinds of Shield TBM prediction Method. and compared with those of prediction methods by conducting field test. As a result, Volume Loss Prediction Method is the most similar to the field tests. However, It is needed to additional studies, such as decision of the factors and adaptability for total settlement predictions of non-excavation method.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.91-98
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• 2016

We propose a method to train a model that can predict the probability of a crime being committed. CCTV data by matching criminal events are required to train the crime prediction model. However, collecting CCTV data appropriate for training is difficult. Thus, we collected actual criminal records and converted them to an appropriate format using variables by considering a crime prediction environment and the availability of real-time data collection from CCTV. In addition, we identified new specific crime types according to the characteristics of criminal events and trained and tested the prediction model by applying neural network partial least squares for each crime type. Results show a level of predictive accuracy sufficiently significant to demonstrate the applicability of CCTV to real-time crime prediction.

• Journal of Ocean Engineering and Technology
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• v.9 no.2
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• pp.112-122
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• 1995

In this work, prediction of fatigue life and fatigue crack growth are studied. 4th order polynominal function is presented to describe the crack growth behaviors from artifical pit of SM45C steel. Crack growth curves obtained from 4th order polyminal growth equations are in good agreement with experimental data The crack growth behaviors at arbitrary stress levels and investigated by the concept of elastic-plastic fracture mechanics using ${\Delta}J$. Fatigue life prediction are carried out by numerical integral method. Prediction lives obtained by proposed method in this study, is in good agreement with the experimental ones. Life prediction results calculated by using of ${\Delta}J$ better than those of ${\Delta}K$.