• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.879-889
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• 2014

In this study, to estimate loss of life considered flood characteristics using the relationship derived from analysis of historical dam break cases and the factors determining loss of life, the loss of life module applying in LIFESim and loss of life estimation by means of a mortality function were suggested and applicability for domestic dam watershed was examined. The flood characteristics, such as water depth, flow velocity and arrival time were simulated by FLDWAV model and flood risk area were predicted by using inundation depth. Based on this, the effects of warning, evacuation and shelter were considered to estimate the number of people exposed to the flood. In order to estimate fatality rates based on the exposed population, flood hazard zone is assigned to three different zones. Then, total fatality numbers were predicted after determining lethality or mortality function for each zone. In the future, the prediction of loss of life due to dam break floods will quantitatively evaluate flood risk and employ to establish flood mitigation measures at downstream applying probabilistic flood scenarios.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.327-333
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• 2019

The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.6
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• pp.931-939
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• 2001

The slot coupling characteristics was analysed in a radial-line slot antenna for its design. The previously proposed waveguide model with a periodic boundary condition on its narrow walls and periodically arranged slots on its wide wall was used. The magnetic field integral equation and two dyadic Green\`s functions for respective regions was derived and the method of moments was used. To maximize the efficiency of numerical analysis and to extract singularities, two different kinds of basis functions, the entire domain basis function and the sub-domain one, are used. In addition, the Ewald sum technique for the rectangular waveguide and the Shanks transform for the half space were used to accelerate the computation of the slowly convergent potential Green\`s functions. Simulation results expressed the effects of the various design parameters on the slot coupling.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.109-120
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• 2006

This paper addresses techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus their attention on objects moving in Euclidean space. A variety of applications such as telematics, however, deal with objects that move only over road networks in most cases, thereby requiring an effective method of future prediction of moving objects on road networks. In this paper, we propose a novel method for predicting a future path of an object by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. We devise a new function that measures a similarity between trajectories by reflecting the characteristics of road networks. By using this function, we predict a future path of a given moving object as follows: First, we search for candidate trajectories that contain subtrajectories similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories thus retrieved. Also, we suggest a method that improves the accuracy of path prediction by regarding moving paths that have just small differences as the same group.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.6
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• pp.645-657
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• 2015

In this study floor impact noise and structure acceleration response of bare concrete slabs were predicted by using Finite Element Analysis(FEA). Prediction results were compared with experimental results to prove the accuracy of numerical model. Acoustic absorption were addressed by using panel impedance coefficients with frequency characteristics and structural modal damping of numerical model were applied by modal testing results and analysis of prediction and test results. By using frequency response function, the floor acceleration and acoustic pressure responses for various impact sources were calculated at the same time. In the FEA, the natural frequencies and the shapes of vibration and acoustic modes can be estimated through the eigen-value analysis, and it can be visually seen the vibration and sound pressure field and the contribution of major modes.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.271-278
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• 2011

Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.989-996
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• 2021

In the case of an unmanned aerial vehicle using a battery as a power source, there are restrictions in performing the mission because the battery capacity is limited. To extend the mission capability, it is important to minimize battery usage while the flight to the mission area. In addition, by using the battery usage prediction model, the possibility of mission completeness can be determined and it can be a criterion for selecting an emergent landing point in the mission planning stage. In this paper, we propose a battery usage prediction model considering as one of the environmental factors in the three-dimensional space. The required power is calculated according to the flight geometry of an unmanned aerial vehicle. True battery usage which is predicted from the required power is verified through the comparison with the battery usage prediction model. The optimal flight trajectory that minimizes battery usage is produced and compared with the shortest travel distance.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.281-286
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• 2006

본 연구의 목적은 차세대 대체에너지로 각광받는 풍력발전 중에서 육상발전보다 여러 가지 이점이 있는 한국형 해상풍력터빈 블레이드의 최적형상설계를 위한 알고리즘을 구현하는 것이다. 블레이드 단면 익형의 양력과 항력 분포는 XFOIL을 이용하여 예측하였다. 첫 번째 수준의 설계변수인 각각의 블레이드 지름과 축 회전수에서 익형의 공력변수들과 최소에너지손실 조건을 이용하여 두 번째 설계변수인 각 블레이드 단면에서의 시위길이와 피치각 분포를 최적화하였다. 그리고 성능결과를 바탕으로 반응면을 구성하고, 확률적 방법을 이용하여 타당성 있는 설계공간까지 첫 번째 설계변수를 이동시키고 구배최적화 기법을 통해 각각의 제약함수를 만족하면서 목적함수를 죄대로 하는 최적블레이드 형상을 구현하였다. 설계된 최적형상에 대해 탈설계점 해석을 수행하여 성능을 구하였다.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.85-95
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• 2009

In the binaural synthesis environments, wide varieties of the head-related transfer functions (HRTFs) that have measured with a various direction would be desirable to obtain the accurate and various spatial sound images. To reduce the size' of HRTFs, interpolation has been often employed, where the HRTF for any direction is obtained by a limited number of the representative HRTFs. In this paper, we study on the distortion measures for interpolation, which has an important role in interpolation. With lhe various objective distortion metrics, the differences between the interpolated and the measured HRTFs were computed. These were then compared and analyzed with the results from the listening tests. From the results, the objective distortion measures were selected, that reflected the perceptual differences in spatial sound image. This measure was employed in a practical interpolation technique. We applied the proposed method to four kinds of an HRTF set, measured from three human heads and one mannequin. As a result, the Mel-frequency cepstral distortion was shown to be a good predictor for the differences in spatial sound location, when three HRTF measured from human, and the time-domain signal to distortion ratio revealed good prediction results for the entire four HRTF sets.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.19-26
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• 2008

In this paper, we propose a dynamic prediction algorithm to predict the bond price using actual data set of treasure note (T-Note). The proposed algorithm is based on term structure model of the interest rates, which takes place in various financial modelling, such as the standard Gaussian Wiener process. To obtain cumulative distribution functions (CDFs) of actual data for the interest rate measurement used, we use the natural cubic spline (NCS) method, which is generally used as numerical methods for interpolation. Then we also use the random number generation scheme (RNGS) to calculate the pricing of bond through the obtained CDF. In empirical computer simulations, we show that the lower values of precision in the proposed prediction algorithm corresponds to sharper estimates. It is very reasonable on prediction.