• Journal of the Korean Society for Marine Environment & Energy
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• v.14 no.2
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• pp.107-113
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• 2011

To evaluate the performance of the hydrodynamic model, we introduced 10 skill assessments that are assorted by two groups: quantitative skill assessments (Absolute Average Error or AAE, Root Mean Squared Error or RMSE, Relative Absolute Average Error or RAAE, Percentage Model Error or PME) and qualitative skill assessments (Correlation Coefficient or CC, Reliability Index or RI, Index of Agreement or IA, Modeling Efficiency or MEF, Cost Function or CF, Coefficient of Residual Mass or CRM). These skill assessments were applied and calculated to evaluate the hydrodynamic modeling at one of Florida estuaries for water level, current, and salinity as comparing measured and simulated values. We found that AAE, RMSE, RAAE, CC, IA, MEF, CF, and CRM are suitable for the error assessment of water level and current, and AAE, RMSE, RAAE, PME, CC, RI, IA, CF, and CRM are good at the salinity error assessment. Quantitative and qualitative skill assessments showed the similar trend in terms of the classification for good and bad performance of model. Furthermore, this paper suggested the criteria of the "good" model performance for water level, current, and salinity. The criteria are RAAE < 10%, CC > 0.95, IA > 0.98, MEF > 0.93, CF < 0.21 for water level, RAAE < 20%, CC > 0.7, IA > 0.8, MEF > 0.5, CF < 0.5 for current, and RAAE < 10%, PME < 10%, CC > 0.9, RI < 1.15, CF < 0.1 for salinity.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.2
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• pp.235-243
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• 2012

In this paper, we propose a method to improve performance of pilot symbol assisted modulation(PSAM) channel estimation method for OFDM systems over frequency selective channel. When channel values are estimated, the low pilot density used for channel estimation increases not only the effective data rate but also power efficiency. Thus, the lower pilot density which is used for channel estimation is better for OFDM system. At first, we estimate the channel values which are located at the middle of adjacent pilots, and then all of the possible channel values are estiamted by using original pilot values and previously estimated pilot values. Furthermore, the error of estimated channel values is reduced by introducing guard interval which is designed acccording to maximum channel delay. Performance achieved with the proposed method is illustrated by simulation experiments in comparison with the existing methods in terms of mean squared error(MSE).

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.49-49
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• 2003

목적 : 심초음파는 비침습적이므로 반복적으로 정확히 심질환의 경과를 관찰하여 치료효과 및 수술시기를 정할 수 있는 검사로서 임상적으로 매우 유용하다. 실시간 심근조영심초음파에 의한 time intensity 평가는 부위별로 수행됨으로 연속적으로 위치하는 관심영역이 intensity에 있어 심장의 움직임 변화에 영향을 받는다. Time intensity 곡선의 최적의 곡선맞춤을 위해 주기적인 심장 운동 매개변수를 조합해 기존의 모델을 보정한 안정적인 측정방법을 제시한다. 방법 : 심장의 운동에 의한 특징적인 정보를 설명하기 위해 기존의 문헌에 제시된 지수 함수에 주어진 심박수로 만들어진 시간에 관한 일반적인 정형파 함수를 추가한다. C(t) = A[1 - exp($\beta$t)] + Dsine(2$\pi$ft + $\theta$) C(t): videointensity A: plateau videointensity (blood volume) $\beta$: capillary blood velocity (rate constant of rise in videointensity) t: pulsing interval (ms) D: displacement from the periodic variance of the curve (estimated motion field from the ejection point for the ratio between systole and diastole) f: heart rate $\theta$: transit time issue A $\times$ $\beta$ : myocardial blood flow 관상동맥의 관류 데이터에 대한 실험이 펄스간격에 대한 비디오 세기로 수행되었다. 그리고 이러한 결과들이 the sum of squares due to error, R square, root mean squared error로 평가되었다. 결과 : 실험결과, 주기적인 심장의 움직임과 심박출 시점으로부터의 변위를 잘 기술하고 곡선에서의 측정 점들이 예측된 심장 움직임에 따라 성공적으로 표시되었다. 뿐만 아니라 보정된 모델이 현저한 적합도의 향상을 보여주었다. 결론 : 제시된 접근방법은 각각의 측정에서 심장 운동 영역의 변화에 독립적이며 측정 시점에 의해 영향받지 않고 심근 관류의 안정적인 측정이 가능하다. 심장의 움직임에 관한 매개변수를 조합한 모델로 곡선접합을 수행함으로써 관류의 정량적 정보를 좀더 정확하게 얻을 수 있으며 임상적 이용을 가능하게 할 것으로 기대된다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.11
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• pp.24-29
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• 2010

Selection-type relaying protocol, which is one of cooperative relaying protocols, provides low decoding complexity and improved system performance due to selection diversity. In this paper, we deal with linear precoding technique that minimize the error probability of cooperative MIMO system. Under the assumption that full channel state information is available at whole nodes, linear source and relay precoders, which minimize mean squared error of the estimated symbol vector, are proposed. Moreover, unlikely to the conventional selection-type relaying protocol using a fixed threshold signal-to-noise-ratio, new transmission link selection algorithm selects direct link or relay link as a transmission link, is introduced. Simulation results show that the proposed linear precoder with the transmission link selection algorithm outperforms the conventional precoders for two-hop relaying protocols or selection-type relaying protocols.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.8-16
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• 2007

Finite element analysis (FEA) is an effective means for the analysis of bioelectromagnetism. It has been successfully applied to various problems over conventional methods such as boundary element analysis and finite difference analysis. However, its utilization has been limited due to the overwhelming computational load despite of its analytical power. We have previously developed a novel mesh generation scheme that produces FE meshes that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with far less number of nodes and elements, thus lessen the computational load. In general, the cMesh generation is affected by the quality of feature maps derived from MRI. In this study, we have tested various feature maps created based on the improved differential geometry measures for more effective cMesh head models. As performance indices, correlation coefficient (CC), root mean squared error (RMSE), relative error (RE), and the quality of cMesh triangle elements are used. The results show that there is a significant variation according to the characteristics of specific feature maps on cMesh generation, and offer additional choices of feature maps to yield more effective and efficient generation of cMeshes. We believe that cMeshes with specific and improved feature map generation schemes should be useful in the FEA of bioelectromagnetic problems.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.3
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• pp.16-21
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• 2010

In this paper, the linear source and relay precoders are designed for AF MIMO relay systems. In order to minimize mean squared error (MSE) of received symbol vector, the source and relay precoders are proposed, and MMSE receiver which is suitable to those precoders is utilized at the destination node. As the optimal precoders for source and relay nodes are not represented in closed form and induced by iterative method, we suggest a simple precoder design scheme. Simulation results show that the performance of the proposed precoding scheme is comparable with that of optimal scheme and outperforms other relay precoding schemes. Moreover, in high SNR region, it is revealed that SNR between source and relay node is more influential than SNR between relay and destination node in terms of bit error rate.

• Journal of the military operations research society of Korea
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• v.34 no.2
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• pp.79-101
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• 2008

This study is for improvement of repair part demand forecasting method of Republic of Korea Air Force aircraft. Recently, demand prediction methods are Weighted moving average, Linear moving average, Trend analysis, Simple exponential smoothing, Linear exponential smoothing. But these use fixed weight and moving average range. Also, NORS(Not Operationally Ready upply) is increasing. Recommended method of Box-Jenkins' ARIMA can solve problems of these method and improve estimate accuracy. To compare recent prediction method and ARIMA that use mean squared error(MSE) is reacted sensitively in change of error. ARIMA has high accuracy than existing forecasting method. If apply this method of study in other several Items, can prove demand forecast Capability.