• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1078-1081
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• 2002

In microcell or picocell mobile communication using cellular method, we suggested propagation prediction model which can accurately and rapidly interpret mobile communication propagation environment in urban, when subscriber service is done based on the main road in urban. Further, we simulated suggested propagation prediction model under the hypothesis of urban propagation environment of PCS mobile communication, analyzed receiving field strength by area within a cell, and finally suggested the optimal transmitting power and location condition of microcell or picocell mobile communication base station

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.204-209
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• 2003

In an urban area telecommunication using wireless system, the accurate prediction and analysis of wave propagation characteristics are very important to determine the service area, optimized selection of base station, and cell design, etc. In the stage of these analyses, we have to present the propagation prediction model which is varied with the type of antenna, directional angle, and configuration of the ground in our urban area. In addition we need to perform an analysis of the conventional model which is similar to ours and dig out the parameters to evaluate the wave environment before the cell design for the selected area. In this paper, we propose a wave propagation prediction model concerning the topography and obstacles in our urban area. We extract the parameters and apply them to the proposed wave environment for the simulation analyzing the propagation characteristics. Throughout these analyzing procedure, we extracted the essential parameters such as the position of the base station, the height of topography, and adequate type and height of the antenna with our preferable correctness.

• Journal of KSNVE
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• v.8 no.1
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• pp.187-194
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• 1998

In order to predict the train noise propagation from a level railroad, this paper presents the model of train noise source and the prediction model based on the results by using the sound intensity method. The prediction model gives the effects of geometric attenuation, ground attenuation, and barrier attenuation of noise. There are several principal assumption in developing model: (a) the train noise is primarily rolling noise; (b) the rail head and wheels are in good condition; (c) the height of source is 10cm above track; (d) the directivity pattern of train noise sources is a dipole source. Calculated results based on this model are compared with available field data and good agreement has been obtained.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.103-112
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• 1998

This paper proposes a propagation prediction model which can calculate a propagation path loss easily at option point in case of the propagation processing by repeat reflection when we analysis a propagation route, it makes the calculation speed which is the defect of a geometrical of image method and a ray-launching method improve and we develop and apply the algorithms which can do an angle of incidence, an angle of reflection with a propagation direct path, a reflection path and a maximum reflection number arithmetic process synchronously. Finally we choose as a sample which is the real road condition where is around SK telecoms chunnam branch office in wolgok-dong, kwangsan-ku, kwangju and simulate proposition model then we demonstrate the relative superiority with comparing the results.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.132-137
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• 2003

This paper concerns the prediction of railway noise propagation using scale model experiment in acoustics. In order to make acoustical experiment the digital signal processing technique are applied and spark discharge sound sources have been developed in which impulse response measured in 1/20 scale model railway. In the case of scale model experiment, it is difficult to realize sufficiently small size and directivity and to get sufficient sound energy and to get repeatability. Several type of Spark discharge sound source is made in laboratory. Experiment results are compared with the calculated results by the prediction model. As the results, it was found that railway noise could be predicted in acoustical scale model experiment using spark discharge sound source.

• Journal of the Korean Applied Science and Technology
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• v.25 no.4
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• pp.438-445
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• 2008

For recipe prediction of colorant proportion showing nonlinear behavior, we modeled the effects of colorant proportion of basic colors on the target colors and predicted colorant proportion necessary for making target colors. First, colorant proportion of basic colors and color information indicated by the instrument was applied by a linear model and a multi-layer perceptrons model with back-propagation learning method. However, satisfactory results were not obtained because of nonlinear property of colors. Thus, in this study the neuro-fuzzy model with merit of artificial neural networks and fuzzy systems was presented. The proposed model was trained with test data and colorant proportion was predicted. The effectiveness of the proposed model was verified by evaluation of color difference(${\Delta}E$).

• ETRI Journal
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• v.15 no.3
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• pp.27-34
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• 1994

There have been traditional approaches to model radio propagation path loss mechanism both theoretically ad empirically. Theoretical approach is simple to explain and effective in certain cases. Empirical approach accommodates the terrain configuration and distance between base station and mobile unit along the propagation path only. In other words, it does not accommodate natural terrain configuration over a specific area. In this paper, we propose a spatial prediction technique for the mobile radio propagation path loss accommodating complete natural terrain configuration over a specific area. Statistical uncertainty analysis is also considered.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.256-262
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• 2004

Conventionally, profile non-uniformity has been characterized by relying on approximated profile with angle or anisotropy. In this study, a new non-uniformity model for etch profile is presented by applying a discrete wavelet to the image obtained from a scanning electron microscopy (SEM). Prediction models for wavelet-transformed data are then constructed using a back-propagation neural network. The proposed method was applied to the data collected from the etching of tungsten material. Additionally, 7 experiments were conducted to obtain test data. Model performance was evaluated in terms of the average prediction accuracy (APA) and the best prediction accuracy (BPA). To take into account randomness in initial weights, two hundred models were generated for a given set of training factors. Behaviors of the APA and BPA were investigated as a function of training factors, including training tolerance, hidden neuron, initial weight distribution, and two slopes for bipolar sig-moid and linear function. For all variations in training factors, the APA was not consistent with the BPA. The prediction accuracy was optimized using three approaches, the best model based approach, the average model based approach and the combined model based approach. Despite the largest APA of the first approach, its BPA was smallest compared to the other two approaches.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1143-1155
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• 1999

셀룰러 방식의 이동통신 시스템에서 전파의 유효신호 도달범위를 예측하기 위해서는 전파전파 모델을 이용한 예측기법이 주로 사용된다. 그러나, 전파과정에서 주변 지형지물에 의해 발생하는 전파손실은 매우 복잡한 비선형적인 특성을 가지며 수식으로는 정확한 표현이 불가능하다. 본 논문에서는 신경회로망의 함수 근사화 능력을 이용하여 전파손실 예측모델을 생성하는 방법을 제안한다. 즉, 전파손실을 송수신 안테나간의 거리, 송신안테나의 특성, 장애물 투과영향, 회절특성, 도로, 수면에 의한 영향 등과 같은 전파환경 변수들의 함수로 가정하고, 신경회로망 학습을 통하여 함수를 근사화한다. 전파환경 변수들이 신경회로망 입력으로 사용되기 위해서는 3차원 지형도와 벡터지도를 이용하여 전파의 반사, 회절, 산란 등의 물리적인 특성이 고려된 특징 추출을 통해 정량적인 수치들을 계산한다. 이와 같이 얻어진 훈련데이타를 이용한 신경회로망 학습을 통해 전파손실 모델을 완성한다. 이 모델을 이용하여 서울 도심 지역의 실제 서비스 환경에 대한 타 모델과의 비교실험결과를 통해 제안하는 모델의 우수성을 보인다.Abstract In cellular mobile communication systems, wave propagation models are used in most cases to predict cell coverage. The amount of propagation loss induced by the obstacles in the propagation path, however, is a highly non-linear function, which cannot be easily represented mathematically. In this paper, we introduce the method of producing propagation loss prediction models by function approximation using neural networks. In this method, we assume the propagation loss is a function of the relevant parameters such as the distance from the base station antenna, the specification of the transmitter antenna, obstacle profile, diffraction effect, road, and water effect. The values of these parameters are produced from the field measurement data, 3D digital terrain maps, and vector maps as its inputs by a feature extraction process, which takes into account the physical characteristics of electromagnetic waves such as reflection, diffraction and scattering. The values produced are used as the input to the neural network, which are then trained to become the propagation loss prediction model. In the experimental study, we obtain a considerable amount of improvement over COST-231 model in the prediction accuracy using this model.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.