• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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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

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2003년도 추계종합학술대회 논문집
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• pp.204-209
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• 2003

도심지역 이동통신에 있어서 전파특성을 정확히 예측ㆍ분석하는 것은 통신 서비스영역의 결정, 최적 기지국 선정, 셀 설계 등에 있어 매우 중요한 사안이다. 이러한 분석에 있어서 사용되는 안테나의 종류, 지향각, 지형지물의 형태에 따라 변화하는 전파예측모델이 정확히 제시되어야 한다. 또한, 선택된 지역에 대하여 셀 설계를 수행하기 전에 기존에 제시된 다양한 모델 중 유사성을 가진 모델을 분석하고 그 파라미터를 측정하여 평가하는 작업을 진행하여야 한다. 본 논문에서는 도심지역의 지형 및 장애물 등을 고려한 전파예측모델을 제안하고 그에 따르는 파라미터를 추출하여 분석된 전파환경에 적용하고 그 전파특성을 분석하기 위한 시뮬레이션을 실시하였다. 이러한 과정을 통하여 우리는 주어진 전파환경에 적절한 기지국의 위치, 지형고도, 안테나의 종류 및 높이 등 핵심적인 파라미터들을 원하는 정확도로 추출하였다.

• 소음진동
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• 제8권1호
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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.

• 전자공학회논문지T
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• 제35T권3호
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• pp.103-112
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• 1998

본 논문에서는 전파경로를 해석할 때 기하광학적 영상법과 전파송출법의 단점을 보완하여 계산속도를 향상시킴은 물론 전파의 입사각과 반사각에 따른 전파경로, 진행파의 수평경로 그리고 반사횟수를 동시에 처리 할 수 있는 알고리즘을 제안하였다. 제안 알고리즘을 활용한 전파예측모델을 제안하고, 반복된 반사에 의해 전파가 진행하는 경우, 임의 지점의 전파경로 손실을 쉽게 계산할 수 있다. 마지막으로 광주광역시 광산구 월곡동에 있는 SK텔레콤 전남지사 주변의 실제 도로 상황을 샘플로 취하여 제안 전파예측 모델을 시뮬레이션하여, 일반적인 타당성을 입증하였다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(III)
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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.

• 한국응용과학기술학회지
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• 제25권4호
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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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• 제15권3_4호
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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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• 제2권2호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권10호
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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.

• 한국경영과학회지
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• 제30권1호
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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.