• 한국통신학회논문지
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• 제23권6호
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• pp.1592-1601
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• 1998

The growing use of unlicensed wireless systems has spurred interest in the 2.4 Ghz ISM band. In order to facilitate the efficient design of such systems, understandings of the propserties of radio wave propagation in buildings is necessary. Many authors have reported about statistical propagation models based on the extensive measurements in buildings. However, measurement based statistical analysis will not be enough for the optimum deployment of the communication systems in the specific building. Aviding expensive measurements in the individual buildings prior to installation, or adjustments afterwards, theoretical prediction models have been developed to predict the path loss and delay spread from the building floor plane. Predictions shows good agreements with measurements except for a few environments which was surrounded by heavy scatterers.

• 정보처리학회논문지D
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• 제10D권4호
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• pp.689-696
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• 2003

설계 개체의 결함경향성을 판별하는 위험도 예측 모델은 분석이나 설계 같은 소프트웨어 개발 초기 단계에서 시스템의 문제 부분들을 찾아 내는데 사용된다. 복잡도 메트릭에 기반한 많은 위험도 예측 모델들이 제안되었지만 그들 대부분은 모델 훈련을 위한 훈련데이터 집합을 필요로 하는 모델들이었다. 하지만 대부분의 개발집단은 훈련데이터 집합을 보유하고 있지 않기 때문에 이들 모델들은 대부분의 개발집단에서 사용될 수 없다는 커다란 문제점이 있었다. 이러한 문제점을 해결하기 위해 본 논문에서는 Kohonen SOM 신경망을 이용하여 훈련데이터 집합을 사용하지 않는 새로운 예측 모델 KSM을 제안한다. 여러 내부 특성들과 모델 사용의 용이성 그리고 모의실험을 통한 예측 정확도 비교를 통해 KSM을 잘 알려진 예측 모델인 역전파 신경망 모델(BPM)과 비교하였으며 그 결과 KSM의 성능이 BPM에 근접하다는 것을 보였다.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• International Journal of Contents
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• 제12권1호
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

• 한국소음진동공학회논문집
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• 제12권3호
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• pp.236-242
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• 2002

This paper presents some advanced and supplemental methods to enhance the accuracy In case of calculating geometric divergence attenuation, attenuation by multiple screening structures, ground attenuation at unflat surfaces of sound during propagation outdoors by the methods specified in ISO 9613-2. Moreover, a calculation method for considering short-term wind effect, specified in ASJ Model-1998, is also introduced. To verity the accuracy of adopted methods, we have carried out highway traffic noise prediction and measurement at tile twelve locations appearing representative road shapes and structures, such as flat, retained cut, elevated, barrier-constructed roads. From the results, we have confirmed the predicted results show good correspondence with the measured at direct, diffracted and reflected sound fields within 30 m from the center of near side lane.

• Agricultural and Biosystems Engineering
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• 제3권1호
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• pp.18-28
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• 2002

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.564-569
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• 2013

An accurate railway environmental noise prediction model is required to make the proper solution of the railway noise problems. In this paper, an engineering model for predicting the noise of conventional passenger cars is presented considering the acoustic source strength in octave-band frequencies and the propagation over grounds with varying surface properties. Since the formation of a train can be variable, the source strength of each locomotive and passenger car was estimated by measuring the pass-by noise and analysing the wheel-rail rolling noise. Some validation cases show on the average small differences between the predictions of the present model and the measurement results.

• 한국전자파학회논문지
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• 제7권3호
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• pp.219-229
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• 1996

육상 이동통신의 서비스 범위 예측에 가장 기본이 되는 전송 손실 계산 방식은 그 적용범위 및 모델도출 방법에 따라 많은 발전을 거듭하여 왔다. 그러나, 전파는 이것이 지나가는 환경, 즉 빌딩의 특성이나, 나무, 그리고 지형형태 등에 의해 너무나 많은 영향을 받으므로 미국,일본 동 외국의 환경에서 만들어져 국내에 도입된 전파 예측모델들은 우리나라의 실정에 적합하지 않는 점이 많다. 본 논문에서는 국내 지형을 분석하여 그 특성에 따라 여섯가지의 종류로 분류하고 각각에 해당하는 국내 지역을 선정하여 무선호출 주파수 대역에서 전계강도 측정을 수행하였다. 또한, 이 측정 데이타를 이용한 실험식과 함께 산악지역에서의 회절 계산식을 포함하여 가시거리 및 비가시거리를 구분하여 전계강도를 계산하는 예측모델을 만들었다. 제안된 모델을 국내 지형 데이타 베이스와 연결하여 전계강도 예측을 수행한 값과 실측된 데이타와 비교한 결과, 최소 3dB에서 최고 9dB 정도로 오차가 나타났으므로 실용성이 있을 것으로 판단된다.

• 한국통신학회논문지
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• 제38C권3호
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• pp.263-270
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• 2013

최근 세계적으로 바다가 자원의 보고로 주목 받으면서 해양 환경 분석 및 예측 기술에 대한 연구가 활발히 진행 되고 있다. 자동화된 해양 환경 자료의 수집과 수집된 자료를 분석하여서 해양재해를 예측하면 기름 유출에 의한 해양오염의 피해, 적조에 의한 수산업의 피해, 해양환경 이변에 의한 수산업 및 재해 피해를 최소화하는데 기여할 수 있다. 그러나 국내 해양 환경에 대한 조사 및 분석 연구는 제한적이다. 본 논문은 국내의 원해 및 근 해역에서 수집된 해양 환경 자료를 분석하여 해양재해를 예측할 수 있는 시스템 모델을 연구한다. 이를 위해서 본 논문에서는 해양재해 예측 시스템을 위해서 통신시스템 모델, 해양환경 자료 수집 시스템 모델, 예측분석 시스템 모델, 상황전파시스템에 대한 모델을 제시하였다. 또한 예측분석 시스템을 위한 적조 예측 모델과 요약분석 모델을 제시하였다.

• 한국정밀공학회지
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• 제34권1호
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• pp.23-27
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• 2017

This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.