• Wind and Structures
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• 제18권6호
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• pp.619-631
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• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

• 정보과학회 논문지
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• 제45권1호
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• pp.69-75
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• 2018

시간에 따라 순차적으로 들어오는 스트리밍 데이터에서는 전체 데이터 셋을 한꺼번에 모두 이용하는 배치 학습에 기반한 차원축소 기법을 적용하기 어렵다. 따라서 스트리밍 데이터에 적용하기 위한 점층적 차원 감소 방법이 연구되어왔다. 이 논문에서는 최소제곱오차해를 통한 점층적 선형 판별 분석법을 제안한다. 제안 방법은 분산행렬을 직접 구하지 않고 새로 들어오는 샘플의 정보를 이용하여 차원 축소를 위한 사영 방향을 점층적으로 업데이트한다. 실험 결과는 이전에 제안된 점층적 차원축소 알고리즘과 비교하여 이 논문에서 제안한 방법이 더 효과적인 방법임을 입증한다.

• ETRI Journal
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• 제37권3호
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• pp.533-540
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• 2015

In wireless localization, several linear closed-form solution (LCS) methods have been investigated as a direct result of the drawbacks that plague the existing iterative methods, such as the local minimum problem and heavy computational burden. Among the known LCS methods, both the direct solution method and the difference of squared range measurements method are considered in this paper. These LCS methods do not have any of the aforementioned problems that occur in the existing iterative methods. However, each LCS method does have its own individual error property. In this paper, a hybrid LCS method is presented to reduce these errors. The hybrid LCS method integrates the two aforementioned LCS methods by using two check points that give important information on the probability of occurrence of each LCS's individual error. The results of several Monte Carlo simulations show that the proposed method has a good performance. The solutions provided by the proposed method are accurate and reliable. The solutions do not have serious errors such as those that occur in the conventional standalone LCS and iterative methods.

• 한국통신학회논문지
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• 제41권8호
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• pp.896-902
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• 2016

본 논문에서는 다른 셀로부터 발생하는 간섭을 고려하여 다중사용자 다중안테나 하향링크 채널에서 최소평균제곱오차(MMSE: minimum mean-squared error)에 기반한 비선형 연속 프리코딩(precoding) 기법을 제안한다. 기존의 zero forcing(ZF) 기반의 방법과 달리 제안하는 방식은 다른 셀로부터의 간섭을 고려하여 셀 내의 다중사용자 간섭을 억제하는 방식을 취하여 수신단에서의 신호 대 간섭 잡음비를 향상시킬 수 있다. 모의 실험 결과를 통해 제안하는 최소평균제곱오차에 기반한 비선형 전처리 기법이 다양한 셀 간섭 환경에서 기존 방법보다 좋은 성능을 가지는 것을 확인할 수 있다.

• 제어로봇시스템학회논문지
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• 제17권10호
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• pp.1059-1066
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• 2011

This paper makes a close inquiry into ill-conditioning that may be occurred in wireless localization of the sensor nodes based on network signals in the wireless sensor network and provides the clue for solving the problem. In order to estimate the location of a node based on the range information calculated using the signal propagation time, LS (Least Squares) method is usually used. The LS method estimates the solution that makes the squared estimation error minimal. When a nonlinear function is used for the wireless localization, ILS (Iterative Least Squares) method is used. The ILS method process the LS method iteratively after linearizing the nonlinear function at the initial nominal point. This method, however, has a problem that the final solution may converge into a LM (Local Minimum) instead of a GM (Global Minimum) according to the deployment of the fixed nodes and the initial nominal point. The conditions that cause the problem are explained and an adaptive method is presented to solve it, in this paper. It can be expected that the stable location solution can be provided in implementation of the wireless localization methods based on the results of this paper.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• 제어로봇시스템학회논문지
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• 제19권7호
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• pp.633-639
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• 2013

Several estimation methods used in the range measurement based wireless localization area have individual problems. These problems may not occur according to certain application areas. However, these problems may give rise to serious problems in particular applications. In this paper, three methods, ILS (Iterative Least Squares), DS (Direct Solution), and DSRM (Difference of Squared Range Measurements) methods are considered. Problems that can occur in these methods are defined and a simple hybrid solution is presented to solve them. The ILS method is the most frequently used method in wireless localization and has local minimum problems and a large computational burden compared with closed-form solutions. The DS method requires less processing time than the ILS method. However, a solution for this method may include a complex number caused by the relations between the location of reference nodes and range measurement errors. In the near-field region of the complex solution, large estimation errors occur. In the DSRM method, large measurement errors occur when the mobile node is far from the reference nodes due to the combination of range measurement error and range data. This creates the problem of large localization errors. In this paper, these problems are defined and a hybrid localization method is presented to avoid them by integrating the DS and DSRM methods. The defined problems are confirmed and the performance of the presented method is verified by a Monte-Carlo simulation.

• Journal of the Optical Society of Korea
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• 제12권4호
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• pp.249-254
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• 2008

In this paper we present an electronic domain solution for chromatic dispersion (CD) monitoring algorithm based on the estimated time domain channel in electronic domain using channel estimation methods. The proposed scheme utilizes kurtosis as a CD measurement, directly computed from the estimated inter-symbol-interference (ISI) channel due to the CD distortion. Hence, the proposed scheme exhibits robust performance under OSNR variation, in contrast to the existing electronic domain approach based on minimum mean squared error (MMSE) fractionally-spaced equalizer taps [1]. The simulation results verify the CD monitoring ability of the proposed scheme.

• 한국수자원학회논문집
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• 제55권11호
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• pp.903-911
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• 2022

물을 공급하기 위한 자원 중 하나인 지하수는 다양한 자연적 요인에 의해 수위의 변동이 발생한다. 최근, 인공신경망을 이용하여 지하수위의 변동을 예측하는 연구가 진행되었다. 기존에는 인공신경망 연산자 중 학습에 영향을 미치는 Optimizer로 경사하강법(Gradient Descent, GD) 기반 Optimizer를 사용하였다. GD 기반 Optimizer는 초기 상관관계 의존성과 해의 비교 및 저장 구조 부재의 단점이 존재한다. 본 연구는 GD 기반 Optimizer의 단점을 개선하기 위해 GD와 화음탐색법(Harmony Search, HS)를 결합한 새로운 Optimizer인 Gradient Descent combined with Harmony Search(GDHS)를 개발하였다. GDHS의 성능을 평가하기 위해 다층퍼셉트론(Multi Layer Perceptron, MLP)을 이용하여 이천율현 관측소의 지하수위를 학습 및 예측하였다. GD 및 GDHS를 사용한 MLP의 성능을 비교하기 위해 Mean Squared Error(MSE) 및 Mean Absolute Error(MAE)를 사용하였다. 학습결과를 비교하면, GDHS는 GD보다 MSE의 최대값, 최소값, 평균값 및 표준편차가 작았다. 예측결과를 비교하면, GDHS는 GD보다 모든 평가지표에서 오차가 작은 것으로 평가되었다.

• 한국항행학회논문지
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• 제10권4호
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• pp.371-376
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• 2006

스피어 디코더의 초기 반지름 결정 문제는 비트 오율 (bit error rate)과 복잡도에 있어서 많은 영향을 미친다. 이런 초기 반지름은 채널의 통계적 특성을 고려함으로 설정되거나, MMSE 결정 값을 이용하여 설정할 수 있다. 채널의 통계적 특성을 이용한 방법은 초기 반지름이 송신 신호에 해당하는 격자점을 매우 높은 확률로 포함한다. MMSE 결정 값을 이용하는 방법은 먼저 수신 신호에서 MMSE 연 판정 부호(soft output information)을 얻은 후, 경 판정(hard decision)을 내린 다음, 수신 신호 공간에서 경 판정 부호에 해당하는 격자점을 찾는다. 그리고 수신 신호와 경 판정 부호에 해당하는 격자점 사이의 유클리디안 거리(Euclidean distance)를 초기 반지름으로 설정한다. 본 논문에서는 채널의 통계적 특성을 이용한 방법에 있어서 기존의 복잡한 수식에 비해 간단한 새로운 식을 유도하고, MMSE 결정값을 이용한 방법과 비교 연구 하였다. 비교를 위해 'Tightness'라는 새로운 측도를 이용하였다. 전산 실험 결과, 낮은 SNR 영역과 중간 정도의 SNR 영역에서는 MMSE를 이용한 방법의 더 많이 디코딩 복잡도 감소를 보였고, 높은 SNR 영역에서는 채널의 통계적 특성을 이용한 방법이 더 낮은 디코딩 복잡도를 보였다.