• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• 상하수도학회지
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• 제36권4호
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 가을 학술발표논문집 Vol.27 No.2 (2)
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• pp.308-310
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• 2000

관찰학습(OLA: Observational Learning Algorithm)은 앙상블 네트워크의 각 구성 모델들이 아른 모델들을 관찰함으로써 얻어진 가상 데이터와 초기에 bootstrap된 실제 데이터를 학습에 함께 이용하는 방법이다. 본 논문에서는, 초기 학습 데이터 셋을 분할하고 분할된 각 데이터 셋에 대하여 앙상블의 구성 모델들을 전문화(specialize)시키는 방법을 적용하여 기존의 관찰학습 알고리즘을 개선시켰다. 제안된 알고리즘은 bagging 및 boosting과의 비교 실험에 의하여, 보다 적은 수의 구성 모델로 동일 내지 보다 나은 성능을 나타냄이 실험적으로 검증되었다.

• Structural Engineering and Mechanics
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• 제39권6호
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• pp.813-831
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• 2011

This paper presents an efficient version of Hilbert-Huang transform for nonlinear non-stationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the "best" outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.

• International Journal of Fluid Machinery and Systems
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• 제9권3호
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• pp.265-276
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• 2016

Energy systems working coherently in different conditions may not have a specific design which can provide optimal performance. A system working for a longer period at lower efficiency implies higher energy consumption. In this effort, a methodology demonstrated by a jet pump design and optimization via numerical modeling for fluid dynamics and implementation of an evolutionary algorithm for the optimization shows a reduction in computational costs. The jet pump inherently has a low efficiency because of improper mixing of primary and secondary fluids, and multiple momentum and energy transfer phenomena associated with it. The high fidelity solutions were obtained through a validated numerical model to construct an approximate function through surrogate analysis. Pareto-optimal solutions for two objective functions, i.e., secondary fluid pressure head and primary fluid pressure-drop, were generated through a multi-objective genetic algorithm. For the jet pump geometry, a design space of several design variables was discretized using the Latin hypercube sampling method for the optimization. The performance analysis of the surrogate models shows that the combined surrogates perform better than a single surrogate and the optimized jet pump shows a higher performance. The approach can be implemented in other energy systems to find a better design.

• 전기학회논문지
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• 제65권1호
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• pp.142-149
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• 2016

This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3438-3454
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• 2018

In this paper, we propose a robust blind watermarking scheme for high-definition video. In the embedding process, luminance component of each frame is transformed by 2-dimensional fast Fourier transform (2D FFT). A secret key is used to generate a matrix of random numbers for the security of watermark information. The matrix is transformed by inverse steerable pyramid transform (SPT). We embed the watermark into the low and mid-frequency of 2D FFT coefficients with the transformed matrix. In the extraction process, the 2D FFT coefficients of each frame and the transformed matrix are transformed by SPT respectively, to produce two oriented sub-bands. We extract the watermark from each frame by cross-correlating two oriented sub-bands. If a video is degraded by some attacks, the watermarks of frames contain some errors. Thus, we use an ensemble position-based error correcting algorithm to estimate the errors and correct them. The experimental results show that the proposed watermarking algorithm is imperceptible and moreover is robust against various attacks. After embedding 64 bits of watermark into each frame, the average peak signal-to-noise ratio between original frames and embedded frames is 45.7 dB.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권6호
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• pp.187-194
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• 2020

본 논문은 클라이언트의 익명성과 개인 정보를 보장하는 토르 네트워크에서 앙상블 학습을 이용한 웹사이트 핑거프린팅 방법을 제안한다. 토르네트워크에서 수집된 트래픽 패킷들로부터 웹사이트 핑거프린팅을 위한 훈련 문제를 구성하며, 트리 기반 앙상블 모델을 적용한 웹사이트 핑거프린팅 시스템의 성능을 비교한다. 훈련 특징 벡터는 트래픽 시퀀스에서 추출된 범용 정보, 버스트, 셀 시퀀스 길이, 그리고 셀 순서로부터 준비하며, 각 웹사이트의 특징은 고정 길이로 표현된다. 실험 평가를 위해 웹사이트 핑거프린팅의 사용에 따른 4가지 학습 문제(Wang14, BW, CW_T, CW_H)를 정의하고, CUMUL 특징 벡터를 사용한 지지 벡터 기계 모델과 성능을 비교한다. 실험 평가에서, BW 경우를 제외하고 제안하는 통계 기반 훈련 특징 표현이 CUMUL 특징 표현보다 우수하다.

• 한국정보통신학회논문지
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• 제19권2호
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• pp.279-286
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• 2015

EMD는 미리 정의된 어떠한 기저함수도 사용하지 않으며 사용자에 의해 미리 정의된 파라미터값도 필요치 않은 완전히 데이터에 기반한 신호 처리의 특징을 갖는다. 그러나 유사한 스케일을 갖는 신호 모드로 분해하는 것을 방해하는 모드 혼합이 발생하는 단점이 있다. 이를 해결하기 위해 EEMD 알고리즘이 도입되었으며, EEMD는 처리하고자 하는 신호에 가우시안 백색 잡음을 혼합하여 앙상블 수만큼 신호를 만들어 EMD 방법을 적용함으로써 모드 혼합 문제를 해결한다. 그럼에도 EEMD는 잡음이 추가된 신호 분해 시 원 신호와 상이한 모드 수를 만들어 내며, 분해된 신호들을 원 신호로 재구성 시에도 레지듀 잡음이 포함된다. 본 논문은 개선된 EEMD알고리즘으로 EMD의 모드 혼합 문제를 해결하고 원신호를 정확히 재구성하며 EEMD 보다 적은 연산 비용으로 신호 모드 분리를 제안한다. 실험결과는 EEMD 방법과 비교하여 적은 체과정의 반복으로 빠른 모드 분리를 보여 주었으며 EEMD 방법의 20.87%의 비용만으로 완전한 신호 분해가 가능하였고, 신호 복원에 있어서도 EEMD 보다 우수한 성능을 보여주었다.

• 한국전산구조공학회논문집
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• 제34권4호
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• pp.183-189
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• 2021

구조물에 장기적으로 발생하는 노후화를 정량적으로 파악하기 위해 상시진동 데이터를 활용한 일반화된 모니터링 시스템에 관한 연구가 세계적으로 활발히 수행중이다. 본 연구에서는 구조물에서 장기적으로 취득되는 동특성을 앙상블 학습에 활용하여 구조물의 이상을 감지하기 위한 보급형 엣지 컴퓨팅 시스템을 구축하였다. 시스템의 하드웨어는 라즈베리파이와 보급형 가속도계, 기울기센서, GPS RTK 모듈, 로라 모듈로 구성됐다. 실험실 규모의 구조물 모형 진동실험을 통해 동특성을 활용한 앙상블 학습의 구조물 이상감지를 검증하였으며, 실험을 기반으로 한 실시간 동특성 추출 분산처리 알고리즘을 라즈베리파이에 탑재하였다. 구축된 시스템을 하우징하고 포항시 행정복지센터에 설치하여 데이터를 취득함으로써 개발된 시스템의 현장 적용성을 검증하였다.