• Structural Engineering and Mechanics
• /
• 제63권6호
• /
• pp.751-760
• /
• 2017

In recent years, multifractal-based analysis methods have been widely applied in engineering. Among these methods, multifractal detrended cross-correlation analysis (MFDXA), a branch of fractal analysis, has been successfully applied in the fields of finance and biomedicine. For its great potential in reflecting the subtle characteristic among signals, a structural health monitoring (SHM) system based on MFDXA is proposed. In this system, damage assessment is conducted by exploiting the concept of multifractal theory to quantify the complexity of the vibration signal measured from a structure. According to the proposed algorithm, the damage condition is first distinguished by multifractal detrended fluctuation analysis. Subsequently, the relationship between the q-order, q-order detrended covariance, and length of segment is further explored. The dissimilarity between damaged and undamaged cases is visualized on contour diagrams, and the damage location can thus be detected using signals measured from different floors. Moreover, a damage index is proposed to efficiently enhance the SHM process. A seven-story benchmark structure, located at the National Center for Research on Earthquake Engineering (NCREE), was employed for an experimental verification to demonstrate the performance of the proposed SHM algorithm. According to the results, the damage condition and orientation could be correctly identified using the MFDXA algorithm and the proposed damage index. Since only the ambient vibration signal is required along with a set of initial reference measurements, the proposed SHM system can provide a lower cost, efficient, and reliable monitoring process.

• 응용통계연구
• /
• 제29권7호
• /
• pp.1445-1458
• /
• 2016

본 논문에서는 1976년부터 2015년까지 40년 간, 59개 지역 기온 시계열을 대상으로 degrended cross-correlation analysis(DCCA) 방법을 이용한 상관 계수를 도출하였다. 4년 단위의 평균기온, 최고기온, 최저기온 시계열을 분석하여 상관계수 값이 0.9 이상이면 단위 기간 동안 두 지역의 온도 상관성이 존재하는 것으로 판단하고, 두 지역 간의 연결선을 만드는 방식으로 네트워크를 구축하였다. 이후 네트워크 이론을 바탕으로 평균 경로 길이, 결집 계수, 유사성, 모듈성 등의 값들을 도출하였다. 그 결과, 기온 네트워크는 좁은 세상 성질을 만족하고, 유사성과 모듈성이 높은 네트워크임을 알 수 있었다.

• 산업경영시스템학회지
• /
• 제41권1호
• /
• pp.84-93
• /
• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.