Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10−5) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications

Online Customer Service System Using Hybrid Model

In a traditional customer service support environment, service engineers typically provide a worldwide customer base support through the use of telephone calls. Such a mode of support is inefficient, ineffective and generally results in high costs, long service cycles, and poor quality of service. The rapid growth of the World Wide Web and Intelligent Agent technology, with its widespread acceptance and accessibility, have resulted in the emergence of Web-based and AI Agent-based systems. Depending on the functionality provided by such systems, most of the associated disadvantages of the traditional customer service support environment can be eliminated. This paper describes a framework for Web-based and AI Agent-based online customer service support system, and discusses the method to use Rough Set Theory and Neural Network Theory to support intelligent fault diagnosis by customers or service engineers

Three-Dimensional Stochastic Distribution Characteristics of Void Fraction in Longwall Mining-Disturbed Overburden of Inclined Coal Seam

AbstractFractures in the overburden induced by mining disturbances provide a channel for fluid flow between the surface and the underground. Mining-induced strata movement and fracture distribution are influenced by the gravity and dip angles of rock seams. In this paper, a new three-dimensional theoretical distribution model for void fraction in each partition of overlying rock strata disturbed by inclined coal seam mining was constructed. Based on the theoretical determination model, the three-dimensional random distribution characteristics for void fraction were obtained by combining the random distribution law of void fraction obtained by similar physical simulation experiments and image processing techniques. Theoretical deterministic models, stochastic theoretical models, and similar physical simulations all show that void fraction distribution in the tendency direction of the coal seam shows a bimodal asymmetric distribution with high and low peaks and a symmetric distribution in the strike direction. The void fraction of the overburden in the central part of the mining area is smaller than that of the surrounding area. The results of the theoretically determined model and stochastic model of the void fraction for the strata with different mining lengths and different coal seam inclinations were compared with the results of similar simulation experiments, respectively. The results are in agreement, further verifying the practicality of the model

Spectrally-Resolved Raman Lidar to Measure Atmospheric Three-Phase Water Simultaneously

We report on a spectrally-resolved Raman lidar that can simultaneously profile backscattered Raman spectrum signals from water vapor, water droplets and ice crystals as well as aerosol fluorescence in the atmosphere. The lidar emits a 354.8-nm ultraviolet laser radiation and samples echo signals in the 393.0-424.0 nm wavelength range with a 1.0-nm spectral resolution. A spectra decomposition method is developed to retrieve fluorescence spectra, water vapor Raman spectra and condensed (liquid and/or ice) water Raman spectra successively. Based on 8 different clear-sky nighttime measurement results, the entire atmospheric water vapor Raman spectra are for the first time obtained by lidar. The measured normalized water vapor Raman spectra are nearly invariant and can serve as background reference for atmospheric water phase state identification under various weather conditions. For an ice virga event, it’s found the extracted condensed water Raman spectra are highly similar in shape to theoretical ice water Raman spectra reported by Slusher and Derr (1975). In conclusion, the lidar provides an effective way to measure three-phase water simultaneously in the atmosphere and to study of cloud microphysics as well as interaction between aerosols and clouds

Spectrally-Resolved Raman Lidar to Measure Atmospheric Three-Phase Water Simultaneously

We report on a spectrally-resolved Raman lidar that can simultaneously profile backscattered Raman spectrum signals from water vapor, water droplets and ice crystals as well as aerosol fluorescence in the atmosphere. The lidar emits a 354.8-nm ultraviolet laser radiation and samples echo signals in the 393.0-424.0 nm wavelength range with a 1.0-nm spectral resolution. A spectra decomposition method is developed to retrieve fluorescence spectra, water vapor Raman spectra and condensed (liquid and/or ice) water Raman spectra successively. Based on 8 different clear-sky nighttime measurement results, the entire atmospheric water vapor Raman spectra are for the first time obtained by lidar. The measured normalized water vapor Raman spectra are nearly invariant and can serve as background reference for atmospheric water phase state identification under various weather conditions. For an ice virga event, it’s found the extracted condensed water Raman spectra are highly similar in shape to theoretical ice water Raman spectra reported by Slusher and Derr (1975). In conclusion, the lidar provides an effective way to measure three-phase water simultaneously in the atmosphere and to study of cloud microphysics as well as interaction between aerosols and clouds

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability