Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

Mechanism of methanol decomposition on the Pd/WC(0001) surface unveiled by first-principles calculations

In this study, the decomposition of methanol into the CO and H species on the Pd/tungsten carbide (WC)(0001) surface is systematically investigated using periodic density functional theory (DFT) calculations. The possible reaction pathways and intermediates are determined. The results reveal that saturated molecules, i.e., methanol and formaldehyde, adsorb weakly on the Pd/ WC(0001) surface. Both CO and H prefer three-fold sites, with adsorption energies of −1.51 and −2.67 eV, respectively. On the other hand, CH₃O stably binds at three-fold and bridge sites, with an adsorption energy of −2.58 eV. However, most of the other intermediates tend to adsorb to the surface with the carbon and oxygen atoms in their sp³ and hydroxyl-like configurations, respectively. Hence, the C atom of CH₂OH preferentially attaches to the top sites, CHOH and CH₂O adsorb at the bridge sites, while COH and CHO occupy the three-fold sites. The DFT calculations indicate that the rupture of the initial C–H bond promotes the decomposition of CH₃OH and CH₂OH, whereas in the case of CHOH, O–H bond scission is favored over the C–H bond rupture. Thus, the most probable methanol decomposition pathway on the Pd/WC(0001) surface is CH₃OH → CH₂OH → trans-CHOH → CHO → CO. The present study demonstrates that the synergistic effect of WC (as carrier) and Pd (as catalyst) alters the CH₃OH decomposition pathway and reduces the noble metal utilization.     

An overlapping domain decomposition dual reciprocity method

We present a domain decomposition boundary integral equation method for two-dimensional partial differential equations. The overlapping Schwarz method is employed to improve the dual reciprocity boundary element method. The resulting algorithm turns out to be more accurate than a non-overlapping approach previously proposed. Some numerical results showing the improved accuracy and efficiency of the methods are given.     

基于调谐频率与分频处理的高分辨率反演技术

讨论了基于调谐频率与分频处理的高分辨率反演技术，给出了方法的基本原理，即利用地震数据的调谐频率和分频处理分析，半定量地确定储层厚度，并将其作为地震反演的约束条件。方法的实现过程是：通过层状地质模型的地震正演模拟，建立不同主频子波时地层厚度与调谐频率的关系；利用地震资料时-频分析技术，实现地震资料分频处理；利用合成地震记录标定地震子波主频；利用分频处理结果，借助于调谐频率分析与调谐振幅分析，给出地震反演约束条件；利用反演算法消除地震子波影响，提高和拓展地震资料的频带范围，实现储层的高分辨率成像。普光地区的应用实例表明，调谐频率与分频处理的高分辨率反演方法无需对储层空间变化规律进行假设，适用于强非均质碳酸盐岩储层的高分辨率成像。     

提取地震信号高频信息方法的若干问题

岩性油气藏勘探已成为当代油气勘探的重要组成部分,因此提取地震资料高频信息已成为一个重要研究课题。文中对分辨率、分频处理地震信号、波阻抗反演中的一些问题进行了较深入的讨论,并得出以下认识:1要提高垂向和横向分辨率,必须设法提高野外采集地震资料的频率;2地震分频处理是偏移后提高分辨率的常用方法之一,这种方法应该限定在地震波的有效频带内,而超出有效频带,可能会带来很多假像;3低频合成地震记录中高频反射系数存在不确定性。在基于模型波阻抗反演的方法中,反演结果受初始模型的影响非常大,初始模型是依据测井资料建立的,初始模型与实际情况越接近,反演效果就越好;否则,会产生模型化的现象;4在相对波阻抗数据体(相当于地震数据体)作为约束条件下的SP测井参数协克里金插值是正演过程,消除了模型化的问题,使反演结果更接近真实情况,提高了砂层真实的分辨率。