Stress distribution in thick-walled cylinder due to non-uniform radial pressure on the example of reinforcement corrosion in concrete

This paper presents an analytical solution to the non-uniform pressure on thick-walled cylinder. The formulation is based on the linear elasticity theory (plain strain) and stress function method. As an example, the proposed solution is used to model the stress distribution due to non-uniform steel reinforcement corrosion in concrete. The model is formulated considering different scenarios of corrosion pressure distribution. It is validated against the finite element model for different cases of non-uniform pressure distributions. The results show that the corrosion-induced cracks are likely to start just beyond the anodic zone. This is confirmed by the experimental tests on concrete cylinder exposed to non-uniform accelerated corrosion of steel reinforcement. The model can be effectively used to calculate the distribution of corrosion-induced stresses in concrete.     

A new design strategy for DC/DC LLC resonant converter: Concept,modeling, and fabrication

In this paper, a new design procedure for LLC converter has been introduced. In fact, this method is a computer-based design algorithm based on a numerical technique. In the process of designing, the value of the resonant element is obtained by solving the LLC converter fundamental equation. This converter will be controlled by using state feedback, such as output voltage variable. As a matter of fact, in a control system, the change of output voltage (because of load variation) will affect the switching frequency, so the output voltage will be tuned. In the designing process, the fundamental equations of LLC converter are obtained, and the value of the resonant elements is calculated. Also, a comparison analysis is carried out between the proposed and typical methods. The simulation is done to investigate the validity of the proposed method. Moreover, a prototype is manufactured, and the experimental test is done to evaluate its applicability.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

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.     

通信交流供电方案初探

在通信供电系统中,与直流供电系统相比,从设计、设备技术,到运行维护管理,交流供电系统都有所滞后。借鉴直流供电系统经验,通过应用N X并联逆变器模块热插拔技术,实行分散式供电,进而改变传统的以UPS为核心的集中供电模式,则可在很大程度上解决一直困扰着我们的交流供电系统成本高和可靠性低的难题。     

宝浪油田宝北区块低渗透油藏层系优化研究

针对宝浪油田宝北区块低孔低渗油藏地质特征以及油层层数多、分布井段长、部分零散层动用难度大等问题 ,研究了宝北区有效厚度、油层层数、单井产能及单井控制地质储量等层系划分与组合的技术经济界限 ,并在此基础上进行了宝北区层系划分与组合优化论证 ,最终优选出宝北区开发层系     

可并联逆变器中的同步控制技术及其实现

论述了可并联逆变器中的同步控制技术及其实现方法。在分析了同步的重要性之后，提出了抢占与并发相结合的同步控制策略及其实现方法，实践证明该方法具有很高的控制精度。     

声光效应拉曼-内斯方程矩阵级数解法及声光调制器优化设计

提出一种求解正常声光相互作用拉曼 内斯 (Raman Nath)方程的矩阵级数解法 ,该解法直观方便且具有普遍性。计算结果表明 ,对Q =4 1π ,Bragg衍射的效率只有 97 5 % ;对非对称入射 ,以往的Raman Nath近似解误差较大 ;指出提高Bragg衍射效率的有效途径在于提高声光频率比并给出计算声光器件最优长度的计算公式。     

A time series bootstrap procedure for interpolation intervals

A sieve bootstrap procedure for constructing interpolation intervals for a general class of linear processes is proposed. This sieve bootstrap provides consistent estimators of the conditional distribution of the missing values, given the observed data. A Monte Carlo experiment is used to show the finite sample properties of the sieve bootstrap and finally, the performance of the proposed method is illustrated with a real data example.     

Developing Cost Response Models for Company-Level Cost Flow Forecasting of Project-Based Corporations

The difficulty in applying the standard curve (S-curve) and cost-schedule integration (CSI) techniques for company-level cost flow forecasting in a project-based industry is the prerequisite of forecasting future unknown individual projects and contract classifications. By analyzing cost flows at the company level through a pool of macroeconomic and internal financial data, this paper proposes an innovative approach to firm-specific model estimation. First, a series of data transformations introduce linear relationships between cost, macroeconomic, and internal financial variables. Second, multivariate regression analysis is employed for initial model building. Third, for the purposes of model restructuring, a subsequent application of Yule–Walker estimates and incomplete principal component analysis is used. This paper uses a sample of four project-based construction firms to demonstrate model performance. Using this methodology, mean absolute percentage error (MAPE) values of the forecasting models range from 0.27 to 0.60%. As such, the transformed cost, macroeconomic, internal financial data could strongly predict company-level cost flow forecasting. While converting the predicted cumulative cost data to periodic cost flows, the MAPE values were augmented, ranging from 7.04 to 17.55%, thus, requiring future research.