双时基频率校准电路

提供精确的时间基准是精密仪器和高精度设备计时和工作的关键技术之一。文中采用两套晶振电路，通过分频产生高频和低频两个频率信号，采用编码的方法对两个频率信号在每一个时钟周期内进行动态校核，只有两个晶振的频率在允许的精度范围内，或同时增大（缩小）相同的倍数，校核后频率信号才是正确的，且能给系统提供精确的时间基准。该方法能有效防止系统的时钟周期因意外的原因伸长或缩短。整个电路可以作为精密仪器或高精度设备的一个模块电路。     

济钢中厚板厂3500mm精轧机快速换辊自控系统

中厚板厂3500mm精轧机快速换辊自控系统，集可编程序控制器、变频传动技术、液压控制于一体。实现了工作辊、支承辊准确、快速地更换。通过网络技术与其他控制系统进行数据交换、操作及连锁，使轧制生产线实现了全自动化轧制，不仅改善了工人的劳动强度，还提高了产品的产量及质量，取得了巨大的社会效益及经济效益。     

A versatile data acquisition system for time resolved X-ray scattering using gas proportional detectors with delay line readout

A data acquisition system for time resolved X-ray scattering experiments using linear, quadrant or area gas proportional detectors with delay line readout based on commercially available hardware (National Instruments) is described. The system can easily be configured for recording data from point detectors (e.g. photomultipliers and photodiodes) and/or ancillary data only. Applications involving measurements with two different types of time to digital converters illustrate the features and performances of the system.     

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.     

高压变频器在煤气加压机中的应用

2005年11月份邯郸钢铁厂8万煤气柜外送煤气加压机采用高压变频技术改造。其原因是该加压机外送量波动较大，采用普通阀门调节打回流，将会浪费大量电能，采用变频技术改造后，节能效果非常明显。     

变频器在暖通空调行业中的应用

本文提出中央空调系统的一种变频调速改造方案，介绍了该系统的结构组成及运行原理，对系统各部分的作用进行了讨论。     

Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays

In this paper, we discuss average consensus problem in undirected networks of dynamic agents with fixed and switching topologies as well as multiple time-varying communication delays. By employing a linear matrix inequality method, we prove that all the nodes in the network achieve average consensus asymptotically for appropriate communication delays if the network topology is connected. Particularly, several feasible linear matrix inequalities are established to determine the maximal allowable upper bound of time-varying communication delays. Numerical examples are given to demonstrate the effectiveness and the sharpness of the theoretical results.     

Cycle detection using a stack

We present an algorithm for detecting periodicity in sequences produced by repeated application of a given function. Our algorithm uses logarithmic memory with high probability, runs in linear time, and is guaranteed to stop within the second loop through the cycle. We also present a partitioning technique that offers a time/memory tradeoff. Our algorithm is especially well suited for sequences where the cycle length is typically small compared to the length of the acyclic prefix.     

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.     

Modeling of Temperature–Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique

A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, a method of combining principal component analysis (PCA) and support vector regression (SVR) technique is proposed for modeling temperature-caused variability of modal frequencies for structures instrumented with long-term monitoring systems. PCA is first applied to extract principal components from the measured temperatures for dimensionality reduction. The predominant feature vectors in conjunction with the measured modal frequencies are then fed into a support vector algorithm to formulate regression models that may take into account thermal inertia effect. The research is focused on proper selection of the hyperparameters to obtain SVR models with good generalization performance. A grid search method with cross validation and a heuristic method are utilized for determining the optimal values of SVR hyperparameters. The proposed method is compared with the method directly using measurement data to train SVR models and the multivariate linear regression (MLR) method through the use of long-term measurement data from a cable-stayed bridge. It is shown that PCA-compressed features make the training and validation of SVR models more efficient in both model accuracy and computational costs, and the formulated SVR model performs much better than the MLR model in generalization performance. When continuously measured data is available, the SVR model formulated taking into account thermal inertia effect can achieve more accurate prediction than that without considering thermal inertia effect.