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.     

两相邻边固定另两边任意支承矩形厚板问题

在Ｒｅｉｓｓｎｅｒ厚板理论中，利用分离变量法得到挠度函数Ｗ（ｘ，ｙ）和应力函数ψ（ｘ，ｙ）的解。在这些解中．选择一些三角级数和多项式作为该问题的挠度函数Ｗ（ｘ，ｙ）和应力函数ψ（ｘ，ｙ），从而得到了两相邻边固定另两边任意支承矩形厚板弯曲问题的精确解．这里不需要繁琐地叠加．     

A neural network model for fault detection in conjunction with a programmable logic controller

This paper discusses the feasibility of using neural networks as a tool in the fault detection process. A neural network is integrated with a state language programmable logic controller, an important device in an automatic control system. Time series data related to time spent in a state is gathered and used as input into a neural network, for the purpose of identifying when a fault has occurred. A feedforward neural network is used to identify which (if any) of three types of faults may have occurred. Experimental results related to sensitivity and accuracy measures are presented. A brief review of related applications and research is also presented.     

概率空间中的一个不等式

设｛Ｚ，（Ｗ）｝是定义在概率空间上的独立的随机变量序列，Ｚ（Ｗ）具有方差为期望Ｅ（Ｚｎ）＝０．本文证明了对任意算数序列｛ｂｎ｝及任意的存在充分大的整数Ｎ＞０使当Ｎ＇＞Ｎ″＞Ｎ时．有此不等式在随机级数研究中有重要作用．     

Stabilization of inter-area and/or oscillatory modes in large-scale power system by voltage impedance power system (VIPS) apparatus based on decentralized control scheme

New technologies such as power electronics have made it possible to change continuously the impedance of a power system not only to control power flow but also to enhance stability. A power system incorporating a variable impedance apparatus such as a variable series capacitor (VSrC) and high-speed phase shifter (HSPS) is called VIPS (Variable Impedance Power System) by the authors. This paper proposes a novel control method of VIPS apparatus such as VSrC and HSPS installed at an interconnecting point for stabilizing inter-area unstable and/or oscillatory modes. The proposed design method of the control system is a kind of hierarchical decentralized control method of a large-scale power system based on a Lyapunov function. Under the proposed control scheme, each subsystem can be stabilized independently by local controllers such as AVR, speed governor and PSS, and then the whole interconnected system can be stabilized by VIPS apparatus taking into account interactions between subsystems. The effectiveness and robustness of the VIPS apparatus control are shown by numerical examples with model systems including a large-scale power system.     

Nonlinear adaptive algorithms for equalisation in mobile satellite communications

This paper considers the equalisation problem in Quadrature Phase-Shift Keying (QPSK) modulated signals which have been distorted by the passage through a transmission channel. The channel is modelled as a Rician fading channel to simulate the behaviour of the transmission channel in the mobile satellite context. The equalisation is treated as the generalisation of the channel behaviour, and some algorithms with the structure of an artificial neural network using the Multilayer Perceptron, Volterra Series and Radial Basis Function are described. Results for the BER performance of typical transversal equalisers, with Square-Root Kalman adaptation algorithm, and algorithms with artificial neural network structure are also reported and evaluated. Improved performance is exhibited by the artificial neural network approaches.     

An automatic torque ripple compensation system for direct drive motors

A serious problem in motion control is the occurrence of torque ripple. Since a direct drive (DD) motor with a magnet rotor uses a rare-earth magnet, the torque ripple is more serious than that of conventional servomotors. This paper presents an auto-compensation of torque ripple using a software-implemented torque observer. The frequency and amplitude of torque ripple can be obtained in the controller by using the estimated torque from an observer, and the autonomous generation of a compensating current component for a torque ripple is possible. The proposed algorithm has been verified by experiments.     

Trends in Hydrological Parameters of a Southern Brazilian Watershed and its Relation to Human Induced Changes

The Piracicaba river basin is a subtropical watershed located in the southeastern region of Brazil. With an area of 12 400 km², the basin is a typical example of new landscape resulting from development in tropical and sub-tropical regions: establishment of intensive industrial and agricultural processes were followed by significant population growth and water management. This scenario has led to significant increase in water demand and decrease in water quality. The main objective of this study is the detection of changes in the patterns of flow and precipitation in the basin, and its possible relation to man-induced changes. Statistical analyses were performed on records of precipitation, evapotranspiration and streamflow, from 1947 to 1991. Precipitation and evapotranspiration totals showed significant increasing trends for the entire basin. From eight streamflow gauge stations, half showed significant decreasing trend. The most probable cause of such trends is the export of water from the basin to the metropolitan region of São Paulo city.     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.     

Minimum variance control and performance assessment of time-variant processes

This paper is concerned with (1) an explicit solution of a minimum variance control law for linear time-variant (LTV) processes in the transfer function form, and (2) performance assessment of LTV processes using minimum variance control as the benchmark. It is shown that there exists a time-variant, absolute lower bound of process variance that is achievable under LTV minimum variance control and can be estimated from routine operating data. This lower bound can subsequently be used to assess the benefit of implementing LTV control such as adaptive control. The proposed methods are illustrated through simulated examples and an industrial case study.