An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

数字化变电站级联网络时延上界计算方法

提出一种改进的数字化变电站级联网络时延上界计算方法,从级联系统末端开始逐步细化数据流,逐步求取其后端级联系统对级联入口数据流的服务曲线,达到了只需考虑单次突发的效果。运用多种方法对采用交换式以太网环形拓扑构建数据采集网和GOOSE网合并的变电站过程总线网络进行计算,结果表明所提出的方法比采用上级离开曲线即下级到达曲线思路的方法更有效。     

Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt compaction analyzer (IACA) is then trained to convert these vibration levels into a “number” indicative of the asphalt mat density at a given location. This two-step process eliminates the need for regression analysis and produces more accurate density measurements than those reported elsewhere in the literature. Compaction studies of HMA mixes on a stiff subgrade indicate that the changes in the vibration characteristics of the roller are attributable to an increased compaction of the HMA base. The results also show that, with the neural network working as a classifier, the IACA can estimate the density continuously, and in real time, with accuracy levels adequate for quality control in the field.     

The super connectivity of exchanged hypercubes

The exchanged hypercube EH(s,t) where s?1 and t?1 are two positive integers, proposed by Loh et al. [The exchanged hypercube, IEEE Transactions on Parallel and Distributed Systems 16 (9) (2005) 866-874], is obtained by systematically removing links from a binary hypercube Qn. This paper determines that the super connectivity and the super edge-connectivity of EH(s,t) are 2s where s?t. That is, for s?t, at least 2s vertices (resp. 2s edges) of EH(s,t) are removed to get a disconnected graph that contains no isolated vertex.     

基于Tikhonov正则化的WSN多边定位算法研究

节点定位是无线传感器网络实现监测和跟踪的一个重要前提.针对多边定位中的不适定问题.提出了一种基于Tikhonov正则化方法的定位算法,研究了定位模型的建立、正则化参数的选取方法以及最优定位参考点数的选取等问题.实验结果表明本算法与典型的极大似然估计法相比,较大幅度地提高了定位精度,当a值选取600,采用5个参考节点时,定位精度可达到1米.     

大型复杂河网一维动态水流-水质数值模型

建立了非恒定河网一维水流-水质数值模型（THU-River1D）,水动力模块利用最近提出的汊点水位预测-校正（junction-point water stage prediction and correction,JPWSPC）法处理缓流河网汊点处的回流效应,采用Preissmann格式离散Saint-Venant方程组,并采用Newton-Raphson方法求解非线性离散方程;水质模块采用分步法,分别求解对流项、源（汇）项、以及纵向离散项,其中,对流项处理采用改进的四阶显式Holly-Preissmann格式。模型无需特殊的河道编码,既适用于树状又适用于环状河网,还适用于潮汐流动。     

神经网络及其在工程管理中的应用

详细地介绍了神经网络的模型、结构、应用与最新发展等,对神经网络技术在工程管理方面的应用也作了详细的说明与分析。     

All‐MOS implementation of RC networks for time‐controlled Gaussian spatial filtering

This paper addresses the design and VLSI implementation of MOS‐based RC networks capable of performing time‐controlled Gaussian filtering. In these networks, all the resistors are substituted one by one by a single MOS transistor biased in the ohmic region. The design of this elementary transistor is carefully realized according to the value of the ideal resistor to be emulated. For a prescribed signal range, the MOSFET in triode region delivers an interval of instantaneous resistance values. We demonstrate that, for the elementary 2‐node network, establishing the design equation at a particular point within this interval guarantees minimum error. This equation is then corroborated for networks of arbitrary size by analyzing them from a stochastic point of view. Following the design methodology proposed, the error committed by an MOS‐based grid when compared with its equivalent ideal RC network is, despite the intrinsic nonlinearities of the transistors, below 1% even under mismatch conditions of 10%. In terms of image processing, this error hardly affects the outcome, which is perceptually equivalent to that of the ideal network. These results, extracted from simulation, are verified in a prototype vision chip with QCIF resolution manufactured in the AMS 0.35µm CMOS‐OPTO process. This prototype incorporates a focal‐plane MOS‐based RC network that performs fully programmable Gaussian filtering. Copyright © 2011 John Wiley & Sons, Ltd.     

Realization of nth-order series impedance function using only (n-1) current-feedback amplifiers

A new configuration for realizing a higher-order series impedance function using current-feedback amplifiers (CFAs) is presented. The nth-order series impedance function employs only (n–1) CFAs and (2n–1) passive elements. The higher order series impedance function can be applied to realize various higher-order highpass and lowpass filters. The passive element values can be easily decided from the coefficients of the filter transfer functions. The use of CFAs simplifies the circuit implementation and makes the proposed circuit attractive for application. Experiments and simulation results are given to confirm the theoretical analysis.