A theory of rectangular dual graphs

Arectangular graph is a plane graph where all regions are four-sided and all edges are oriented in either the vertical or the horizontal direction. In addition the graph enclosure must also be rectangular. Given a plane graph representing a desired component connectivity, itsrectangular dual can be used to build afloorplan. This indicates that a system implementation can allocate rectangular floorplan regions to components that can be pairwise connected through common borders.This paper proves that constructing a rectangular dual graph is equivalent to a matching problem in a bipartite graph derived from the given plane graph. A simple existence theorem in terms of the graph structure is obtained as a corollary.This work was supported by the National Science Council, Taiwan, Republic of China, under Contract NSC 78-0404-E006-14.     

Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems.     

Modularity,Combining and Artificial Neural Nets

In this paper, the modular combination of artificial neural nets is considered. A modular approach to combining can be contrasted with an ensemble-based approach in that it implies individual modules, each responsible for some specialist aspect of a task, as opposed to each approximating the same function. It is possible to characterize modular systems in terms of (i) reasons for the task decomposition, (ii) the method for accomplishing the decomposition and (iii) the relationship between the modules. These characteristics are considered in brief outlines of the papers in the issue. Reasons for task decomposition include the exploitation of specialist capabilities of individual nets, performance improvement, and making the system easier to understand and modify. Task decomposition may be either automatic (based on the blind application of a data partitioning algorithm) or explicit (based on prior knowledge of the task or the specialist capabilities of the modules), and the relationship between the modules may be successive, cooperative or supervisory.     

再谈水质稳定、管网改造及运行管理的相互关系

针对当前在改善管网水质上,偏重于管道的更新改造,忽略水质稳定及管网运行管理的问题,从腐蚀、结垢机理、水质稳定性等方面,论述了影响管网水质的诸多因素,并提出了制定供水企业出厂水稳定性要求,加强管网技术改造及管网运行管理科学化等具体建议。     

压力渗碳工艺试验与应用

对20CrMnTi钢压力渗碳工艺研究结果表明,在保证产品质量的前提下,显著提高渗碳速度,缩短工艺周期,降低 生产成本。同时,对压力渗碳工艺的机理进行了初步探讨。     

Controlled degradation of epoxy networks: analysis of crosslink density and glass transition temperature changes in thermally reworkable thermosets

The characteristics of networks formed in cured ‘reworkable’ epoxy thermosets capable of controlled thermal degradation were studied. Dynamic mechanical thermal analysis, swelling measurements, and glass transition temperature measurements were used to obtain information regarding the time and temperature dependence of the crosslink densities of these materials. By applying isothermal conditions, networks containing up to 36 mol% non-degradable components could be completely degraded, i.e. progress from a network of infinite molecular weight to a finite one with zero crosslink density. Percolation theory was used to facilitate the interpretation of these results. The degradation behavior of the reworkable thermosets were well-described by gel degradation theory, i.e. the reverse of the gelation process, and the experimental results were in good agreement with calculated values obtained by replacing the extent of reaction, p, in Macosko and Miller's branching theory with the extent of degradation, 1−p.     

常用温度控制法的对比

常用温度控制包括常规PID、模糊、神经网络、Fuzzy-PID、神经网络PID、模糊神经网络、遗传PID及广义预测等控制方法.常规PID控制易于建立线性温度控制系统被控对象模型.模糊控制基于规则库,并以绝对或增量形式给出控制决策.神经网络控制采用数理模型模拟生物神经细胞结构,并用简单处理单元连接成复杂网络;Fuzzy-PID为线性控制,且结合模糊与PID控制优点.并给出了各方法的控制特性、功能及主要应用场合.     

Study on Service Level Management in Integrated Satellite Information Network

Integrated Satellite Information Network (ISIN) includes those nodes in space and those on ground. It is the way to realize the fusion of satellite communication and traditional network technology. A satellite network management system based on Multiplex Network Management Protocol (MNMP) has accomplished traditional management, such as configuration, performance and fault management. An architecture of Service Level Management (SLM) in ISIN is proposed, and a service topology management and Service Level Agreement (SLA) are deeply researched. At last, service security and fault management are briefly discussed, and a simulation system is accomplished.     

FPGA Implementation of a Pipelined On-Line Backpropagation

The paper describes the implementation of a systolic array for a multilayer perceptron with a hardware-friendly learning algorithm. A pipelined modification of the on-line backpropagation algorithm is shown and explained. It better exploits the parallelism because both the forward and backward phases can be performed simultaneously. The neural network performance for the proposed modification is discussed and compared with the standard so-called on-line backpropagation algorithm in typical databases and with the various precisions required. Although the preliminary results are positive, subsequent theoretical analysis and further experiments with different training sets will be necessary. For this reason our VLSI systolic architecture—together with the combination of FPGA reconfiguration properties and a design flow based on generic VHDL—can create a reusable, flexible, and fast method of designing a complete ANN on a single FPGA and can permit very fast hardware verifications for our trials of the Pipeline On-line Backpropagation algorithm and the standard algorithms.     

Multiclassification of tool wear with support vector machine by manufacturing loss consideration

Tool wear is a dynamic process, as a tool progresses from sharp to worn state and possibly to breakage. Thus the multiclassification of tool states is preferred, which can provide more timely and accurate estimation of tool states. Based on acoustic emission (AE) sensing, this paper proposes a new performance evaluation function for tool condition monitoring (TCM) by considering manufacturing loss. Firstly, two types of manufacturing loss due to misclassification (loss caused by under prediction and loss caused by over prediction) are analyzed, and both are utilized to compute corresponding weights of the proposed performance evaluation function. Then the expected loss of future misclassification is introduced to evaluate the recognition performance of TCM. Finally, a revised support vector machine (SVM) approach coupled with one-versus-one method is implemented to carry out the multiclassification of tool states. With this approach, a tool is replaced or continued not only based on the tool condition alone, but also the risk in cost incurred due to underutilized or overused tool. The experimental results show that the proposed method can reliably perform multiclassificaion of tool flank wear, and reduce the potential manufacturing loss.