新型荫罩式PDP中电极对放电特性影响模拟分析

SM-PDP中可以通过改变电极宽度,使放电空间的等位面沿电极增加的方向扩展,在等位面扩展的区域电场得到增强,从而改善SM-PDP的放电性能.但在性能改善的同时,相邻电极间的电感加大,输入功率增加,这对SM-PDP性能的提高是不利的.本文采用三维模拟方法,详细研究了在不同电极宽度情况下,SM-PDP放电单元的放电特性,为在设计SM-PDP时选择合适的电极宽度提供帮助.     

旋转式室温磁制冷机用永磁磁路

随着室温磁制冷技术的发展,许多具有巨磁热效应的材料被开发出,但多是在高场强的超导磁体中获得的。为应用磁制冷技术到家用空调和冰箱,除了巨磁热效应的磁致冷材料外,设计出高场强的永磁磁路也尤其重要。针对旋转式室温磁制冷机,采用有限元方法设计并制作了具有较高场强的永磁磁路,在磁路工作气隙为20mm时,工作气隙中心的场强的计算值及实测值均高于1.5T,且计算值与实测值基本相符。     

C194铜合金的强化机制研究

为了研究C194铜合金的强化机制，对比分析了两种成分略有不同的C194铜合金带材的力学性能及其微观组织。研究发现C194合金组织中的析出相包括α—Fe和Fe3P两种，其中α—Fe是C194铜合金的主要强化相，而不是Fe3P。如果合金中P含量较高，则多余的P与合金元素Fe结合形成金属间化合物Fe3P，并以粗大颗粒形式析出，这将导致起主要强化作用的α-Fe相数量减少，从而使材料强度降低，同时由于P对合金的导电、导热性能均有显著影响，对材料综合性能不利，因此在生产中应严格控制P的含量，且尽量取下限，抑制Fe3P的形成和析出，促进α-Fe在时效过程的弥散析出，从而达到理想的强化效果。     

NdFeB粘结磁体加工性能

应用高压毛细管流变仪和HAAKE转矩流变仪，研究了粘结剂尼龙．6质量分数10％时的NdFeB粘结磁体流变和加工性能。结果表明：NdFeB粘结磁性材料的喂料体系的流动为非牛顿型假塑性液体，具有超高的熔体粘度；润滑剂能有效地降低喂料流动粘度，改善加工条件；而偶联剂会恶化其加工性能；磁粉的粒度分布对喂料的粘度和加工性能有明显的影响。本实验结果可为NdFeB粘结磁体注射、挤出成型的工艺参数确定、模具和机械设计以及加工质量的控制等提供技术依据。     

硫酸铵-硫氰酸铵-甲基紫液-固体系浮选分离汞(Ⅱ)的研究

研究了硫酸铵-硫氰酸铵-甲基紫液-固体系浮选分离汞（Ⅱ）的行为及其与常见离子分离的条件。实验结果表明，在1．0g（NH4）2SO4存在下，当0，01mol／L硫氰酸铵和0.001mol／L甲基紫（MV）溶液的用量分别为0.5ml、0.6ml时，NH4SCN-MV-Hg三元缔合物可浮于盐水相上形成界面清晰的液-固两相，从而使Hg^2＋被定量浮选，而Zn^2＋、Cd^2＋、Cu^2＋、Mn^2＋、Ni^2＋、Fe^2＋、Al^3＋等离子不被浮选，实现了Hg^2＋与这些离子的定量分离。该方法对合成水样中微量汞（Ⅱ）进行的定量浮选分离测定，结果令人满意。     

MR智能阻尼器试验研究及径向基网络模型

制作了MR智能阻尼器，并在不同的位移，速度和电流输入情况下，对自制的MR智能阻尼器进行了性能试验研究，建立了MR智能阻尼器的径向基函数网络模型。结果表明：神经网络模型能精确的描述阻尼器正向和逆向工作特性，并且对液体沉降、液体磁响应时间、蓄能器刚度等因素的影响也能很好的解决。     

造纸过程水分智能控制

针对造纸过程的复杂性、工艺特点及要求，本文对造纸过程的水分控制，提出模糊控制和专家系统结合的智能控制方法。实践证明该控制器满足了造纸过程对水分的工艺要求，且具有良好的鲁棒性。     

水声信道的建模与仿真研究

本文简要分析了水声信道的传播特性，介绍了一种水声信道的仿真模型，并对模型进行了一定的理论分析，提出了一些改进的建议，最后给出了实现仿真的方法，并对仿真结果进行了分析。     

Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems

Graph-based systems are models wherein the nodes represent the components and the edges represent the fault propagation between the components. For critical systems, some components are equipped with smart sensors for on-board system health management. When an abnormal situation occurs, alarms will be triggered from these sensors. This paper considers the problem of identifying the set of potential failure sources from the set of ringing alarms in graph-based systems. However, the computational complexity of solving the optimal multiple fault diagnosis (MFD) problem is exponential. Based on Lagrangian relaxation and subgradient optimization, we present a heuristic algorithm to find approximately the most likely candidate fault set. A computationally cheaper heuristic algorithm - primal heuristic - has also been applied to the problem so that real-time MFD in systems with several thousand failure sources becomes feasible in a fraction of a second. This paper also considers systems with asymmetric and multivalued alarms (tests).     

A Knowledge Discovery System with Support for Model Selection and Visualization

The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.