脉冲等子体软X射线探测系统

研制了一种脉冲重复频率为１０Ｈｚ的激光等离子体软Ｘ射线源；设计并建立了以掠入射恒偏单色仪为基础的脉冲等离子体软Ｘ射线辐射测试系统。光源实验使用能量１０００ｍＪ（波长０．５３μｍ）的激光脉冲，聚焦在不同的金属靶上，使之离子化。这种高温等离子体产生的辐射包含了波长０．４～２０ｎｍ的软Ｘ光连续谱和迭加在其上的线谱。靶材有Ｍｇ、Ａｌ、Ｔｉ、Ｆｅ、Ｃｕ、Ｚｎ、Ｓｎ、Ｐｂ和Ｂｉ。得到了不同原子序数的靶材辐射的光谱分布特性。     

核桃软糖的防粘结研究

主要针对核桃软糖在气温稍高、湿度较大的环境中容易粘结，造成质量下降和贮藏期缩短的现象，通过改进配方和工艺，防止和延缓核桃软糖粘结，延长了产品的贮藏时间。     

一种新的信号去噪方法及在激光信号中的应用

小波去噪是信号去噪的主要方法之一,目前的研究主要集中于如何选取阈值使去噪达到较好的效果,其中Donoho的阈值方法应用广泛.与传统双通道小波变换相比,三通道小波变换具有很多优势.基于这一理论,结合非参数自适应估计,推导出了一种适用于三通道小波域的自适应的信号去噪方法.实验证明,与三通道小波域中基于Donoho方法的去噪相比,该方法可以更好地保护细节以及提高重建信号的信噪比,在激光超声信号处理中也表现出良好的性能.     

计算机软件在多媒体化学教育中的应用

本文阐述了计算机软件在多媒体化学课件开发中的应用，着重介绍了一些普通软件及专业化学软件的特点及功能。     

“三软”煤层综放面特殊情况下支架拆除及沿空留巷

超化煤矿21091(西)综采工作面因出水造成工作面停产,在不超前15 m铺金属网的情况下进行支架拆除,并把原工作面留做炮采面切巷。根据实际情况制定了施工方案,并介绍了实施情况。     

垫层载荷试验应注意的几个问题

介绍了垫层上载荷试验的类型，通过工程实例对不同类型垫层上的载荷试验应注意的问题进行了分析和探讨，这对通过载荷试验确定双层地基承载力具有一定的实际意义，可供同类工程参考借鉴。     

A New Matlab De-noising Algorithm for Signal Extraction

The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors addressed a new Matlab algorithm for de-noising. A key method of the algorithm is selecting an optimal basis from a library of wavelet bases for ideal de-noising. The algorithm with an optimal basis from a library of wavelet bases for de-noising was created through making use of Matlab＇s Wavelet Toolbox. The experimental results show that the new algorithm is efficient in signal de-nosing.     

形状记忆聚氨酯的结构与性能研究

以 2 ,4 -甲苯二异氰酸酯 (2 ,4 -TDI)、不同分子量的聚己二酸丁二醇酯 (PBAG)和 1,4-丁二醇 (BDO)为原料合成了具有形状记忆功能的聚氨酯材料。通过DSC、弯曲实验和力学实验 ,研究了形状记忆聚氨酯的性能 ,发现软段高度结晶和硬段聚集形成硬段微区是其具有较好形状记忆性能的必要条件。     

时空效应规律在软土深基坑工程中的运用

阐述了时空效应规律在软土深基坑工程中的运用情况，实践证明，运用时空效应规律，能可靠而合理地利用土体自身在基坑开挖过程中控制土体位移的潜力而达到保护环境的目的。     

Use of a neural network to link AFM etch patterns to plasma parameters

Plasmas play a critical role in depositing thin films or etching fine patterns while manufacturing integrated circuits. A new model for plasma diagnosis is presented. This was accomplished by linking atomic force microscopy (AFM) to plasma parameters using a neural network. Experimental AFM data were collected during the etching of silicon oxynitride films in C₂F₆ inductively coupled plasma. Surface roughness of etched patterns was characterized by means of discrete wavelet transformation. This led to the construction of three vertical (type I), diagonal (type II), and horizontal (type III) wavelet coefficient-based models. The performance of diagnosis models was evaluated in terms of the prediction and recognition accuracies. Both accuracies were optimized as a function of the number of hidden neurons. Comparisons revealed that the type I model yielded the largest recognition and the smallest prediction error. This was demonstrated even under stricter monitoring conditions. More improved diagnosis is expected by enhancing AFM resolution.