高水头船闸输水廊道反弧门水弹性振动试验研究

本文简要介绍水弹性振动模型试验的相似准则，用近似满足水弹性相似准则的有机玻璃模型对－高水头船闸反弧形阀门进行流激振动试验研究．试验表明阀门的振动是随机振动，不仅有质量振动而且有结构振动，有若干阶模态被激发参与振动。在开门和关门过程中，阀门面板脉动压力和阀门振动是不大的，与葛洲坝船闸类似阀门间歇开启的振动水平相当。由于常压模型不能正确模拟空化现象，对于廊道内可能产生的空泡溃灭所引起的阀门振动尚需通过减压模型进行研究。     

ERD facility for analysis of hydrogen and deuterium in solids

Hydrogen is the lightest element in nature, and so, its detection and quantitative analysis is difficult by the conventional methods utilized for other elements. In the recent years the technique of elastic recoil detection analysis (ERD) using 1–2 MeV He⁺ beam has been developed to quantitatively and simultaneously analyze hydrogen and its isotopes in solids. Such a facility has been set up using the 2 MeV Van-de-Graaff accelerator at IIT Kanpur. It facilitates H and D analysis in a material up to a depth of ∼ 1μm with a detection sensitivity of 0·1 at.% and depth resolution of about 300 ?. The application potential of this setup is illustrated by presenting the results of measurements performed on Al:H:D systems prepared by plasma source ion implantation and highT _c YBCO pellets exposed to humid atmosphere.     

非晶硅基薄膜的等离子体刻蚀光发射谱检测

本文研究了采用锁定放大相干检测技术的等离子体光发射谱检测系统。用该系统检测了仅用CF4作为刻蚀气体刻蚀非晶硅基薄膜的等离子体光发射谱。分析了检测结果和刻蚀机理。     

铝液夹杂物检测DMIRT－Ⅲ法的试验研究

提出了一种新的铝液夹杂物快速检测方法。导出了该法的检测原理方程。分析并试验研究了影响检测重复性和分辨率的因素。试验选择了较好的检测参数，并获得了较好的检测结果。     

红外图像序列小目标检测预处理技术

文中就如何复杂背景条件下低信噪比的小目标检测概率问题展开了讨论，提出了用空间高通滤的方法改善图像质量，达以抑制背景噪声，增强小目标的目的，并交坑通滤波和中值滤波做了一个简单的比较，随后采用分层投票表决法对目标进行检测，取得了良好效果。     

直接基于灰度图象的多尺度角点检测方法

在计算机视觉领域中角点特征的提取具有相当重要的地位，因为角点包含了丰富的信息。本文提出了一种新的利用微分几何和小波变换的多尺度角点检测方法，它具有以下特点：一是无须提取边缘图象，而是直接在灰度图象上提取角点。二是由于利用了小波变换技术从多个尺度上进行角点的提取，所以它既较为有效的抑制了由于噪音等原因造成的诸多错误角点位置的出现，又能得到较为精确的有用角点位置。最后通过实验证明了本算法有效性。     

AVO 异常检测技术及其应用

本文介绍了一系列简单而实用的 AVO 异常显示、检测和增强的新技术。包括:共炮检距(或相近)显示技术、振幅比值显示技术、滚动显示技术、线性转换显示技术、抛物线转换显示技术和 AVO 增强技术。文中以实例说明了上述各种方法的应用情况。     

圆锥破碎机躯体裂纹的超声波探伤

重点介绍了圆锥破碎机躯体裂纹的超声波检测及缺陷判定的方法,为矿山其它大型设备的裂纹检测提供了经验。     

On‐Line Detection Of State‐Of‐Charge In Lead Acid Battery Using Radial Basis Function Neural Network

To realize a stable supply of electric power in an automobile, an accurate and reliable detection method of SOC (state‐of‐charge) in a lead acid battery is required. However the dynamics of the battery is very complicated. The characteristics of the battery greatly change due to its degradation. Moreover a automobile has many driving patterns, which are unknown beforehand. Thus it is not easy to detect the SOC analytically. In this paper, to overcome this problem, a new on‐line SOC detection method with a radial basis function neural network is proposed. In order to increase the detection accuracy of degraded batteries, physical values related to the degradation degree are used as input signal in the neural network. The detection accuracies for different sized batteries and various degradation states are investigated.     

箕斗卸载状况的检测方法

介绍了箕斗卸载状况检测方法的原理,分析了各种方法的优点和缺点。使用时应根据具体情况选择合理的检测方法。