钢管内径光电检测装置

介绍了一种检测细长钢管内径的光电检测装置。该装置利用线阵ＣＣＤ作传感器，采用８０３１单片机作为主控制。     

二元光栅照明研究

采用部分相干成象理论对二元光机照明进行了较为详细的研究。分析了二元光栅照明对分辨力，焦，照明能量和照明均匀性的影响，并通过实验研究验证了二元光栅对分辨力和焦深有较大程度的提高，同时能量损失不大。     

涂胶设备背面冲洗功能的开发与研制

本文根据涂胶设备在加工制品过程中所遇到的制品背面沾污问题，提出改进措施，并根据设备的具体情况，增加了涂胶设备的背面冲洗功能。使制品在涂胶过程中，及时地对制品背面进行冲洗，从而保证了制品背面的洁净度，对提高产品质量和提高产品的成品率有非常重要的意义，同时对开发一些技术要求较高的新品也有深远的影响。     

夜视系统鉴别率测试的新方法

用室内低光照高对比鉴别率取代野外探测目标距离作为衡量夜视系统性能优劣的标准,将给验收工作带来方便。本文给出一种实验室内测试夜视系统鉴别率的方法。文中导出了夜视系统鉴别率的一种新的表达式,从而提出了一种改进后的物理模型及其实验方案。分析表明,采用新方案,即在实验室中人为增加环境照度,由此测得的系统工作鉴别率,用以取代野外探测目标距离,其可信度可望得到明显的提高。     

泌阳凹陷古城地区地震资料连片处理及认识

地震资料连片处理的目的是尽可能地克服不同年度、不同仪器、不同施工参数等造成的影响,同时消除不同的处理参数及处理流程所导致的差异。连片地震资料可以用于整体区域地震解释和地质认识。根据新老资料采集参数的特点,在静校正、叠前去噪、振幅补偿、叠前叠后提高分辨率等方面进行攻关,系统地解决了新老资料连片处理中的关键问题。通过处理资料的分析认为连片资料比较适合构造解释,而属性研究必须根据具体情况,一般是面元均化的网格与单块网格相比,网格差别大的,最好使用单块处理的资料,差别小的或者网格没有差别的,采用连片处理的资料进行研究。     

高分辨力地震是提高中国南方碳酸盐岩地震勘探效能的基础——一个由失败到成功的史例

南方碳酸盐岩复杂地区的地震工作经历了长期艰难曲折的道路,其中异常判别的经验教训极为深刻。本文从地震异常分析入手,揭示其实质,寻求问题的关键,探讨解决的办法,介绍高分辨率地震方法技术,展示高分辨率地震新成果,以得出有意义的结论和建议。     

Sub-wavelength resolution in air-coupled ultrasound images of spot welds

Resistance spot welds joining steel sheets were examined using air-coupled ultrasound at 1 MHz. Transmission C-scan images were obtained using two axially aligned transducers, with each focused on its adjacent surface of the specimen. Because of the enormous impedance mismatch between air and steel, the transmitted signal was exceedingly weak, requiring several hours to collect an image. Although the air-coupled images resembled the weld shapes as determined by an independent technique, surface topography introduced considerable image distortion. The smearing of the images was substantially less than the 5.9-mm wavelength in steel, with the steepness of the transition between welded and unwelded areas indicating a transverse resolution about 1 mm. Similarities to near-field imaging are mentioned as a possible explanation for the observed sub-wavelength resolution.     

Limits of Learning-Based Superresolution Algorithms

Learning-based superresolution (SR) is a popular SR technique that uses application dependent priors to infer the missing details in low resolution images (LRIs). However, their performance still deteriorates quickly when the magnification factor is only moderately large. This leads us to an important problem: “Do limits of learning-based SR algorithms exist?” This paper is the first attempt to shed some light on this problem when the SR algorithms are designed for general natural images. We first define an expected risk for the SR algorithms that is based on the root mean squared error between the superresolved images and the ground truth images. Then utilizing the statistics of general natural images, we derive a closed form estimate of the lower bound of the expected risk. The lower bound only involves the covariance matrix and the mean vector of the high resolution images (HRIs) and hence can be computed by sampling real images. We also investigate the sufficient number of samples to guarantee an accurate estimate of the lower bound. By computing the curve of the lower bound w.r.t. the magnification factor, we could estimate the limits of learning-based SR algorithms, at which the lower bound of the expected risk exceeds a relatively large threshold. We perform experiments to validate our theory. And based on our observations we conjecture that the limits may be independent of the size of either the LRIs or the HRIs.     

On topology optimization of linear and nonlinear plate problems

In this paper we propose a new restriction method based on employing C ⁰-continuous fields of density defined on a set of meshes different from the one used for the finite element analysis. The optimization procedure starts with using a coarse density-mesh compared to the finite element one. Once the convergence is obtained in the optimization steps, a finer density-mesh is nominated for the further steps. Linear and nonlinear plate behaviors are considered and formulated by Kirchhoff or Mindlin–Reissner hypothesis. Comparison is made with element/nodal based approaches using filter. The results show excellent and robust performance of the proposed method.