50钢板裂纹分析

通过对50钢裂纹的宏微观分析，并结合轧制过程，认为该钢板产生裂纹的原因为此50钢连铸坯在拉钢过程中，产生内裂，低熔点物质向裂纹处填充，靠近表面处在轧制过程中形成了横向裂纹。     

矿山巷道支护用热轧25U型钢的开发

简要介绍了莱钢中型型钢生产线的概况 ,以及采用万能轧机生产矿山巷道支护用热轧 2 5U型钢的工艺技术要点、孔型设计和生产情况。     

Induced effects of Cu underlayer on（111） orientation of Fe50 Mn50 thin films

Effects of Cu underlayer on the structure of Fe50 Mn50 films were studied. Samples with a structure of Fe50 Mn50 (200 nm)/Cu(tcu) were prepared by rnagnetron sputtering on thermally oxidized silicon substrates at room temperature. The thickness of Cu underlayer varied from 0 to 60 nm in the intervals of 10 nm. High-vacuum annealing treatments, at different temperatures of 200, 300 and 400℃ for 1 h, respectively, on the Fe50Mn50 (200 nm)/Cu(20 nm) thin films were performed. The surface morphologies and textures of the samples were measured by field emission scan electronic microscope (FE-SEM) and X-ray diffraction(XRD). Energy dispersive X-ray spectroscopy (EDX) and Auger electron spectroscopy(AES) were used to analyze the compositional distribution. It is found that Cu underlayer has an obvious induce effect on (111) orientation of Fe50 Mn50 thin films. The induce effects of Cu on (111) orientation of Fe50 Mn50 changed with the increase of Cu layer thickness and the best effect was obtained at the Cu layer thickness of 20 nm. High-vacuum annealing treatments cause the migration of Mn atoms towards surface of the film and interface between Cu layer and substrate. With the increasing annealing temperature, migration of Mn atoms is more obvious, which leads to a Fe-riched Fe-Mn alloy film.     

U71Mn钢钢轨轨端热处理工艺改进

对钢轨轨端采用加热空冷、加热风冷、淬火 回火3种不同的工艺进行热处理，并对处理后钢轨踏面的组织、性能进行对比分析。结果表明，钢轨轨端采用加热风冷的热处理工艺是可行的，用该工艺处理后钢轨轨端的组织、性能能够满足标准要求。     

基于Cortex-A8的智能视频传感器的设计

视频监控在安全防范系统中有着重要作用。为了提高视频监控系统的智能化水平,设计并实现一种基于Cortex-A8的智能视频传感器。首先,以s5pv210为处理核心,TVP5150为视频采集模块,电源、网口、串口等外围辅助电路为基础,搭建视频传感器的硬件平台;然后,以嵌入式Linux为系统平台,采用TVP5150进行视频解码,Cortex-A8内置MFC进行视频压缩,在Vi Be等算法的基础上实现对检测区域的区域警戒,烟雾检测等;最后,融合硬件与软件为一体,实现智能视频传感器的设计。实验表明,该设计提高了智能视频监控系统检测报警及智能识别的实时性与准确性。     

基于CH378嵌入式存储系统设计

随着军事工业飞速发展,高速数据存储设备成为导弹测试领域不可或缺的一部分。通过对导弹测试领域中现有数据存储设备研究发现当今存储系统存在传输速度低、存储容量小、可靠性差、数据易丢失、严重依赖PC机等缺点,首次提出以U盘作为存储介质用于某型号导弹参数测试领域,以最新的USB串口芯片CH378来设计读写U盘,并独特运用FPGA读写大容量的FLASH构成存储系统的数据缓存部分这一设计理念,最终以STM32F103VE单片机控制CH378将缓存数据写入U盘的设计方案;文中详细介绍了基于CH378的嵌入式存储系统软硬件设计,最后通过软件测试U盘存储速度并用信号发生器模拟传感器信号输出,对比U盘和FLASH中数据并对数据解码分析,验证系统达到高速可靠存储等设计要求。     

一种高速图像数据记录器设计

针对摄像机输出的高速数字视频信号的存储需求,设计了一种图像数据记录器,实现了对数据的存储、回传和转发等功能;图像数据接收采用点到点LVDS模式,存储电路选用K9WBG08U1M作为存储介质,使用CY7C4285V-15构成数据缓冲电路;通过采用交错双平面模式对FLASH进行页编程和对坏块的特殊处理使记录器很好地实现了对30.72MB/s高速数字视频信号的存储;测试结果表明,该记录器能够准确记录图像信息,图像还原后清晰准确.     

一种高速正弦信号发生器的设计

文章提出了基于高速DSP芯片TMS320C5402实现正弦信号发生器的设计原理与方法,介绍了所设计的正弦信号发生器的硬件结构电路图和软件程序结构图。结合DSP硬件特性,通过使用泰勒级数展开法得到设定参数的正弦波波形输出,从而达到设计目的。     

Suboptimal robust synthesis for MIMO plant under coprime factor perturbations

Suboptimal robust synthesis for MIMO nominal system under coprime factor perturbations is considered in classical and non-classical statements. In the classical statement, weights of perturbations and upper bound on magnitude bounded exogenous disturbance are assumed to be known to controller designer. Suboptimal synthesis within ε tolerance is reduced to the solution of log₂(1/ε) standard mixed sensitivity problems of ℓ₁ optimization. In the non-classical statement, the upper bounds on perturbations and exogenous disturbance are to be estimated from measurement data and suboptimal synthesis is reduced to the solution of 1/ε mixed sensitivity problems.     

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.