基于分级存储的石油勘探地震处理应用研究

石油勘探中“两宽一高”采集技术的深入应用，对地震资料并行处理能力提出了更高的要求，针对存储对地震资料处理应用的影响进行研究。为提高各类应用的运算效率，设计并实现了基于SSD、集群NAS多级分层存储地震资料处理方法，针对高IOPS应用、大数据块并行应用进行算法流程级设计。通过DD与IOZONE基准测试、两种应用实际运行效果，验证了设计方案具备在地震资料处理中应用效率与性价比上的优势，适用于勘探数据中心地震资料处理，具有一定的普适性。     

基于SSD的眨眼与打哈欠检测

提出了一种新的基于SSD网络的眨眼与打哈欠检测。首先,对原SSD架构进行四个方面的修改。第一,主干网用MobileNetV3替换了VGG,使得架构尺寸缩减三分之一。第二,重新设计了输出检测器,实现了目标检测与分类、目标定位与高宽比回归多任务的融合。第三,网络直接回归高宽比,比传统的从关键点离线计算高宽比的方法鲁棒性要高。第四,省去了非最大抑制层(NMS)中的重叠度(IOU)计算和阈值设定,使得单目标检测速度提高了40%。另外,在数据集YawDD上验证了所提出方法的有效性,眨眼与打哈欠的测全率为95.1%与100.0%,在单卡2080Ti上实现了41fps的高检测速度。     

CAWR: Buffer Replacement with Channel‐Aware Write Reordering Mechanism for SSDs

A typical solid‐state drive contains several independent channels that can be operated in parallel. To exploit this channel‐level parallelism, a variety of works proposed to split consecutive write sequences into small segments and schedule them to different channels. This scheme exploits the parallelism but breaks the spatial locality of write traffic; thus, it is able to significantly degrade the efficiency of garbage collection. This paper proposes a channel‐aware write reordering (CAWR) mechanism to schedule write requests to different channels more intelligently. The novel mechanism encapsulates correlated pages into a cluster beforehand. All pages belonging to a cluster are scheduled to the same channels to exploit spatial locality, while different clusters are scheduled to different channels to exploit the parallelism. As CAWR covers both garbage collection and I/O performance, it outperforms existing schemes significantly. Trace‐driven simulation results demonstrate that the CAWR mechanism reduces the average response time by 26% on average and decreases the valid page copies by 10% on average, while achieving a similar hit ratio to that of existing mechanisms.     

Machine learning assisted OSP approach for improved QoS performance on 3D charge-trap based SSDs

Three-dimensional (3D) charge-trap based solid-state-drivers (SSDs) have become an emerging storage solution in recent years. One-shot-programming in 3D charge-trap based SSDs could deliver a maximized system input/output (I/O) throughput at the cost of degraded Quality-of-Service (QoS) performance. This paper proposes reinforcement-learning based one-shot-programming (RLOSP), a reinforcement learning based approach to improve the QoS performance for 3D charge-trap based SSDs. By learning the I/O patterns of the workload environments as well as the device internal status, the proposed approach could properly choose requests in the device queue, and allocate physical addresses for these requests during one-shot-programming. In this manner, the storage device could deliver an improved QoS performance. Experimental results reveal that the proposed approach could reduce the worst-case latency at the 99.9th percentile by 37.5%–59.2%, with an optimal system I/O throughput.     

基于SSD和MPE的滚动轴承故障诊断方法

为了解决滚动轴承故障特征难以提取的问题,提出了一种奇异谱分解(SSD)和多尺度排列熵(MPE)的故障特征提取方法,结合K近邻(KNN)算法识别滚动轴承故障类型。首先对滚动轴承振动信号用SSD进行分解,得到3个奇异谱分量(SSC),根据峭度最大原则选择主分析分量;然后用MPE计算主分析分量的熵值,实现滚动轴承的故障特征进行提取;最后将熵值作为特征向量输入KNN分类器中,完成滚动轴承的状态识别。将该方法应用于实验数据分析,并与VMD和MPE相结合的故障诊断方法做比较,结果证明,该方法能够有效地提取故障特征,实现故障诊断。     

U.2 NVMe SSD热插拔导致服务器蓝屏死机的解决方案

本文将探索一种在服务器运行状态下热拔除固体硬盘,而能够避免蓝屏死机的方案。希望对U.2 NVMe SSD热插拔方案在服务器上的应用推广起到一定的帮助。     

基于深度学习的车辆检测算法之特点分析

车辆检测是智能交通系统的基础架构。文章分析了三种经典的车辆检测算法——Faster R-CNN算法、YOLOv3算法、SSD算法的特点,在此基础上指出了它们的优点和缺点,为下一步的车辆检测算法改进做好了理论知识基础。     

基于Windows7平台的高校计算机机房改造与实施

以郧阳师范高等专科学校计算机中心机房为例,介绍了基于Windows 7平台下机房建设改造过程中的硬件、软件及网络设备的方案选用,及工程实施过程中需要注意的问题和实践经验。     

基于STM32F407和OV7670的低端视频监控系统

本系统采用了意法半导体公司的STM32F407微处理器作为核心,以OV7670作为监控系统的视频采集传感器,并将传感器采集的数据通过DMA控制器成功地发送至液晶模块(SSD1289)显示。该系统运行流畅,为低端视频监控系统提供了一种新的解决方案。     

基于SSD 的小目标特征强化检测算法

为解决原始单次多框目标检测(single shot multibox detector,SSD)目标检测算法中对小目标物体检测能力不足的问题,提出一种改进的SSD目标检测算法.采用VGG19作为特征提取网络,在低层特征图部分引入Conv3_3卷积特征图,对Conv4_4进行转置卷积操作,将转置卷积后得到的Conv4_3同Conv3_3的特征图进行特征拼接,实验部分使用VOC数据集对模型进行训练与测试.结果表明:该算法可提高检测能力,目标检测精度能比原始SSD算法提高3.6％,小目标检测效果比改进前也有明显提升.