Monolithic silica rod columns for high-efficiency reversed-phase liquid chromatography

Chromatographic properties of a new type of monolithic silica rod columns were examined. Silica rod columns employed for the study were prepared from tetramethoxysilane, modified with octadecylsilyl moieties, and encased in a stainless-steel protective column with two polymer layers between the silica and the stainless-steel tubing. A 25 cm column provided up to 45,000 theoretical plates for aromatic hydrocarbons, or a minimum plate height of about 5.5 μm, at optimum linear velocity of ca. 2.3 mm/s and back pressure of 7.5 MPa in an acetonitrile-water (80/20, v/v) mobile phase at 40°C. The permeability of the column was similar to that of a column packed with 5 μm particles, with K(F) about 2.4×10(-14) m(2) (based on the superficial linear velocity of the mobile phase), while the plate height value equivalent to that of a column packed with 2.5 μm particles. Generation of 80,000-120,000 theoretical plates was feasible with back pressure below 30 MPa by employing two or three 25 cm columns connected in series. The use of the long columns enabled facile generation of large numbers of theoretical plates in comparison with conventional monolithic silica columns or particulate columns. Kinetic plot analysis indicates that the monolithic columns operated at 30 MPa can provide faster separations than a column packed with totally porous 3-μm particles operated at 40 MPa in a range where the number of theoretical plates (N) is greater than 50,000.     

Faster analysis by reversed-phase high-performance liquid chromatography

Summary Many analysts are not taking full advantage of the high speed possibilities of modern LC. Some analytical procedures reported in the literature, and many in regular use in control laboratories, could be achieved in less time without loss in precision. Some factors which affect retention times are discussed and the advantages and disadvantages of employing shorter column lengths and finer packing materials in reversed-phase HPLC are examined. The effect on efficiency of increased flow rates with 10,5 and 3 m ODS materials is shown. The ability to couple shorter column lengths without loss of efficiency is also demonstrated. This allows a minimum length to be selected that gives adequate resolution. Examples of high speed separations are shown and limitations in state of the art HPLC equipment and chromatographic data systems are discussed briefly.Presented at the 14th International Symposium on Chromatography London, September, 1982     

深度学习在绝缘子红外图像异常诊断的应用

绝缘子的红外图像分析一般采用图像处理的方法,易受背景环境和数据量的影响,准确率和效率均较低,本文提出一种深度学习的异常诊断方法,基于改进的Faster R-CNN方法搭建检测网络,开展不同类型的绝缘子测试.研究结果表明:相对于神经网络(Back Propagation,BP)、Faster R-CNN方法,本文方法可高...     

基于Mask R-CNN的肝脏CT影像自动分割算法研究

精准医疗正日益成为诊断、分析、治疗和评估不可或缺的一部分,尤其是对于肝部病变的患者,精确的分析肝部影像,可以使肝病患者得到精确的诊断,并缩短治疗时间.本文提出了一种基于Mask R-CNN算法的CT图像肝脏区域自动分割方法,对肝脏区域检测起到了重要的自动识别作用;对于肝脏海量图像样本计算,本文算法框架采用PyTorch...     

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Changes in the ungulate population density in the wild has impacts on both the wildlife and human society. In order to control the ungulate population movement, monitoring systems such as camera trap networks have been implemented in a non-invasive setup. However, such systems produce a large number of images as the output, hence making it very resource consuming to manually detect the animals. In this paper, we present a new dataset of wild ungulates which was collected in Latvia. Moreover, we demonstrate two methods, which use RetinaNet and Faster R-CNN as backbones, respectively, to detect the animals in the images. We discuss the optimization of training and impact of data augmentation on the performance. Finally, we show the result of aforementioned tune networks over the real world data collected in Latvia.     

基于旋转候选区域的船舶检测

利用图像进行船舶检测可以为海上侦察监视提供重要信息.以往的检测方法对水平或近水平的目标都有很强的检测能力,但对于任意方向的船舶检测,都不能给出满意的检测结果.在此提出了一种新型的船舶检测算法,可以检测任意方向的船舶.在该方法中,提出了一种利用旋转候选区域来生成具有船舶方位角信息的多方向候选信息,并且还对边框的方位角进行...     

An OpenCL-Based FPGA Accelerator for Faster R-CNN

In recent years, convolutional neural network (CNN)-based object detection algorithms have made breakthroughs, and much of the research corresponds to hardware accelerator designs. Although many previous works have proposed efficient FPGA designs for one-stage detectors such as Yolo, there are still few accelerator designs for faster regions with CNN features (Faster R-CNN) algorithms. Moreover, CNN’s inherently high computational complexity and high memory complexity bring challenges to the design of efficient accelerators. This paper proposes a software-hardware co-design scheme based on OpenCL to implement a Faster R-CNN object detection algorithm on FPGA. First, we design an efficient, deep pipelined FPGA hardware accelerator that can implement Faster R-CNN algorithms for different backbone networks. Then, an optimized hardware-aware software algorithm was proposed, including fixed-point quantization, layer fusion, and a multi-batch Regions of interest (RoIs) detector. Finally, we present an end-to-end design space exploration scheme to comprehensively evaluate the performance and resource utilization of the proposed accelerator. Experimental results show that the proposed design achieves a peak throughput of 846.9 GOP/s at the working frequency of 172 MHz. Compared with the state-of-the-art Faster R-CNN accelerator and the one-stage YOLO accelerator, our method achieves 10× and 2.1× inference throughput improvements, respectively.     

优化卷积网络及低分辨率热成像的夜间人车检测与识别

夜间环境下人车的检测与识别在自动驾驶,安防等领域具有重要意义.本文提出使用性价比较高的低分辨率红外热成像摄像机拍摄的图像来进行夜间的人车检测与识别,并根据图像独特的性质对Faster RCNN网络进行了优化.增加多通道卷积层来适应热成像图像的灰度特性.使用全局平均池化层来适应较少的图像及类别数量,增加批标准化层来防止加深加宽网络后可能出现的梯度消失或爆炸.使用在城市夜间环境中采集的2000张低分辨率热成像图像对网络进行训练与测试,平均准确识别率达到71.3％.相比于传统的检测手段,本组合方法在真实的场景中取得了较好的识别效果,同时提升了准确识别率,有效解决了夜间环境下人车检测与识别的问题,鲁棒性及应用价值较强.     

Self-Balanced R-CNN for instance segmentation

Current state-of-the-art two-stage models on instance segmentation task suffer from several types of imbalances. In this paper, we address the Intersection over the Union (IoU) distribution imbalance of positive input Regions of Interest (RoIs) during the training of the second stage. Our Self-Balanced R-CNN (SBR-CNN), an evolved version of the Hybrid Task Cascade (HTC) model, brings brand new loop mechanisms of bounding box and mask refinements. With an improved Generic RoI Extraction (GRoIE), we also address the feature-level imbalance at the Feature Pyramid Network (FPN) level, originated by a non-uniform integration between low- and high-level features from the backbone layers. In addition, the redesign of the architecture heads toward a fully convolutional approach with FCC further reduces the number of parameters and obtains more clues to the connection between the task to solve and the layers used. Moreover, our SBR-CNN model shows the same or even better improvements if adopted in conjunction with other state-of-the-art models. In fact, with a lightweight ResNet-50 as backbone, evaluated on COCO minival 2017 dataset, our model reaches 45.3% and 41.5% AP for object detection and instance segmentation, with 12 epochs and without extra tricks. The code is available at https://github.com/IMPLabUniPr/mmdetection/tree/sbr_cnn.     

面向室内动态场景的VSLAM

传统VSLAM算法基于静态场景实现,其在室内动态场景下定位精度退化,三维稀疏点云地图也会出现动态特征点误匹配等问题.文中在ORB-SLAM2框架上进行改进,结合Mask R-CNN进行图像的语义分割,剔除位于动态物体上的动态特征点,优化了相机位姿,得到了静态的三维稀疏点云地图.在公开的TUM数据集上的实验结果表明,结合...