A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

改进的YOLOv3安检包裹中危险品检测算法

针对目前的安检工作中检测效率较低的问题,提出一种改进的YOLOv3安检包裹中危险品检测算法,用于辅助安检人员完成安检工作,提升安检机的自动化和智能化。将YOLOv3中原来每个网格预测3个边界框减少到预测2个边界框,检测速度提升了约6%。利用K-means聚类根据数据集计算出先验框,平均精度均值提高了约1.13个百分点。为了解决样本量少的问题,采用数据增强方法,平均精度均值提升了约7.8个百分点。采用多尺度输入训练策略,不仅增强了模型检测不同尺度图像时的鲁棒性,而且平均精度均值提升了约1.22个百分点。     

非参数贝叶斯分类字典学习的MRI重建方法

为提高磁共振图像的重构质量,提出一种基于非参数贝叶斯分类字典学习的重建方法.通过差分变换,在梯度域中利用无限高斯混合模型将图像块自动聚类,对具有相似结构的图像块进行分类训练字典.采用非参数贝叶斯字典学习方法训练字典,克服传统字典学习对参数选择的依赖性.实验结果表明,与目前几种典型的磁共振图像重建方法相比,该方法的峰值信噪比平均提高2.9 dB;在同一噪声水平下,该方法抗噪性能更强,重构质量更优.     

基于多网络融合的缺失测井数据复原方法

为解决在实际地质储层特性分析过程中存在测井数据不完整的问题,提出一种基于多网络融合的缺失测井数据复原模型.该模型由两个网络组成,其中通过交叉网络捕获有限度的有效特征之间的相互作用关系,并通过残差思想构造网络模型,降低模型复杂度.另一网络先通过构造的树模型进行特征选择,将树模型得到的稀疏向量通过嵌入层转成稠密向量作为神经网络的输入,从而获得高度非线性的组合特征.通过组合两个网络的输出对缺失测井数据进行复原,实验结果表明,该方法在相关评价指标上优于现有方法.     

A GPU-Accelerated In-Memory Metadata Management Scheme for Large-Scale Parallel File Systems

Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing nodes.Applications running on such a large-scale computing system are likely to spawn millions of parallel processes,which usually generate a burst of I/O requests,introducing a great challenge into the metadata management of underlying parallel file systems.The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner,which will inevitably confront with serious network and consistence problems.This work instead pursues to enhance the metadata performance in the scale-up manner.Specifically,we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel.Our proposal designs a novel metadata server architecture,which employs CPU to interact with file system clients,while offloading the computing tasks about metadata into GPU.To take full advantages of the parallelism existing in GPU,we redesign the in-memory data structure for the name space of file systems.The new data structure can perfectly fit to the memory architecture of GPU,and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently.We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal,and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50％under typical metadata operations.The superiority is strengthened further on high concurrent scenarios,e.g.,the high-performance computing systems supporting millions of parallel threads.     

云计算环境基于客体属性匹配的逆向混合访问控制方案

云计算提高了大数据的使用、分析和管理的效率,但也给数据贡献者带来了对云服务的数据安全及隐私信息泄露的担忧。针对这个问题,结合了基于角色的访问控制、基于属性的访问控制方法并采用了下一代访问控制的体系结构,提出了云计算环境下的基于客体属性匹配的逆向混合访问控制方法。首先,数据贡献者设置共享文件访问权限级别,逆向规定了访问客体的最低权值;然后,采用变异系数加权的方法直接计算各属性的权值,取消了以属性为中心的基于角色的访问控制中策略规则匹配的过程;最后,把数据贡献者对数据文件设定的权限值定为数据访问者被允许访问的阈值,这样既实现了数据访问控制,又保障了对隐私数据的保护。实验结果表明,随着访问次数的增多,所提方法对恶意行为、权限不足行为等的判断基准趋于稳定,检测能力越来越强,成功率趋于一个较为平稳的水平。该方法在用户访问数量较大的环境下相较传统的访问控制方法能够实现更高的决策效率,验证了所提方法的有效性和可行性。     

正则稀疏化的多因子量化选股策略

针对高维度数据集特征之间的复杂性,而传统的L1惩罚项不满足Oracle性质的无偏性,将逻辑回归弹性网（LR-Elastic Net）中的L1惩罚项替换为SCAD（Smoothly Clipped Absolute Deviation）和MCP（Minimax Concave Penalty）惩罚项,分别构建了LR-SCAD和LR-MCP模型,在保留稀疏性的同时满足了无偏性,并利用ADMM（Alternating Direction Method of Multipliers）算法进行求解。通过模拟实验发现,LR-Elastic Net模型能很好地处理特征存在相关性的小样本数据,而LR-SCAD和LR-MCP模型在特征存在相关性的大样本数据中表现较好;建立LR-Elastic Net、LR-SCAD和LR-MCP策略,并应用于沪深300指数成分股数据。回测结果显示,LR-SCAD和LR-MCP策略在股票相关性很强的数据中比LR-Elastic Net策略表现更好。     

小说人物的分布表示及其应用研究

小说是以刻画人物为中心,通过完整的故事情节和具体的环境描写反映社会生活的一种文学体裁。对小说人物进行建模,是小说文本理解和小说文本挖掘的基础性工作。该文构建了大规模的小说语料库,抽取人物及其依存特征,提出基于skip-gram的人物向量训练方法,以人物为目标,以依存特征为上下文,基于训练出的人物向量,探索了小说人物相似度计算、小说人物聚类分析及小说人物画像等应用。实验结果表明,小说人物的分布表示有较好的应用效果。     

Further Study on Networked Control Systems with Unreliable Communication Channels

This paper focuses on the fundamental problems of linear quadratic gaussian (LQG) control and stabilization problems for networked control systems (NCSs) with unreliable communication channels (UCCs) where packet dropout, input delay and observation delay occur. These basic issues have attracted extensive attentions due to broad applications. Our contributions are as follows. For the finite horizon case, without time-stamping technique, the optimal estimator is derived by using the novelty method of innovation sequences based on the delayed intermittent observations; A necessary and sufficient condition for the optimal control problem is presented on the basis of the solution to the forward and backward difference equations (FBDEs) and two coupled Riccati equations. For the infinite horizon case, it is shown that under certain assumption, the system can stay bounded in the mean square sense if and only if the algebraic Riccati equation admits the unique positive solution.

     

基于CPU+FPGA的临近空间信道模拟器

针对临近空间通信信道中的气象性损耗和非气象性损耗进行了理论分析,着重研究大气吸收损耗、降雨衰减和多径衰落等信道传播特性的建模方法,建立临近空间信道模型;提出了一种基于中央处理器(central processing unit,CPU)和现场可编辑门阵列(field-programmable gate array,FPGA)的临近空间信道模拟器软硬结合实现方案,并对希尔伯特变换和多普勒频移的实现方法进行分析;结合卫星导航模拟器和信号发生器产生的实际信号对信道模拟器的性能指标和统计特性进行测试;实测结果表明,信道模拟器输出信道衰落的各项统计特性符合理论结果,信道模拟器可实现分辨率和最大值分别是5 ns和5115 ns的多径相对时延以及分辨率和最大值分别是1 Hz和1.6 MHz的多普勒频移;该信道模拟器可辅助分析临近空间信道环境对信号传播的影响.