新时代背景下数据科学与大数据专业人才培养的若干思考

聚焦新时代复合型数据科学与大数据专业人才的培养,提出要围绕育人目标制定人才培养方案,并不断优化;要坚持"引进来,走出去"原则建设师资队伍;要坚持高标准、宽视野的要求进行课程建设;要调用校内外一切资源建设实验室.文章提出要从系统论、控制理论的视角构建数据科学与大数据专业人才培养协同系统.     

最优知识传递宽残差网络输电线路螺栓缺陷图像分类

目的 输电线路螺栓图像具有分辨率低和视觉信息较差的特点,针对螺栓缺陷图像分类时教师网络（大模型）参数量大、学生网络（小模型）分类精度低的问题,提出了一种最优知识传递宽残差网络输电线路螺栓缺陷图像分类方法,来弥补使用大小模型进行分类任务的局限性。方法 首先改变大模型宽度,即拓宽网络特征表达维度来增加向小模型传递的螺栓缺陷知识和简化小模型结构至3个残差块;然后为了选出传递螺栓缺陷知识性能最优的大模型,提出知识偏差的概念来可视化大模型向小模型螺栓缺陷知识传递的程度,综合分析不同宽度大模型利用知识蒸馏算法与注意力转移算法分别指导小模型训练后的分类准确率,并用知识偏差来确定最优知识传递模型;最后将最优知识传递模型利用知识蒸馏算法与注意力转移算法相结合指导小模型训练,尽可能提升小模型的螺栓缺陷分类性能。结果 在自建螺栓缺陷图像分类数据集上进行了验证,结果表明宽度为5的大模型向小模型传递螺栓缺陷知识性能最优,使小模型的螺栓缺陷分类准确率提高了5.56%,小模型与最优大模型的分类准确率只差2.17%,知识偏差为0.28,且小模型的参数量仅为大模型参数量的0.56%。结论 本文提出的最优知识传递宽残差网络输电线路螺栓缺陷图像分类方法,弥补了大小模型螺栓缺陷图像分类的局限性,实现了精度与资源消耗的平衡。     

An improved model training method for residual convolutional neural networks in deep learning

Multimedia Tools and Applications - Residual convolutional neural network (R-CNN) has become a promising method for image recognition in deep learning applications. The application accuracy, as a...     

Video super-resolution reconstruction method based on deep Back projection and motion feature fusion

How to effectively utilize inter-frame redundancies is the key to improve the accuracy and speed of video super-resolution reconstruction methods. Previous methods usually process every frame in the whole video in the same way, and do not make full use of redundant information between frames, resulting in low accuracy or long reconstruction time. In this paper, we propose the idea of reconstructing key frames and non-key frames respectively, and give a video super-resolution reconstruction method based on deep back projection and motion feature fusion. Key-frame reconstruction subnet can obtain key frame features and reconstruction results with high accuracy. For non-key frames, key frame features can be reused by fusing them and motion features, so as to obtain accurate non-key frame features and reconstruction results quickly. Experiments on several public datasets show that the proposed method performs better than the state-of-the-art methods, and has good robustness.

     

Target re-aware deep tracking based on correlation filters updated online

Pattern Analysis and Applications - Deep trackers often use convolutional neural networks (CNNs) pre-trained to extract features. The training dataset always does not contain the tracking objects....     

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.

     

A Fast Vision-inertial Odometer Based on Line Midpoint Descriptor

Machine Intelligence Research - Visual simultaneous localization and mapping (VSLAM) are essential technologies to realize the autonomous movement of vehicles. Visual-inertial odometry (VIO) is...     

Observer‐based compensation control of servo systems with backlash

For compensating backlash phenomenon in servo systems, the authors propose an observer method in this paper to estimate both system states and vibration torque before controller design. First, a systematic scheme is given to obtain plant parameters, which is very important in observing system states. This is a parameter estimation principle that gives a crude estimation and computes the differences between the crude and true values. As a result, the precise value of the parameters is obtained by adding together the crude value and the difference. Then, based on the precise estimated parameters, an extended state observer (ESO) is designed to obtain feedback and feedforward signals. Consequently, robust compensation control is achieved by designing an output feedback controller, consisting of a feedback term and a feedforward term. Finally, in order to validate the proposed approach, extensive experiments are performed on a practical servo system with backlash nonlinearity.     

图计算加速器中稀疏向量比较单元的设计与实现

绝大多数图计算应用都可以映射为稀疏矩阵和向量运算,稀疏向量的比较运算是稀疏矩阵向量运算的计算瓶颈,为解决图计算加速器中稀疏矩阵列向量比较问题,设计一种稀疏向量比较运算电路,主要包括64个比较运算电路和一个共享存储单元.基于Verilog HDL语言完成电路设计,以SNAP(Stanford Network Analysis Project)上的Flickr数据集为例,在ModelSim SE-6410.1 c完成验证工作,采用Xilinx公司的ISE开发环境对硬件电路进行综合,实验结果表明,该电路的工作频率可以达到264 MHz.     

自然图像抠图技术综述

着重对近几年自然图像抠图研究新进展进行综述,针对近年来基于启发式优化的抠图研究以及基于深度学习的抠图研究新成果对抠图研究工作进行重新分类和梳理,分析不同类型自然图像抠图研究主要的方法并整理其中的关键问题,介绍常用的自然图像抠图评价指标。最后通过实例分析总结了当前自然图像抠图研究面临的挑战,并对未来的研究前景进行了探讨。