Two low illuminance image enhancement algorithms based on grey level mapping

Two image enhancement contrast methods are proposed in this paper for low-intensity images. The first method (LEAM) is a new greyscale mapping function, and it can be significantly enhanced in the low grey range and compressed slowly in the high grey range, which is beneficial for retaining more image details; the second method (LEAAM) is based on the data characteristics of a histogram combined with the first mapping function, which adaptively sets the gamma value to correct the image. The experimental results show that compared with a traditional mapping function, LEAM is more effective at enriching image details and enhancing visual effects, and LEAAM, compared with a recent low-illumination image enhancement algorithm, achieves good performance for average gradient, information entropy and contrast index; additionally, the overall visual effect is the best compared with other methods.

     

基于OTN技术的电力通信网络业务路由优化算法

本文基于OTN(Optical Transport Network;光传送网)技术提出了电力通信网络业务路由优化算法,通过仿真分析,结果表明此算法可优化电力通信网络既有OTN业务,实时有效均衡网络风险,提升OTN业务运行可靠性与稳定性;此算法的适应度函数优化设计,可促使现有网络业务路由分配的时候,更加趋向选取光信噪比参数值相对较高的光通道,所以通过此网络业务路由优化算法,OSNR(Optical Signal Noise Ratio;光信噪比)均值可显著提高;可实现网络业务路由优化,实现最佳风险均衡,且算法效率与收敛性较高,值得大力推广.     

基于风电规模化入网技术的机组启停间歇联合协调方法研究

传统方法在控制机组启停间歇时,受到电波波动特性影响,对机组启停间歇协调效率低.为此,本文引入风电规模化入网技术,优化机组机组启停间歇协调效果.依据机组启停间歇控制结构,采用断点控制方式设计机组启停间歇控制位置;通过自启停APS技术分析机组启停电波规律,并根据做功特点控制机组启停容量可信度;规范设计机组启停间歇联合协调方案,确定机组启停间歇联合协调步骤,实现机组启停间歇联合协调控制.结果可知,该方法协调率较高,可行性较高.     

基于滑动窗口的影像中建筑物特征提取方法研究

针对多尺度分割算法在工程项目应用中使用成本高的问题,提出采用基于滑动窗口的影像对象获取方法.将主成分第一分量作为输入,通过灰度共生矩阵计算影像对象纹理特征,并与光谱均值特征构建面向对象的建筑物信息提取方法,将多尺度分割后的预测结果与滑动窗口分割后的预测结果进行对比实验验证.结果表明,滑动窗口提取结果的总体精度比多尺度分割提取结果的总体分类精度高出0.94％,Kappa系数高出0.0125,验证了滑动窗口预测结果的准确性与普适性.     

基于蚁群算法的能耗均衡的安全路由

针对无线传感网络(Wireless Sensor Networks,WSNs)路由能耗及安全问题,提出基于蚁群算法的能耗均衡的安全路由(Ant Colony based Energy Balancing Secure,ACES).ACES路由利用蚁群算法搜索从源节点至汇聚节点的路径,并利用节点的剩余能量,离汇聚节点距离以及节点信任值对蚁群算法的信息素启发函数,状态转移函数和信息素的更新函数进行优化,使寻径蚂蚁能够快速建立从源节点至汇聚节点的路径,提高数据包传递率,均衡节点能耗.仿真结果表明,提出的ACES路由有效地延长了网络寿命,并提高了数据包传递率.     

基于深度网络的出租车Pick-up需求预测

在智慧城市建设中,区域的出租车需求预测是一个十分重要的模块。为了预测指定区域未来时刻的出租车需求,本文通过拓展已有序列模型,提出一个多时间分辨率的基于层次注意力机制的循环高速网络（Multi-Time Resolution Hierarchical Attention-Based Recurrent Highway Networks, MTR-HRHN）。MTR-HRHN将对外生数据时空特征的提取和目标数据的时空建模整合到单个框架中,并且通过多分辨率（例如每个小时或者每天）对序列数据不同的时间属性建模,从而捕获更全面的时间模式。最后,在纽约市出租车数据集上评估MTR-HRHN的预测性能。实验结果表明,与其他经典时间序列预测方法相比,MTR-HRHN在多个高需求区域的短期需求预测上表现出更好的预测性能。     

侵入物高速撞击下铰链式动车组的安全性

为探究侵入物高速撞击下铰链式动车组的安全性,在实物三维扫描重构的基础上,构建一种新的活体三维有限元层叠模型,并在LS-DYNA中进行摆锤侧面碰撞分析验证;通过铰链式动车组与活体在110 km/h速度下的碰撞仿真计算,讨论动车组运行的安全性以及吸能装置的可靠性.结果 显示:活体的有限元层叠模型既能保证计算精度,又能提高计算效率;在110 km/h的碰撞速度下,车体加速度为0.117g,轮对抬升量为0.238 mm,车钩每5 m长度的压缩量最大约为1.89 mm.各项指标都低于EN 15227标准,动车组行车安全性没有受到影响,吸能装置也能可靠工作.     

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.

     

fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Machine Intelligence Research - One of the most significant challenges in the neuroscience community is to understand how the human brain works. Recent progress in neuroimaging techniques have...