基于改进Hough变换的电梯轿厢内人数统计算法

考虑电梯轿厢内人数的电梯群控调度算法,可以在有限的时间内,找到更优解,对电梯的调度具有重要意义。针对目前基于视觉的人数统计算法正确率不够稳定,速度较慢的问题,提出了一种基于改进Hough变换检测人头轮廓的电梯轿厢内人数统计算法。该算法将标准Hough变换检测圆时的三维空间转换为一维空间,利用圆的几何特性遍历图像数据,通过一维Hough变换进行半径累积,并和阈值进行比较来确定人头轮廓的半径和圆心。结果表明,和用Hough变换检测人头轮廓的传统方法相比,该算法具有速度快,正确率高,抗噪性能好等优点。     

基于无线疲劳监测系统的铝合金材料疲劳损伤研究

铝合金材料具有重量轻、强度高等特点,是国民经济发展的重要基础材料,在诸多领域(如飞行器设计与制造、建筑装饰等)中占有十分重要的地位。然而,其抗疲劳性能较差,在交变荷载作用下容易产生疲劳裂纹和发生疲劳破坏。提出了一种基于无线疲劳监测系统的铝合金材料疲劳损伤研究方法,实时监测了铝合金构件的疲劳应变,并实时分析了构件的疲劳损伤状况,为研究铝合金材料的疲劳性能提供了可借鉴性的方法。     

基于XML Web Service技术的校园网用户管理和计费系统的设计与实现

结合高校校园网管理的实际情况,提出了利用基于XML Web Service技术实现校园网用户管理和计费系统,并重点地分析了用户管理和计费管理系统的开发应用,可以高效地解决校园网管理中的一些复杂问题.     

电动汽车锂电池SOC估算研究

为了精确估算电动汽车锂电池的荷电状态(state of charge,SOC),本文通过对主流SOC估算方法进行分析与比较,提出了一种基于卡尔曼滤波算法的电动汽车能量管理系统(energy management system,EMS)SOC估算方法,同时采用联合模型以保证估算过程中有较好的精度,并在实验室条件下进行实测数据及MATLAB仿真分析。仿真结果表明,卡尔曼滤波算法对锂电池SOC进行在线实时估计是有效的,能够较为准确地计算出SOC值,且估算结果与实测值基本一致,该方法可以用于电动汽车锂电池SOC的估算,具有很强的实际应用价值。     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

Random Bichromatic Matchings

Given a graph with edges colored Red and Blue, we study the problem of sampling and approximately counting the number of matchings with exactly k Red edges. We solve the problem of estimating the number of perfect matchings with exactly k Red edges for dense graphs. We study a Markov chain on the space of all matchings of a graph that favors matchings with k Red edges. We show that it is rapidly mixing using non-traditional canonical paths that can backtrack. We show that this chain can be used to sample matchings in the 2-dimensional toroidal lattice of any fixed size ℓ with k Red edges, where the horizontal edges are Red and the vertical edges are Blue. An extended abstract appeared in J.R. Correa, A. Hevia and M.A. Kiwi (eds.) Proceedings of the 7th Latin American Theoretical Informatics Symposium, LNCS 3887, pp. 190–201, Springer, 2006. N. Bhatnagar’s and D. Randall’s research was supported in part by NSF grants CCR-0515105 and DMS-0505505. V.V. Vazirani’s research was supported in part by NSF grants 0311541, 0220343 and CCR-0515186. N. Bhatnagar’s and E. Vigoda’s research was supported in part by NSF grant CCR-0455666.     

基于多视角信息融合的棒材识别计数方法

棒材出厂前要按根数打捆,基于单视角视觉识别计数方法难以处理多个目标之间的互遮挡等问题。文章提出多视角信息融合方法,从2个视角分别捕获目标图像,分别识别,再进行融合,对单视角目标识别信息的补偿与纠错,达到消除互遮挡、准确计数的目的。实践证明,该方法能很好地解决单视角的遮挡目标识别问题,对于因存在遮挡和光照条件限制而难以处理的多目标识别和跟踪问题具有一定的普适性。     

计算机测控系统的可靠性预计方法研究

采用元器件计数法和相似产品法相结合的可靠性预计方法对研制的某大型设备计算机测控系统进行MTBF值的可靠性预计,通过预计发现设计中的薄弱环节,并为后期的可靠性指标分配奠定基础.     

一种基于扩展规则的#SAT 求解系统

#SAT 问题是SAT 问题的扩展,需要计算出给定命题公式集合的模型个数.通过将问题求解沿着归结的反方向进行,并利用容斥原理解决由此带来的空间复杂性问题,提出了一种基于扩展规则的模型计数和加权模型计数问题求解框架,可以看作是目前所有模型计数问题求解方法的一种补方法.证明了该方法的完备性和有效性,设计了基于扩展规则的#SAT 求解系统:JLU-ERWMC.实验结果表明,JLU-ERWMC 在有些问题中优于目前最为高效的#SAT 问题求解系统.     

融合双注意力机制的人群计数算法

针对背景复杂、遮挡、人群分布不均等人群计数常见问题,提出了一种结合联合损失的空间-通道双注意力机制卷积神经网络模型(joint loss-based space-channel dual attention network, JL-SCDANet).该网络前端进行图像粗粒度特征提取,中间加入空间注意力机制以及通道注意力机制突出图像重点区域,后端使用可加大感受野且不丢失图像分辨率的空洞卷积提取深层二维特征.此外,该模型结合联合损失函数进行训练,以增强模型的鲁棒性.为了验证模型的改进效果,在3个公共数据集(ShanghaiTech Part B、mall和UCF＿CC＿50)上分别进行了对比实验,在ShanghaiTech Part B数据集中平均绝对误差(MAE)和均方误差(MSE)分别达到了8.13和13.13;在mall数据集中MAE、MSE达到了1.78和2.28;在UCF＿CC＿50数据集中MAE、MSE分别达到了182.12和210.24,实验结果证明了该网络在提高人数统计准确率上的有效性.