基于ARM和μC/OS-Ⅱ的全自动测硫仪的设计与实现

针对传统测硫仪存在实时性差的问题,设计一种基于ARM和μC/OS-Ⅱ的嵌入式全自动测硫仪.本文给出了系统的软硬件设计与实现,并对μC/OS-Ⅱ进行了移植.测试结果表明,任务切换时间约为510us,各任务运行无明显滞后.与传统的测硫仪相比,极大的提高了系统的实时性和稳定性.     

利用多时相Landsat遥感图像数据对土法炼焦点进行动态监测——以山西省东南部地区为例

国家明确规定对土法炼焦进行取缔,然而,土法炼焦并没有得到有效地控制,形势相当严峻,提高监测的技术水平是解决问题的重要措施之一。以土焦生产大省山西省东南部为试验区,以1999年和2004年Landsat5数据为背景资料,采用TM751和TM721波段组合技术,对土法炼焦进行了信息提取和挖掘。结果表明,TM7通道是土焦的敏感通道,TM5通道次之,在TM7通道上,土焦点呈现出明显地“峰”值特征,温度达到一定值,对应的五波段也出现“峰”值特征,给出了具体的临界值;TM751波段合成图像具有解译标志明显,解译判对率达90％以上;试验区样区土焦点数量增长10倍以上,且主要分布在人口集中的平原区,对环境造成了严重的污染,最后探讨了控制策略。研究结果为大面积、准确监测土法炼焦动态变化奠定了基础,并为土焦监管提供了一种全新、快速的技术手段。     

红外低硫传感器信噪比改善方法研究

低硫的红外探测是一个非常重要的研究领域,但由于红外硫的吸收峰在一个特殊的波段,与人体红外波段重合,所以国外的先进技术大都对中国封锁,而国内的低硫传感器存在交叉吸收误差,检出限不够、检测精度低等缺陷;文章以煤中硫元素的测试为基础,研究采用了电调制热释电红外传感器在测量硫元素过程中存在的以上问题,主要开展了探测器稳定性、恒温测试、气室噪声、滤波等改善传感器信噪比的相关改进设计和实验,并提出了优化的方案,优化后经过实验测试,整个测试系统的信噪比提升近50%,检出限从原来的100 ppm提升到近10 ppm;低硫测量平均值为0.382%,SD为0.006%,RSD为1.2%,瓶内RSD为0.9%;达到了低硫的高精密测试。     

基于TM影像天山北坡地表反照率反演方法的研究

地表反照率是气候模型和地表能量平衡方程中的重要参数。基于6S模型估算地表反照率,忽略了地形起伏的影响,不同波段组合的地表反照率也有待进一步研究。本文选取干旱区典型流域——天山北坡三工河流域为研究区域,以TM影像为数据源逐步进行地形校正、大气校正等,从而提取窄波段地表反照率。在此基础上,根据亮度、绿度、湿度3个特征变量的物理意义,以各波段能量权重为转换参数对窄波段地表反照率进行组合,实现研究区宽波段地表反照率的反演,得出基于不同波段的物理意义的地表反照率。     

基于热导传感器的高精密测氮仪设计

介绍一种基于热导传感器的高精密测氮仪的研究与设计.对测氮仪中的核心部件热导传感器进行了理论分析与设计,并在此基础上,采用CPLD ATF1508AS的架构平台,对控制系统进行了硬件设计和软件设计,通过对热导传感器的检测和整机的实验,该测氮仪具有测量速度快、测量精度高、信噪比高等优点,准确度和精密度均远高于国家标准.     

一种多尺度分形特征目标检测方法

自然背景干扰下的自动目标检测是目标检测的一个基本问题.根据尺度变化时自然 场景中人造目标的分形特征变化剧烈这一特点,提出了一种分形参数极值特征的自动目标检 测方法.大量的实验结果表明,分形参数极值特征的自动目标检测方法能较好地完成自然背景 干扰中人造小目标的自动检测.     

井下复杂环境人员重识别研究

对煤矿井下视频监控中的人员身份进行智能识别,对提高人员监管效率、减少安全事故发生具有重要意义。受井下环境复杂、视频监控设备性能局限性影响,井下视频监控图像存在分辨率低、遮挡、背景干扰等问题,导致井下人员间差异性较小,人员重识别准确率低。针对上述问题,提出了一种基于通道注意力和距离度量的网络结构,并将其用于井下复杂环境人员重识别。针对监控图像中人员与背景不易区分的问题,在骨干网络中引入通道注意力模块,使其更加关注人员的前景特征而抑制背景信息,并将骨干网络最后一层输出的特征图大小扩大1倍,以获得更多的细粒度特征,丰富人员的特征信息,增强网络对特征的学习能力;在实现不同身份人员分类的基础上,利用人员图像间的绝对距离信息,通过距离度量模块对难以识别的人员图像进行采样和加权处理,增加难样本在反向传播时的权重,使网络更加关注具有判别力的人员特征;联合使用身份损失和距离度量损失优化特征层,使网络提取出更具判别力的人员特征,从而提高重识别准确度。采用Miner-CUMT数据集对提出的井下复杂环境人员重识别方法进行验证,结果表明该方法可充分利用井下不同身份人员的关键信息,使识别网络具有更强的判别能力,提高了井下作业人员身份识别准确度。     

基于深度分割模型的小目标检测

小目标检测广泛应用于视频监控等各种任务,在各领域均有着重要作用.由于待测目标尺寸小、特征弱等原因,目前的检测算法对小目标的检测性能仍值得进一步提升.现有基于设计特征的传统方法在复杂背景的应用场景下检测精度低、鲁棒性弱,基于深度学习的检测算法存在数据集难获取、小目标特征难提取等问题.面向解决低信杂比图像中小目标因面积占比小导致的特征提取难的问题,提出了一个深度分割模型用于小目标检测.为进一步提升检测性能、降低漏检率,充分应用多波段图像信息,设计了一个基于深度分割模型的多波段融合小目标检测方法.在仿真数据集上的实验结果表明,该方法有效提高了小目标检测的准确率,为小目标检测的后续研究提供了新的思路.     

一种高增益宽频带圆极化微带阵列天线的研制

针对微带天线阻抗匹配带宽一般较窄的自身缺陷,基于相控阵雷达天线的应用背景,设计了一种工作在X波段的双层圆极化微带天线结构,且优化发现,其各电磁参数良好。为提高其增益,还在此基础上设计并最终制作了双层2×2结构的微带天线阵列,其实测性能与设计值相符,增益达到10.7dB,带宽1.2GHz,相应轴比为4dB,符合圆极化要求。     

粒子群局部优化的反距离权重插值算法

参考点与参数的选取会对反距离权重（IDW）的精度产生影响。针对多参数协同优化反距离权重算法（PIDW）忽略局部特性的问题,提出一种利用粒子群对IDW进行局部优化的改进算法——PLIDW。首先,分别对研究区域中各个样本点的参数进行寻优,利用交叉验证方法进行评估,记录各自最优取值的一组参数;同时,为提高查询效率,使用K维树（KD-Tree）保存空间位置与最优参数;最后,根据空间邻近程度从K维树选取最近的一组参数优化IDW。基于模拟数据与真实的温度数据集上的实验结果表明,相较于PIDW,PLIDW在真实数据集上的准确度提高4.18%以上,改善了PIDW存在的因忽略局部特性导致部分场景准确度低的问题,适应能力更强。     

An output error algorithm for piecewise affine system identification

Within Hybrid systems, piecewise affine systems are a common class to be identified from input/output data. In this paper an improved algorithm for identifying piecewise affine systems is developed. The algorithm stems from clustering-based system identification. An affine output error algorithm is used to identify final models. The performance of the new Piecewise Affine Output Error (PWA-OE) algorithm is demonstrated using experimental data from a Radio Frequency MicroElectroMechanical Systems switch. Compared to the existing state-of-the-art, the PWA-OE algorithm generates a potential 62% improvement in model coefficient accuracy. Furthermore the PWA-OE algorithm is less sensitive to two additional input parameter selections.     

Term frequency combined hybrid feature selection method for spam filtering

Feature selection is an important technology on improving the efficiency and accuracy of spam filtering. Among the numerous methods, document frequency-based feature selections ignore the effect of term frequency information, thus always deduce unsatisfactory results. In this paper, a hybrid method (called HBM), which combines the document frequency information and term frequency information is proposed. To maintain the category distinguishing ability of the selected features, an optimal document frequency-based feature selection (called ODFFS) is chosen; terms which are indeed discriminative will be selected by ODFFS. For the remaining features, term frequency information is considered and the terms with the highest HBM values are selected. Further, a novel method called feature subset evaluating parameter optimization (FSEPO) is proposed for parameter optimization. Experiments with support vector machine (SVM) and Naïve Bayesian (NB) classifiers are applied on four corpora: PU1, LingSpam, SpamAssian and Trec2007. Six feature selections: information gain, Chi square, improved Gini-index, multi-class odds ratio, normalized term frequency-based discriminative power measure and comprehensively measure feature selection are compared with HBM. Experimental results show that, HBM is significantly superior to other feature selection methods on four corpora when SVM and NB are applied, respectively.     

Ground-based method for measuring thermal infrared effective emissivities: Implications and perspectives on the measurement of land surface temperature from satellite data

Thermal infrared emissivity is an important parameter for surface characterization and for determining surface temperature. The field-based measurements for ground and vegetation emissivities in 8-14 w m waveband were performed with an emissivity box. A theoretical analysis was carried out using the box and a correcting factor has been determined. The average value for thermal band emissivity of the exposed bare soil was found to be around 0.909; the average value measured for most of the varieties of vegetation present were in the range of 0.980-0.985. A theoretical model is used for obtaining effective emissivity in the 8-14 w m region from Advanced Very High Resolution Radiometer (AVHRR) data considering the proportion of vegetation cover in a pixel and the field-measured emissivity values. The error of the methodology is found to be within 1.5%. Narrow band emissivities for AVHRR channels 4 and 5 have been derived from the emissivity values in the 8-14 w m waveband. The surface temperature has been derived from AVHRR data using a split-window algorithm as a function of emissivities derived in narrow bands. The split-window algorithm accounted for absorption effects of the atmosphere by incorporating the water vapour concentration measured in the campaign. A good agreement was obtained between the satellite-derived surface temperature and the in situ observations. The result suggest that the methodology allows us to derive land surface temperature with an accuracy better than 1.5° C provided the surface emissivity is known. The paper describes the field-based emissivity measurement and approach for deriving surface temperature over land surface.     

The effects of variable mechanical parameters on peak power and energy consumption of ankle-foot prostheses at different speeds

In recent years, powered ankle-foot prostheses are developed to restore human ankle behaviour. Compared with common passive ankle prostheses, a powered ankle-foot prosthesis can provide amputees with better performance on walking gait by providing additional energy. However, the electrical peak power of powered ankle-foot prostheses at high speed is still a big challenge for the current actuation system. While most researchers focus on the energy consumption on one walking speed, we investigate the influences of several variable mechanical parameters to reduce electrical peak power during different locomotion speeds. Then we apply this concept to current ankle-foot models. It's found that the variable mechanical parameters can significantly reduce the demand of electrical peak power at different speeds. Meanwhile, the reduction of the electrical peak power does not lead to a large increment of the energy consumption. The results also show that every variable mechanical parameter has a different influence on the peak power and energy consumption of the ankle-foot prosthesis. It means the most effective and sensitive variable mechanical parameter need to be found for a specific ankle prosthesis system.     

考虑星间测量的航天器自主导航并行滤波器

针对基于星间测量的航天器自主导航问题,本文考虑测量中存在野值的情况,提出了一种轨道根数辅助估计的并行无迹卡尔曼滤波算法.系统由两个并行滤波器组成,通过副滤波器的状态估计识别观测野值,进而在主滤波器中修正导航定位结果.文章选择了星间相对观测两卫星编队的基本构型,研究了算法的阈值参数选择,对不同参数条件下的滤波结果进行了对比.数值仿真说明了该算法在观测量变化率较大时能够有效降低连续野值对自主导航系统的影响,和传统算法相比具有更高的滤波精度和收敛速度.     

Managing Spatial Selections With Contextual Snapshots

Spatial selections are a ubiquitous concept in visualization. By localizing particular features, they can be analysed and compared in different views. However, the semantics of such selections often depend on specific parameter settings and it can be difficult to reconstruct them without additional information. In this paper, we present the concept of contextual snapshots as an effective means for managing spatial selections in visualized data. The selections are automatically associated with the context in which they have been created. Contextual snapshots can also be used as the basis for interactive integrated and linked views, which enable in‐place investigation and comparison of multiple visual representations of data. Our approach is implemented as a flexible toolkit with well‐defined interfaces for integration into existing systems. We demonstrate the power and generality of our techniques by applying them to several distinct scenarios such as the visualization of simulation data, the analysis of historical documents and the display of anatomical data.     

A comparison of SPOT and Landsat-TM data for use in conducting inventories of forest resources

Abstract

SPOT multispectral (XS) and Landsat Thematic Mapper (TM) digital data were studied in an attempt to evaluate the use of this data in detailed assessments of forest conditions. Forest type, basal area, and age class information were collected from 256 sample sites within an intensively managed 80000acre experimental forest in North Carolina, U.S.A. A comparison of the SPOT and TM data with the sample site information showed that XS3, the near-infrared waveband, and TM bands 2, 3, 4, 5, and 7 were significantly correlated with basal area. Age class was not found to be significantly correlated with any of the three SPOT XS wavebands. TM bands 2, 3, 4, 5, and 7 were, however, shown to be significantly correlated with age class. Although significant, the correlation coefficients between the TM or SPOT waveband data and basal area or age class were low (<0.65).

Six forest cover types, and an additional water category, were selected as the basis of a land cover classification system for use with the TM and SPOT data. Verification of the classification of the seven cover types using the SPOT XS waveband data resulted in an estimated accuracy of 74.4 per cent. Classification accuracy was slightly reduced (70.8 per cent) when the TM wavebands corresponding to the SPOT XS bands were used as inputs to the classifier. When each of the six visible and reflective infrared TM wavebands were included in the classification process overall accuracy increased to 885 per cent.     

飞机发电机故障诊断的多特征参数组合分析

针对飞机发电机振动特征参数多、故障特征参数难以准确识别飞机发电机健康状况的现状,设计了发电机振动信号实时采样装置对飞机发电机转动时的多种频域参数及幅域参数进行采样,并引入小波分析计算各频带能量值,构建神经网络进行故障判定,选用不同的振动特征参数组合对检验样本进行验证以期获得指向性较好的飞机发电机故障特征参数.诊断结果表明,利用RBF网络对发电机故障诊断,采用基于幅值域的特征参数峭度指标、峰值因子、脉冲指标、裕度指标、歪度和基于频域的重心频率、均方根频率、频率标准差,再考虑进小波包分频带能量值作为神经网络的输入参数指标,可取得良好的诊断准确率.     

基于退化数据和DBN算法的IGBT健康参数预测方法

绝缘栅双极型晶体管(IGBT)等电子元器件被广泛用于运输和能源部门,其健康状态对于设备安全和有效至关重要;在对IGBT的结构和损伤机制分析基础上,结合NASA艾姆斯中心开展的IGBT加速退化试验,选择集电极-发射极关断峰值电压作为失效特征参数,提出了一种基于深度信念网络的预测模型对其进行分析和预测;以Levenberg-Marquardt(LM)算法模型作为对比,实验结果显示文章提出的三隐藏层DBN模型相比于LM模型有更好的预测性能和更高的预测精度。