山东栖霞—大柳行地区金矿找矿靶区地质评价及优选

栖霞—大柳行地区位于胶东中部栖霞—蓬莱金矿成矿带内，是胶东重要的金成矿区，成矿条件优越。为了对圈定靶区进行优选分类，通过对该区地质背景、找矿靶区评价、找矿靶区优选方面展开综合研究，并结合勘查工作程度和矿业权设置情况，划分出3类预测区，选定了5个靶区为今后优先部署勘查工作的方向。     

模糊数学在零件分组中的应用研究

在充分研究减法聚类法、模糊C均值聚类法和最大隶属度原则的基础上,针对机械零件分组中的模糊性,将以上的方法或者原则应用到其中,并结合工程实际情况作适当修改,使其更符合工程实际。通过实例验证了此方法的正确性和实用性,并分析了这种方法的优缺点。     

基于谱减法及频谱方差的语音端点检测方法

为提高在强背景噪声条件下语音端点检测的性能,提出一种将改进的谱减法和频谱方差相结合的方法。先利用谱减法对带噪语音信号进行增强处理,然后使用基于频谱方差的方法进行检测,并以一男一女对10个数字和26个英文字母各发音2次的录音作为样本进行仿真验证。实验结果表明:该方法计算简单,可靠性高,能有效减少背景噪声,提高语音信号的信噪比,在较低的信噪比下仍能比较准确地检测到语言信号的端点。     

建筑师和城市的背景建筑

城市背景建筑的质量决定着城市空间的整体品质，中国城市背景建筑的质量有待提高，这取决于建筑师的城市整体意识、建构意识、过程意识和大众意识的提高。     

Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites

Background concentrations of nitrogen dioxide (NO₂) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO₂ concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO₂ concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO₂ in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO₂ at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling studies.     

钻削振动信号小波包分频谱减特征增强方法

针对在钻削加工噪声背景下振动信号微弱特征识别和提取困难的问题,提出了一种基于小波包分频谱减的钻削振动信号特征增强方法。首先,在经典谱减法原理的基础上,将钻削前机床空转信号视为监测信号的"加性噪声";其次,根据钻削过程振动信号的特点,采用小波包分解方法将"加性噪声"和监测信号分成多个子频带;最后,对每个子频带内"加性噪声"的相应频带进行谱减处理后,重构钻削振动信号。仿真和实验结果表明,该方法能有效降低环境噪声对钻削过程特征提取的影响,从而达到增强监测信号特征的目的,同时建立了钻削过程与监测信号之间很好的映射关系模型。     

Experimental investigation of spherical-flame acceleration in lean hydrogen-air mixtures

Large-scale experiments examining spherical-flame acceleration in lean hydrogen-air mixtures were performed in a 64 m³ constant-pressure enclosure. Equivalence ratios ranging from 0.33 to 0.57 were examined using detailed front tracking for flame diameters up to 1.2 m through the use of a Background Oriented Schlieren (BOS) technique. From these measurements, the critical radii for onset of instability for these mixtures, on the order of 2–3 cm, were obtained. In addition, the laminar burning velocity and rate of flame acceleration as a function of radius were also measured.     

Adaptive visual target detection and tracking using weakly supervised incremental appearance learning and RGM-PHD tracker

Multiple visual target tracking is a challenging problem due to various uncertainties including occlusion, miss-detection and noisy measurement. Most tracking approaches utilize an object-specific detector, pre-trained on many labeled images, to provide suitable measurements for their tracking system. In this paper, we use a simple background subtraction detector which only needs the background image to localize targets independent of their shape or type. In order to cope with the uncertainties resulted by the detector, we propose an adaptive appearance model and develop an incremental appearance learning algorithm to learn the target appearances in time. The proposed method employs the background information and our defined keypoints’ miss-matched history to adapt the target appearances within different frames. Furthermore, we combine Refined Gaussian Mixture Probability Hypothesis Density (RGM-PHD) tracker with the detectors to keep target trajectories and handle uncertainties. The experiments conducted on several video datasets show the effectiveness of our proposed method.     

Robust techniques for abandoned and removed object detection based on Markov random field

This paper presents a novel framework for detecting abandoned objects by introducing a fully-automatic GrabCut object segmentation. GrabCut seed initialization is treated as a background (BG) modelling problem that focuses only on unhanded objects and objects that become immobile. The BG distribution is constructed with dual Gaussian mixtures that are comprised of high and low learning rate models. We propose a primitive BG model-based removed object validation and Haar feature-based cascade classifier for still-people detection once a candidate for a released object has been detected. Our system can obtain more robust and accurate results for real environments based on evaluations of realistic scenes from CAVIAR, PETS2006, CDnet 2014, and our own datasets.