A simulation method of aircraft plumes for real-time imaging

Real-time infrared simulation technology can provide a large number of infrared images under different conditions to support the design, test and evaluation of a system having infrared imaging equipment with very low costs. By synthesizing heat transfer, infrared physics, fluid mechanics and computer graphics, a real-time infrared simulation method is proposed based on the method of characteristics to predict the infrared feature of aircraft plumes, which tries to obtain a good balance between simulation precision and computation efficiency. The temperature and pressure distribution in the under-expansion status can be rapidly solved with dynamically changing flight statuses and engine working states. And a modified C–G (Curtis–Godson) spectral band model that combines the plume streamlines with the conventional C–G spectral band model was implemented to calculate the non-uniformly distributed radiation parameters inside a plume field. The simulation result was analyzed and compared with the CFD++, which validates the credibility and efficiency of the proposed simulation method.     

A method for C(alpha) direct-detection in protonless NMR

Attempts are made to efficiently decouple (13)C nuclei without significant loss of coherence during the application of the decoupling package. Such attempts are based on the S(3)E spin-state selection method. A newly developed double S(3)E (DS(3)E) is particularly efficient for C(alpha) detection for proteins as large as 480 kDa.     

基于机器视觉的车辆保险盒在线检测研究

摘要: 车辆保险盒作为汽车电控系统中的一个重要的元器件,其质量好坏直接影响汽车的性能,传统的车辆保险盒检测主要依靠人工检测,检测费时费力,针对该问题,提出一种基于视觉的车辆保险盒在线检测方法,分析了产品图像校正到标准模板图像的位置误差,采用SURF(Speeded Up Robust Feature)算法和平面单应性理论将待检产品图像变换到标准模板位置,利用颜色直方图匹配和模板匹配完成保险盒上元件的检测。实验结果证明,该方法检测效率高,稳定可靠,能够满足在线检测的要求,具有一定实用价值。     

电拓扑状态预测有机磷酸酯类化合物的气相色谱保留指数

以原子类型电拓扑状态指数(ETSI)有效表征35个有机磷酸酯类化合物(OP)的分子结构, 应用基于预测的变量选择与模型化(VSMP)方法建立OP化合物在3种不同固定相上的气相色谱保留指数(RI)与分子结构(ETSI)的定量相关模型. 结果表明, 影响不同固定相上OP色谱保留的主要结构因素都是由7个ETSI描述子对应的子结构碎片, 即: ＝CH₂,≡C—, aaC—, ＝O, —O—, Cl和Br. 其中子结构aaC—, ＝O和—O与OP化合物母体骨架密切相关, 而＝CH₂,≡C—, —Cl和—Br反映支链或取代基的变化. 通过多元线性回归法建立OP化合物在三个固定相上的定量结构-保留相关模型(QSRR)发现, 各QSAR模型的估计相关系数均在0.99以上, LOO检验相关系数在0.98以上, 表明模型具有良好估计能力与稳定性. 应用28个OP训练集样本构建的QSRR模型预测外部7个检验集RI结果表明训练集模型具有良好预测能力.     

基于视觉辐条图案识别方法的蓝宝石引晶机制研究

工业生产蓝宝石晶体过程中,引晶步骤有着至关重要的地位。引晶必须在温度梯度较小,温度分布趋于稳定的条件下进行。目前,工业生产蓝宝石主要依靠人工经验操控籽晶杆实现引晶操作,但是人工引晶操作的准确性不高会导致成品品质不佳、资源浪费。为此,本文提出一种基于蓝宝石视觉辐条图案识别方法来检测蓝宝石熔体状态自由液面状态,从而实现一种高效率引晶的机制。此方法利用经典骨架化算法细化辐条图案,Harris算子实现特征信息的提取,提取的特征信息放入运动轨迹模型中判断熔体稳定性,分析液面温度分布稳定性从而实现引晶。结果表明,此算法具有有效性,蓝宝石晶体引晶效率大大提高,生产出的成品良率也有提升,可有效指导蓝宝石的工业生产。     

Optimization of STEM-HAADF Electron Tomography Reconstructions by Parameter Selection in Compressed Sensing Total Variation Minimization-Based Algorithms

A novel procedure to optimize the 3D morphological characterization of nanomaterials by means of high angle annular dark field scanning-transmission electron tomography is reported and is successfully applied to the analysis of a metal- and halogen-free ordered mesoporous carbon material. The new method is based on a selection of the two parameters (μ and β) which are key in the reconstruction of tomographic series by means of total variation minimization (TVM). The parameter-selected TVM reconstructions obtained using this approach clearly reveal the porous structure of the carbon-based material as consisting of a network of parallel, straight channels of ≈6 nm diameter ordered in a honeycomb-type arrangement. Such an unusual structure cannot be retrieved from a TVM 3D reconstruction using default reconstruction values. Moreover, segmentation and further quantification of the optimized 3D tomographic reconstruction provide values for different textural parameters, such as pore size distribution and specific pore volume that match very closely with those determined by macroscopic physisorption techniques. The approach developed can be extended to other reconstruction models in which the final result is influenced by parameter choice.     

吸收峰混叠的太赫兹光谱区间组合特征提取算法

太赫兹光谱是物质识别的前沿方法之一。由于不同物质的分子组成或结构各异,许多物质的太赫兹吸收谱会在特定频率上出现吸收峰,可以作为混合物成分检测的重要特征。有效准确地提取这些吸收峰的参数,是提高识别率的关键。多峰拟合算法将光谱曲线拟合成若干个标准峰函数之和,能够同时提取到吸收峰的频率、峰高、峰宽等信息。但是该算法以寻峰算法结果为基础确定吸收峰的大致位置和数量,寻峰结果不一定是最优的拟合结果,而且很难准确识别定位混叠状态的吸收峰。为了提高混叠光谱中吸收峰的识别定位精度,提出以大幅度平滑后的曲线波谷为分界点,将预处理后的光谱分成若干个子区间。然后将子区间组合起来进行多峰拟合,通过遗传算法得到最优的拟合子区间组合和吸收峰频率近似值,拟合时每个子区间中通过峰数递增最优化方法确定拟合的吸收峰数,最后微调优化得到最优的吸收峰频率、峰高值。为了实现物质的识别,通过密度聚类算法得到同一类纯净物在多次测量中的共同吸收峰,以此作为标准数据,通过提出的基于吸收峰特征的光谱匹配算法实现了纯净物和不同含量混合物的快速识别。对10类纯净物的实际光谱数据进行拟合聚类,得到其吸收峰参数,结果与太赫兹光谱数据库一致。通过识别算法对纯净物测试集进行识别的识别率为100%,证明了特征提取和物质识别算法的有效性。对于含有混叠峰的混合物光谱,二阶导数法对葡萄糖-乳糖混合物光谱中被掩盖吸收峰(1.280 THz)的识别率仅为70%,提取到的频率平均值为1.316 THz;而该算法提高识别率至95%,频率平均值为1.281 THz,该算法提高了对混叠峰的分辨能力,能够精确定位混叠峰。对10类纯净物构成的6类不同程度混叠的二元混合物前二、三识别率分别达到90.8%和98.3%,提取到的特征能够有效应用于混合物的成分检测。该算法能够以纯净物数据为标准数据实现成分各异的混合物成分检测,对于太赫兹光谱混合物成分检测有重要意义。     

Aerial Video Trackers Review

Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.     

Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.