杭州市园林鸟类群落结构及其季节变化

园林鸟类是城市环境质量的重要指标,也是城市自然保护和生态建设的重要组成部分.1997年10月至1998年12月,通过选取杭州市区20个主要园林,根据季节每月一至两次调查园林鸟类的物种和数量,对杭州市园林鸟类的群落结构及其季节变化进行了分析探讨,结果显示:全部园林中共记录到鸟类82种,其中迁徙鸟类50种,占61%,留鸟32种,占39%;园林鸟类群落呈现出较强的季节性,从全部园林总体上看,物种数和总数量呈现春秋两个高峰,夏季最低;从单个园林来看,多数园林鸟类群落物种数的最高峰集中在冬季,而各园林鸟类总数量的季节变化呈现不同程度的波动性.     

基于Mel频谱值和深度学习网络的鸟声识别算法*

为了增强网络对鸟鸣声信号的特征学习能力并提高识别精度,提出一种基于深度残差收缩网络和扩张卷积的鸟声识别方法。首先,提取鸟鸣声信号的对数梅尔特征及其一阶和二阶差分系数组成logMel特征集作为网络模型的输入;其次,通过深度残差收缩网络自动学习噪声阈值,减少噪声干扰;然后,引入扩张卷积增大卷积核感受野并利用注意力机制使网络更关注关键帧特征;最后,通过双向长短时记忆网络从学到的局部特征中学习长期依赖关系。以百鸟数据birdsdata鸟声库中的19种中国常见鸟类作为实验对象,识别正确率可以达到96.58%,并对比模型在不同信噪比数据下的识别结果,结果表明该模型在噪声环境下的识别效果优于现有模型。     

Experimental and FEM study of windshield subjected to high speed bird impact

Both experimental and finite element model (FEM) results are presented for the dynamic strength behavior of windshield subjected to bird impact. The experimental data taken from a series of high speed photographs are compared with the numerical results predicted by using FEM in which the windshield was modeled entirely with solid elements and the bird body was approximately simulated by an elastic-plastic material with failure element behavior. Effective plastic strain and element pressure were adopted as the failure criteria and once the pressure or the effective plastic strain of an element reached the critical value, the element would lose the tensile resistance capability completely. The deflection and stress distribution in the windshield were obtained. It is shown that the result from the finite element analysis agrees with those from the full-scale bird impact test. The project supported by the National Natural Science Foundation of China (10272011)     

分散固相萃取-超高效液相色谱-串联质谱法测定禽蛋中氟虫腈及其代谢产物

建立了分散固相萃取-超高效液相色谱-串联质谱检测禽蛋中氟虫腈及其代谢产物氟甲腈、氟虫腈硫醚和氟虫腈砜的方法。样品用含1%(体积分数)乙酸的乙腈提取后,加无水硫酸钠、十八烷基键合硅胶(C18-N)及氨基-丙基乙二胺(NH2-PSA)3种净化剂,旋涡振荡,对样品进行净化。以Shim-pack GIST C18色谱柱(50 mm×2.1 mm,2μm)进行分离,以甲醇和1 mmol/L乙酸铵水溶液为流动相进行梯度洗脱,在电喷雾离子源负离子模式和多反应检测(MRM)模式下进行定性定量分析。考察了净化剂中无水硫酸钠、C18-N和NH2-PSA的用量对加标回收率的影响,优化了实验条件。结果表明:4种化合物在0.4~100μg/L范围内线性关系良好,相关系数r2≥0.998 9;回收率为95.09%~103.26%;定量限为0.2μg/kg。该方法前处理简单,回收率高,重复性好,可作为禽蛋中氟虫腈及其代谢产物的有效检测方法。     

中国鸟类的两个新纪录

新中国成立后,钱燕文等(1963)、陈服官等(1980)、向礼陔(1980)等曾先后报导过分布于新疆的若干鸟类新纪录.近来,我们在整理新疆大学生物系所收藏的鸟类标本中,又发现两种鸟类为我国首次纪录,现报导如下;     

飞机圆弧风挡抗鸟撞试验研究

进行了某型飞机圆弧风挡抗鸟撞试验研究,为了解风挡边界及相关附件的影响,实验系统包括飞机前舱较大范围的部件。采用高速摄像系统记录了风挡动力响应变形的全过程,得到了该机型圆弧风挡抗鸟撞极限速度的范围及响应过程中关注点的位移、应变-时间曲线等数据。分析了风挡关键点位移随撞击速度的变化规律,给出了撞击临界速度与应变之间的变化规律,同时计算了风挡受鸟撞时的动态应力值,为进一步设计研制具有高抗鸟撞能力的风挡提供了有价值的数据。     

Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy

The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.     

Use of projection moiré for measuring the instantaneous out-of-plane deflections of composite plates subject to bird strike

For the new generation aircraft families, the use of fibre-reinforced plastics is considered for the leading edge of the wings. However, this leading edge is very prone to bird strike impact.This paper presents the use of the projection moiré technique to measure the instantaneous out-of-plane deflections of composite plates subject to bird strike. Very strict constraints with regard to (i) high-speed image acquisition, (ii) vibrations of the impact chamber, and (iii) projection and observation angles, complicated substantially the development of the set-up. Moreover, the high frame rates (12,000 fps) required a very intensive illumination.In the optimized configuration, a specially designed grating with gradually changing period is projected by means of special halide hydride lamps through one of the side windows of the impact chamber onto the composite plate riveted in a steel frame. The digital high-speed camera is mounted on the roof of the impact chamber and records through a mirror the object surface with the projected fringe pattern on it.Numerical routines based on local Fourier transform were developed to process the digital images to extract the phase and the out-of-plane displacements. The phase evaluation is possible due to the carrier frequency nature of the projected moiré pattern. This carrier frequency allows separation of the unwanted additive and multiplicative fringe pattern components in the frequency domain via the application of a proper mask. The numerical calculations were calibrated for the bird strike on an aluminum plate, where the plastic deformation could be checked after the test.     

Understanding chicken walks on n × n grid: Hamiltonian paths,discrete dynamics,and rectifiable paths

Understanding animal movements and modeling the routes they travel can be essential in studies of pathogen transmission dynamics. Pathogen biology is also of crucial importance, defining the manner in which infectious agents are transmitted. In this article, we investigate animal movement with relevance to pathogen transmission by physical rather than airborne contact, using the domestic chicken and its protozoan parasite Eimeria as an example. We have obtained a configuration for the maximum possible distance that a chicken can walk through straight and nonoverlapping paths (defined in this paper) on square grid graphs. We have obtained preliminary results for such walks which can be practically adopted and tested as a foundation to improve understanding of nonairborne pathogen transmission. Linking individual nonoverlapping walks within a grid‐delineated area can be used to support modeling of the frequently repetitive, overlapping walks characteristic of the domestic chicken, providing a framework to model fecal deposition and subsequent parasite dissemination by fecal/host contact. We also pose an open problem on multiple walks on finite grid graphs. These results grew from biological insights and have potential applications. © 2014 The Authors. Mathematical Methods in the Applied Sciences published by John Wiley & Sons Ltd.     

Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs

Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of three SFIMs (result fusion mode). The SFIMs were trained with three different kinds of spectrograms, which were calculated through short-time Fourier transform, mel-frequency cepstrum transform and chirplet transform, respectively. To overcome the shortage of the huge number of trainable model parameters, transfer learning was used in the multi-channel models. Using our own vocalization dataset as a sample set, it is found that the result fusion mode model outperforms the other proposed models, the best mean average precision (MAP) reaches 0.914. Choosing three durations of spectrograms, 100 ms, 300 ms and 500 ms for comparison, the results reveal that the 300 ms duration is the best for our own dataset. The duration is suggested to be determined based on the duration distribution of bird syllables. As for the performance with the training dataset of BirdCLEF2019, the highest classification mean average precision (cmAP) reached 0.135, which means the proposed model has certain generalization ability.