基于超静定方程的橡胶材料本构模型参数识别

由于橡胶材料具有非线性和大变形特性,使得描述橡胶力学特性的本构模型参数的确定比较烦琐和困难。为了提高橡胶本构模型参数识别的准确性,基于超静定方程求解原理推导出一种新的识别方法。以某橡胶衬套为例,识别的参数应用于有限元分析,对比试验数据,结果表明该识别方法可以准确识别橡胶材料参数,并且精度相对于最小二乘法有了明显改善,显示了超静定识别法的有效性和可靠性。     

基于深度卷积神经网络的储粮害虫图像识别

[目的]为了防治储粮害虫带来的危害,借助计算机对储粮害虫进行有效的图像识别是具有重要意义的。[方法]针对基于图像的储粮害虫多分类识别问题,引入了基于深度卷积神经网络的储粮害虫图像识别方法。该方法与传统的储粮害虫识别方法相比,大幅度简化了数据预处理过程,[结果]同时该方法在识别精确度方面达到了97.61%,也明显优于传统方法。[结论]因此基于深度卷积神经网络的储粮害虫识别方法具有较高的实用性,且具有进一步研究和推广的意义。     

作物类型遥感识别研究进展

及时获取作物种植面积是研究粮食区域平衡,预测农业综合生产力和人口承载力的基础。遥感技术已经成为提取作物种植面积的重要手段,而前提是识别作物。为了理清当前该领域的国内外研究现状,以遥感在作物类型识别中的应用为主线,归纳了国内外作物类型识别研究中常用的各类遥感数据,如资源遥感影像、气象遥感影像、高分辨率影像、高光谱影像和微波影像等,分析其优缺点和适用性;同时总结了利用遥感进行作物类型识别的3 类研究方法,包括基于光谱的识别方法、基于物候差异的识别方法以及光谱与物候相结合的方法,分析了各种方法的特点;指出目前作物类型遥感识别中存在的主要问题,如影像空间精度与价格的平衡问题,多分辨率遥感数据的综合应用问题,物候差异对作物识别的影响问题等;认为不同分辨率遥感数据的结合可以弥补各自不足,遥感影像的时相选择是提高精度的关键,另外需要应用除光谱和物候以外的更多解译标志;建议进一步深入研究作物识别机理和多尺度数据融合方法。以期为遥感技术在作物类型识别中的深入研究提供参考和借鉴。     

STR基因座在奶牛个体识别中的应用

通过分析2份冷冻精液样本和4头可疑种公牛血样的BM1862、BM2113、BM720和TGLA122 STR基因座遗传多样性,旨在判断该2份冷冻精液的种公牛来源,为建立奶牛个识别方法奠定基础。结果表明,应用STR基因座对个体进行识别,鉴别能力达0.9999。说明四个STR基因座可以用于冻精质量监测或奶牛个体识别和亲子鉴定。     

基于Matlab的玉米根系图像拼接、根系提取分析系统

为了对玉米根系生长进行准确的观察、取样、测定。通过采用沈阳大气环境研究所自主研发的根系监测系统(SYIAE-01)获取玉米根系的图片,基于Matlab数字图像识别方法(IBR)采用SIFI和OTSU法编写了由图像几何畸变校正、图像拼接、根系图像提取及根系面积计算三部分组成的根系图像软件,利用这一软件研究分析所采集的根系数字图像。结果表明,以Matlab为基础编写的根系图像拼接、提取分析系统初步实现了玉米根系特征的原位识别,通过该系统可以为提高玉米根系图像整理的工作效率,为计算根系面积提供帮助。     

基于形状特征和SVM多分类的铜仁地区茶叶病害识别研究

针对茶叶常见叶部病斑图像的形状特点,将机器学习应用于茶叶病害识别当中。以茶叶3种常见病害作为研究对象,运用支持向量机方法进行分类识别研究,对有病害的茶叶图像进行处理和特征提取,利用径向基核函数进行分类来提高茶叶病害识别率。运用分类识别方法对茶叶病害进行研究,使茶叶在发病初期就能得到更好的预防以及后期能保证茶叶的质量和产量,提高当地茶叶的销量,促进经济发展。     

STR基因座进行奶牛个体识别的应用

通过分析2份冷冻精液样本和4头可疑种公牛血样的BM1862、BM2113、BM720和TGLA122 STR基因座遗传多样性，旨在判断该2份冷冻精液的种公牛来源，为建立奶牛个识别方法奠定基础。结果表明，应用STR基因座对个体进行识别，鉴别能力达0.9999。说明四个STR基因座可以用于冻精质量监测或奶牛个体识别和亲子鉴定。     

基于深度卷积神经网络的玉米病害识别

为了提高玉米病害的识别率,本文提出了一种在自然环境条件下基于深度卷积神经网络的玉米病害识别方法。该方法以玉米常见的10类病害为研究对象。算法模型是先将图像预处理,应用Triplet loss双卷积神经网络结构学习玉米图像特征,再使用SIFT算法提取图像纹理细节,最后通过Softmax对图像进行标签分类。训练集采用正常玉米图像与玉米病害图像相结合的方式,使用深度相似性网络学习正常玉米图像特征表示,再使用迁移学习方法学习玉米病害图像的特征,最后对特征进行分类识别。研究结果表明,该方法可准确识别10种常见玉米病害,正确率可达90%以上,为玉米病害的防治提供了有效的技术支持。     

农作物的几种常见肥害类型

在农业生产实践中，时常可见到农药药害和施肥造成的肥害，药害发生后症状一般都很明显，而肥害的发生比较难以识别，有时和农药药害有一些混淆。因此，区别不同情况，识别常见肥害很有必要。下面介绍几种肥害类型和识别方法。     

高山杜鹃

商家选择江苏如皋花木大世界、福建华瑞花场。植物名西洋杜鹃,也叫西鹃。挑选提示市场有一些流动花贩用地货杜鹃(大田培养的后起花苗)栽入相应的大盆中,再加入腐叶土进行伪装,冒充原盆(指在盆中栽培2～3年以上)的。地货杜鹃花期只有1个月左右,花叶还会不断掉落,给以后莳养带来诸多麻烦。一般原盆的价格是地货的2倍以上。识别方法:用手指沿盆口向下探摸,摸不到根系就不是原盆。     

Single sample face recognition based on B-spline and image gradient

A single sample face recognition algorithm based on B-spline and image gradient is proposed. Image gradient method for face recognition has advantage of illumination invariant. But the recognition rate will be greatly decreased when the image contains noise which will seriously influence gradient information. Traditional methods to reduce noise smooth image at the same time and image gradient reorganization rate will be reduced. As the B-spline filter has the feature which can adjust the order, B-spline filter with different orders can be selected according to the image noise value to minimize noise while preserve image gradient information. Experiments prove that using B-spline and image gradient algorithm can achieve a better recognition rate than traditional filtering method on single sample face recognition problem.     

A face recognition method based on modular PCA and SVD

In order to solve the problem that modular PCA method is sensitive to translation, rotation and other geometric transform, a face recognition method based on modular PCA and singular value decomposition (SVD) is proposed. The PCA features of sub image and SVD features are extracted respectively. The distance measure that fuses information of modular PCA and SVD is obtained. Minimum distance classifier is used to face recognition. Experimental results on ORL human face database show that the proposed method can obtain higher recognition rate.     

Automatic head recognition by integrating mean shift with multiple features

To improve the head detection accuracy in video sequences captured with fixed vertical monocular camera, a novel method of head recognition based on mean shift and multiple features is proposed. Firstly, mean shift based image segmentation algorithm with color information and spatial information is suggested to derive the candidate head components in images. Furthermore, by referring to two features that the contour of human head regions are approximate round and the hair color distribution is clustered, the evaluation models based on the contour information and inside color information of candidate head components are presented for head recognition. The experimental results show that the proposed algorithm can effectively reduce the light interfere and eliminate fake target whose color information is similar to hair color distribution. The detection rate for static images can reach about 89.4%.     

语谱特征的身份认证向量识别方法

针对采用梅尔频率倒谱系数（mel-frequency cepstrum coefficient,MFCC）作为身份认证向量（identity vector,i-vector）进行说话人识别存在语音信息不全的问题,提出一种基于语谱特征的身份认证向量识别说话人的方法。语音信号经过预加重、分帧加窗预处理之后,通过短时傅立叶变换转换成语谱图,语谱图被提交到高斯通用背景模型,在高维均值超向量空间中选择合适的低维线性子空间流型结构以构造符合正态分布的向量作为身份认证向量。这些获取的身份认证向量经过线性判别性分析实现降维并存储。最后采用对数似然比（log-likelihood ratio,LLR）方法对训练和测试阶段的i-vector进行评分,完成说话人识别。以TIMIT数据库为标准的数值实验结果表明,相比采用MFCC作为特征的识别方法,研究的等错误率（equal error rate,EER）更低。     

Gender recognition of speakers based on MFCC and SVM

A Chinese speech (mandarin) database was established for speakers gender recognition. A combination method is proposed for gender recognition of speakers based on support vector machine and Mel frequency cepstrum coefficients (MFCC) for classification and feature extraction respectively. The comparative result shows that the accuracy of SVM is 98.7%, which is better than other methods.     

Partial discharge pattern recognition based on S transform and two-directional 2DPCA

A new feature extraction method is proposed to recognize different types of partial discharge (PD) signals. Firstly,four typical categories of PD artificial defect models are made and S transform (ST) is employed to obtain a time-frequency representation of the recorded UHF signals. Then,two-directional two-dimensional principal component analysis ((2D) 2PCA) is applied to compress the ST amplitude (STA) matrix to extract features. Finally,support vector machine (SVM) combined with particle swarm optimization (PSO) algorithm is introduced to accomplish the recognition of experimental samples. Classification results demonstrate that the average recognition rate of (10,5) combination is the highest while the one of (5,5) combination is the lowest among four kinds of feature dimension combinations. Moreover,PSO can obviously improve the classification performance of SVM. Specifically,all the average recognition rates of PSO-SVM are higher than 94.43%and the maximum value comes to 97.67%. Therefore,the feature sets extracted by ST and (2D) 2PCA can not only achieve dramatic dimension reduction,but also retain the major information of original data. It is proved that the proposed algorithm can obtain ideal results in PD pattern recognition.     

光强倒数色度空间的彩色人脸光照预处理

为了解决彩色人脸识别中色彩信息易受光照影响的问题,提出一种基于光强倒数色度空间(IICS)的彩色人脸图像预处理方法。本方法首先将图像均匀地分割成子块;将每个图像块变换为IICS空间中的一个二维数据集,并根据数据集的线性分布特性估计图像块的光照颜色;然后对全部图像块的光照估计进行颜色直方图统计,根据直方图对分块估计的结果进行合并;最后,利用估计得到的光照和对角模型将图像光照校正到标准白光下,用于人脸识别。在AR和FERET人脸库上的实验表明,通过引入本光照预处理,有效增强了彩色人脸识别方法对光照变化的鲁棒性,提高了识别精度。     

Digital modulation mode recognition based on multi-classclassification of support vector machine

To solve the overfitting, underfitting and local minimum existing in neural networks, a digital modulation mode recognition method based on support vector machine (SVM) is proposed. Seven characteristic parameters are extracted from instantaneous amplitude, instantaneous phase, instantaneous frequency, frequency spectrum, and changes in characteristics of the envelope to train support vector machine. Compared with the existing algorithms, using binary tree theory to design multi-class classifier has the features of simple, high-speed, high-precision. The simulation results indicate that the scheme can achieve 97% recognition accuracy when the signal to noise ratio (SNR) is above 15 dB with the AWGN channel.     

Application of Gobor wavelet and SLLE in face recognition

In order to improve the recognition rate of face recognition algorithm, a new algorithm of face recognition is proposed based on Gabor wavelet transform and Supervised Locally Linear Embedding (SLLE). Gabor wavelet is introduced as a method to extract Gabor magnitude features by convolving the normalized face image with multi scale and multi orientation Gabor filters. In the feature extraction module, the dimension of Gabor features is reduced by SLLE. A minimum distance classifier is trained for classification. With the test of the ORL and YALE face database, it is found that 3.5 %~37.8% increase in recognition rate can be achieved compared with other algorithms.     

Face recognition method with local matching

From human cognition, a face recognition method with local matching based on statistical learning is proposed. The image is divided into several subimages and each subimage is considered as a weak classifier. The Adaboost learning algorithm is used to train the weak classifiers and construct a strong classifier. As a result, all subimages are effectively combined together to explore the best discriminating power and improve the classification accuracy. Compared with the holistic matching methods, the local matching method is robust to variations in illumination, expression, and pose, etc. The experimental results show that the proposed method can improve the face recognition accuracy and is robust to variations in illumination and expression.