Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals

The potential of the electronic nose to monitor Longjing tea different grade based on dry tea leaf, tea beverages and tea remains volatiles was studied. The original feature vector was obtained from the response signals of the E-nose, and was analyzed by principal component analysis (PCA). To decrease the data dimension and optimize the feature vector, the front five principal component values of the PCA were extracted as the final feature vectors by PCA. The linear discrimination analysis (LDA) and the back-propagation neural network (BPNN) were proposed to identify Longjing tea grade. The results showed that the discrimination results and testing results for the tea grade were better based on the tea beverages than those based on the tea leaf and the tea remains based on the new five feature vectors; both of the LDA and BPNN methods achieved better discrimination for the tea grades based on the tea beverages and the analysis results of the two methods were accordance.     

基于传感器阵列多特征优化融合的茶叶品质检测研究

为提高电子鼻对不同品质茶叶的识别能力,分别提取电子鼻传感器信号的总体平均值、上升阶段斜率平均值和相对稳态平均值作为特征值,对电子鼻传感器阵列进行多特征数据融合优化.首先对原始数据进行归一化处理,统一值的量纲和数量级;通过因子载荷分析,去除各个象限内主成分投影较小和投影重叠的因子,对多特征向量矩阵进行优化;最后采用单因素方差分析,缩小不同品质茶叶组内间距,增大组间间距,更利于实现茶叶品质的区分.结果显示,主成分分析(PCA)可有效区分3种不同等级茶叶,因子载荷优化使各品质茶叶组内间距减小,单因素方差优化使一级与二级茶叶区分效果更明显;线性判别分析(LDA)效果要优于PCA分析,3个不同等级的茶叶可得到极为明显的区分.研究结果表明,用多特征优化融合可有效提取电子鼻对茶叶的响应信息,有利于对不同品质茶叶进行识别.     

基于电子鼻系统的番茄苗不同种类损伤的区分效果研究

通过对早疫病病害番茄苗、灰霉病病害番茄苗、机械损伤番茄苗和对照番茄苗的电子鼻响应信号的对比,可以看出不同处理的番茄苗样本电子鼻的响应信号是不同的,表明用电子鼻响应信号对番茄苗不同种类损伤进行预测是可行的.从PCA结果来看,早疫病病害的番茄苗和灰霉病病害的番茄苗能很好区分开,机械损伤的番茄苗和正常处理的番茄苗产生了重叠现...     

基于RF-SVM的发动机缸体孔组加工质量分级监控

为实时动态监控发动机缸体顶面孔组的加工质量,提出基于随机森林(random forest,RF)和支持向量机(support vector machine,SVM)相结合的工序节点处加工质量分级监控模型。设计在工序间快速获取发动机缸体孔组图像的机器视觉系统,提取单缸孔7个特征参数及3个相邻孔间距;用主成分分析法对特征参数进行降维处理,建立样本集合训练孔组整体加工质量RF分级监控模型及单孔加工质量SVM分级监控模型。应用该模型对某发动机缸体顶面孔组加工质量进行在线监控,结果表明,与决策树模型、RF模型和SVM模型相比,所提模型对孔组整体加工质量分级精度可达97.778%,单孔分级精度可达99.167%,能快速响应发动机缸体制造过程质量反馈控制,可有效解决相关工程实际问题。     

Remote sensing of tea plantations using an SVM classifier and pattern-based accuracy assessment technique

Tea (Camellia sp.) and its plantation are very important on a worldwide scale as it is the second-most consumed beverage after water. Therefore, it becomes necessary to map the widely distributed tea plantations under various geographies and conditions. Remote-sensing techniques are effective tools to map and monitor the impact of tea plantation on land-use/land-cover (LULC). Remote sensing of tea plantations suffers from spectral mixing as these plantation areas are generally surrounded by similar types of green vegetation such as orchards and bushes. This problem is mainly tied to planting style, topography, and spectral characteristics of tea plantations, and the side effects are observed as low classification accuracies after the classification process. In this study, to overcome this problem, a three-step approach was proposed and implemented on a test area with high slope. As a first step, spectral and multi-scale textural features based on Gabor filters were extracted from high resolution multispectral digital aerial images. Similarly, based on the wavelength range of the sensor, a modified normalized difference vegetation index (MNDVI) was applied to distinguish the green vegetation cover from other LULCs. The second step involves the classification of multidimensional textural and spectral feature combinations using a support vector machine (SVM) algorithm. As a final step, two different techniques were applied for evaluating classification accuracy. The first one is a traditional site-specific accuracy assessment based on a confusion matrix calculating statistical metrics for different feature combinations. The overall accuracy and kappa values were calculated as 93.68% and 0.92, 93.82% and 0.92, and 97.40% and 0.97 for LULC maps produced by red, green, and blue (RGB), RGB + MNDVI, and RGB + MNDVI + Gabor features, respectively. The second accuracy assessment technique was the pattern-based accuracy assessment. The technique involves polygon-based fuzzy local matching. Three comparison maps showing local matching indices were obtained and used to compute the global matching index (g) for LULC maps of each feature set combination. The g values were g_(RGB) (0.745), g_(RGB+MNDVI) (0.745), and g_{(RGB+MNDVI+Gabor)} (0.765) for comparison maps. Finally, based on accuracy assessment metrics, the study area was successfully classified and tea plantation features were extracted with high accuracy.     

基于KL散度的支持向量机方法及应用研究

针对ICA提取的说话人语音特征,导出以库尔贝克—莱布勒（KL）散度作为距离测度的KL核函数用来设计支持向量机,实现了一个高分辨率的ICA/SVM说话人确认系统.说话人确认的仿真实验结果表明,使用ICA特征基函数系数比直接使用语音数据训练SVM得到的分类间隔大,支持向量少,而且使用KL核函数的ICA/SVM系统确认的等差率也低于其它传统SVM方法,证明了基于KL散度的支持向量机方法在实现分类和判决上具有高效性能.     

电子鼻技术在茶叶品质检测中的应用研究

以电子鼻作为检测手段,对同类不同等级的茶叶、茶水和茶底挥发性成分进行检测,并对采集到的数据进行分析。首先通过主成分分析进行特征提取来压缩数据维数,减少数据计算量,进而优化特征向量。然后采用线性判别和BP神经网络的方法对茶叶的不同等级进行分类判别。结果显示,误判样本都发生在T60和T100之间,两种判别方法结果比较一致。相对于茶叶和茶底,以各等级茶水为研究对象时,两种方法对茶叶品质等级的判别及测试结果相对都比较好。     

Vision-based rock-type classification of limestone using multi-class support vector machine

Rock-type classification is a challenging and difficult job due to the heterogeneous properties of rocks. In this paper, an image-based rock-type analysis and classification method is proposed. The study was conducted at a limestone mine in western India using stratified random sampling from a case study mine. The analysis of collected sample images was performed in laboratory. Color, morphology, and textural features were extracted from the captured image and a total of 189 features were recorded. The multi-class support vector machine (SVM) algorithm was then applied for rock-type classification. The hyper-parameters and the number of input features of the SVM model were selected by genetic algorithm. The results revealed that the SVM model performed best when 40 features were selected out of the 189 extracted features. The results demonstrated that the overall accuracy of the proposed technique for rock type classification is 96.2 %. A comparative study shows that the proposed SVM model performed better than a competing neural network model in this case study mine.     

一种基于电子舌技术的绿茶分类方法

提出了一种基于对铜电极的循环伏安信号的主成分分析来进行绿茶分类的方法.绿茶中含有蛋白质,脂类,碳水化合物及氨基酸等.实验结果显示在强碱性溶液中铜电极对这些物质具有丰富的响应.我们采用主成分分析法对五种不同的绿茶在铜电极上的循环伏安信号进行分析,结果显示在第一和第二主成分的得分图上,五种不同绿茶可以清楚地区分开.同时也进一步研究了一种带窗的时间切割法对原始的循环伏安信号的数据压缩.     

Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA

Technological progresses in the gas sensor fields provide the possibility of designing and construction of Electronic nose (E-nose) based on the Biological nose. E-nose uses specific hardware and software units; Sensor array is one of the critical units in the E-nose and its types of sensors are determined based on the application. So far, many achievements have been reported for using the E-nose in different fields of application. In this work, an E-nose for handling multi-purpose applications is proposed, and the employed hardware and pattern recognition techniques are depicted. To achieve higher recognition rate and lower power consumption, the improved binary gravitational search algorithm (IBGSA) and the K-nearest neighbor (KNN) classifier are used for automatic selecting the best combination of the sensors. The designed E-nose is tested by classifying the odors in different case studies, including moldy bread recognition in food and beverage field, herbs recognition in the medical field, and petroleum products recognition in the industrial field. Experimental results confirm the efficiency of the proposed method for E-nose realization.     

Comparison of support vector machine,artificial neural network,and spectral angle mapper algorithms for crop classification using LISS IV data

The Resourcesat-2 is a highly suitable satellite for crop classification studies with its improved features and capabilities. Data from one of its sensors, the linear imaging and self-scanning (LISS IV), which has a spatial resolution of 5.8 m, was used to compare the relative accuracies achieved by support vector machine (SVM), artificial neural network (ANN), and spectral angle mapper (SAM) algorithms for the classification of various crops and non-crop covering a part of Varanasi district, Uttar Pradesh, India. The separability analysis was performed using a transformed divergence (TD) method between categories to assess the quality of training samples. The outcome of the present study indicates better performance of SVM and ANN algorithms in comparison to SAM for the classification using LISS IV sensor data. The overall accuracies obtained by SVM and ANN were 93.45% and 92.32%, respectively, whereas the lower accuracy of 74.99% was achieved using the SAM algorithm through error matrix analysis. Results derived from SVM, ANN, and SAM classification algorithms were validated with the ground truth information acquired by the field visit on the same day of satellite data acquisition.     

基于核主元分析和支持向量机的人脸识别

核主元分析(KPCA,Kernel Principal Components Analysis)具有能较好地提取非线性特征的优势;支持向量机(SVM,Support Vector Machine)具有较好的非线性映射能力,且泛化能力强。结合核主元分析与支持向量机的特点,提出了一种基于核主元分析与支持向量机的人脸识别方法。该方法首先利用核主元分析对人脸图像进行特征提取,然后依据支持向量机与最近邻准则对所提取的核主元特征进行分类识别。基于ORL(Olivetti Research Laboratory)人脸数据库的实验结果表明了该方法的有效性。     

Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm

Tea category classification is of vital importance to industrial applications. We developed a tea-category identification system based on machine learning and computer vision with the aim of classifying different tea types automatically and accurately. 75 photos of three categories of tea were obtained with 3-CCD digital camera, they are green, black, and oolong. After preprocessing, we obtained 7 coefficient subbands using 2-level wavelet transform, and extracted the entropies from the coefficient subbands as the features. Finally, a weighted k-Nearest Neighbors algorithm was trained for the classification. The experiment results over 5 × 5-fold cross validation showed that the proposed approach achieved sensitivities of 95.2 %, 90.4 %, and 98.4 %, for green, oolong, and black tea, respectively. We obtained an overall accuracy of 94.7 %. The average time to identify a new image was merely 0.0491 s. Our method is accurate and efficient in identifying tea categories.

     

Comparative study on classifying human activities with miniature inertial and magnetic sensors

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.     

Hyperspectral image classification based on adaptive optimisation of morphological profile and spatial correlation information

ABSTRACT

Hyperspectral image feature extraction generally does not consider the optimisation of structure element types, resulting in the loss of spatial correlation information. To solve this issue, this paper proposes a novel adaptive classification method (MPDTNC-SVM), in which spatial information is extracted by Morphological Profile Filter (MPF) and Domain Transform Normalised Convolution Filter (DTNCF). First, MPF extracts the spatial features on multiple principal component analysis (PCA) components of the hyperspectral image, and DTNCF works over all spectral bands to extract spatially correlated features. The two spatial features are then combined and fed into Support Vector Machine (SVM). Second, a two-step optimisation is implemented in the classification process. Specifically, the best structure of MPF is chosen for classification. Next, the optimal parameters of the structural elements are obtained through iterative classification optimisation, with the best classification performance produced in the process. Experimental results of actual hyperspectral images show that the proposed MPF and DTNCF with SVM (MPDTNC-SVM) method is superior to other classification methods, including Edge-Preserving Filter and Recursive Filter method, morphological feature-based methods, and the SVM methods with raw spectral features, reduced-dimensional and spatial-spectral information.     

A Dempster Shafer-based fuzzy multisensor fusion system using airborne lidar and hyperspectral imagery

ABSTRACT

Nowadays, accurate spectral reflectance information is provided by hyperspectral (HS) data while light detection and ranging (lidar) data provides precise information about the height and geometrical properties of the surfaces. In the most research papers, data fusion of disparate sensors significantly improves object classification performance compared to that of just an individual sensor. Previous researches on fusion of these two sensors had problems such as crisp classifiers or simple fuzzy decision-making systems. This article tries to overcome these weaknesses by accurate support vector machine (SVM) and Fuzzy SVM as classifiers in crisp and fuzzy decision fusion system and fusion of two sensors by two different methods based on precise theories of Bayesian and Shafer. Also, the proposed method tries to compare the results of fusion of both data using decision fusion system with stacked features strategy. This study focuses on HS and lidar fusion through three main phases. The first phase is based on the using of Noise Weighted Harsanyi-Farrand-Chang method and principal component analysis to overcome the high dimensionality problem of HS data. The second phase is based on the feature extraction and selection strategy on lidar data. Finally, fuzzy SVM and Dempster Shafer methods are applied as fuzzy classification and fuzzy decision fusion strategies on the feature spaces. A co-registered HS and lidar data set from Houston of U.S.A. by 15 classes was available to examine the effectiveness of the proposed method. The results of this study highlight that the combination of HS and lidar data enable reliable mapping of land cover.     

Color image segmentation using pixel wise support vector machine classification

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.     

Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens

Plant-emitted volatiles can change after herbivore attack. Monitoring the change in volatile profiles can offer a non-destructive method for determining plant health. An electronic nose (E-nose) equipped with a headspace sampling unit was used to discriminate between volatile profiles emitted by uninfested rice plants and those emitted by rice plants exposed to different numbers of Nilaparvata lugens adults. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the E-nose was able to distinguish among the different pest treatments. The results indicate that it is possible to separate differently treated rice plants using E-nose signals. The stepwise discriminant analysis (SDA) and a 3-layer back-propagation neural network (BPNN) were developed for pattern recognition models. Calculations show that the discrimination rates were over 92.5% for the training data set and 70% for the testing set using SDA. The correlation coefficient between predicted and real numbers of the pest was found to be over 0.78 using BPNN. Moreover, gas chromatography–mass spectrometry (GC–MS) analysis confirmed the E-nose results. These studies demonstrate that the E-nose technology has clear potential for use as an effective insect monitoring method.     

基于融合特权信息支持向量机的模拟电路故障诊断新方法

针对模拟电路故障诊断复杂多样难于辨识的问题,提出了基于融合特权信息支持向量机的模拟电路故障诊断新方法。首先对采集的信号进行主成分分析(PCA)——特征提取;然后将训练集输入融合特权信息支持向量机进行训练获得故障诊断模型;最后将测试集输入训练好的支持向量机分类模型,实现对不同故障类型的识别。Sallen-Key滤波电路故障诊断仿真实验结果表明,该方法有效提高了分类的性能,不仅能够正确分类单故障而且能够有效分类多故障,其中单硬故障情况下平均故障诊断率达到了99%以上,为模拟电路故障诊断提供了新的途径。