Automatic Identification of Asian Rice Plant-Hopper Based on Image Processing

2017 ◽  
Vol 33 (5) ◽  
pp. 591-602 ◽  
Author(s):  
Jialiang Wang ◽  
Rui Kang ◽  
Kunjie Chen ◽  
Deying Liu ◽  
Haiming Yu
Keyword(s):  
Rice Plant ◽  
Fourier Spectrum ◽  
Image Acquisition ◽  
Species Recognition ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Automatic Identification ◽  
Asian Rice ◽  
Image Pixels

Abstract. To realize the automatic forecasting of rice plant-hoppers, a rice plant-hopper species recognition method is proposed using the windowed data of two-dimensional (2-D) Fourier spectrum of back images of insects and support vector machine. A self-made insect image acquisition device was used in field to collect the back images of rice plant-hoppers in natural environment. According to the statistical analysis of image pixels, we chose the blue component, B=140, as the color threshold to binary the original images. The segmented and morphologically filtered insect images were first obtained, and then logical AND operation was applied to them, generating the images of back regions of rice plant-hoppers. Then 2-D Fourier transform was used to obtain the 2-D Fourier spectrum of rice plant-hoppers’ back images. We extracted the data from an l*l (l = 1, 2, …, 9) windowed data of 2-D logarithmic spectrum to describe the features of a rice plant-hopper’s back by analysis. Then we developed a classification model for rice plant-hoppers based on support vector machines. Experimental results showed that a discrimination model using the data from a 3×3 windowed data of 2-D Fourier spectrum can achieve a recognition rate up to over 90%for rice plant-hoppers. Keywords: Image acquisition, Images, Recognition, Rice plant-hopper.

scholarly journals A Fast Detection Method for Wheat Mould Based on Biophotons

2020 ◽  
Author(s):  
Gong Yue-hong ◽  
Yang Tie-jun ◽  
Liang Yi-tao ◽  
Ge Hong-yi ◽  
Chen Liang
Keyword(s):  
Recognition Rate ◽  
Implementation Process ◽  
Approximate Entropy ◽  
Classification Model ◽  
Support Vector ◽  
Fast Detection ◽  
Materials Used ◽  
Specific Implementation ◽  
Wheat Kernels

AbstractMould is a common phenomenon in stored wheat. First, mould will decrease the quality of wheat kernels. Second, the mycotoxins metabolized by mycetes are very harmful for humans. Therefore, the fast and accurate examination of wheat mould is vitally important to evaluating its storage quality and subsequent processing safety. Existing methods for examining wheat mould mainly rely on chemical methods, which always involve complex and long pretreatment processes, and the auxiliary chemical materials used in these methods may pollute our environment. To improve the determination of wheat mould, this paper proposed a type of green and nondestructive determination method based on biophotons. The specific implementation process is as follows: first, the ultra-weak luminescence between healthy and mouldy wheat samples are measured repeatedly by a biophotonic analyser, and then, the approximate entropy and multiscale approximate entropy are separately introduced as the main classification features. Finally, the classification performances have been tested using the support vector machine(SVM). The ROC curve of the newly established classification model shows that the highest recognition rate can reach 93.6%, which shows that our proposed classification model is feasible and promising for detecting wheat mould.

Study about Recognition of Digital Meter Dial Reading Based on SVM

2014 ◽  
Vol 615 ◽  
pp. 194-197
Author(s):  
Zhen Yuan Tu ◽  
Fang Hua Ning ◽  
Wu Jia Yu
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Feature Points ◽  
Svm Classification ◽  
Performance Factors ◽  
Model Combining ◽  
Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

Quantitative inspection for identifying broken coal mine wire rope based on wavelet packet sparse representation

2021 ◽  
Vol 63 (2) ◽  
pp. 102-110
Author(s):  
Junying Zhou ◽  
Jie Tian ◽  
Peng Cheng ◽  
Xu Li ◽  
Decheng Wang
Keyword(s):  
Sparse Representation ◽  
Matching Pursuit ◽  
Wavelet Packet ◽  
Median Filter ◽  
Recognition Rate ◽  
Wire Rope ◽  
Classification Model ◽  
Filter Method ◽  
Support Vector ◽  
Reconstructed Signal

The magnetic flux leakage (MFL) signal of steel wire rope is easily affected by background noise, rope strands and so on. A preprocessing method for the damage signal based on wavelet packet sparse representation is proposed. This method is suitable for the damage signal of the wire rope. The original signal is decomposed into three layers of wavelet packets and the wavelet packet coefficients are sparsely represented by the matching pursuit (MP) and orthogonal matching pursuit (OMP) algorithms. The signal-to-noise ratio (SNR) of the reconstructed signal is much higher than that obtained through the wavelet threshold shrinkage method, the median filter method and the singular value difference spectrum method. The proposed method can significantly improve the noise reduction effect of the damage signal. A principal component analysis (PCA)-based particle swarm optimisation support vector machine (PSO-SVM) model for quantitative recognition is proposed. Seven global eigenvalues and wavelet packet energy entropy details of damage signals are extracted as effective eigenvalues. The eight eigenvalues are used as the input for the SVM that is designed and trained. A PSO-SVM classification model based on PCA is proposed. The results show that the recognition rate of the SVM is 94.73%. The quantitative recognition accuracy is improved.

Research On Exercise Fatigue Estimation Method of Pilates Rehabilitation Based On ECG and sEMG Feature Fusion

2021 ◽  
Author(s):  
Dujuan Li ◽  
Caixia Chen
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Estimation Method ◽  
Classification Model ◽  
Support Vector ◽  
Ecg Signal ◽  
Data Types ◽  
Semg Signal ◽  
Linear Discriminant ◽  
Fatigue Estimation

Abstract Purpose. Fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury in Pilates rehabilitation. Surface electromyography (sEMG) is used to estimate fatigue with low and unstable recognition rates. To improve the rate, this paper fused electrocardiogram (ECG) signal and sEMG signal under three different states, and the classification model of the improved proved particle swarm optimization support vector machine (IPSO-SVM) algorithm was established. Methods. Twenty subjects performed 150 minutes of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. After necessary preprocessing, the IPSO-SVM classification model based on feature fusion was established to identify three different fatigue states (relaxed, transition, and tired). The model effects of different classification algorithms and different fused data types were compared. Results. Compared with common physiological signal classification methods such as BP neural network algorithm(BPNN), K-nearest neighbor(KNN), and Linear discriminant analysis(LDA), IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion. The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

Morlet-Kernel Principal Component Analysis Features for Speech Recognition

2020 ◽  
Vol 17 (9) ◽  
pp. 4482-4486
Author(s):  
H. Y. Vani ◽  
M. A. Anusuya ◽  
M. L. Chayadevi
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Morlet Wavelet ◽  
Classification Model ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Least Mean Square ◽  
Proposed Model ◽  
Comparative Results

The aim of this paper is to present the application of Morlet wavelet to extract the speech features in place of MFCC features. KPCA is applied for selecting and reducing the large features obtained from Morlet wavelet. NLMS (Normalized Least Mean Square) filter is used to reduce additive noise levels ranging from ±5 dB to ±15 dB. Features are modeled using Ensembled Support Vector Machine classification model for FSDD and Kannada multi speaker data sets. The comparative results are discussed over logistic regression model. The proposed model reduces the noise with 99% of recognition rate for isolated words. The efficiency of ensembled classification model is explored.

scholarly journals Automatic Identification of Sedimentary Facies Based on a Support Vector Machine in the Aryskum Graben, Kazakhstan

2019 ◽  
Vol 9 (21) ◽  
pp. 4489 ◽  
Author(s):  
Ai ◽  
Wang ◽  
Sun
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Amplitude Ratio ◽  
Sedimentary Facies ◽  
Field Application ◽  
Classification Model ◽  
Support Vector ◽  
Automatic Identification ◽  
Depositional Facies ◽  
Svm Classification

The Aryskum Depression in the South Turgay Basin has shown improving exploration prospects for subtle reservoirs, due to investment in the exploration workload and more comprehensive geological research. Among them, lithologic stratigraphic reservoirs have gradually become one of the focuses of oil and gas exploration. At present, deduction of the sedimentary characteristics of the target layer through core wells using artificial exploration has become an urgent problem to be solved. We selected 16 artificially interpreted coring wells in the Aryskum Graben for this study. Using the parameters of the gamma-ray (GR) curve of coring wells and support vector machine (SVM) classification algorithms, we developed an automatic identification model of sedimentary facies in the study area. The application of the SVM includes the following steps: Firstly, using the GR curve of 16 coring wells, six quantitative indexes defined as standard deviation, relative gravity, curve amplitude ratio, average median, average slope, and mutation amplitude, are selected to quantify the logging curve in the study area, thus realizing the description of the logging curve form. Secondly, training samples are selected to establish an SVM classification model. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional facies. Field application shows that this solution can be effectively used in uncored wells to identify depositional facies with a rate of accuracy approaching 70%. Our results provide new methods for the identification of sedimentary facies in the study area. The results will also provide a theoretical basis, as well as data basis, for further fine division of microfacies in the study area.

Research on Functional Brain Network Metrics for Depression Patients Automatic Identification

2013 ◽  
Vol 427-429 ◽  
pp. 1440-1446
Author(s):  
Chen Cheng ◽  
Wen Zhao Liu ◽  
Jun Jie Chen
Keyword(s):  
Network Topology ◽  
Neurological Diseases ◽  
Brain Network ◽  
Classification Model ◽  
Support Vector ◽  
Automatic Identification ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Metrics ◽  
Abnormal Brain

Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.

scholarly journals Research on the Open-Categorical Classification of the Internet-of-Things Based on Generative Adversarial Networks

2018 ◽  
Vol 8 (12) ◽  
pp. 2351 ◽  
Author(s):  
Caidan Zhao ◽  
Mingxian Shi ◽  
Zhibiao Cai ◽  
Caiyun Chen
Keyword(s):  
Support Vector Machine ◽  
Internet Of Things ◽  
Recognition Rate ◽  
Classification Model ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Real Samples ◽  
The Real ◽  
Adversarial Networks ◽  
Categorical Classification

Nowadays, it is more and more important to deal with the potential security issues of internet-of-things (IoT). Indeed, using the physical layer features of IoT wireless signals to achieve individual identity authentication is an effective way to enhance the security of IoT. However, traditional classifiers need to know all the categories in advance to get the recognition models. Realistically, it is difficult to collect all types of samples, which will result in some mistakes that the unknown target class may be decided as a known one. Consequently, this paper constructs an improving open-categorical classification model based on the generative adversarial networks (OCC-GAN) to solve the above problems. Here, we have modified the loss function of the generative model G and the discriminative model D. Compared to the traditional GAN model which can generate the fake sample overlapping with the real samples, our proposed G model generates the fake samples as negative samples which are evenly surrounding with the real samples, while the D model learns to distinguish between real samples and fake samples. Besides, we add auxiliary training not only to gain a better recognition result but also to improve the efficiency of the model. Furthermore, Our proposed model is verified through experimental study. Compared to other common methods, such as one-class support vector machine (OC-SVM) and one-versus-rest support vector machine (OvR-SVM), the OCC-GAN model has a better performance. The recognition rate of the OCC-GAN model can reach more than 90% with a recall rate of 97% by the data of the IoT module.

scholarly journals Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jian-Jun Yan ◽  
Rui Guo ◽  
Yi-Qin Wang ◽  
Guo-Ping Liu ◽  
Hai-Xia Yan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fractal Dimension ◽  
Chinese Medicine ◽  
Healthy Subjects ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Male And Female ◽  
Multiclass Support Vector Machine

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.

scholarly journals Recognition of Design Fixation via Body Language Using Computer Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zhongliang Yang ◽  
Yumiao Chen ◽  
Song Zhang
Keyword(s):  
Recognition Rate ◽  
Body Language ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Design Fixation ◽  
Average Recognition Rate ◽  
Svm Model ◽  
Three Body ◽  
The Impact

The main objective of this study is to recognize design fixation accurately and effectively. First, we conducted an experiment to record the videos of design process and design sketches from 12 designers for 15 minutes. Then, we executed a video analysis of body language in designers, correlating body language to the presence of design fixation, as judged by a panel of six experts. We found that three body language types were significantly correlated to fixation. A two-step hybrid recognition model of design fixation based on body language was proposed. The first-step recognition model of body language using transfer learning based on a pretrained VGG-16 convolutional neural network was constructed. The average recognition rate achieved by the VGG-16 model was 92.03%. Then, the frames of recognized body language were used as input vectors to the second-step fixation classification model based on support vector machine (SVM). The average recognition rate for the fixation state achieved by the SVM model was 79.11%. The impact of the work could be that the fixation can be detected not only by the sketch outcomes but also by monitoring the movements, expressions, and gestures of designers, as it is happening by monitoring the movements, expressions, and gestures of designers.

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