scholarly journals Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

2009 ◽  
Vol 27 (No. 6) ◽  
pp. 393-402 ◽  
Author(s):  
H. Lin ◽  
J. Zhao ◽  
Q. Chen ◽  
J. Cai ◽  
P. Zhou
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Crack Detection ◽  
Support Vector Machine Model ◽  
Acoustic Resonance ◽  
Support Vector ◽  
Machine Model ◽  
Nearest Neighbours

A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by recursive least squares adaptive filter, which resulted in the signal-to-noise ratio of the acoustic impulse response reing remarkably enhanced. Five features variables were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison to k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.

Research on Maize Varieties Recognition System Based on Image Processing

2013 ◽  
Vol 397-400 ◽  
pp. 2335-2339
Author(s):  
Li Miao Deng ◽  
Tao Luan ◽  
Wen Jie Ma
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Recognition System ◽  
Support Vector ◽  
Test Accuracy ◽  
Machine Model ◽  
Maize Varieties ◽  
Texture Characteristics

In order to realize highly intelligent and automatic species identification and recognition, we obtained the images of 11 varieties and each variety includes 50 seeds. For each image, we acquired 33 characteristics including shape, color and texture characteristics. And then we constructed the Artificial Neural Network and Support Vector Machine model to train and identify different varieties. We built the recognition system based on Visual C++ 6.0 and OpenCV library.Results shows that the SVM method has higher recognition effect than neural network overall and the recognition effect is more stability, the overall self-_recognition performance can reach 100% and test accuracy can reach 85%. The recoginition System base on Visual C++ runs faster than that of Matlab, which is more suitable for real-time varieties identification.

scholarly journals SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot

2021 ◽  
Vol 11 (12) ◽  
pp. 5573
Author(s):  
Zeyu Yin ◽  
Jianbin Zheng ◽  
Liping Huang ◽  
Yifan Gao ◽  
Huihui Peng ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Simulated Annealing ◽  
Support Vector Machine Model ◽  
The State ◽  
Industrial Robots ◽  
Support Vector ◽  
Machine Model ◽  
Exoskeleton Robot ◽  
Locomotion Pattern

An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% ± 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots.

scholarly journals Implementation Model Linear Regression, Neural Network and Support Vector Machine in Simple Mathematical Pendulum Experiments to Determine the Value of Gravity

2019 ◽  
Vol 8 (2S7) ◽  
pp. 268-272
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Standard Deviation ◽  
Linear Regression ◽  
Support Vector Machine Model ◽  
Training Data ◽  
Support Vector ◽  
Mathematical Pendulum ◽  
Machine Model ◽  
Implementation Model

machine in mathematical pendulum experiments to find the value of gravity. There were 4 data obtained from mathematical pendulum experiments which were then interpolated to obtain more data (13 data), then the data was used as training data for each model. Each model is tested to get a gravity value of 26 including training data, then compared with reference gravity values [17,18,19]. The results of the model Neural network proved to be the most accurate with an error value of 2.53%. The support vector machine model is the most accurate model with a standard deviation value of 0.03 and the error deviation of 0.058 is the smallest value of the three models in this paper.

Deformation Prediction of Deep Excavation Using Support Vector Machine

2012 ◽  
Vol 157-158 ◽  
pp. 66-69 ◽  
Author(s):  
Xu Chao Shi ◽  
Wu Xin Chen ◽  
Xiu Juan Lv
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Prediction Problem ◽  
Deep Excavation ◽  
Comparison Analysis ◽  
Recognition Method ◽  
Machine Model ◽  
Deformation Prediction

Support Vector Machine (SVM) is a new pattern recognition method developed in recent years on the foundation of statistical learning theory. It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. Due to the complexity of the deep excavation, deformation prediction problem has not been a good solution. In the paper the support vector machine model was proposed to predict the deep excavation deformation. On the basis of deep excavation displacement data measured with real time series, the model of deep excavation displacement with time was built by SVM. Typical deformation data of deep excavation is used as learning and test samples. Comparison analysis is made between calculated values generated by SVM method and observed values. The result shows this method is feasible and effective.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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