On-Line Fan Monitoring System Based on Improved Intelligent Regression Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 1874-1879 ◽  
Author(s):  
Xiao Gang Xu ◽  
Song Ling Wang ◽  
Jin Lian Liu ◽  
Fei Li ◽  
Hui Jie Wang
Keyword(s):  
Monitoring System ◽  
Flow Curve ◽  
Differential Pressure ◽  
Support Vector ◽  
Pressure Flow ◽  
Svm Model ◽  
Curve Model ◽  
On Line ◽  
Matlab Programming ◽  
Fitting Error

The running state of the fan has significant influence on the safety and economy of the power plant unit, so it is necessary to monitor the fan performance and running state in real time. According to the basic theory of the fan, there is a stable, good nonlinear mapping relation between the inlet pressure difference and flow, which can be utilized to monitor the flow of the fan. Thus, the fan differential pressure - flow curve model is established by the optimized BP neural network and the modified Support Vector Machine (SVM). The fitting error shows that the improved SVM model is better. Finally, the on-line fan monitoring system software is established by using Visual Basic (VB) language and Matlab programming based on the improved SVM fan differential pressure - flow curve model, which can accurately monitor the fan operation.

Application of the GRA and SVM for Forecast of China Grain Production

2012 ◽  
Vol 433-440 ◽  
pp. 1106-1111
Author(s):  
Jin Fa Shi ◽  
He Jun Jiao
Keyword(s):  
Support Vector Machine Model ◽  
Production Control ◽  
Food System ◽  
Support Vector ◽  
Production Data ◽  
Grain Production ◽  
Machine Model ◽  
Factors Influencing ◽  
Svm Model ◽  
On Line

This paper proposes a new method: GRA-SVM model which is composed of GRA and SVM to predict grain production through annual production data. In view of the fact that the complexity and incomplete information of grain production system, the primary factors influencing the grain production is decided on the basis of the grey ralational analysis of the grain producing system, then, the grey ralational analysis and support vector machine model is established by the principle of the support vection machine regression. The application case proved that the proposed method can improve the feasibility of the program in grain production, and it is suitable for on-line grain production control for food system.

scholarly journals Error Compensation Technique for a Resistance-Type Differential Pressure Flow Sensor

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Guimei Wang ◽  
Tao Chu ◽  
Lijie Yang ◽  
Fang Sun
Keyword(s):  
Least Squares ◽  
Error Compensation ◽  
Differential Pressure ◽  
Flow Sensor ◽  
Support Vector ◽  
Model Parameters ◽  
Filling Material ◽  
Pressure Flow ◽  
Compensation Technique ◽  
Fourier Series Approximation

A flow sensor is designed based on resistance-type differential pressure flow (RDPF) method, and the flow data is measured during a coal gangue paste-filling process. The measurement error characteristics of a RDPF sensor are analyzed. Periodic and aperiodic errors are then modeled separately. The model for the periodic error is established by Fourier series approximation using least squares solution of an overdetermined equation to solve for the model parameters. The model for the aperiodic error is established using an online least squares support vector machine (LS-SVM) method. The cross-validation is used to solve model parameters. Simulations and experiments show that the dynamic measurement accuracy of the sensor is greatly improved by error compensation, thereby reducing filling material waste and improving the economic efficiency.

scholarly journals NEW CALCULATING METHOD OF NASAL RESISTANCE APPLICABLE TO ANY PREDETERMINED POINTS ON DIFFERENTIAL PRESSURE/FLOW CURVE

1999 ◽  
Vol 38 (4) ◽  
pp. 422-426
Author(s):  
Toshiko Mamiya ◽  
Kensei Naito ◽  
Yuka Kondo ◽  
Sho Miyata ◽  
Tatsuyoshi Okada ◽  
...  
Keyword(s):  
Flow Curve ◽  
Differential Pressure ◽  
Nasal Resistance ◽  
Pressure Flow ◽  
Calculating Method

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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