Application of Support Vector Machine to Predicting Mechanical Properties of TC4

2011 ◽  
Vol 189-193 ◽  
pp. 1854-1857 ◽  
Author(s):  
Zhe Zhe Hou ◽  
Yan Liang Du ◽  
Wei Gang Zhao ◽  
Meng Zhao ◽  
Shuang Chao Peng
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Support Vector Machine ◽  
Mechanical Performance ◽  
Heating Temperature ◽  
Heating Time ◽  
Experimental Results ◽  
Support Vector ◽  
Machine Method ◽  
Tc4 Alloy

On the basis of numerous experimental results the effect of heat treatment on mechanical properties of TC4 alloy is studied. A computer model expressing the relationships between heat treatment and mechanical properties has been established with supported vector machine method. The input parameters were determined by the heating temperature and heating time which are important factors of the mechanical performance, and the output parameters are tensile and yield strength and elongation. The model is established by libsvm with RBF kernel function, e-SVR and proper parameters. Experimental results show that prediction accuracy made by using support vector machine reached over 95%, and the model has good learning precision and generalization and it can be used for predicting the mechanical properties of TC4 alloy.

Design and calibration method for a novel six-component piezoelectric balance

2013 ◽  
Vol 227 (8) ◽  
pp. 1841-1852 ◽  
Author(s):  
Zhenyuan Jia ◽  
Yifei Gao ◽  
Zongjin Ren ◽  
Shengnan Gao ◽  
Yongyan Shang
Keyword(s):  
Support Vector Machine ◽  
Wind Tunnel ◽  
Calibration Method ◽  
Experimental Results ◽  
Step Response ◽  
Small Samples ◽  
Support Vector ◽  
Dynamic Impact ◽  
Machine Method ◽  
Wind Tunnel Balance

Wind tunnel balance is one of the most important measurement equipments in aerodynamic testing. In this paper, a new six-component piezoelectric balance is developed to measure the dynamic impact loading force in the wind tunnel. The arrangement mode of the triaxial piezoelectric load cells is confirmed based on the theory analysis. Furthermore, the mathematical model is established according to the calibration experimental results. Support vector machine is proposed to develop the piezoelectric balance calibration. It is an effective method to predict the model using small samples and reduce the duration of the calibration. The results of prediction are compared to the conventional calibration and the dynamic step response. The linearity and repeatability of the balance are within 0.2% and 0.5%, respectively, and the interference error has been reduced using the support vector machine method. The experimental results have shown that the four supports arrangement mode can reduce the area of attack and enhance the measuring range of the balance. The dynamic characteristics of the piezoelectric balance performed by the step response test show that the designed balance is feasible to measure the dynamic impact airloads in a wind tunnel.

scholarly journals Research on the Physico-Mechanical Properties of Moso Bamboo with Thermal Treatment in Tung Oil and Its Influencing Factors

Materials ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 599 ◽  
Author(s):  
Tong Tang ◽  
Xiufang Chen ◽  
Bo Zhang ◽  
Xianmiao Liu ◽  
Benhua Fei
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Mechanical Performance ◽  
Heating Temperature ◽  
Moso Bamboo ◽  
Modulus Of Rupture ◽  
Chemical Compositions ◽  
Cellulose Content ◽  
Remarkable Change ◽  
Tung Oil

In this study, the effects of tung oil heat treatment on the physico-mechanical properties of moso bamboo were investigated. Here, heat treatment in tung oil at 100–200 °C was used to modify natural bamboo materials. The changes in the nanostructures of cell walls in bamboo caused by oil heat treatment, like density, chemical compositions, and cellulose crystalline, were evaluated to study their correlation with mechanical properties. Results showed that the mechanical performance of bamboo, such as ultimate stress, modulus of elasticity (MOE), and modulus of rupture (MOR), didn’t reduce after heat treatment below 200 °C, compared with the untreated bamboo, which was mainly due to the tung oil uptake, stable cellulose content, and the increment of cellulose crystalline. No remarkable change in the ultimate strain occurred for bamboo materials thermally treated below 140 °C, but it decreased obviously at the heating temperature over 180 °C, mainly due to the degradation of hemicellulose resulting in a decrease in the viscoelasticity of cell wall.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

Detection and Recognition of RF Devices Using Support Vector Machine

2013 ◽  
Vol 5 (4) ◽  
pp. 13-20
Author(s):  
Shikhar P. Acharya ◽  
Ivan G. Guardiola
Keyword(s):  
Support Vector Machine ◽  
Radio Frequency ◽  
Experimental Results ◽  
Support Vector ◽  
Noise Band ◽  
Detection And Identification ◽  
Electromagnetic Emissions ◽  
Rf Devices ◽  
Unintended Electromagnetic Emissions ◽  
Detection And Recognition

Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be used as a signature for the purpose of detection and identification. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band. The research herein provides the application of Support Vector Machine (SVM) for detection and identification of RF devices using their UEEs. Experimental Results shows that SVM can detect RF devices within the noise band, and can also identify RF devices using their UEEs.

Analog Circuits Fault Diagnosis Using Multifractal Analysis

2013 ◽  
Vol 721 ◽  
pp. 367-371
Author(s):  
Yong Kui Sun ◽  
Zhi Bin Yu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Multifractal Analysis ◽  
Multifractal Spectrum ◽  
Experimental Results ◽  
Support Vector ◽  
Diagnosis Accuracy ◽  
Circuit Under Test

Analog circuits fault diagnosis using multifractal analysis is presented in this paper. The faulty response of circuit under test is analyzed by multifratal formalism, and the fault feature consists of multifractal spectrum parameters. Support vector machine is used to identify the faults. Experimental results prove the proposed method is effective and the diagnosis accuracy reaches 98%.

Effects of Heat Treatment on Microstructure Parameters, Mechanical Properties and Cold Resistance of Sparingly Alloyed High-Strength Steel

2021 ◽  
Vol 410 ◽  
pp. 197-202
Author(s):  
Pavel P. Poleckov ◽  
Olga A. Nikitenko ◽  
Alla S. Kuznetsova
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Cold Resistance ◽  
Heating Temperature ◽  
High Strength ◽  
Steel Microstructure ◽  
Treatment Conditions ◽  
Microstructure Parameters ◽  
Temperature Impact ◽  
Low Temperature Impact Toughness

This study considers the influence of various heat treatment conditions on the change of steel microstructure parameters, mechanical properties and cold resistance at a temperature of-60 °C. The common behavior of these properties is considered depending on the heating temperature used for quenching and subsequent tempering. Based on the obtained results, heat treatment conditions are proposed that provide a combination of a guaranteed yield point σ0.2 ≥600 N/mm2 with a low-temperature impact toughness KCV-60 ≥50 J/cm2 and plasticity δ5 ≥17%. The obtained research results are intended for industrial use at the mill "5000" site of MMK PJSC.

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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