The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Siqi Shi - One of the best experts on this subject based on the ideXlab platform.
-
predicting the Onset Temperature tg of gexse1 x glass transition a feature selection based two stage support vector regression method
Chinese Science Bulletin, 2019Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the Onset Temperature (Tg) of GexSe1−x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1−x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1−x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.
-
Predicting the Onset Temperature (Tg) of GexSe1−x glass transition: a feature selection based two-stage support vector regression method
Science Bulletin, 2019Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the Onset Temperature (Tg) of GexSe1−x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1−x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1−x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.
-
the Onset Temperature tg of asxse1 x glasses transition prediction a comparison of topological and regression analysis methods
Computational Materials Science, 2017Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract AsxSe1−x glasses are promising candidates as matrix for mid-infrared applications, but it is usually invasive, costly and time-consuming or even impossible to measure the Onset Temperature (Tg) of glass transition of each composition in the system for glass preparation and fiber processing by experimental methods. In this paper, topological and regression analysis (ridge regression, support vector regression and back-propagation neural network) methods are used to predict the Tg of AsxSe1−x glass system and compared with each other. The topological method predicts the Tg of AsxSe1−x glass system by composition dependence of quantitative structure, although its calculation range is limited in the composition range of 0 ≤ x ≤ 0.5 due to no enough knowledge of quantitative structures and their variation. In contrast, regression analysis methods can model the relationships between physical attributes and Tg without complex domain knowledge, thus extending the calculation range to x = 0.6 and achieving much higher prediction accuracy. Among them, back-propagation neural network achieves the highest prediction accuracy with an RMSE of 1.21 K (7.87 K) and MAPE of 0.33% (1.96%) for training data (testing data). Significantly, a three-attribute correlation equation based on ridge regression is obtained, possessing much higher prediction accuracy than that of the topological method.
-
The Onset Temperature (Tg) of AsxSe1−x glasses transition prediction: A comparison of topological and regression analysis methods
Computational Materials Science, 2017Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract AsxSe1−x glasses are promising candidates as matrix for mid-infrared applications, but it is usually invasive, costly and time-consuming or even impossible to measure the Onset Temperature (Tg) of glass transition of each composition in the system for glass preparation and fiber processing by experimental methods. In this paper, topological and regression analysis (ridge regression, support vector regression and back-propagation neural network) methods are used to predict the Tg of AsxSe1−x glass system and compared with each other. The topological method predicts the Tg of AsxSe1−x glass system by composition dependence of quantitative structure, although its calculation range is limited in the composition range of 0 ≤ x ≤ 0.5 due to no enough knowledge of quantitative structures and their variation. In contrast, regression analysis methods can model the relationships between physical attributes and Tg without complex domain knowledge, thus extending the calculation range to x = 0.6 and achieving much higher prediction accuracy. Among them, back-propagation neural network achieves the highest prediction accuracy with an RMSE of 1.21 K (7.87 K) and MAPE of 0.33% (1.96%) for training data (testing data). Significantly, a three-attribute correlation equation based on ridge regression is obtained, possessing much higher prediction accuracy than that of the topological method.
David R Reichman - One of the best experts on this subject based on the ideXlab platform.
-
Mean-field theory, mode-coupling theory, and the Onset Temperature in supercooled liquids.
Physical review. E Statistical nonlinear and soft matter physics, 2004Co-Authors: Yisroel Brumer, David R ReichmanAbstract:We consider the relationship between the Temperature at which averaged energy landscape properties change sharply (T(o)) and the breakdown of mean-field treatments of the dynamics of supercooled liquids. First, we show that the solution of the wave vector dependent mode-coupling equations undergoes an ergodic-nonergodic transition consistently close to T(o). Generalizing the landscape concept to include hard-sphere systems, we show that the property of inherent structures that changes near T(o) is governed more fundamentally by packing and free volume than potential energy. Lastly, we study the finite-size random orthogonal model (ROM), and show that the Onset of noticeable corrections to mean-field behavior occurs at T(o). These results highlight connections between the energy landscape and mode-coupling approach to supercooled liquids, and identify which features of the relaxation of supercooled liquids are properly captured by mode-coupling theory.
-
Mean-field theory, mode-coupling theory, and the Onset Temperature in supercooled liquids.
Physical Review E, 2004Co-Authors: Yisroel Brumer, David R ReichmanAbstract:We consider the relationship between the Temperature at which averaged energy landscape properties change sharply ($T_{o}$), and the breakdown of mean-field treatments of the dynamics of supercooled liquids. First, we show that the solution of the wavevector dependent mode-coupling equations undergoes an ergodic-nonergodic transition consistently close to $T_{o}$. Generalizing the landscape concept to include hard-sphere systems, we show that the property of inherent structures that changes near $T_{o}$ is governed more fundamentally by packing and free volume than potential energy. Lastly, we study the finite-size Random Orthogonal Model (ROM), and show that the Onset of noticeable corrections to mean-field behavior occurs at $T_{o}$. These results highlight new connections between the energy landscape and mode-coupling approach to supercooled liquids, and identify what features of the relaxation of supercooled liquids are properly captured by mode-coupling theory.Comment: 4 pages and 3 figure
Yue Liu - One of the best experts on this subject based on the ideXlab platform.
-
predicting the Onset Temperature tg of gexse1 x glass transition a feature selection based two stage support vector regression method
Chinese Science Bulletin, 2019Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the Onset Temperature (Tg) of GexSe1−x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1−x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1−x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.
-
Predicting the Onset Temperature (Tg) of GexSe1−x glass transition: a feature selection based two-stage support vector regression method
Science Bulletin, 2019Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the Onset Temperature (Tg) of GexSe1−x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1−x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1−x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.
-
the Onset Temperature tg of asxse1 x glasses transition prediction a comparison of topological and regression analysis methods
Computational Materials Science, 2017Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract AsxSe1−x glasses are promising candidates as matrix for mid-infrared applications, but it is usually invasive, costly and time-consuming or even impossible to measure the Onset Temperature (Tg) of glass transition of each composition in the system for glass preparation and fiber processing by experimental methods. In this paper, topological and regression analysis (ridge regression, support vector regression and back-propagation neural network) methods are used to predict the Tg of AsxSe1−x glass system and compared with each other. The topological method predicts the Tg of AsxSe1−x glass system by composition dependence of quantitative structure, although its calculation range is limited in the composition range of 0 ≤ x ≤ 0.5 due to no enough knowledge of quantitative structures and their variation. In contrast, regression analysis methods can model the relationships between physical attributes and Tg without complex domain knowledge, thus extending the calculation range to x = 0.6 and achieving much higher prediction accuracy. Among them, back-propagation neural network achieves the highest prediction accuracy with an RMSE of 1.21 K (7.87 K) and MAPE of 0.33% (1.96%) for training data (testing data). Significantly, a three-attribute correlation equation based on ridge regression is obtained, possessing much higher prediction accuracy than that of the topological method.
-
The Onset Temperature (Tg) of AsxSe1−x glasses transition prediction: A comparison of topological and regression analysis methods
Computational Materials Science, 2017Co-Authors: Yue Liu, Tianlu Zhao, Guang Yang, Siqi ShiAbstract:Abstract AsxSe1−x glasses are promising candidates as matrix for mid-infrared applications, but it is usually invasive, costly and time-consuming or even impossible to measure the Onset Temperature (Tg) of glass transition of each composition in the system for glass preparation and fiber processing by experimental methods. In this paper, topological and regression analysis (ridge regression, support vector regression and back-propagation neural network) methods are used to predict the Tg of AsxSe1−x glass system and compared with each other. The topological method predicts the Tg of AsxSe1−x glass system by composition dependence of quantitative structure, although its calculation range is limited in the composition range of 0 ≤ x ≤ 0.5 due to no enough knowledge of quantitative structures and their variation. In contrast, regression analysis methods can model the relationships between physical attributes and Tg without complex domain knowledge, thus extending the calculation range to x = 0.6 and achieving much higher prediction accuracy. Among them, back-propagation neural network achieves the highest prediction accuracy with an RMSE of 1.21 K (7.87 K) and MAPE of 0.33% (1.96%) for training data (testing data). Significantly, a three-attribute correlation equation based on ridge regression is obtained, possessing much higher prediction accuracy than that of the topological method.
Tao Jin - One of the best experts on this subject based on the ideXlab platform.
-
Numerical and experimental study of a two-phase thermofluidic oscillator with regenerator achieving low Temperature-differential oscillation
Applied Thermal Engineering, 2020Co-Authors: Jingqi Tan, Jiaqi Luo, Jianjian Wei, Tao JinAbstract:Abstract In this work, a two-phase thermofluidic oscillator with regenerator is numerically and experimentally investigated, focusing on the mechanism of regenerator in lowering the Onset Temperature. A modified acoustic-electric analogy model, considering the thermophysical properties of regenerator, is developed to predict the Onset Temperature difference and resonant frequency at Onset point, which is then verified by experiments. The influences of the material and type of regenerator on the Onset Temperature difference, resonant frequency and pressure amplitude have been investigated. By inserting the regenerator packed with copper mesh screens, the Onset Temperature difference is decreased from 35.1 °C to 7.0 °C, which is lowest ever reported in the literatures. Additionally, the pressure amplitude is increased from 4.5 kPa to 10.7 kPa at a hot Temperature of 46 °C. The merits of simple construction, free maintenance and low Temperature-differential Onset enable this updated two-phase thermofluidic oscillator suitable for low-grade heat harvesting.
-
Thermodynamic characteristics during the Onset and damping processes in a looped thermoacoustic prime mover
Applied Thermal Engineering, 2016Co-Authors: Tao Jin, Rui Yang, Yuanliang Liu, Ke TangAbstract:Abstract The Onset and damping processes of self-excited working-fluid oscillation in thermoacoustic prime movers, related to the quality of the utilizable thermal energy, have attracted everlasting research interest and effort. This work is intended to observe and analyze the Temperature and pressure characteristics of the Onset and damping processes in a recently developed looped travelling-wave thermoacoustic prime mover with enlarged thermoacoustic cores. A hysteretic loop, caused by the discrepancy between the Onset Temperature difference and the damping Temperature difference, can be found in the looped structure, which indicates the potential in decreasing the Onset Temperature difference. Additionally, in order to optimize the acoustic field in the thermoacoustic prime mover, a compliance unit was inserted into the loop. The experimental results show that the Onset Temperature difference drops by up to 223 K with the appropriately located compliance unit, which is advantageous to the utilization of low-grade thermal energy.
-
Basic analysis on a thermoacoustic engine with gas and liquid
Journal of Applied Physics, 2011Co-Authors: Ke Tang, Tian Lei, A. T. A. M De Waele, Tao JinAbstract:This paper analyzes the basics of a thermoacoustic engine with gas and liquid as working fluids. The governing equations for the engine are deduced from the dynamics of each individual component. From the governing equations, analytical expressions are obtained for oscillation frequency and Onset Temperature. The relations for the dependence of the displacement amplitude of liquid column, the velocity amplitude at the end of resonant tube, the pressure amplitude gradient and the enthalpy flow in stack, on the pressure amplitude in resonant tube are formulated. The calculation with the deduced formulae shows that an oscillation frequency below 10 Hz can be achieved in the thermoacoustic engine with gas and liquid. Meanwhile, a lower oscillation frequency, as well as a lower Onset Temperature, requires a larger liquid mass and a lower mean pressure. Experiments, focusing on the oscillation frequency and the Onset Temperature, have been performed to validate the computation.
Ke Tang - One of the best experts on this subject based on the ideXlab platform.
-
Thermodynamic characteristics during the Onset and damping processes in a looped thermoacoustic prime mover
Applied Thermal Engineering, 2016Co-Authors: Tao Jin, Rui Yang, Yuanliang Liu, Ke TangAbstract:Abstract The Onset and damping processes of self-excited working-fluid oscillation in thermoacoustic prime movers, related to the quality of the utilizable thermal energy, have attracted everlasting research interest and effort. This work is intended to observe and analyze the Temperature and pressure characteristics of the Onset and damping processes in a recently developed looped travelling-wave thermoacoustic prime mover with enlarged thermoacoustic cores. A hysteretic loop, caused by the discrepancy between the Onset Temperature difference and the damping Temperature difference, can be found in the looped structure, which indicates the potential in decreasing the Onset Temperature difference. Additionally, in order to optimize the acoustic field in the thermoacoustic prime mover, a compliance unit was inserted into the loop. The experimental results show that the Onset Temperature difference drops by up to 223 K with the appropriately located compliance unit, which is advantageous to the utilization of low-grade thermal energy.
-
Basic analysis on a thermoacoustic engine with gas and liquid
Journal of Applied Physics, 2011Co-Authors: Ke Tang, Tian Lei, A. T. A. M De Waele, Tao JinAbstract:This paper analyzes the basics of a thermoacoustic engine with gas and liquid as working fluids. The governing equations for the engine are deduced from the dynamics of each individual component. From the governing equations, analytical expressions are obtained for oscillation frequency and Onset Temperature. The relations for the dependence of the displacement amplitude of liquid column, the velocity amplitude at the end of resonant tube, the pressure amplitude gradient and the enthalpy flow in stack, on the pressure amplitude in resonant tube are formulated. The calculation with the deduced formulae shows that an oscillation frequency below 10 Hz can be achieved in the thermoacoustic engine with gas and liquid. Meanwhile, a lower oscillation frequency, as well as a lower Onset Temperature, requires a larger liquid mass and a lower mean pressure. Experiments, focusing on the oscillation frequency and the Onset Temperature, have been performed to validate the computation.
-
A 115 K thermoacoustically driven pulse tube refrigerator with low Onset Temperature
Cryogenics, 2004Co-Authors: Ke Tang, Guobang Chen, Bo KongAbstract:After the modifications of jacket type water coolers and stacks, and the optimizations of the openings of orifice and double inlet valves, a refrigeration Temperature as low as 115.4 K has been achieved by a thermoacoustically driven pulse tube refrigerator. By operating the double inlet valve of the pulse tube refrigerator, the Onset Temperature of the thermoacoustic system decreases from 550 to 340 °C. It provides the possibility of utilizing the low-grade heat energy.
