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The First Potential Energy Surfaces For The C₆Hˉ-H₂ And C₆Hˉ-He Collisional Systems And Their Corresponding Inelastic Cross Sections, Kyle M. Walker, Fabien Dumouchel, François Lique, Richard Dawes
The First Potential Energy Surfaces For The C₆Hˉ-H₂ And C₆Hˉ-He Collisional Systems And Their Corresponding Inelastic Cross Sections, Kyle M. Walker, Fabien Dumouchel, François Lique, Richard Dawes
Chemistry Faculty Research & Creative Works
Molecular anions have recently been detected in the interstellar and circumstellar media. Accurate modeling of their abundance requires calculations of collisional data with the most abundant species that are usually He atoms and H2 molecules. In this paper, we focus on the collisional excitation of the first observed molecular anion, C6H-, by He and H2. Theoretical calculations of collisional cross sections rely generally on ab initio interaction potential energy surfaces (PESs). Hence, we present here the first PESs for the C6H--H2 and C6H--He van …
Parameter Estimates For A Pemfc Cathode, Qingzhi Guo, Vijay A. Sethuraman, Ralph E. White
Parameter Estimates For A Pemfc Cathode, Qingzhi Guo, Vijay A. Sethuraman, Ralph E. White
Ralph E. White
Five parameters of a model of a polymer electrolyte membrane fuel cell (PEMFC) cathode (the volume fraction of gas pores in the gas diffusion layer, the volume fraction of gas pores in the catalyst layer, the exchange current density of the oxygen reduction reaction, the effective ionic conductivity of the electrolyte, and the ratio of the effective diffusion coefficient of oxygen in a flooded spherical agglomerate particle to the square of that particle radius) were determined by least-squares fitting of experimental polarization curves. The values of parameters obtained in this work indicate that ionic conduction and gas-phase transport are two …
Constant-Stress Accelerated Life Test Of White Organic Light-Emitting Diode Based On Least Square Method Under Weibull Distribution, J Zhang, C Liu, G Cheng, X Chen, J Wu, Q Zhu, Laichang Zhang
Constant-Stress Accelerated Life Test Of White Organic Light-Emitting Diode Based On Least Square Method Under Weibull Distribution, J Zhang, C Liu, G Cheng, X Chen, J Wu, Q Zhu, Laichang Zhang
Research outputs 2014 to 2021
It is currently hard to estimate the reliability parameters of organic light-emitting diodes (OLEDs) when conducting a life test at normal stress, due to the remarkably improved life of OLEDs to thousands hours. This work adopted three constant-stress accelerated life tests (CSALTs) to predict the life of white OLEDs in a short time. The Weibull function was applied to describe the life distribution, and the shape and scale parameters were estimated using the least square method. The experimental test data were statistically analyzed using a self-developed software. The life of white OLEDs predicted via this software is in good agreement …
Mmse-Optimal Approximation Of Continuous-Phase Modulated Signal As Superposition Of Linearly Modulated Pulses, Xiaojing Huang, Y. Li
Mmse-Optimal Approximation Of Continuous-Phase Modulated Signal As Superposition Of Linearly Modulated Pulses, Xiaojing Huang, Y. Li
Faculty of Informatics - Papers (Archive)
The optimal linear modulation approximation of any M-ary continuous-phase modulated (CPM) signal under the minimum mean-square error (MMSE) criterion is presented in this paper. With the introduction of the MMSE signal component, an M-ary CPM signal is exactly represented as the superposition of a finite number of MMSE incremental pulses, resulting in the novel switched linear modulation CPM signal models. Then, the MMSE incremental pulse is further decomposed into a finite number of MMSE pulse-amplitude modulated (PAM) pulses, so that an M-ary CPM signal is alternatively expressed as the superposition of a finite number of MMSE PAM components, similar to …
Efficient Training Algorithms For A Class Of Shunting Inhibitory Convolutional Neural Networks, Fok Hing Chi Tivive, Abdesselam Bouzerdoum
Efficient Training Algorithms For A Class Of Shunting Inhibitory Convolutional Neural Networks, Fok Hing Chi Tivive, Abdesselam Bouzerdoum
Faculty of Informatics - Papers (Archive)
This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the …
Parameter Estimates For A Pemfc Cathode, Qingzhi Guo, Vijay A. Sethuraman, Ralph E. White
Parameter Estimates For A Pemfc Cathode, Qingzhi Guo, Vijay A. Sethuraman, Ralph E. White
Faculty Publications
Five parameters of a model of a polymer electrolyte membrane fuel cell (PEMFC) cathode (the volume fraction of gas pores in the gas diffusion layer, the volume fraction of gas pores in the catalyst layer, the exchange current density of the oxygen reduction reaction, the effective ionic conductivity of the electrolyte, and the ratio of the effective diffusion coefficient of oxygen in a flooded spherical agglomerate particle to the square of that particle radius) were determined by least-squares fitting of experimental polarization curves. The values of parameters obtained in this work indicate that ionic conduction and gas-phase transport are two …
Robust Partial Least-Squares Regression: A Modular Neural Network Approach, Thomas M. Mcdowall, Fredric M. Ham
Robust Partial Least-Squares Regression: A Modular Neural Network Approach, Thomas M. Mcdowall, Fredric M. Ham
Electrical Engineering and Computer Science Faculty Publications
We have developed a robust Partial Least-Squares Regression (PLSR) neural network approach to statistical calibration model development. Generalized neural network learning rules derived from a weighted statistical representation error criterion that grows less than quadratically are presented. This optimization criterion allows for higher-order statistics associated with the inputs to be taken into account and also serves to robustify the results when the empirical data contains impulsive and colored noise and outliers. The learning rules presented are considered generalized because they can be used to implement several specialized cases including: robust PLSR, linear PLSR, weighted least-squares, and variance scaling. The same …
A Recursive Least Squares Training Algorithm For Multilayer Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
A Recursive Least Squares Training Algorithm For Multilayer Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Recurrent neural networks have the potential to perform significantly better than the commonly used feedforward neural networks due to their dynamical nature. However, they have received less attention because training algorithms/architectures have not been well developed. In this study, a recursive least squares algorithm to train recurrent neural networks with an arbitrary number of hidden layers is developed. The training algorithm is developed as an extension of the standard recursive estimation problem. Simulated results obtained for identification of the dynamics of a nonlinear dynamical system show promising results.
Identification Of Cutting Force In End Milling Operations Using Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
Identification Of Cutting Force In End Milling Operations Using Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The problem of identifying the cutting force in end milling operations is considered in this study. Recurrent neural networks are used here and are trained using a recursive least squares training algorithm. Training results for data obtained from a SAJO 3-axis vertical milling machine for steady slot cuts are presented. The results show that a recurrent neural network can learn the functional relationship between the feed rate and steady-state average resultant cutting force very well. Furthermore, results for the Mackey-Glass time series prediction problem are presented to illustrate the faster learning capability of the neural network scheme presented here
Robust Linear Quadratic Regulation Using Neural Network, Kisuck Yoo, Michael Thursby
Robust Linear Quadratic Regulation Using Neural Network, Kisuck Yoo, Michael Thursby
Electrical Engineering and Computer Science Faculty Publications
Using an Artificial Neural Network (ANN) trained with the Least Mean Square (LMS) algorithm we have designed a robust linear quadratic regulator for a range of plant uncertainty. Since there is a trade-off between performance and robustness in the conventional design techniques, we propose a design technique to provide the best mix of robustness and performance. Our approach is to provide different control strategies for different levels of uncertainty. We describe how to measure these uncertainties. We will compare our multiple strategies results with those of conventional techniques e.g. H∞ control theory. A Lyapunov equation is used to define stability …