Corpus-based extraction of F0 contour generation process model parameters

2005 ◽  
Author(s):  
Keikichi Hirose ◽  
Yusuke Furuyama ◽  
Nobuaki Minematsu
Keyword(s):  
Process Model ◽  
Model Parameters ◽  
Generation Process ◽  
F0 Contour

scholarly journals Characterization of Moisture Diffusion in Cured Concrete Slabs at Early Ages

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Zhang ◽  
Hongduo Zhao
Keyword(s):  
Process Model ◽  
Model Simulation ◽  
Moisture Diffusion ◽  
Experimental Results ◽  
Concrete Slabs ◽  
Model Parameters ◽  
Early Age ◽  
Water Curing ◽  
Early Age Concrete

The objective of this paper is to investigate the characterization of moisture diffusion inside early-age concrete slabs subjected to curing. Time-dependent relative humidity (RH) distributions of three mixture proportions subjected to three different curing methods (i.e., air curing, water curing, and membrane-forming compounds curing) and sealed condition were measured for 28 days. A one-dimensional nonlinear moisture diffusion partial differential equation (PDE) based on Fick’s second law, which incorporates the effect of curing in the Dirichlet boundary condition using a concept of curing factor, is developed to simulate the diffusion process. Model parameters are calibrated by a genetic algorithm (GA). Experimental results show that the RH reducing rate inside concrete under air curing is greater than the rates under membrane-forming compound curing and water curing. It is shown that the effect of water-to-cement (w/c) ratio on self-desiccation is significant. Lower w/c ratio tends to result in larger RH reduction. RH reduction considering both effect of diffusion and self-desiccation in early-age concrete is not sensitive to w/c ratio, but to curing method. Comparison between model simulation and experimental results indicates that the improved model is able to reflect the effect of curing on moisture diffusion in early-age concrete slabs.

scholarly journals Determination of Combustion Process Model Parameters in Diesel Engine with Variable Compression Ratio

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Saša Milojević ◽  
Radivoje Pešić
Keyword(s):  
Diesel Engine ◽  
Compression Ratio ◽  
Process Model ◽  
Internal Combustion Engines ◽  
Combustion Process ◽  
Exhaust Emission ◽  
Model Parameters ◽  
Connecting Rod ◽  
Variable Compression Ratio ◽  
Variable Compression

Compression ratio has very important influence on fuel economy, emission, and other performances of internal combustion engines. Application of variable compression ratio in diesel engines has a number of benefits, such as limiting maximal in cylinder pressure and extended field of the optimal operating regime to the prime requirements: consumption, power, emission, noise, and multifuel capability. The manuscript presents also the patented mechanism for automatic change engine compression ratio with two-piece connecting rod. Beside experimental research, modeling of combustion process of diesel engine with direct injection has been performed. The basic problem, selection of the parameters in double Vibe function used for modeling the diesel engine combustion process, also performed for different compression ratio values. The optimal compression ratio value was defined regarding minimal fuel consumption and exhaust emission. For this purpose the test bench in the Laboratory for Engines of the Faculty of Engineering, University of Kragujevac, is brought into operation.

scholarly journals Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1798
Author(s):  
Stephen Dankwa ◽  
Lu Yang
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Process Model ◽  
Good Practice ◽  
Knowledge Exchange ◽  
Turing Test ◽  
The Internet ◽  
Generation Process ◽  
Adversarial Attack ◽  
Mixed Batch

The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.

Model Calibration for the High-Purity Oxygen Activated Sludge Process – Algorithm Development and Evaluation

1993 ◽  
Vol 28 (11-12) ◽  
pp. 163-171 ◽  
Author(s):  
Weibo (Weber) Yuan ◽  
David Okrent ◽  
Michael K. Stenstrom
Keyword(s):  
Activated Sludge ◽  
High Purity ◽  
Model Calibration ◽  
Process Model ◽  
Model Parameters ◽  
Complex Method ◽  
Activated Sludge Process ◽  
Activated Sludge Model ◽  
Kinetic Coefficients ◽  
Calibration Algorithm

A model calibration algorithm is developed for the high-purity oxygen activated sludge process (HPO-ASP). The algorithm is evaluated under different conditions to determine the effect of the following factors on the performance of the algorithm: data quality, number of observations, and number of parameters to be estimated. The process model used in this investigation is the first HPO-ASP model based upon the IAWQ (formerly IAWPRC) Activated Sludge Model No. 1. The objective function is formulated as a relative least-squares function and the non-linear, constrained minimization problem is solved by the Complex method. The stoichiometric and kinetic coefficients of the IAWQ activated sludge model are the parameters focused on in this investigation. Observations used are generated numerically but are made close to the observations from a full-scale high-purity oxygen treatment plant. The calibration algorithm is capable of correctly estimating model parameters even if the observations are severely noise-corrupted. The accuracy of estimation deteriorates gradually with the increase of observation errors. The accuracy of calibration improves when the number of observations (n) increases, but the improvement becomes insignificant when n>96. It is also found that there exists an optimal number of parameters that can be rigorously estimated from a given set of information/data. A sensitivity analysis is conducted to determine what parameters to estimate and to evaluate the potential benefits resulted from collecting additional measurements.

Knowledge Generation in Mid Size Entrepreneurial Firms

2005 ◽  
Vol 9 (4) ◽  
pp. 51-63 ◽  
Author(s):  
Anurag Mishra ◽  
M. Akbar
Keyword(s):  
Process Model ◽  
Case Analysis ◽  
Knowledge Generation ◽  
Generic Model ◽  
Entrepreneurial Firms ◽  
Generation Process ◽  
Knowledge Assets ◽  
Developed Markets ◽  
Knowledge Based ◽  
Line Diagram

Literature on medium sized enterprises (MSEs) is limited both in developed markets and emerging markets. This paper addresses this gap and explores MSEs from a knowledge-based perspective. Grounded in the case based research often MSEs, the paper identifies the knowledge assets employed by highly successful firms. The paper performs a detailed case analysis of three such firms from our sample. We trace the knowledge generation process through a detailed line diagram and based on the case analysis, build a generic model for analyzing the knowledge conversion process in MSEs. The contribution of this work is articulated in the process model that integrates the various classes of knowledge assets in the context of transitional firms in India. The paper also develops a few empirically testable propositions, filling a major gap in existing literature on knowledge management.

Numerical and Experimental Analysis of Self Piercing Riveting Process with Carbon Fiber-Reinforced Plastic and Aluminium Sheets

2013 ◽  
Vol 554-557 ◽  
pp. 1045-1054 ◽  
Author(s):  
Welf Guntram Drossel ◽  
Reinhard Mauermann ◽  
Raik Grützner ◽  
Danilo Mattheß
Keyword(s):  
Finite Element ◽  
Carbon Fiber ◽  
Finite Element Model ◽  
Simulation Model ◽  
Process Parameters ◽  
Process Model ◽  
Element Model ◽  
Model Parameters ◽  
Fiber Reinforced ◽  
Carbon Fiber Reinforced

In this study a numerical simulation model was designed for representing the joining process of carbon fiber-reinforced plastics (CFRP) and aluminum alloy with semi-tubular self-piercing rivet. The first step towards this goal is to analyze the piercing process of CFRP numerical and experimental. Thereby the essential process parameters, tool geometries and material characteristics are determined and in finite element model represented. Subsequently the finite element model will be verified and calibrated by experimental studies. The next step is the integration of the calibrated model parameters from the piercing process in the extensive simulation model of self-piercing rivet process. The comparison between the measured and computed values, e.g. process parameters and the geometrical connection characteristics, shows the reached quality of the process model. The presented method provides an experimental reliable characterization of the damage of the composite material and an evaluation of the connection performances, regarding the anisotropic property of CFRP.

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