scholarly journals The Object According State Prediction to Diagnostic Data

2021 ◽  
Vol 2096 (1) ◽  
pp. 012121
Author(s):  
L A Baranov ◽  
E P Balakina ◽  
A I Godyaev
Keyword(s):  
Process Model ◽  
Least Squares Method ◽  
Measurement Data ◽  
The State ◽  
Random Component ◽  
Degree Polynomial ◽  
Sampling Step ◽  
Diagnostic Data ◽  
Object Based ◽  
Error Method

Abstract The predicting methodology the state of the object based on diagnostic data is considered. With the selected parameter that determines the state of the object, it is measured in real time at a fixed sampling step. According to the measurement data, the value of this parameter is predicted in the future. This operation is implemented by an extrapolator of the l order - a l degree polynomial, built using the least squares method based on the previous measurements results. The changing process model of the diagnosed parameter is a random time function described by the stationary centered random component sum and a mathematical expectation deterministic change. The estimating prediction error method and the extrapolator parameters influence on its value are presented.

scholarly journals Under the Umbrella: Goal-Derived Category Construction and Product Category Nesting

2021 ◽  
pp. 000183922110123
Author(s):  
Johnny Boghossian ◽  
Robert J. David
Keyword(s):  
Process Model ◽  
Product Category ◽  
Derived Category ◽  
The State ◽  
Product Attributes ◽  
Product Categories ◽  
Role Of The State ◽  
Market Intermediaries ◽  
Category Construction

Categories are organized vertically, with product categories nested under larger umbrella categories. Meaning flows from umbrella categories to the categories beneath them, such that the construction of a new umbrella category can significantly reshape the categorical landscape. This paper explores the construction of a new umbrella category and the nesting beneath it of a product category. Specifically, we study the construction of the Quebec terroir products umbrella category and the nesting of the Quebec artisanal cheese product category under this umbrella. Our analysis shows that the construction of umbrella categories can unfold entirely separately from that of product categories and can follow a distinct categorization process. Whereas the construction of product categories may be led by entrepreneurs who make salient distinctive product attributes, the construction of umbrella categories may be led by “macro actors” removed from the market. We found that these macro actors followed a goal-derived categorization process: they first defined abstract goals and ideals for the umbrella category and only subsequently sought to populate it with product categories. Among the macro actors involved, the state played a central role in defining the meaning of the Quebec terroir category and mobilizing other macro actors into the collective project, a finding that suggests an expanded role of the state in category construction. We also found that market intermediaries are important in the nesting of product categories beneath new umbrella categories, notably by projecting identities onto producers consistent with the goals of the umbrella category. We draw on these findings to develop a process model of umbrella category construction and product category nesting.

scholarly journals ESTIMATING PURITY AND ENTROPY IN STABILIZER STATE EXPERIMENTS

2010 ◽  
Vol 08 (01n02) ◽  
pp. 325-335 ◽  
Author(s):  
HARALD WUNDERLICH ◽  
MARTIN B. PLENIO
Keyword(s):  
Quantum Information ◽  
Measurement Data ◽  
Simulated Data ◽  
The State ◽  
Graph State ◽  
Graph States ◽  
Underlying Graph ◽  
Moderate Number ◽  
Full State ◽  
State Tomography

Many experiments in quantum information aim at creating graph states. Quantifying the purity of an experimentally achieved graph state could in principle be accomplished using full-state tomography. This method requires a number of measurement settings growing exponentially with the number of constituents involved. Thus, full-state tomography becomes experimentally infeasible even for a moderate number of qubits. In this paper, we present a method to estimate the purity of experimentally achieved graph states with simple measurements. The observables we consider are the stabilizers of the underlying graph. Then, we formulate the problem as: "What is the state with the least purity that is compatible with the measurement data?" We solve this problem analytically and compare the obtained bounds with results from full-state tomography for simulated data.

Calibration and Uncertainty Quantification of Gas Turbine Performance Models

2015 ◽  
Author(s):  
Manuel Arias Chao ◽  
Darrel S. Lilley ◽  
Peter Mathé ◽  
Volker Schloßhauer
Keyword(s):  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Bayesian Approach ◽  
Least Squares Method ◽  
Measurement Data ◽  
Operating Conditions ◽  
Performance Models ◽  
Regression Problem ◽  
Calibration Problem ◽  
Calibration Parameters

Calibration and uncertainty quantification for gas turbine (GT) performance models is a key activity for GT manufacturers. The adjustment between the numerical model and measured GT data is obtained with a calibration technique. Since both, the calibration parameters and the measurement data are uncertain the calibration process is intrinsically stochastic. Traditional approaches for calibration of a numerical GT model are deterministic. Therefore, quantification of the remaining uncertainty of the calibrated GT model is not clearly derived. However, there is the business need to provide the probability of the GT performance predictions at tested or untested conditions. Furthermore, a GT performance prediction might be required for a new GT model when no test data for this model are available yet. In this case, quantification of the uncertainty of the baseline GT, upon which the new development is based on, and propagation of the design uncertainty for the new GT is required for risk assessment and decision making reasons. By using as a benchmark a GT model, the calibration problem is discussed and several possible model calibration methodologies are presented. Uncertainty quantification based on both a conventional least squares method and a Bayesian approach will be presented and discussed. For the general nonlinear model a fully Bayesian approach is conducted, and the posterior of the calibration problem is computed based on a Markov Chain Monte Carlo simulation using a Metropolis-Hastings sampling scheme. When considering the calibration parameters dependent on operating conditions, a novel formulation of the GT calibration problem is presented in terms of a Gaussian process regression problem.

scholarly journals First definitive record of a whip scorpion Labochirus tauricornis (Pocock, 1900) from Goa, India: with notes on its morphometry and pedipalp micro-morphology

2018 ◽  
Vol 10 (7) ◽  
pp. 11955
Author(s):  
Manoj Ramakant Borkar
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Sexual Dimorphism ◽  
Diagnostic Value ◽  
The State ◽  
Diagnostic Data ◽  
Micro Morphology ◽  
Scanning Electron

Bastawade & Borkar in 2008 made a passing reference to the presence of a single uropygid species in Goa, though without much primary diagnostic data on the collected specimen of four females.  The present study puts in place a definitive record of the uropygid, Labochirus tauricornis Pocock, (1900) in the state of Goa, and addresses an important gap in our understanding of its occurrence, morphology, and ecology. Besides documenting the species of this cryptozoic, nocturnal arachnid predator commonly known as ‘Vinegaroon’ on account of their vinegary allomone spray; the present study also describes the gross morphology , morphometry and micro-morphology of non-ambulatory sub-raptorial pedipalps which are of taxonomic-diagnostic value, elucidated using scanning electron microscopy, in addition to routine stereomicroscopy. The paper also examines in detail, sexual dimorphism and morphometry of this uropygid species. 

scholarly journals Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4457 ◽  
Author(s):  
Antončič ◽  
Papič ◽  
Blažič
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Low Voltage ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Measurement Data ◽  
Estimation Algorithm ◽  
Distribution Networks ◽  
Voltage Distribution ◽  
State Estimator

This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.

Fabric Simulation Using Measurement Data for Dress Design

2014 ◽  
Author(s):  
Ayumi Hara ◽  
Hideki Aoyama ◽  
Tetsuo Oya
Keyword(s):  
Mechanical Properties ◽  
Evaluation System ◽  
Contact Point ◽  
Measurement Data ◽  
Previous Method ◽  
The State ◽  
Design Element ◽  
Simple Mass ◽  
Mass Spring ◽  
Fashion Designers

The state of wrinkles and folds formed on our dress according to human postures and movements is an important design element. Fashion designers must envisage the fabric state as wrinkling and folding. However, this is not easy because the fabric state strongly depends on the mechanical properties of the fabric, and in this sense, fabric simulation can aid designers in envisaging the fabric state. In previous works on fabric simulation, fabric models are proposed and developed based on the simple mass spring model. Since none of the models proposed so far take into account the state of slipping at the contact point of the warp and weft, simulated results differ from real fabric states. This paper proposes a method to simulate real fabric state taking into consideration slipping. In order to obtain real simulation results, the mechanical properties of fabric obtained by KES: Kawabata Evaluation System [1], were used in the simulation. The effectiveness of the proposed model was confirmed by comparing simulated results obtained by the proposed method with simulated results obtained by a previous method. In addition, it was verified by comparing the simulated results obtained by the proposed method with real cloth states.

Developing an E-Government Training Program

2016 ◽  
Vol 12 (3) ◽  
pp. 37-54 ◽  
Author(s):  
Annie D. A. Abdullah ◽  
Calvin M. L. Chan ◽  
Syamimi Ariff Lim
Keyword(s):  
Education Research ◽  
Training Program ◽  
Process Model ◽  
Training Programs ◽  
Research Question ◽  
The State ◽  
Future Research ◽  
Government Education ◽  
Stage Process

Education and training is recognized to be important to the success of e-government. Nonetheless, research in e-government education has remained at a nascent phase. This paper advances the state of e-government education research through a case study. It answers the research question “How to develop an e-government training program.” Through the adoption of stakeholder theory as the theoretical foundation, and the analysis of the case data, a two-stage process model is developed. This model not only provides a theoretical explanatory basis for the process of developing e-government training programs, but also a practical guide for practitioners in developing such training programs. In addition, it is hoped that this paper will serve as a basis upon which future research can take reference in order to develop a cumulative tradition of employing theoretically-based approach to advance the state of e-government education research.

scholarly journals Generalized Nonlinear Least Squares Method for the Calibration of Complex Computer Code Using a Gaussian Process Surrogate

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 985
Author(s):  
Youngsaeng Lee ◽  
Jeong-Soo Park
Keyword(s):  
Gaussian Process ◽  
Least Squares ◽  
Covariance Matrix ◽  
Process Model ◽  
Least Squares Method ◽  
Nonlinear Least Squares ◽  
Computer Code ◽  
Test Functions ◽  
Unknown Parameters ◽  
Complex Computer

The approximated nonlinear least squares (ALS) method has been used for the estimation of unknown parameters in the complex computer code which is very time-consuming to execute. The ALS calibrates or tunes the computer code by minimizing the squared difference between real observations and computer output using a surrogate such as a Gaussian process model. When the differences (residuals) are correlated or heteroscedastic, the ALS may result in a distorted code tuning with a large variance of estimation. Another potential drawback of the ALS is that it does not take into account the uncertainty in the approximation of the computer model by a surrogate. To address these problems, we propose a generalized ALS (GALS) by constructing the covariance matrix of residuals. The inverse of the covariance matrix is multiplied to the residuals, and it is minimized with respect to the tuning parameters. In addition, we consider an iterative version for the GALS, which is called as the max-minG algorithm. In this algorithm, the parameters are re-estimated and updated by the maximum likelihood estimation and the GALS, by using both computer and experimental data repeatedly until convergence. Moreover, the iteratively re-weighted ALS method (IRWALS) was considered for a comparison purpose. Five test functions in different conditions are examined for a comparative analysis of the four methods. Based on the test function study, we find that both the bias and variance of estimates obtained from the proposed methods (the GALS and the max-minG) are smaller than those from the ALS and the IRWALS methods. Especially, the max-minG works better than others including the GALS for the relatively complex test functions. Lastly, an application to a nuclear fusion simulator is illustrated and it is shown that the abnormal pattern of residuals in the ALS can be resolved by the proposed methods.

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