scholarly journals On the UPMSat-2 Attitude Determination and Control Subsystem’s magnetometers integration

2021 ◽  
Vol 2090 (1) ◽  
pp. 012099
Author(s):  
Elena Rodríguez-Rojo ◽  
Javier Cubas ◽  
Santiago Pindado
Keyword(s):  
Least Squares ◽  
Attitude Determination ◽  
Ordinary Least Squares ◽  
Mathematical Methods ◽  
Data Set ◽  
Least Squares Fitting ◽  
Earth Magnetic Field ◽  
Fitting Algorithm ◽  
Magnetometer Calibration ◽  
And Control

Abstract In the present work, a method for magnetometer calibration through least squares fitting is presented. This method has been applied over the magnetometer’s data set obtained during the integration tests of the Attitude Determination and Control Subsystem (ADCS) of UPMSat-2. The UPMSat-2 mission is a 50-kg satellite designed and manufactured by the Technical University of Madrid (Universidad Politécnica de Madrid), and finally launched in September 2020. The satellite has three fluxgate magnetometers (one of them experimental) whose calibration is critical to obtain correct measurements to be used by the ADCS. Among several mathematical methods suitable to obtain the calibration parameters, an ordinary least squares fitting algorithm is selected as a first step of the calibration process. The surface estimated is an ellipsoid, surface represented by the magnetometer’s measures of the Earth magnetic field in a point of the space. The calibration elements of the magnetometers are related to the coefficients of the estimated ellipsoid.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

A DETERMINISTIC METHODOLOGY FOR ESTIMATION OF PARAMETERS IN DYNAMIC MARKOV CHAIN MODELS

2011 ◽  
Vol 19 (01) ◽  
pp. 71-100 ◽  
Author(s):  
A. R. ORTIZ ◽  
H. T. BANKS ◽  
C. CASTILLO-CHAVEZ ◽  
G. CHOWELL ◽  
X. WANG
Keyword(s):  
Inverse Problem ◽  
Markov Chain ◽  
Least Squares ◽  
Large Population ◽  
Ordinary Least Squares ◽  
Large Sample Size ◽  
Data Set ◽  
Markov Chain Models ◽  
Sample Paths ◽  
Dynamic Stochastic

A method for estimating parameters in dynamic stochastic (Markov Chain) models based on Kurtz's limit theory coupled with inverse problem methods developed for deterministic dynamical systems is proposed and illustrated in the context of disease dynamics. This methodology relies on finding an approximate large-population behavior of an appropriate scaled stochastic system. The approach leads to a deterministic approximation obtained as solutions of rate equations (ordinary differential equations) in terms of the large sample size average over sample paths or trajectories (limits of pure jump Markov processes). Using the resulting deterministic model, we select parameter subset combinations that can be estimated using an ordinary-least-squares (OLS) or generalized-least-squares (GLS) inverse problem formulation with a given data set. The selection is based on two criteria of the sensitivity matrix: the degree of sensitivity measured in the form of its condition number and the degree of uncertainty measured in the form of its parameter selection score. We illustrate the ideas with a stochastic model for the transmission of vancomycin-resistant enterococcus (VRE) in hospitals and VRE surveillance data from an oncology unit.

scholarly journals Least-Squares Fitting of Multidimensional Spectra to Kubo Lineshape Models

2021 ◽  
Author(s):  
Kevin Robben ◽  
Christopher Cheatum
Keyword(s):  
Least Squares ◽  
Source Code ◽  
Model Fitting ◽  
Scale Invariant ◽  
Linear Absorption ◽  
Time Constants ◽  
Least Squares Fitting ◽  
Propagation Of Uncertainty ◽  
Fitting Algorithm ◽  
Line Slope

We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of dephasing parameters is between 8× to 50× better for nonlinear model fitting compared to the CLS method, which effectively increases data acquisition efficiency by one to two orders of magnitude. Whereas the center-line-slope (CLS) method requires sequential fitting of both the nonlinear and linear spectra, our model fitting algorithm only requires nonlinear spectra, but accurately predicts the linear spectrum. We show an experimental example in which the CLS time constants differ by 60% for independent measurements of the same system, while the Kubo time constants differ by only 10% for model fitting. This suggests that model fitting is a far more robust method of measuring spectral diffusion than the CLS method, which is more susceptible to structured residual signals that are not removable by pure solvent subtraction. Statistical analysis of the CLS method reveals a fundamental oversight in accounting for the propagation of uncertainty by Kubo time constants in the process of fitting to the linear absorption spectrum. A standalone desktop app and source code for the least-squares fitting algorithm are freely available with example lineshape models and data. We have written the MATLAB source code in a generic framework where users may supply custom lineshape models. Using this application, a standard desktop fits a 12-parameter generalized Kubo model to a 106 data-point spectrum in a few minutes.

scholarly journals THE EFFECT OF URBAN GREEN SPACES ON HOUSE PRICES IN WARSAW

2018 ◽  
Vol 22 (5) ◽  
pp. 358-371 ◽  
Author(s):  
Radoslaw Trojanek ◽  
Michal Gluszak ◽  
Justyna Tanas
Keyword(s):  
Least Squares ◽  
Residential Buildings ◽  
House Prices ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Data Set ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Green Area ◽  
The Impact

In the paper, we analysed the impact of proximity to urban green areas on apartment prices in Warsaw. The data-set contained in 43 075 geo-coded apartment transactions for the years 2010 to 2015. In this research, the hedonic method was used in Ordinary Least Squares (OLS), Weighted Least Squares (WLS) and Median Quantile Regression (Median QR) models. We found substantial evidence that proximity to an urban green area is positively linked with apartment prices. On an average presence of a green area within 100 meters from an apartment increases the price of a dwelling by 2,8% to 3,1%. The effect of park/forest proximity on house prices is more significant for newer apartments than those built before 1989. We found that proximity to a park or a forest is particularly important (and has a higher implicit price as a result) in the case of buildings constructed after 1989. The impact of an urban green was particularly high in the case of a post-transformation housing estate. Close vicinity (less than 100 m distance) to an urban green increased the sales prices of apartments in new residential buildings by 8,0–8,6%, depending on a model.

scholarly journals Voluntary appointment of independent directors: evidence from Taiwan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mao-Feng Kao ◽  
Lynn Hodgkinson ◽  
Aziz Jaafar
Keyword(s):  
Corporate Governance ◽  
Least Squares ◽  
Research Work ◽  
Ordinary Least Squares ◽  
Independent Directors ◽  
Parameter Estimates ◽  
Two Stage ◽  
Data Set ◽  
Content Type ◽  
Two Stage Least Squares

Purpose Using a data set of Taiwanese listed firms from 2002 to 2015, this paper aims to examine the determinants to voluntarily appoint independent directors. Design/methodology/approach This study uses panel estimation to exploit both the cross-section and time-series nature of the data. Further, this paper uses Tobit regression, generalized linear model (GLM) in the additional analysis and the two-stage least squares to mitigate for a possible endogeneity issue. Findings The main findings show that Taiwanese firms with large board sizes tend to voluntarily appoint independent directors and firms that already have independent supervisors more willingly to accept additional independent directors onto the board. Furthermore, ownership concentration and institutional ownership are positively associated with the voluntary appointment of independent directors. On the contrary, firms controlled by family members are generally reluctant to voluntarily appoint independent directors. Research limitations/implications The findings are important for managers, shareholders, creditors and policymakers. In particular, when considering the determinants of the voluntary appointment of independent directors, the results indicate that independent supervisors, outside shareholders and institutional investors are significant factors in influencing effective internal and external corporate governance mechanisms. This research work focuses on the voluntary appointment of independent directors. It would be interesting to compare the effectiveness of voluntary appointments with a mandatory appointment within Taiwan and with other jurisdictions. Originality/value This study incrementally contributes to the corporate governance literature in several ways. First, this study extends the earlier research by using a more comprehensive data set of non-financial Taiwanese firms and using alternative methodologies to investigate the determinants of voluntary appointment of independent directors. Second, prior studies tend to neglect the possible issue of using a censored and fractional dependent variable, the proportion of independent directors, which might yield biased and inconsistent parameter estimates when using ordinary least squares regression estimation. Finally, this study addresses the relevant econometric issues by using the Tobit, GLM and the two-stage least squares for a possible endogeneity concern.

Design and Analysis of High-precision Weighted Linear Least-Squares Fitting Algorithm for Fluorescent Optical Fiber Temperature Sensor

2021 ◽  
pp. 2250031
Author(s):  
Jian Yang ◽  
Xiangliang Jin ◽  
Yan Peng ◽  
Jun Luo
Keyword(s):  
Optical Fiber ◽  
Least Squares ◽  
High Precision ◽  
Temperature Sensor ◽  
Heating Temperature ◽  
Experimental Tests ◽  
Linear Least Squares ◽  
Least Squares Fitting ◽  
Fitting Algorithm ◽  
Microwave Hyperthermia

Microwave hyperthermia is a new method of treating cancer, where the therapeutic effect is determined by the heating temperature. Traditional active temperature sensors are interfered by high frequency so that the accuracy of temperature measurement cannot be guaranteed. It is of great significance to study the high-precision fluorescent optical fiber temperature sensor with complete insulation. This paper has realized a compact and practical fluorescent optical fiber temperature sensor after studying the optical path, circuit, data processing algorithm. In order to improve the accuracy of the system, the weighted linear least-squares fitting algorithm is improved in this paper. Through experimental tests, compared with the standard linear least-squares fitting algorithm and the unimproved weighted linear least-squares fitting algorithm, the accuracy of the algorithm is improved by about 98% and 65.5%, respectively. In addition, the response time is reduced by about 36.5%, compared with the unimproved weighted linear least-squares fitting algorithm. This algorithm fully meets the precision requirements of microwave hyperthermia.

Calibration of stormwater quality regression models: a random process?

2010 ◽  
Vol 62 (4) ◽  
pp. 875-882 ◽  
Author(s):  
A. Dembélé ◽  
J.-L. Bertrand-Krajewski ◽  
B. Barillon
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Regression Models ◽  
Linear Models ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Data Sets ◽  
Data Set ◽  
Urban Catchments

Regression models are among the most frequently used models to estimate pollutants event mean concentrations (EMC) in wet weather discharges in urban catchments. Two main questions dealing with the calibration of EMC regression models are investigated: i) the sensitivity of models to the size and the content of data sets used for their calibration, ii) the change of modelling results when models are re-calibrated when data sets grow and change with time when new experimental data are collected. Based on an experimental data set of 64 rain events monitored in a densely urbanised catchment, four TSS EMC regression models (two log-linear and two linear models) with two or three explanatory variables have been derived and analysed. Model calibration with the iterative re-weighted least squares method is less sensitive and leads to more robust results than the ordinary least squares method. Three calibration options have been investigated: two options accounting for the chronological order of the observations, one option using random samples of events from the whole available data set. Results obtained with the best performing non linear model clearly indicate that the model is highly sensitive to the size and the content of the data set used for its calibration.

scholarly journals A Synthesis of the Information Given by Temporal Data Series: The Representative Day

2021 ◽  
Author(s):  
Tiziano Tirabassi ◽  
Daniela Buske
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Least Squares ◽  
Data Series ◽  
Data Sets ◽  
Temporal Data ◽  
Concentration Data ◽  
Data Set ◽  
Meaningful Information ◽  
And Control

The recording of air pollution concentration values involves the measurement of a large volume of data. Generally, automatic selectors and explicators are provided by statistics. The use of the Representative Day allows the compilation of large amounts of data in a compact format that will supply meaningful information on the whole data set. The Representative Day (RD) is a real day that best represents (in the meaning of the least squares technique) the set of daily trends of the considered time series. The Least Representative Day (LRD), on the contrary, it is a real day that worst represents (in the meaning of the least squares technique) the set of daily trends of the same time series. The identification of RD and LRD can prove to be a very important tool for identifying both anomalous and standard behaviors of pollutants within the selected period and establishing measures of prevention, limitation and control. Two application examples, in two different areas, are presented related to meteorological and SO 2 and O 3 concentration data sets.

scholarly journals Decision Makers' Ability To Identify Unusual Costs And Implications For Alternative Estimation Procedures

2011 ◽  
Vol 7 (4) ◽  
pp. 36
Author(s):  
MaryAnne Atkinson ◽  
Scott Jones
Keyword(s):  
Cost Function ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Decision Makers ◽  
Data Set ◽  
Influential Points ◽  
Estimation Procedures ◽  
Data Points ◽  
Weighting Rule

This paper reports the results of an experiment in which individuals visually fitted a cost function to data. The inclusion or omission of unusual data points within the data set was experimentally manipulated. The results indicate that individuals omit outliers from their visual fits, but do not omit influential points. Evidence also suggests that the weighting rule used by individuals is more robust that the weighting rule used in the ordinary least squares criterion.

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