Gaussian-Based Kernel for Multi-Agent Aerial Chemical-Plume Mapping

2019 ◽  
Author(s):  
Xiang He ◽  
Jake A. Steiner ◽  
Joseph R. Bourne ◽  
Kam K. Leang
Keyword(s):  
Mean Squared Error ◽  
New Approach ◽  
Aerial Robots ◽  
Squared Error ◽  
Gas Leak ◽  
Mapping Process ◽  
Multi Agent ◽  
Gaussian Plume ◽  
Chemical Plume ◽  
Error Simulation

Abstract This paper presents a multi-vehicle chemical-plume mapping process that incorporates onboard wind speed and direction estimation. A Gaussian plume model is exploited to develop the kernel for extrapolating the measured data. Compared to the uni- or bi-variate kernels, the proposed kernel uses the estimated wind information to refine the chemical concentration prediction downwind of the source. This new approach, compared to previous mapping methods, relies on fewer parameters and provides 30% reduction in the mapping mean-squared error. Simulation and experimental results are presented to validate the approach. Specifically, outdoor flight tests show three aerial robots with chemical sensing capabilities mapping a real propane gas leak to demonstrate feasibility of the approach.

A simple variance estimator for the trapezoidal area-under-the-curve estimator of the spawner abundance of Pacific salmon

2013 ◽  
Vol 70 (8) ◽  
pp. 1231-1239 ◽  
Author(s):  
Russell B. Millar ◽  
Chris E. Jordan
Keyword(s):  
Survey Data ◽  
Mean Squared Error ◽  
Pacific Salmon ◽  
Area Under The Curve ◽  
Variance Estimator ◽  
New Approach ◽  
Squared Error

The trapezoidal area-under-the-curve (TAUC) method is the simplest and most widely used method for estimating the spawning abundance of Pacific salmon from periodic surveys of spawner counts within a surveyed area. However, there is currently no method to estimate the precision of the estimated spawner abundance from the survey data. For this reason the Gaussian area-under-the-curve (GAUC) method was recently presented as an easily implemented alternative that has the advantage of having a variance estimator. However, under a diverse variety of simulation scenarios, the TAUC estimator was seen to be a slightly better estimator of escapement on the basis of mean squared error. It is shown here that a variance estimator for the TAUC method can be obtained as an immediate by-product of the GAUC method. Furthermore, this new approach can easily be extended to accommodate asymmetric and multimodal patterns of spawner abundance by the use of basis splines.

A new approach to mean squared error estimation of response surfaces

Biometrika ◽  
1973 ◽  
Vol 60 (3) ◽  
pp. 573-579 ◽  
Author(s):  
LAWRENCE L. KUPPER ◽  
EDWARD F. MEYDRECH
Keyword(s):  
Error Estimation ◽  
Mean Squared Error ◽  
Response Surfaces ◽  
New Approach ◽  
Squared Error

scholarly journals A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting

2021 ◽  
Vol 13 (21) ◽  
pp. 4359
Author(s):  
Tim R. Hammond ◽  
Øivind Midtgaard ◽  
Warren A. Connors
Keyword(s):  
Mean Squared Error ◽  
Grid Cell ◽  
New Approach ◽  
Squared Error ◽  
Recognition Probability ◽  
Bayesian Fusion ◽  
Complex Reasoning ◽  
Recorded Data ◽  
Evaluation Approaches

This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relies on through-the-sensor (TTS) assessment, by which the sensors’ performances can be measured in situ through processing of their recorded data, yielding the local mine recognition probability, and false alarm rate. The method constructs a risk map of the minefield area composed of small grid cells (~4 m2) that are colour coded according to the remaining mine probability. The new approach can produce this map using the available evidence whenever decision support is needed during the mine hunting operation, e.g., for replanning purposes. What distinguishes the new technique from other recent TTS methods is its use of Bayesian networks that facilitate more complex reasoning within each grid cell. These networks thus allow for the incorporation of two types of evidence not previously considered in evaluation: the explosions that typically result from mine neutralization and verification of mine destruction by visual/sonar inspection. A simulation study illustrates how these additional pieces of evidence lead to the improved estimation of the number of deployed mines (M), compared to results from two recent TTS evaluation approaches that do not use them. Estimation performance was assessed using the mean squared error (MSE) in estimates of M.

Credible Loss Ratio Claims Reserves: the Benktander, Neuhaus and Mack Methods Revisited

2009 ◽  
Vol 39 (1) ◽  
pp. 81-99 ◽  
Author(s):  
Werner Hürlimann
Keyword(s):  
Mean Squared Error ◽  
Cape Cod ◽  
Loss Ratio ◽  
New Approach ◽  
Standard Methods ◽  
Straightforward Calculation ◽  
Mean Squared Errors ◽  
Squared Error ◽  
The Mean ◽  
Chain Ladder

AbstractThe Benktander (1976) and Neuhaus (1992) credibility claims reserving methods are reconsidered in the framework of a credible loss ratio reserving method. As a main contribution we provide a simple and practical optimal credibility weight for combining the chain-ladder or individual loss ratio reserve (grossed up latest claims experience of an origin period) with the Bornhuetter-Ferguson or collective loss ratio reserve (experience based burning cost estimate of the total ultimate claims of an origin period). The obtained simple optimal credibility weights minimize simultaneously the mean squared error and the variance of the claims reserve. We note also that the standard Chain-Ladder, Cape Cod and Bornhuetter-Ferguson methods can be reinterpreted in the credible context and extended to optimal credible standard methods. The new approach is inspired from Mack (2000). Two advantages over the Mack method are worthwhile to be mentioned. First, a pragmatic estimation of the required parameters leads to a straightforward calculation of the optimal credibility weights and mean squared errors of the credible reserves. An advantage of the collective loss ratio claims reserve over the Bornhuetter-Ferguson reserve in Mack (2000) is that different actuaries come always to the same results provided they use the same actuarial premiums.

scholarly journals Marginalized Maximum Likelihood for Parameters Estimation of the Three Parameter Weibull Distribution

2021 ◽  
Vol 10 (4) ◽  
pp. 62
Author(s):  
Ouindllassida Jean-Etienne Ou´edraogo ◽  
Edoh Katchekpele ◽  
Simplice Dossou-Gb´et´e
Keyword(s):  
Weibull Distribution ◽  
Mean Squared Error ◽  
Likelihood Function ◽  
Information Loss ◽  
Parameters Estimation ◽  
New Approach ◽  
Mm Algorithm ◽  
First Order ◽  
Squared Error ◽  
Distributed Scheme

The aims of this paper is to propose a new approach for fitting a three-parameter weibull distribution to data from an independent and identically distributed scheme of sampling. This approach use a likelihood function based on the n - 1 largest order statistics. Information loss by dropping the first order statistic is then retrieved via an MM-algorithm which will be used to estimate the model’s parameters. To examine the properties of the proposed estimators, the associated bias and mean squared error were calculated through Monte Carlo simulations. Subsequently, the performance of these estimators were compared with those of two concurrent methods.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Sign in / Sign up

Export Citation Format

Share Document