On optical mass estimation methods for galaxy groups

ABSTRACT Identifying galaxy groups from redshift surveys of galaxies plays an important role in connecting galaxies with the underlying dark matter distribution. Current and future high-z spectroscopic surveys, usually incomplete in redshift sampling, present both opportunities and challenges to identifying groups in the high-z Universe. We develop a group finder that is based on incomplete redshift samples combined with photometric data, using a machine learning method to assign halo masses to identified groups. Test using realistic mock catalogues shows that $\gtrsim \! 90{{\ \rm per\ cent}}$ of true groups with halo masses $\rm {\it M}_h \gtrsim 10^{12} M_{\odot }\,h^{-1}$ are successfully identified, and that the fraction of contaminants is smaller than $10{{\ \rm per\ cent}}$. The standard deviation in the halo mass estimation is smaller than 0.25 dex at all masses. We apply our group finder to zCOSMOS-bright and describe basic properties of the group catalogue obtained.

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BODY-MASS ESTIMATION IN PALEONTOLOGY: A REVIEW OF VOLUMETRIC TECHNIQUES

The Paleontological Society Papers ◽

10.1017/scs.2017.12 ◽

2016 ◽

Vol 22 ◽

pp. 133-156 ◽

Cited By ~ 11

Author(s):

Charlotte A. Brassey

Keyword(s):

Body Mass ◽

Laser Scanning ◽

Imaging Techniques ◽

Three Dimensional ◽

The Body ◽

Estimation Methods ◽

Mass Estimation ◽

Surface Laser ◽

Body Mass Estimation ◽

Future Work

AbstractBody mass is a key parameter for understanding the physiology, biomechanics, and ecology of an organism. Within paleontology, body mass is a fundamental prerequisite for many studies considering body-size evolution, survivorship patterns, and the occurrence of dwarfism and gigantism. The conventional method for estimating fossil body mass relies on allometric scaling relationships derived from skeletal metrics of extant taxa, but the recent application of three-dimensional imaging techniques to paleontology (e.g., surface laser scanning, computed tomography, and photogrammetry) has allowed for the rapid digitization of fossil specimens. Volumetric body-mass estimation methods based on whole articulated skeletons are therefore becoming increasingly popular. Volume-based approaches offer several advantages, including the ability to reconstruct body-mass distribution around the body, and their relative insensitivity to particularly robust or gracile elements, i.e., the so-called ‘one bone effect.’ Yet their application to the fossil record will always be limited by the paucity of well-preserved specimens. Furthermore, uncertainties with regards to skeletal articulation, body density, and soft-tissue distribution must be acknowledged and their effects quantified. Future work should focus on extant taxa to improve our understanding of body composition and increase confidence in volumetric model input parameters.

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Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

Remote Sensing ◽

10.3390/rs12111903 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1903

Author(s):

Cheng Hu ◽

Shaoyang Kong ◽

Rui Wang ◽

Fan Zhang ◽

Lianjun Wang

Keyword(s):

Support Vector Regression ◽

Cross Section ◽

Radar Cross Section ◽

Estimation Methods ◽

Estimation Accuracy ◽

Support Vector ◽

Feature Subset ◽

Mass Estimation ◽

Optimal Feature Subset ◽

Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

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A review of aircraft wing mass estimation methods

Aerospace Science and Technology ◽

10.1016/j.ast.2017.11.006 ◽

2018 ◽

Vol 72 ◽

pp. 256-266 ◽

Cited By ~ 13

Author(s):

Odeh Dababneh ◽

Timoleon Kipouros

Keyword(s):

Estimation Methods ◽

Aircraft Wing ◽

Mass Estimation

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Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2015.2437452 ◽

2015 ◽

Vol 16 (6) ◽

pp. 3138-3149 ◽

Cited By ~ 27

Author(s):

Richard Alligier ◽

David Gianazza ◽

Nicolas Durand

Keyword(s):

Machine Learning ◽

Estimation Methods ◽

Mass Estimation

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Halo Mass Estimation for Galaxy Groups: The Role Of Magnitude Gaps

Proceedings of the International Astronomical Union ◽

10.1017/s1743921315007085 ◽

2015 ◽

Vol 11 (S317) ◽

pp. 332-333

Author(s):

Yi Lu ◽

Xiaohu Yang ◽

Shiyin Shen

Keyword(s):

Mass Estimation ◽

Galaxy Groups ◽

The Galaxy ◽

Massive Halos

AbstractWe find that for the galaxy groups, the luminosity gap between the brightest and the subsequent brightest member galaxies in a halo (group) can be used to significantly reduce the scatter in the halo mass estimation based on the luminosity of the brightest galaxy alone. These corrections can significantly reduce the scatter in the halo mass estimations by ~ 50% to ~ 70% in massive halos.

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Body mass estimates of an exceptionally complete Stegosaurus (Ornithischia: Thyreophora): comparing volumetric and linear bivariate mass estimation methods

Biology Letters ◽

10.1098/rsbl.2014.0984 ◽

2015 ◽

Vol 11 (3) ◽

pp. 20140984 ◽

Cited By ~ 24

Author(s):

Charlotte A. Brassey ◽

Susannah C. R. Maidment ◽

Paul M. Barrett

Keyword(s):

Soft Tissue ◽

Body Mass ◽

Close Agreement ◽

Prediction Interval ◽

Linear Equations ◽

Estimation Methods ◽

Predictive Equations ◽

Mass Estimation ◽

Estimation Equations ◽

Biological Variable

Body mass is a key biological variable, but difficult to assess from fossils. Various techniques exist for estimating body mass from skeletal parameters, but few studies have compared outputs from different methods. Here, we apply several mass estimation methods to an exceptionally complete skeleton of the dinosaur Stegosaurus . Applying a volumetric convex-hulling technique to a digital model of Stegosaurus , we estimate a mass of 1560 kg (95% prediction interval 1082–2256 kg) for this individual. By contrast, bivariate equations based on limb dimensions predict values between 2355 and 3751 kg and require implausible amounts of soft tissue and/or high body densities. When corrected for ontogenetic scaling, however, volumetric and linear equations are brought into close agreement. Our results raise concerns regarding the application of predictive equations to extinct taxa with no living analogues in terms of overall morphology and highlight the sensitivity of bivariate predictive equations to the ontogenetic status of the specimen. We emphasize the significance of rare, complete fossil skeletons in validating widely applied mass estimation equations based on incomplete skeletal material and stress the importance of accurately determining specimen age prior to further analyses.

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Potential of Pressure Sensor Based Mass Estimation Methods for Electric Buses

Electronics ◽

10.3390/electronics9050711 ◽

2020 ◽

Vol 9 (5) ◽

pp. 711

Author(s):

Utz Spaeth ◽

Heiko Fechtner ◽

Michele Weisbach ◽

Benedikt Schmuelling

Keyword(s):

Influencing Factor ◽

Pressure Sensors ◽

Measurement Data ◽

Urban Traffic ◽

Estimation Methods ◽

Mass Estimation ◽

Advantages And Disadvantages ◽

Available Energy ◽

Passenger Transportation ◽

Pressure Changes

One approach to improve the economic efficiency of trolleybuses in the so-called BOB Project in the German town of Solingen is to use them as mobile energy storages in a smart grid. To achieve this, reliable information on available energy is essential, which in turn needs to be derived from a precise range calculator. As shown in this article, vehicle mass is a strong influencing factor, especially in urban traffic. Depending on passenger volume, the total mass and range of the bus varies by about 30 percent. The currently available mass on the bus fluctuates by more than 2 tons for constant payloads, and there is no proven solution for a more accurate mass estimation for buses in public passenger transportation. Therefore, this article presents a viable methodology to detect changes in payload, using high precision pressure sensors on the bus’s tires and air suspensions. These mass inducted pressure changes are extracted from the measurement data, using a filter to be later converted back into the corresponding masses. As the article will show, both approaches have their respective advantages and disadvantages, but have high potential and should therefore be investigated further.

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A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

Energies ◽

10.3390/en13071649 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1649

Author(s):

DaeYi Jung ◽

Gyoojae Choi

Keyword(s):

Driving Forces ◽

Rolling Friction ◽

Estimation Methods ◽

Engine Torque ◽

Mass Estimation ◽

Estimation Strategy ◽

Other Information ◽

Actual Field ◽

Road Grade ◽

Local Convex

This paper proposes a new mass estimation for a vehicle system, utilizing the characteristics of engine torque local convex minimum, where the mass can be estimated based on the driving forces and the longitudinal accelerations only. Fundamentally, this approach generally requires no other information about an aerodynamic effect, a road grade, or a rolling friction, which is usually demanded by the existing well-known longitudinal dynamics and adaptive filter-based estimation methods. The effectiveness of the proposed approach was evaluated and validated by both TruckSim/Simulink co-simulation and actual field test data. It is found that the proposed estimation technique is more favorable for a situation where the vehicle is exposed to low-speed regions. In addition to this new mass estimation strategy, other new and current existing methods were explored and are reviewed here. Moreover, this study suggested a guideline for a hybrid-type mass estimation strategy to predict a mass by combining a new method with an existing one for every speed.

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