The back-calculation of specific rates of breakage and non-normalized breakage distribution parameters from batch grinding data

1977 ◽  
Vol 4 (1) ◽  
pp. 7-32 ◽  
Author(s):  
R.R. Klimpel ◽  
L.G. Austin
Keyword(s):  
Distribution Parameters ◽  
Back Calculation

Statistical criteria of stellar population types

1966 ◽  
Vol 24 ◽  
pp. 101-110
Author(s):  
W. Iwanowska
Keyword(s):  
Stellar Population ◽  
Chemical Properties ◽  
Spectrophotometric Study ◽  
Physical And Chemical Properties ◽  
Population Type ◽  
The Galaxy ◽  
Distribution Parameters ◽  
Physical And Chemical ◽  
Statistical Criteria ◽  
And Chemical Properties

In connection with the spectrophotometric study of population-type characteristics of various kinds of stars, a statistical analysis of kinematical and distribution parameters of the same stars is performed at the Toruń Observatory. This has a twofold purpose: first, to provide a practical guide in selecting stars for observing programmes, second, to contribute to the understanding of relations existing between the physical and chemical properties of stars and their kinematics and distribution in the Galaxy.

A Stochastic Study on the Distribution Parameters of Random Individual Walking Excitations

AEI 2017 ◽  
2017 ◽  
Author(s):  
Zhiqiang Zhang ◽  
Bill Zhang ◽  
Jieqiang Wei ◽  
Peng Luo ◽  
Changhui Cui
Keyword(s):  
Distribution Parameters ◽  
Random Individual

Back-Calculation Method of Subgrade Modulus Based on Traffic Speed Deflectometer

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Guoshuai Zang ◽  
Haizhu Lu ◽  
Guanglai Jin ◽  
Zhixiang Zhang
Keyword(s):  
Calculation Method ◽  
Back Calculation ◽  
Subgrade Modulus ◽  
Traffic Speed

ESTIMATION OF THE T-YEAR SPECIFIC DISCHARGE USING THE REGIONALISED SKEWNESS COEFFICIENT OF THE LOG-PEARSON TYPE III DISTRIBUTION

2020 ◽  
Author(s):  
Jakub Mészáros ◽  
◽  
Pavol Miklánek ◽  
Pavla Pekárová ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Type Iii ◽  
River Region ◽  
Pearson Type ◽  
Skewness Coefficient ◽  
Distribution Parameters ◽  
Design Values ◽  
Specific Discharge ◽  
Morava River ◽  
Pearson Type Iii Distribution

In this paper the results are presented of estimation of T-year specific discharge of several streams in two regions in Slovakia. The Qmax time series used in the study were observed at water gauges from lowland Slovak part of the Morava River basin, and from the mountainous Belá River basin. For estimating the design values, we have studied the use of only one type of probability distribution, namely the Log-Pearson Type III Distribution (LP3 distribution). The use of only one type of distribution brings several benefits, e.g. possibility of the regionalization of the distribution parameters (in this study skew coefficient). In the first step the design values of the specific discharge series qmax (with historical data) were estimated and regional skew coefficients Gr of the LP3 distribution were computed. Regional skewness coefficient Gr was estimated to be 0.38 in the Morava River region, and 0.73 in the Belá River region. In many cases the estimate of the 1000-year specific discharge is two times higher than the value of the 100-year specific discharge. Then we have derived the empirical relations between station skew coefficient G and the elevation of the water gauge. In the second step we have derived the empirical relationships between 1000-years specific discharge q1000 and the elevation of the water gauge for both regions separately. The derived empirical regional equations can be used to estimate the 1000-years specific discharge of other streams in the region.

GROWTH OF PIKE-PERCH SANDER LUCIOPERCA (L., 1758) (PERCIDAE) IN RESERVOIRS OF K. SATPAYEV’S CHANNEL

2018 ◽  
pp. 59-68
Author(s):  
Saule Zhangirovna Asylbekova ◽  
Kuanysh Baibulatovich Isbekov ◽  
Vladimir Nickolaevich Krainyuk
Keyword(s):  
Growth Rates ◽  
Growth Dynamics ◽  
High Growth ◽  
Water Bodies ◽  
Volga River ◽  
Trophic Resources ◽  
Pike Perch ◽  
The Amur River ◽  
Back Calculation ◽  
Abiotic And Biotic Factors

Pike-perch is an invader for the water basins of Central Kazakhstan. These species have stable self-reproductive populations in the regional waters. Back calculation method was used to investigate pike-perch growth rates in reservoirs of K. Satpayev’s channel. For comparison, the data from the other water bodies (Vyacheslavsky and Sherubay-Nurinsky water reservoirs) were used, as well as literature data. Pike-perch species from the investigated waters don’t show high growth rates. The populations from the reservoirs of K. Satpayev’s channel have quite similar growth rates with populations from the Amur river, from a number of reservoirs in the Volga river basin and from the reservoir in Spain. Sexual differences in growth have not been observed. Evaluating possible influence of various abiotic and biotic factors on the growth rate of pike-perch in the reservoirs of K. Satpayev’s channel was carried out. It has been stated that the availability of trophic resources cannot play a key role in growth dynamics because of their high abundance. Morphology of water bodies also does not play a role, as well as chromaticity, turbidity and other optical water indicators. It can be supposed that the main factor influencing growth of pike perch is the habitat’s temperature. This factor hardly ever approaches optimal values for the species in reservoirs of K. Satpaev’s channel. The possible influence of fishing selectivity on pike-perch growth rates was also evaluated. Currently, there has been imposed a moratorium on pike-perch catch. However, pike-perch is found in by-catches and in catches of amateur fishermen. It should be said that such seizures have an insignificant role in the dynamics of growth rates.

ABOUT INVARIANCE OF ANGLE NOISE DISTRIBUTION PARAMETERS BY AN ARBITRARY N-POINT GEOMETRIC MODEL TO VIEWING ANGLE

2019 ◽  
pp. 32-35
Author(s):  
V. V. Artyushenko ◽  
A. V. Nikulin
Keyword(s):  
Regular Polygon ◽  
Geometric Model ◽  
Sea Surface ◽  
Noise Distribution ◽  
Viewing Angle ◽  
Distributed Objects ◽  
Software Complex ◽  
The Earth ◽  
Distribution Parameters ◽  
Equal Power

In this article we consider a problem of reliable modeling of echo signals and angle noise of distributed objects using twodimensional geometric models with random statistically unrelated signals. The conditions that ensure the invariance of distribution parameters of the angle noise generated by an arbitrary N-point configuration of a two-dimensional geometric model are obtained. In the particular case of a model whose emitters are supplied with signals of equal power, the conditions of invariance are reduced to the location of the model points on the plane in the form of a regular polygon. These results can be used to synthesize mathematical models used for simulating reflections from distributed objects and for developing a hardware-software complex for the simulation of electromagnetic fields reflected from the Earth surface, atmospheric inhomogeneities, the sea surface, etc.

scholarly journals Measuring Hydrometeors Using a Precipitation Microphysical Characteristics Sensor: Sampling Effect of Different Bin Sizes on Drop Size Distribution Parameters

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xichuan Liu ◽  
Taichang Gao ◽  
Yuntao Hu ◽  
Xiaojian Shu
Keyword(s):  
Drop Size ◽  
Rain Rate ◽  
Monte Carlo Model ◽  
Rain Gauge ◽  
Sampling Schemes ◽  
Distribution Parameters ◽  
Optimum Sampling ◽  
Simulation Results ◽  
The Impact ◽  
Good Agreement

In order to improve the measurement of precipitation microphysical characteristics sensor (PMCS), the sampling process of raindrops by PMCS based on a particle-by-particle Monte-Carlo model was simulated to discuss the effect of different bin sizes on DSD measurement, and the optimum sampling bin sizes for PMCS were proposed based on the simulation results. The simulation results of five sampling schemes of bin sizes in four rain-rate categories show that the raw capture DSD has a significant fluctuation variation influenced by the capture probability, whereas the appropriate sampling bin size and width can reduce the impact of variation of raindrop number on DSD shape. A field measurement of a PMCS, an OTT PARSIVEL disdrometer, and a tipping bucket rain Gauge shows that the rain-rate and rainfall accumulations have good consistencies between PMCS, OTT, and Gauge; the DSD obtained by PMCS and OTT has a good agreement; the probability of N0, μ, and Λ shows that there is a good agreement between the Gamma parameters of PMCS and OTT; the fitted μ-Λ and Z-R relationship measured by PMCS is close to that measured by OTT, which validates the performance of PMCS on rain-rate, rainfall accumulation, and DSD related parameters.

scholarly journals Interpretable Variational Graph Autoencoder with Noninformative Prior

2021 ◽  
Vol 13 (2) ◽  
pp. 51
Author(s):  
Lili Sun ◽  
Xueyan Liu ◽  
Min Zhao ◽  
Bo Yang
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Expert Knowledge ◽  
Structural Information ◽  
Standard Normal Distribution ◽  
Noninformative Prior ◽  
Latent Space ◽  
Distribution Parameters ◽  
Standard Normal ◽  
Low Dimensional

Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal distribution which encourages all nodes to gather around 0. That leads to the inability to fully utilize the latent space. Therefore, it becomes a challenge on how to choose a suitable prior without incorporating additional expert knowledge. Given this, we propose a novel noninformative prior-based interpretable variational graph autoencoder (NPIVGAE). Specifically, we exploit the noninformative prior as the prior distribution of latent variables. This prior enables the posterior distribution parameters to be almost learned from the sample data. Furthermore, we regard each dimension of a latent variable as the probability that the node belongs to each block, thereby improving the interpretability of the model. The correlation within and between blocks is described by a block–block correlation matrix. We compare our model with state-of-the-art methods on three real datasets, verifying its effectiveness and superiority.

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