A Novel Two-Step Job Runtime Estimation Method Based on Input Parameters in HPC System

2019 ◽  
Author(s):  
Qiqi Wang ◽  
Jing Li ◽  
Shuo Wang ◽  
Guibao Wu
Keyword(s):  
Estimation Method ◽  
Input Parameters ◽  
Runtime Estimation

INVESTIGATING THE SLOPE STABILITY BASED ON UNCERTAINTY BY USING FUZZY POSSIBILITY THEORY

2014 ◽  
Vol 59 (1) ◽  
pp. 179-188 ◽  
Author(s):  
Navid Hosseini ◽  
Mehran Gholinejad
Keyword(s):  
Slope Stability ◽  
Stability Condition ◽  
Estimation Method ◽  
Possibility Theory ◽  
Statistical Characteristics ◽  
Deterministic Analysis ◽  
Performance Function ◽  
Realistic Interpretation ◽  
Input Parameters ◽  
Grade Of Membership

Abstract The main purpose of this paper is to investigate the slope stability condition by using fuzzy estimation method based on fuzzy possibility theory. Due to use of this theory, the inaccuracy, ambiguity and uncertainty in input parameters are considered and therefore, the calculated factor of safety (FOS) is highly reliable. In this research, first, the input parameters of slope stability analysis, based on statistical characteristics and grade of membership concept, as a fuzzy numbers are defined. Then the performance function of slope behavior is defined and by using the fuzzy parameters, the FOS is calculated. In next step, by using the several α - cut, the calculated FOS is defined as a fuzzy form and subsequently, the slope stability condition based on fuzzy presentation of FOS is evaluated. The results show that, although based on deterministic analysis the studied slope is stable but based on fuzzy interpretation of FOS, the slope stability condition is scare. The fuzzy analysis of slope stability condition, by applying the uncertainty in calculating the FOS and defining the grade of membership for each unknown input parameters in model, a more realistic interpretation of slope stability condition is provided. In addition, the fuzzy presentation of the FOS, allowing more accurate judgments about slope stability condition.

scholarly journals A novel length-based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries

2014 ◽  
Vol 72 (1) ◽  
pp. 217-231 ◽  
Author(s):  
Adrian Hordyk ◽  
Kotaro Ono ◽  
Sarah Valencia ◽  
Neil Loneragan ◽  
Jeremy Prince
Keyword(s):  
Empirical Test ◽  
Estimation Method ◽  
Size Composition ◽  
Cost Effective ◽  
Small Scale ◽  
Empirical Estimation ◽  
Input Parameters ◽  
Maximum Likelihood Methods ◽  
Spawning Potential Ratio ◽  
The Difference

Abstract The spawning potential ratio (SPR) is a well-established biological reference point, and estimates of SPR could be used to inform management decisions for data-poor fisheries. Simulations were used to investigate the utility of the length-based model (LB-SPR) developed in Hordyk et al. (2015). Some explorations of the life history ratios to describe length composition, spawning-per-recruit, and the spawning potential ratio. ICES Journal of Marine Science, 72: 204–216.) to estimate the SPR of a stock directly from the size composition of the catch. This was done by (i) testing some of the main assumptions of the LB-SPR model, including recruitment variability and dome-shaped selectivity, (ii) examining the sensitivity of the model to error in the input parameters, and (iii) completing an initial empirical test for the LB-SPR model by applying it to data from a well-studied species. The method uses maximum likelihood methods to find the values of relative fishing mortality (F/M) and selectivity-at-length that minimize the difference between the observed and the expected length composition of the catch, and calculates the resulting SPR. When parameterized with the correct input parameters, the LB-SPR model returned accurate estimates of F/M and SPR. With high variability in annual recruitment, the estimates of SPR became increasingly unreliable. The usefulness of the LB-SPR method was tested empirically by comparing the results predicted by the method with those for a well-described species with known length and age composition data. The results from this comparison suggest that the LB-SPR method has potential to provide a tool for the cost-effective assessment of data-poor fisheries. However, the model is sensitive to non-equilibrium dynamics, and requires accurate estimates of the three parameters (M/k, L∞, and CVL∞). Care must be taken to evaluate the validity of the assumptions and the biological parameters when the model is applied to data-poor fisheries.

Coaches estimation method for the athletes (CEM)

1995 ◽  
Author(s):  
Nagykaldi Csaba ◽  
Manohar Singh Badhan
Keyword(s):  
Estimation Method

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

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