Study on the Mechanical and Physical Properties of Lotus Kernels

2011 ◽  
Vol 337 ◽  
pp. 735-738
Author(s):  
Qiu Cheng Ma ◽  
Yin Bin Zhou ◽  
Dou Ling
Keyword(s):  
Distribution Function ◽  
Moisture Content ◽  
Physical Properties ◽  
Compression Strength ◽  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Deep Processing ◽  
Shape Parameters ◽  
Mechanical And Physical Properties ◽  
The Relationship

The mechanical and physical properties of lotus kernel play an important role in deep processing of lotus seeds. This paper established an empirical distribution function of random variables of the shape parameters of lotus kernels. The relationship between the moisture content and the hardness was analysed, and the compression strength of the different surfaces of the same lotus kernels were compared. The results indicate that, with the same moisture content, there is a centralized distribution section for lotus kernel shape. The density of a lotus kernel is higher than that of water. The higher the moisture content, the lower hardness. And the lower moisture content, the higher hardness. Compressed from different directions, the compression strengths of the lotus kernels are different.

scholarly journals Nonparametric Estimation of Multivariate Extreme Value Copulas with Known and Unknown Marginal Distributions

2021 ◽  
Vol 2068 (1) ◽  
pp. 012003
Author(s):  
Ayari Samia ◽  
Mohamed Boutahar
Keyword(s):  
Distribution Function ◽  
Empirical Distribution Function ◽  
Extreme Values ◽  
Empirical Distribution ◽  
Extreme Value ◽  
Dependence Function ◽  
Marginal Distributions ◽  
Nonparametric Estimators ◽  
Monte Carlo Experiments ◽  
Multivariate Extreme Values

Abstract The purpose of this paper is estimating the dependence function of multivariate extreme values copulas. Different nonparametric estimators are developed in the literature assuming that marginal distributions are known. However, this assumption is unrealistic in practice. To overcome the drawbacks of these estimators, we substituted the extreme value marginal distribution by the empirical distribution function. Monte Carlo experiments are carried out to compare the performance of the Pickands, Deheuvels, Hall-Tajvidi, Zhang and Gudendorf-Segers estimators. Empirical results showed that the empirical distribution function improved the estimators’ performance for different sample sizes.

scholarly journals PENENTUAN SIFAT MEKANIS DAN FISIS BATU BATA DENGAN PENAMBAHAN ARANG TEMPURUNG KELAPA ASAL ALOR

2018 ◽  
Vol 3 (1) ◽  
pp. 80-85
Author(s):  
Ernawati Kawa ◽  
Minsyahril Bukit ◽  
Albert Zicko Johannes
Keyword(s):  
Mechanical Properties ◽  
Physical Properties ◽  
Compression Strength ◽  
Physical And Mechanical Properties ◽  
Strength Test ◽  
Coconut Shell ◽  
Mechanical And Physical Properties ◽  
Maximum Limit

Abstrak Telah dilakukan penelitian tentang penentuan sifat mekanis dan fisis batu bata dengan penambahan tempurung kelapa asal alor. Penenlitian ini bertujuan mengetahui kualitas batu bata yang memenuhi standar kelayakan sebagai bahan konstruksi dengan penambahan arang tempurung kelapa aal alor dengan presentasi 0%, 5%, 10%, 15% terhadap tanah liat (lempung). Batu bata dicetak dengan prosedur pemadatan, pengringn dan pembakaran. Setelah prosedur pencetakkan selesai kemudian di lanjutkan dengan pengujian sefat mekanis dan sifat fisis, yaitu uji kuat tekan (compression strength), densitas (density), porositas (porosity) hasil  kuat tekan batu bata didapatkan berdasarkan pengujian: a) uji kuat tekan, batu bata tanpa penambahan (0%) : 4,94 meemenuhi standar kuat tekan kelas 50 (SNI 15-2094-2000), b) uji porositas, batu bata 0% dan 5% : 3,82% dan 17,93% memenuhi standar porositas dengan batas maksimum 20% (SNI 15-2094-2000) dan uji densitas, batu bata tidak ada yang memenuhi standar (SII 0021-1978) Kata kunci: sifat mekanis, sifat fisis, tempurung kelapa, densitas, porositas, kuat tekan Abstract A research had been conducted to determine physical and mechanical properties of the bricks with the addition coconut shell charcoal from alor. This research aims at the quality of the bricks to meet the standars of eligibility as a contruction material. The addition of coconut shell charcoal is variate with the presentage 0%, 5%, 10%, 15% to the clay mass. The brick being printed with procedure compaction, drying, and baking. After the printing procedure is done then next is testing the mechanical and physical properties, that is compression strength test, density test, and porosity test. The brick quality result is obtained based on the test: a) compression strength test, the brick without addition (0%) : 4,94  (SNI 15-2094-2000) is comply with the standard compression strength the class 50 , b) porosity test, the brick 0% and 5% (3,82% and 17,93%) meet the standard with the maximum limit 20% ( SNI 15-2094-2000)  , and c) density test, every bricks does not meet the standard (SII 0021- 1978). Keywords: mechanical properties, physical properties, coconut shell, density, porosity, compression strength

Semiparametric estimation and inference

2019 ◽  
pp. 139-164
Author(s):  
M. D. Edge
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Permutation Test ◽  
Cumulative Distribution ◽  
Permutation Testing ◽  
Unknown Distribution ◽  
Regression Parameters ◽  
Independent Observations

Nonparametric and semiparametric statistical methods assume models whose properties cannot be described by a finite number of parameters. For example, a linear regression model that assumes that the disturbances are independent draws from an unknown distribution is semiparametric—it includes the intercept and slope as regression parameters but has a nonparametric part, the unknown distribution of the disturbances. Nonparametric and semiparametric methods focus on the empirical distribution function, which, assuming that the data are really independent observations from the same distribution, is a consistent estimator of the true cumulative distribution function. In this chapter, with plug-in estimation and the method of moments, functionals or parameters are estimated by treating the empirical distribution function as if it were the true cumulative distribution function. Such estimators are consistent. To understand the variation of point estimates, bootstrapping is used to resample from the empirical distribution function. For hypothesis testing, one can either use a bootstrap-based confidence interval or conduct a permutation test, which can be designed to test null hypotheses of independence or exchangeability. Resampling methods—including bootstrapping and permutation testing—are flexible and easy to implement with a little programming expertise.

Sign in / Sign up

Export Citation Format

Share Document