Estimation of the Structural Parameter Distribution of Asphaltene Using a Preparative GPC Technique

2005 ◽  
pp. 65-74
Author(s):  
Shinya Sato ◽  
Toshimasa Takanohashi ◽  
Ryuzo Tanaka
Keyword(s):  
Structural Parameter ◽  
Parameter Distribution ◽  
Preparative Gpc

Precession and Structural Parameter Estimation of the Cone-shaped Warhead Based on the Micro-Doppler

2011 ◽  
Vol 33 (10) ◽  
pp. 2413-2419 ◽  
Author(s):  
Xiao-hai Zou ◽  
Xiao-feng Ai ◽  
Yong-zhen Li ◽  
Feng Zhao ◽  
Shun-ping Xiao
Keyword(s):  
Parameter Estimation ◽  
Structural Parameter

scholarly journals Fast radio burst detection in the presence of coloured noise

2021 ◽  
Vol 503 (4) ◽  
pp. 5223-5231
Author(s):  
C F Zhang ◽  
J W Xu ◽  
Y P Men ◽  
X H Deng ◽  
Heng Xu ◽  
...  
Keyword(s):  
False Alarm ◽  
Radio Burst ◽  
Pulse Width ◽  
False Alarm Probability ◽  
False Positives ◽  
Parameter Distribution ◽  
Correlated Noise ◽  
Width Distribution ◽  
Fast Radio Burst ◽  
The Impact

ABSTRACT In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that (1) the correlated noise significantly increases the false alarm probability; (2) the signal-to-noise ratios (S/N) of the false positives become higher; (3) the correlated noise also affects the pulse width distribution of false positives, and there will be more false positives with wider pulse width. We use 55-h observation for M82 galaxy carried out at Nanshan 26m radio telescope to demonstrate the application of the correlated noise modelling. The number of candidates and parameter distribution of the false positives can be reproduced with the modelling of correlated noise. We will also discuss a low S/N candidate detected in the observation, for which we demonstrate the method to evaluate the false alarm probability in the presence of correlated noise. Possible origins of the candidate are discussed, where two possible pictures, an M82-harboured giant pulse and a cosmological FRB, are both compatible with the observation.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals A deformation energy model reveals sequence-dependent property of nucleosome positioning

Chromosoma ◽  
2021 ◽  
Vol 130 (1) ◽  
pp. 27-40
Author(s):  
Guoqing Liu ◽  
Hongyu Zhao ◽  
Hu Meng ◽  
Yongqiang Xing ◽  
Lu Cai
Keyword(s):  
Budding Yeast ◽  
Nucleosome Occupancy ◽  
Nucleosome Positioning ◽  
Deformation Energy ◽  
Energy Model ◽  
Structural Parameter ◽  
High Deformation ◽  
Dna Deformation ◽  
Genome Wide ◽  
Genomic Regions

AbstractWe present a deformation energy model for predicting nucleosome positioning, in which a position-dependent structural parameter set derived from crystal structures of nucleosomes was used to calculate the DNA deformation energy. The model is successful in predicting nucleosome occupancy genome-wide in budding yeast, nucleosome free energy, and rotational positioning of nucleosomes. Our model also indicates that the genomic regions underlying the MNase-sensitive nucleosomes in budding yeast have high deformation energy and, consequently, low nucleosome-forming ability, while the MNase-sensitive non-histone particles are characterized by much lower DNA deformation energy and high nucleosome preference. In addition, we also revealed that remodelers, SNF2 and RSC8, are likely to act in chromatin remodeling by binding to broad nucleosome-depleted regions that are intrinsically favorable for nucleosome positioning. Our data support the important role of position-dependent physical properties of DNA in nucleosome positioning.

scholarly journals Wood creep data collection and unbiased parameter identification of compliance functions

Holzforschung ◽  
2020 ◽  
Vol 74 (11) ◽  
pp. 1011-1020
Author(s):  
Danyang Tong ◽  
Susan Alexis Brown ◽  
David Corr ◽  
Gianluca Cusatis
Keyword(s):  
Parameter Identification ◽  
Prediction Models ◽  
Tall Buildings ◽  
Building Design ◽  
Parameter Distribution ◽  
Experimental Conditions ◽  
Global Emission ◽  
Wood Construction ◽  
Parameter Values

AbstractRising global emission have led to a renewed popularity of timber in building design, including timber-concrete tall buildings up to 18 stories. In spite of this surge in wood construction, there remains a gap in understanding of long-term structural behavior, particularly wood creep. Unlike concrete, code prescriptions for wood design are lacking in robust estimates for structural shortening. Models for wood creep have become increasingly necessary due to the potential for unforeseen shortening, especially with respect to differential shortening. These effects can have serious impacts as timber building heights continue to grow. This study lays the groundwork for wood compliance prediction models for use in timber design. A thorough review of wood creep studies was conducted and viable experimental results were compiled into a database. Studies were chosen based on correlation of experimental conditions with a realistic building environment. An unbiased parameter identification method, originally applied to concrete prediction models, was used to fit multiple compliance functions to each data curve. Based on individual curve fittings, statistical analysis was performed to determine the best fit function and average parameter values for the collective database. A power law trend in wood creep, with lognormal parameter distribution, was confirmed by the results.

scholarly journals Kumaraswamy Generalized Power Lomax Distributionand Its Applications

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 28-45
Author(s):  
Vasili B.V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Hazard Rate ◽  
Real Data ◽  
Rate Function ◽  
Maximum Likelihood Estimates ◽  
Parameter Estimates ◽  
Parameter Distribution ◽  
Data Sets ◽  
Lomax Distribution ◽  
Entropy Measures ◽  
Modeling Behavior

In this paper, a new five-parameter distribution is proposed using the functionalities of the Kumaraswamy generalized family of distributions and the features of the power Lomax distribution. It is named as Kumaraswamy generalized power Lomax distribution. In a first approach, we derive its main probability and reliability functions, with a visualization of its modeling behavior by considering different parameter combinations. As prime quality, the corresponding hazard rate function is very flexible; it possesses decreasing, increasing and inverted (upside-down) bathtub shapes. Also, decreasing-increasing-decreasing shapes are nicely observed. Some important characteristics of the Kumaraswamy generalized power Lomax distribution are derived, including moments, entropy measures and order statistics. The second approach is statistical. The maximum likelihood estimates of the parameters are described and a brief simulation study shows their effectiveness. Two real data sets are taken to show how the proposed distribution can be applied concretely; parameter estimates are obtained and fitting comparisons are performed with other well-established Lomax based distributions. The Kumaraswamy generalized power Lomax distribution turns out to be best by capturing fine details in the structure of the data considered.

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