Estimation of a common mean of several univariate inverse Gaussian populations

1991 ◽  
Vol 43 (2) ◽  
pp. 357-367 ◽  
Author(s):  
Manzoor Ahmad ◽  
Y. P. Chaubey ◽  
B. K. Sinha
Keyword(s):  
Inverse Gaussian ◽  
Common Mean

Inferences on the common mean of several inverse Gaussian populations

2010 ◽  
Vol 54 (4) ◽  
pp. 906-915 ◽  
Author(s):  
Ren-Dao Ye ◽  
Tie-Feng Ma ◽  
Song-Gui Wang
Keyword(s):  
Inverse Gaussian ◽  
Common Mean ◽  
The Common

PECULIARITIES OF MANIFESTATION OF MEANING DISSONANCE

2020 ◽  
Vol 22 (10) ◽  
pp. 25-28
Author(s):  
Abakumova I.V. ◽  
Grishina A.V. ◽  
Godunov M.V.
Keyword(s):  
Cognitive Dissonance ◽  
Personal Development ◽  
External World ◽  
Personal Meaning ◽  
Formation Processes ◽  
Common Mean ◽  
Personal Meanings ◽  
Modern Psychology ◽  
Meaning Systems ◽  
The Subject

Modern psychology considers meaning regulation, as an integral mechanism of personal development. A system of personal meanings develops in the processes of under-standing reality. Due to their polymodality personal meanings cannot be good or bad, but they are not the same. When confronted with unknown situations, the unevenness of the emerging personal meanings can lead to match or mismatch with the existing system of mean-ings. Coincidence, as agreement with a new fact, means meaning consonance. Mismatch, as a mismatch between new and existing information, means meaning disso-nance, as a kind of cognitive dissonance. An analysis of modern psychological literature shows that there are two main plans for the action of meaning dissonances: the dissonance of individual meanings in the implementation of real interactions and the dissonance of common mean-ings during the translation of interpersonal meaning formations. It is proposed to consider that meaning ac-quires a personal coloring due to the processes of both consonance and dissonance positioning of meaning con-structs in the meaning sphere of the subject. The revealed dichotomy of the meaning formation processes shows the possibility of manifestation of meanings bipolarity, which is revealed in the process of transition from the internal to the external world and in collisions with oth-er meaning systems. Then it can be assumed that the ef-fect of meaning dissonance manifests itself in two ways: firstly, in terms of real interactions as a discord of indi-vidual meanings, and secondly, in terms of translation of interpersonal meaning constructs as a dissonance of common meanings. In the course of such an external for-mation, meaning becomes already a personal meaning in the consciousness of a particular person.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

scholarly journals Lidar-Based Morphometry of Conical Hills in Temperate Karst Areas in Slovenia

2021 ◽  
Vol 13 (14) ◽  
pp. 2668
Author(s):  
Tamás Telbisz
Keyword(s):  
Empirical Distribution ◽  
Inverse Gaussian ◽  
Digital Terrain ◽  
Terrain Models ◽  
Starting Point ◽  
Significant Difference ◽  
Karst Areas ◽  
Hill Area ◽  
The Hill ◽  
Comprehensive Study

Conical hills, or residual hills, are frequently mentioned landforms in the context of humid tropical karsts as they are dominant surface elements there. Residual hills are also present in temperate karsts, but generally in a less remarkable way. These landforms have not been thoroughly addressed in the literature to date, therefore the present article is the first attempt to morphometrically characterize temperate zone residual karst hills. We use the methods already developed for doline morphometry, and we apply them to the “inverse” topography using LiDAR-based digital terrain models (DTMs) of three Slovenian sample areas. The characteristics of hills and depressions are analysed in parallel, taking into account the rank of the forms. A common feature of hills and dolines is that, for both types, the empirical distribution of planform areas has a strongly positive skew. After logarithmic transformation, these distributions can be approximated by Inverse Gaussian, Normal, and Weibull distributions. Along with the rank, the planform area and vertical extent of the hills and dolines increase similarly. High circularity is characteristic only of the first-rank forms for both dolines and hills. For the sample areas, the the hill area ratios and the doline area ratios have similar values, but the total extent of the hills is slightly larger in each case. A difference between dolines and hills is that the shapes of hills are more similar to one another than those of dolines. The reason for this is that the larger, closed depressions are created by lateral coalescence, while the hills are residual forms carved from large blocks. Another significant difference is that the density of dolines is much higher than that of hills. This article is intended as a methodological starting point for a new topic, aiming at the comprehensive study of residual karst hills across different climatic areas.

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