5 DETERMINING THE DISTRIBUTION TYPE of distribution can be established from the original Pearson criteria, or from

2016 ◽  
pp. 202-204
Keyword(s):  
Distribution Type

Noise Classification Based on GMM and AANN

2011 ◽  
Vol 58-60 ◽  
pp. 1847-1853 ◽  
Author(s):  
Yan Zhang ◽  
Cun Bao Chen ◽  
Li Zhao
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Specific Method ◽  
Accuracy Rate ◽  
Noise Classification ◽  
Distribution Type

In this paper, Gaussian Mixture model (GMM) as specific method is applied to noise classification. On this basis, a modified Gaussian Mixture Model with an embedded Auto-Associate Neural Network (AANN) is proposed. It integrates the merits of GMM and AANN. We train GMM and AANN as a whole and they are trained by means of Maximum Likelihood (ML). In the process of training, the parameter of GMM and AANN are updated alternately. AANN reshapes the distribution of the data and improves the similarity of the feature data in the same distribution type of noise. Experiments show that the GMM with embedded AANN improves accuracy rate of noise classification against baseline GMM.

Reliability Analysis of Embankment Slope in Yaan-Luku Expressway

2011 ◽  
Vol 71-78 ◽  
pp. 1360-1365
Author(s):  
Jian Quan Ma ◽  
Guang Jie Li ◽  
Shi Bo Li ◽  
Pei Hua Xu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Latin Hypercube Sampling ◽  
Natural State ◽  
State Function ◽  
Embankment Slope ◽  
The Times ◽  
Distribution Type

Take a typical cross-section of rockfill embankment slope in Yaan-Luku highway as the research object, reliability analysis is studied under the condition of water table of 840.85m, 851.50m, and loading condition of natural state and horizontal seismic acceleration of 0.2g, respectively. Raw data use Kolmogorov-Smirnov test (K-S test) to determine the distribution type of parametric variation. And the parameters were sampling with Latin hypercube sampling (LHS) method and Monte Carlo (MC) method, respectively, to obtain state function and determine safety factors and reliability indexes. A conclusion is drawn that the times of simulation based on LHS method were less than Monte Carlo method. Also the convergence of failure probability is better than the Monte Carlo method. The safety factor is greater than one and the failure probability has reached to 35.45% in condition of earthquake, which indicating that the instability of rockfill embankment slope is still possible.

scholarly journals Integrable Equations with a Forcing of a Distribution Type

1994 ◽  
Vol 92 (2) ◽  
pp. 97-114 ◽  
Author(s):  
A. S. Fokas ◽  
A. R. Its ◽  
V. Yu. Novokshenov
Keyword(s):  
Integrable Equations ◽  
Distribution Type

Algorithms for detecting broadband signals with a constant level of false alarms

2021 ◽  
Author(s):  
I.F. Lozovskiy
Keyword(s):  
Detection Threshold ◽  
Signal Amplitude ◽  
Rayleigh Distribution ◽  
Training Sample ◽  
Detection Algorithm ◽  
Constant Level ◽  
False Alarms ◽  
Interference Power ◽  
Number Of False Alarms ◽  
Distribution Type

The use of broadband souding signals in radars, which has become real in recent years, leads to a significant reduction in the size of resolution elements in range and, accordingly, in the size of the window in which the training sample is formed, which is used to adapt the detection threshold in signal detection algorithms with a constant level of false alarms. In existing radars, such a window would lead to huge losses. The purpose of the work was to study the most rational options for constructing detectors with a constant level of false alarms in radars with broadband sounding signals. The problem was solved for the Rayleigh distribution of the envelope of the noise and a number of non-Rayleigh laws — Weibull and the lognormal, the appearance of which is associated with a decrease in the number of reflecting elements in the resolution volume. For Rayleigh interference, an algorithm is proposed with a multi-channel in range incoherent signal amplitude storage and normalization to the larger of the two estimates of the interference power in the range segments. The detection threshold in it adapts not only to the interference power, but also to the magnitude of the «power jump» in range, which allows reducing the number of false alarms during sudden changes in the interference power – the increase in the probability of false alarms did not exceed one order of magnitude. In this algorithm, there is a certain increase in losses associated with incoherent accumulation of signals reflected from target elements, and losses can be reduced by certain increasing the size of the distance segments that make up the window. Algorithms for detecting broadband signals against interference with non-Rayleigh laws of distribution of the envelope – Weibull and lognormal, based on the addition of the algorithm for detecting signals by non-linear transformation of sample counts into counts with a Rayleigh distribution, are studied. The structure of the detection algorithm remains unchanged in practice. The options for detectors of narrowband and broadband signals are considered. It was found that, in contrast to algorithms designed for the Rayleigh distribution, these algorithms provide a stable level of false alarms regardless of the values of the parameters of non-Rayleigh interference. To reduce losses due to interference with the distribution of amplitudes according to the Rayleigh law, detectors consisting of two channels are used, in which one of the channels is tuned for interference with the Rayleigh distribution, and the other for lognormal or Weibull interference. Channels are switched according to special distribution type recognition algorithms. In such detectors, however, there is a certain increase in the probability of false alarms in a rather narrow range of non-Rayleigh interference parameters, where their distribution approaches the Rayleigh distribution. It is shown that when using broadband signals, there is a noticeable decrease in detection losses in non-Rayleigh noise due to lower detection thresholds for in range signal amplitudes incoherent storage.

scholarly journals MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

2018 ◽  
Vol 14 (2) ◽  
pp. 159 ◽  
Author(s):  
Aulia Ilham ◽  
Marza Ihsan Marzuki
Keyword(s):  
Machine Learning ◽  
Satellite Image ◽  
Image Data ◽  
Information Criterion ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
Distribution Type ◽  
Non Linear System ◽  
Image Data Classification

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for  wide data observation. In remote sensing, machine learning method could be  used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.

scholarly journals Limitations on animacy categorization in ensemble perception

2020 ◽  
Author(s):  
Vladislav Khvostov ◽  
Yuri Markov ◽  
Timothy F. Brady ◽  
Igor Utochkin
Keyword(s):  
Poor Performance ◽  
Object Categorization ◽  
Visual Features ◽  
Multiple Objects ◽  
Individual Object ◽  
Ensemble Perception ◽  
Entire Visual Field ◽  
Good Individual ◽  
Distribution Type ◽  
Individual Objects

Many studies have shown that people can rapidly and efficiently categorize the animacy of individual objects and scenes, even with few visual features available. Does this necessarily mean that the visual system has an unlimited capacity to process animacy across the entire visual field? We tested this in an ensemble task requiring observers to judge the relative numerosity of animate vs. inanimate items in briefly presented sets of multiple objects. We generated a set of morphed “animacy continua” between pairs of animal and inanimate object silhouettes and tested them in both individual object categorization and ensemble enumeration. For the ensemble task, we manipulated the ratio between animate and inanimate items present in the display and we also presented two types of animacy distributions: “segmentable” (including only definitely animate and definitely inanimate items) or “non-segmentable” (middle-value, ambiguous morphs pictures were shown along with the definite “extremes”). Our results showed that observers failed to integrate animacy information from multiple items, as they showed very poor performance in the ensemble task and were not sensitive to the distribution type despite their categorization rate for individual objects being near 100%. A control condition using the same design with color as a category-defining dimension elicited both good individual object and ensemble categorization performance and a strong effect of the segmentability type. We conclude that good individual categorization does not necessarily allow people to build ensemble animacy representations, thus showing the limited capacity of animacy perception.

scholarly journals Analysis of the error distribution density convergence with its orthogonal decomposition in navigation measurements

2021 ◽  
Vol 2090 (1) ◽  
pp. 012126
Author(s):  
Igor Vorokhobin ◽  
Iryna Zhuravska ◽  
Igor Burmaka ◽  
Inessa Kulakovska
Keyword(s):  
Distribution Density ◽  
Hermite Polynomials ◽  
Gaussian Function ◽  
Cauchy Distribution ◽  
Error Distribution ◽  
Orthogonal Decomposition ◽  
Moving Object ◽  
Random Errors ◽  
Density Values ◽  
Distribution Type

Abstract Modern trends towards the expansion of online services lead to the need to determine the location of customers, who may also be on a moving object (vessel or aircraft, others vehicle – hereinafter the “Vehicle”). This task is of particular relevance in the fields of medicine – when organizing video conferencing for diagnosis and/or remote rehabilitation, e.g., for post-infarction and post-stroke patients using wireless devices, in education – when organizing distance learning and when taking exams online, etc. For the analysis of statistical materials of the accuracy of determining the location of a moving object, the Gaussian normal distribution is usually used. However, if the histogram of the sample has “heavier tails”, the determination of latitude and longitude’s error according to Gaussian function is not correct and requires an alternative approach. To describe the random errors of navigation measurements, mixed laws of a probability distribution of two types can be used: the first type is the generalized Cauchy distribution, the second type is the Pearson distribution, type VII. This paper has shown that it’s possible obtaining the decomposition of the error distribution density using orthogonal Hermite polynomials, without having its analytical expression. Our numerical results show that the approximation of the distribution function using the Gram-Charlier series of type A makes it possible to apply the orthogonal decomposition to describe the density of errors in navigation measurements. To compare the curves of density and its orthogonal decomposition, the density values were calculated. The research results showed that the normalized density and its orthogonal decomposition practically coincide.

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