scholarly journals Entropy Estimation Using a Linguistic Zipf–Mandelbrot–Li Model for Natural Sequences

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1100
Author(s):  
Andrew D. Back ◽  
Janet Wiles
Keyword(s):  
Sequence Data ◽  
Estimation Method ◽  
Limited Data ◽  
Coincidence Counting ◽  
New Approach ◽  
Rapid Estimation ◽  
Natural Sequence ◽  
Entropy Estimation ◽  
Estimation Algorithms ◽  
Real World Problems

Entropy estimation faces numerous challenges when applied to various real-world problems. Our interest is in divergence and entropy estimation algorithms which are capable of rapid estimation for natural sequence data such as human and synthetic languages. This typically requires a large amount of data; however, we propose a new approach which is based on a new rank-based analytic Zipf–Mandelbrot–Li probabilistic model. Unlike previous approaches, which do not consider the nature of the probability distribution in relation to language; here, we introduce a novel analytic Zipfian model which includes linguistic constraints. This provides more accurate distributions for natural sequences such as natural or synthetic emergent languages. Results are given which indicates the performance of the proposed ZML model. We derive an entropy estimation method which incorporates the linguistic constraint-based Zipf–Mandelbrot–Li into a new non-equiprobable coincidence counting algorithm which is shown to be effective for tasks such as entropy rate estimation with limited data.

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.

scholarly journals A Deterministic Sensor Deployment Method for Target Coverage

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ye Jiang ◽  
Shuyan Xiao ◽  
Jian Liu ◽  
Bo Chen ◽  
Bangbang Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Gas Sensors ◽  
Estimation Method ◽  
Weather Conditions ◽  
Pso Algorithm ◽  
Gas Diffusion ◽  
Optimization Criterion ◽  
Sensor Deployment ◽  
Entropy Estimation ◽  
Gas Leakage

In order to monitor the gas leakage, the gas sensors are deployed conventionally in chemical industry park, with little considerations given to the gas characteristics and weather conditions, which give rise to the problems of coverage hole and coverage repetition. To solve the problems, this paper proposes a deterministic sensor deployment method with the gas diffusion models which takes into account wind speed and direction and then studies the influence of wind speed and direction on the monitoring error of gas sensors. Then, we research the deterministic deployment method of gas sensors in condition of the main wind speed and direction somewhere. Firstly, we use the CFD theory to simulate the gas diffusion situation so as to obtain the concentration value of the relevant points. Secondly, we put forward a new optimization criterion, namely, the more alarm concentration points covered by gas sensors, the coverage performance is better, and the deployment method is better. Accordingly, a new objection function is built. Thirdly, we obtain the weight values of the function using entropy estimation method. Finally, we deploy the gas sensors determinately using particle swarm optimization (PSO) algorithm. The simulation results show that the proposed method can improve the monitoring efficiency and the coverage performance of gas sensor network.

Genetic Algorithms in Fuzzy Engineering Design

1999 ◽  
Author(s):  
Ralf Schleiffer ◽  
Hans-Jürgen Sebastian ◽  
Erik K. Antonsson
Keyword(s):  
Engineering Design ◽  
Clustering Algorithm ◽  
Computer Application ◽  
Test Problems ◽  
Important Class ◽  
New Approach ◽  
Design Task ◽  
Evolutionary Technique ◽  
The Given ◽  
Real World Problems

Abstract Problems in the field of engineering design represent an important class of real world problems that typically require a fuzzy and imprecise representation. This article presents and discusses a new approach to model this type of problem, by incorporating linguistic descriptions together with a variety of user-defined trade-off strategies. An interactive computer application is introduced, using stochastic optimization to solve the design task by producing a specially desired output under the given environmental conditions which are partly caused by the personal preferences of the engineer and by the expectations of the customer. It utilizes a randomized evolutionary technique, made suitable for the class of problems at hand, to generate and to optimize design solutions that are later identified by a clustering algorithm. Moreover test problems that were solved by the application are considered. In all cases the good solutions were obtained by evaluating only an extremely small fraction of all possible designs.

scholarly journals 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelaaziz Mahdaoui ◽  
El Hassan Sbai
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
3D Point Cloud ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
New Approach ◽  
Entropy Estimation ◽  
3D Objects ◽  
Point Cloud Simplification

While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.

scholarly journals Modeling the potential energy field caused by mass density distribution with Eton approach

Open Physics ◽  
2016 ◽  
Vol 14 (1) ◽  
pp. 106-113 ◽  
Author(s):  
Badr Saad T. Alkahtani ◽  
Abdon Atangana
Keyword(s):  
Potential Energy ◽  
Fractional Derivative ◽  
Mass Density ◽  
Variable Order ◽  
Modified Model ◽  
New Approach ◽  
Energy Field ◽  
Variable Order Derivative ◽  
With Memory ◽  
Real World Problems

AbstractA new approach for modeling real world problems called the “Eton Approach” was presented in this paper. The "Eton approach" combines both the concept of the variable order derivative together with Atangana derivative with memory derivative. The Atangana derivative with memory is used to account for the memory and fractional derivative for its filter effect. The approach was used to describe the potential energy field that is caused by a given charge or mass density distribution.We solve the modified model numerically and present supporting numerical simulations.

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