scholarly journals Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 185
Author(s):  
Martino Trassinelli ◽  
Pierre Ciccodicola
Keyword(s):  
Parameter Space ◽  
Likelihood Function ◽  
Search Algorithm ◽  
Probability Distributions ◽  
Mean Shift ◽  
Computation Time ◽  
Recognition Method ◽  
Nested Sampling ◽  
Bayesian Evidence ◽  
Cluster Recognition

Nested sampling is an efficient algorithm for the calculation of the Bayesian evidence and posterior parameter probability distributions. It is based on the step-by-step exploration of the parameter space by Monte Carlo sampling with a series of values sets called live points that evolve towards the region of interest, i.e., where the likelihood function is maximal. In presence of several local likelihood maxima, the algorithm converges with difficulty. Some systematic errors can also be introduced by unexplored parameter volume regions. In order to avoid this, different methods are proposed in the literature for an efficient search of new live points, even in presence of local maxima. Here we present a new solution based on the mean shift cluster recognition method implemented in a random walk search algorithm. The clustering recognition is integrated within the Bayesian analysis program NestedFit. It is tested with the analysis of some difficult cases. Compared to the analysis results without cluster recognition, the computation time is considerably reduced. At the same time, the entire parameter space is efficiently explored, which translates into a smaller uncertainty of the extracted value of the Bayesian evidence.

scholarly journals Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 878
Author(s):  
C. T. J. Dodson ◽  
John Soldera ◽  
Jacob Scharcanski
Keyword(s):  
Face Recognition ◽  
Cyber Security ◽  
Probability Distributions ◽  
Geodesic Distance ◽  
Recognition Method ◽  
Frequency Distributions ◽  
Face Images ◽  
User Access ◽  
Multidimensional Methods ◽  
Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems

2013 ◽  
Vol 464 ◽  
pp. 352-357
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computation Time ◽  
Finite Sequence ◽  
Bat Algorithm ◽  
Harmony Search Algorithm ◽  
Response Surfaces ◽  
Sample Mean ◽  
Constrained Problems

The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search process and social behavior of bat echolocation for decision direction. These algorithms were implemented under different natures of three optimization, which are single-peak, multi-peak and curved-ridge response surfaces. Moreover, both algorithms were also applied to constrained engineering problems. The results from non-linear continuous unconstrained functions in the context of response surface methodology and constrained problems can be shown that Bat algorithm seems to be better in terms of the sample mean and variance of design points yields and computation time.

A parameter-space search algorithm tested on a Hodgkin–Huxley model

2007 ◽  
Vol 96 (6) ◽  
pp. 625-634 ◽  
Author(s):  
Michael S. Reid ◽  
Edgar A. Brown ◽  
Stephen P. DeWeerth
Keyword(s):  
Parameter Space ◽  
Search Algorithm

scholarly journals Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 341
Author(s):  
Bugra Alkan ◽  
Malarvizhi Kaniappan Chinnathai
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Computation Time ◽  
Population Based ◽  
Performance Comparison ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

Numerical simulation of soliton gas within the Korteweg-de Vries type equations

2019 ◽  
pp. 52-66
Author(s):  
Екатерина Геннадьевна Диденкулова ◽  
Анна Витальевна Кокорина ◽  
Алексей Викторович Слюняев
Keyword(s):  
Numerical Scheme ◽  
Initial Conditions ◽  
Probability Distributions ◽  
Scattering Problem ◽  
Spectral Composition ◽  
Computation Time ◽  
Qualitative Description ◽  
Statistical Characteristics ◽  
De Vries ◽  
Pseudo Spectral Method

Приведены детали численной схемы и способа задания начальных условий для моделирования нерегулярной динамики ансамблей солитонов в рамках уравнений типа Кортевега-де Вриза на примере модифицированного уравнения Кортевега-де Вриза с фокусирующим типом нелинейности. Дано качественное описание эволюции статистических характеристик для ансамблей солитонов одной и разных полярностей. Обсуждаются результаты тестовых экспериментов по столкновению большого числа солитонов. The details of the numerical scheme and the method of specifying the initial conditions for the simulation of the irregular dynamics of soliton ensembles within the framework of equations of the Korteweg - de Vries type are given using the example of the modified Korteweg - de Vries equation with a focusing type of nonlinearity. The numerical algorithm is based on a pseudo-spectral method with implicit integration over time and uses the Crank-Nicholson scheme for improving the stability property. The aims of the research are to determine the relationship between the spectral composition of the waves (the Fourier spectrum or the spectrum of the associated scattering problem) and their probabilistic properties, to describe transient processes and the equilibrium states. The paper gives a qualitative description of the evolution of statistical characteristics for ensembles of solitons of the same and different polarities, obtained as a result of numerical simulations; the probability distributions for wave amplitudes are also provided. The results of test experiments on the collision of a large number of solitons are discussed: the choice of optimal conditions and the manifestation of numerical artifacts caused by insufficient accuracy of the discretization. The numerical scheme used turned out to be extremely suitable for the class of the problems studied, since it ensures good accuracy in describing collisions of solitons with a short computation time.

Swarm Intelligence-Based Optimization for PHEV Charging Stations

2015 ◽  
pp. 374-405 ◽  
Author(s):  
Imran Rahman ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Hybrid Electric Vehicle ◽  
Performance Indicator ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Computation Time ◽  
Charging Infrastructure ◽  
Charging Time ◽  
Battery Capacity ◽  
Hybrid Electric ◽  
Charging Stations

In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging time, they optimized the State-of-Charge (SoC), a key performance indicator in hybrid electric vehicle for the betterment of charging infrastructure. Simulation results obtained for maximizing the highly non-linear objective function evaluates the performance of both techniques in terms of global best fitness and computation time.

scholarly journals Setting Alarm Thresholds in Measurements with Systematic and Random Errors

Stats ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 259-271 ◽  
Author(s):  
Tom Burr ◽  
Elisa Bonner ◽  
Kamil Krzysztoszek ◽  
Claude Norman
Keyword(s):  
Probability Distributions ◽  
Mean Shift ◽  
Measurement Data ◽  
Main Tool ◽  
False Alarm Probability ◽  
Data Uncertainty ◽  
Grouped Data ◽  
Random Errors ◽  
Group Data ◽  
Ordered Data

For statistical evaluations that involve within-group and between-group variance components (denoted σ W 2 and σ B 2 , respectively), there is sometimes a need to monitor for a shift in the mean of time-ordered data. Uncertainty in the estimates σ ^ W 2 and σ ^ B 2 should be accounted for when setting alarm thresholds to check for a mean shift as both σ W 2 and σ B 2 must be estimated. One-way random effects analysis of variance (ANOVA) is the main tool for analysing such grouped data. Nearly all of the ANOVA applications assume that both the within-group and between-group components are normally distributed. However, depending on the application, the within-group and/or between-group probability distributions might not be well approximated by a normal distribution. This review paper uses the same example throughout to illustrate the possible approaches to setting alarm limits in grouped data, depending on what is assumed about the within-group and between-group probability distributions. The example involves measurement data, for which systematic errors are assumed to remain constant within a group, and to change between groups. The false alarm probability depends on the assumed measurement error model and its within-group and between-group error variances, which are estimated while using historical data, usually with ample within-group data, but with a small number of groups (three to 10 typically). This paper illustrates the parametric, semi-parametric, and non-parametric options to setting alarm thresholds in such grouped data.

An Iterated Local Search Algorithm for Task Assignment in Distributed Computing Systems

2012 ◽  
Vol 588-589 ◽  
pp. 1308-1311
Author(s):  
Qin Ma Kang ◽  
Hong He ◽  
Hai Ning Jiang
Keyword(s):  
Distributed Computing ◽  
Local Search ◽  
Search Algorithm ◽  
Task Assignment ◽  
Computation Time ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Wide Range

This paper considers the problem of task assignment in heterogeneous distributed computing systems with the goal of minimizing the total execution and communication costs. An iterated local search algorithm is proposed for finding the suboptimal task assignment in a reasonable amount of computation time. We study the performance of the proposed algorithm over a wide range of parameters such as the problem scales, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness of the algorithm is manifested by comparing it with other competing algorithms in the relevant literature.

scholarly journals Buffer Allocation via Bottleneck-Based Variable Neighborhood Search

2020 ◽  
Vol 10 (23) ◽  
pp. 8569
Author(s):  
Sixiao Gao ◽  
Toshimitsu Higashi ◽  
Toyokazu Kobayashi ◽  
Kosuke Taneda ◽  
Jose I. U. Rubrico ◽  
...  
Keyword(s):  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Computation Time ◽  
State Probability ◽  
Neighborhood Search ◽  
Buffer Allocation ◽  
Neighborhood Structure ◽  
Production Lines ◽  
Solution Quality ◽  
Acceptable Solution

This study addresses the challenging problem of efficient buffer allocation in production lines. Suitable locations for buffer allocation are determined to satisfy the desired throughput, while a suitable balance between solution quality and computation time is achieved. A throughput calculation approach that yields the state probability of production lines is adopted to evaluate the effectiveness of candidate buffer allocation solutions. To generate candidate buffer allocation solutions, an active probability index based on state probability is proposed to rapidly detect suitable locations of buffer allocations. A variable neighborhood search algorithm is used to maintain acceptable solution quality; an additional neighborhood structure is used in the case where no satisfactory solution is generated in the initial neighborhood structure. Extensive numerical experiments demonstrate the efficacy of the proposed approach. The proposed approach can facilitate agile design of production lines in industry by rapidly estimating production line topologies.

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