scholarly journals Estudo comparativo de métricas de ranking em Redes Sociais

2020 ◽  
Author(s):  
Samuel O. Silva ◽  
Bruno O. Goulart ◽  
Maria Júlia M. Schettini ◽  
Carolina Xavier ◽  
João Gabriel Silva
Keyword(s):  
Information Diffusion ◽  
Computational Cost ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Computational Time ◽  
Short Term ◽  
Vertex Ranking ◽  
Influential Nodes ◽  
Low Computational Cost ◽  
High Computational Cost

The use of modeling and application of complex networks in several areas of knowledge have become an important tool for understanding different phenomena; among them some related to the structures and dissemination of information on social medias. In this sense, the use of a network's vertex ranking can be applied in the detection of influential nodes and possible foci of information diffusion. However, calculating the position of the vertices in some of these rankings may require a high computational cost. This paper presents a comparative study between six ranking metrics applied in different social medias. This comparison is made using the rank correlation coefficients. In addition, a study is presented on the computational time spent by each ranking. Results show that the Grau ranking metric has a greater correlation with other metrics and has low computational cost in its execution, making it an efficient indication in detecting influential nodes when there is a short term for the development of this activity.

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

scholarly journals A Simple Time-Varying Sensitivity Analysis (TVSA) for Assessment of Temporal Variability of Hydrological Processes

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2463 ◽  
Author(s):  
Yelena Medina ◽  
Enrique Muñoz
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Variability ◽  
Time Windows ◽  
Computational Cost ◽  
Time Varying ◽  
Moving Window ◽  
Time Sensitivity ◽  
Annual Time ◽  
Low Computational Cost ◽  
High Computational Cost

Time-varying sensitivity analysis (TVSA) allows sensitivity in a moving window to be estimated and the time periods in which the specific components of a model can affect its performance to be identified. However, one of the disadvantages of TVSA is its high computational cost, as it estimates sensitivity in a moving window within an analyzed series, performing a series of repetitive calculations. In this article a function to implement a simple TVSA with a low computational cost using regional sensitivity analysis is presented. As an example of its application, an analysis of hydrological model results in daily, monthly, and annual time windows is carried out. The results show that the model allows the time sensitivity of a model with respect to its parameters to be detected, making it a suitable tool for the assessment of temporal variability of processes in models that include time series analysis. In addition, it is observed that the size of the moving window can influence the estimated sensitivity; therefore, analysis of different time windows is recommended.

scholarly journals Human Activity Recognition Based on Symbolic Representation Algorithms for Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4045 ◽  
Author(s):  
Wesllen Sousa Lima ◽  
Hendrio de Souza Bragança ◽  
Kevin Montero Quispe ◽  
Eduardo Pereira Souto
Keyword(s):  
Inertial Sensors ◽  
Computational Cost ◽  
Symbolic Representation ◽  
Mobile Sensing ◽  
Memory Space ◽  
Symbolic Aggregate Approximation ◽  
Symbolic Data ◽  
Accuracy Rates ◽  
Low Computational Cost ◽  
High Computational Cost

Mobile sensing has allowed the emergence of a variety of solutions related to the monitoring and recognition of human activities (HAR). Such solutions have been implemented in smartphones for the purpose of better understanding human behavior. However, such solutions still suffer from the limitations of the computing resources found on smartphones. In this sense, the HAR area has focused on the development of solutions of low computational cost. In general, the strategies used in the solutions are based on shallow and deep learning algorithms. The problem is that not all of these strategies are feasible for implementation in smartphones due to the high computational cost required, mainly, by the steps of data preparation and the training of classification models. In this context, this article evaluates a new set of alternative strategies based on Symbolic Aggregate Approximation (SAX) and Symbolic Fourier Approximation (SFA) algorithms with the purpose of developing solutions with low computational cost in terms of memory and processing. In addition, this article also evaluates some classification algorithms adapted to manipulate symbolic data, such as SAX-VSM, BOSS, BOSS-VS and WEASEL. Experiments were performed on the UCI-HAR, SHOAIB and WISDM databases commonly used in the literature to validate HAR solutions based on smartphones. The results show that the symbolic representation algorithms are faster in the feature extraction phase, on average, by 84.81%, and reduce the consumption of memory space, on average, by 94.48%, and they have accuracy rates equivalent to conventional algorithms.

Real Gas Effects in Turbomachinery Flows: A Computational Fluid Dynamics Model for Fast Computations

2004 ◽  
Vol 126 (2) ◽  
pp. 268-276 ◽  
Author(s):  
Paolo Boncinelli ◽  
Filippo Rubechini ◽  
Andrea Arnone ◽  
Massimiliano Cecconi ◽  
Carlo Cortese
Keyword(s):  
Fluid Dynamic ◽  
Computational Cost ◽  
Three Dimensional ◽  
Industrial Applications ◽  
Design Tool ◽  
Navier Stokes ◽  
Computational Time ◽  
Real Gas ◽  
Real Gas Effects ◽  
Low Computational Cost

A numerical model was included in a three-dimensional viscous solver to account for real gas effects in the compressible Reynolds averaged Navier-Stokes (RANS) equations. The behavior of real gases is reproduced by using gas property tables. The method consists of a local fitting of gas data to provide the thermodynamic property required by the solver in each solution step. This approach presents several characteristics which make it attractive as a design tool for industrial applications. First of all, the implementation of the method in the solver is simple and straightforward, since it does not require relevant changes in the solver structure. Moreover, it is based on a low-computational-cost algorithm, which prevents a considerable increase in the overall computational time. Finally, the approach is completely general, since it allows one to handle any type of gas, gas mixture or steam over a wide operative range. In this work a detailed description of the model is provided. In addition, some examples are presented in which the model is applied to the thermo-fluid-dynamic analysis of industrial turbomachines.

The Influence of Metamodeling Techniques on the Multidisciplinary Design Optimization of a Radial Compressor Impeller

2012 ◽  
Author(s):  
Christopher Chahine ◽  
Joerg R. Seume ◽  
Tom Verstraete
Keyword(s):  
Fluid Dynamics ◽  
Design Optimization ◽  
Design Space ◽  
Optimization Algorithms ◽  
Computational Cost ◽  
Computational Time ◽  
Radial Compressor ◽  
Compressor Impeller ◽  
The Impact ◽  
Low Computational Cost

Aerodynamic turbomachinery component design is a very complex task. Although modern CFD solvers allow for a detailed investigation of the flow, the interaction of design changes and the three dimensional flow field are highly complex and difficult to understand. Thus, very often a trial and error approach is applied and a design heavily relies on the experience of the designer and empirical correlations. Moreover, the simultaneous satisfaction of aerodynamic and mechanical requirements leads very often to tedious iterations between the different disciplines. Modern optimization algorithms can support the designer in finding high performing designs. However, many optimization methods require performance evaluations of a large number of different geometries. In the context of turbomachinery design, this often involves computationally expensive Computational Fluid Dynamics and Computational Structural Mechanics calculations. Thus, in order to reduce the total computational time, optimization algorithms are often coupled with approximation techniques often referred to as metamodels in the literature. Metamodels approximate the performance of a design at a very low computational cost and thus allow a time efficient automatic optimization. However, from the experiences gained in past optimizations it can be deduced that metamodel predictions are often not reliable and can even result in designs which are violating the imposed constraints. In the present work, the impact of the inaccuracy of a metamodel on the design optimization of a radial compressor impeller is investigated and it is shown if an optimization without the usage of a metamodel delivers better results. A multidisciplinary, multiobjective optimization system based on a Differential Evolution algorithm is applied which was developed at the von Karman Institute for Fluid Dynamics. The results show that the metamodel can be used efficiently to explore the design space at a low computational cost and to guide the search towards a global optimum. However, better performing designs can be found when excluding the metamodel from the optimization. Though, completely avoiding the metamodel results in a very high computational cost. Based on the obtained results in present work, a method is proposed which combines the advantages of both approaches, by first using the metamodel as a rapid exploration tool and then switching to the accurate optimization without metamodel for further exploitation of the design space.

scholarly journals Comparison between Meta-Heuristic Algorithms for Path Planning

2020 ◽  
Author(s):  
Lídia Rocha ◽  
Kelen Vivaldini
Keyword(s):  
Execution Time ◽  
Path Length ◽  
Heuristic Algorithms ◽  
Computational Cost ◽  
Computational Time ◽  
Complex Environments ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Aerial Vehicle ◽  
High Computational Cost

Unmanned Aerial Vehicle (UAV) has been increasingly employed in several missions with a pre-defined path. Over the years, UAV has become necessary in complex environments, where it demands high computational cost and execution time for traditional algorithms. To solve this problem meta-heuristic algorithms are used. Meta-heuristics are generic algorithms to solve problems without having to describe each step until the result and search for the best possible answer in an acceptable computational time. The simulations are made in Python, with it, a statistical analyses was realized based on execution time and path length between algorithms Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Glowworm Swarm Optimization (GSO). Despite the GWO returns the paths in a shorter time, the PSO showed better performance with similar execution time and shorter path length. However, the reliability of the algorithms will depend on the size of the environment. PSO is less reliable in large environments, while the GWO maintains the same reliability.

scholarly journals The revised FLORIDyn model: Implementation of heterogeneous flow and the Gaussian wake

2022 ◽  
Author(s):  
Marcus Becker ◽  
Bastian Ritter ◽  
Bart Doekemeijer ◽  
Daan van der Hoek ◽  
Ulrich Konigorski ◽  
...  
Keyword(s):  
Wind Farm ◽  
Computational Cost ◽  
Parametric Model ◽  
Computational Time ◽  
Flow Conditions ◽  
Heterogeneous Flow ◽  
The Mean ◽  
Wake Model ◽  
Low Computational Cost ◽  
Good Agreement

Abstract. In this paper a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at low computational cost under heterogeneous and changing wind conditions. Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics: this is achieved by creating so called Observation Points (OPs) at each time step at the rotor plane which inherit the turbine state. In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model, but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computational lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible. FLORIDyn is compared to SOWFA simulations in three- and nine-turbine cases under static and changing environmental conditions.The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model based dynamic wind farm control.

scholarly journals WearGP: A UQ/ML Wear Prediction Framework for Slurry Pump Impellers and Casings

2020 ◽  
Author(s):  
Anh Tran ◽  
Yan Wang ◽  
John Furlan ◽  
Krishnan V. Pagalthivarthi ◽  
Mohamed Garman ◽  
...  
Keyword(s):  
High Performance ◽  
Computational Cost ◽  
Operating Conditions ◽  
Wear Prediction ◽  
Wear Rates ◽  
Computing Platform ◽  
Multi Phase ◽  
Performance Computing ◽  
Low Computational Cost ◽  
High Computational Cost

Abstract Dedicated to the memory of John Furlan. Wear prediction is important in designing reliable machinery for slurry industry. It usually relies on multi-phase computational fluid dynamics, which is accurate but computationally expensive. Each run of the simulations can take hours or days even on a high-performance computing platform. The high computational cost prohibits a large number of simulations in the process of design optimization. In contrast to physics-based simulations, data-driven approaches such as machine learning are capable of providing accurate wear predictions at a small fraction of computational costs, if the models are trained properly. In this paper, a recently developed WearGP framework [1] is extended to predict the global wear quantities of interest by constructing Gaussian process surrogates. The effects of different operating conditions are investigated. The advantages of the WearGP framework are demonstrated by its high accuracy and low computational cost in predicting wear rates.

scholarly journals Research on Evaluation Method of Sports Events Based on Edge Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
FangXiao Deng ◽  
A. Gudamu
Keyword(s):  
Evaluation Method ◽  
Computational Cost ◽  
Computational Time ◽  
Learning Technology ◽  
Data Set ◽  
Research On Evaluation ◽  
Sports Events ◽  
Traditional Sports ◽  
Set Up ◽  
High Computational Cost

In view of the high computational cost and long computational time of IoT edge algorithm in traditional sports event evaluation, this paper optimizes IoT edge algorithm by introducing deep reinforcement learning technology. Set the IoT edge algorithm cycle through the IoT topology to obtain the data upload speed. In order to improve the evaluation efficiency of sports events, the process of edge algorithm is designed. The contribution rate of evaluation index is calculated, and the consistency, minimum deviation, and minimum difference of the results are taken as the standard to design the evaluation method of sports events. In order to verify the performance of the optimized edge algorithm, the test data set and test platform are set up and the comparative experiment is designed. Compared with the traditional methods, the edge algorithm based on DSLL has lower computational cost, shorter computational time, higher evaluation accuracy, and more practical results.

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