Browsing by Tema "Algorithm"

Now showing 1 - 20 of 97

2-D Niblett-Bostick magnetotelluric inversion
(Cartographic and Geological Institute of Catalonia, 2010) Juan Luis Polo Rodríguez; Francisco J. Esparza; Enrique Gómez‐Treviño
A simple and robust imaging technique for two-dimensional magnetotelluric interpretations has been developed following the well known Niblett-Bostick transformation for one-dimensional profiles. The algorithm processes series and parallel magnetotelluric impedances and their analytical influence functions using a regularized Hopfield artificial neural network. The adaptive, weighted average approximation preserves part of the nonlinearity of the original problem, yet no initial model in the usual sense is required for the recovery of the model; rather, the built-in relationship between model and data automatically and concurrently considers many half spaces whose electrical conductivities vary according to the data. The use of series and parallel impedances, a self-contained pair of invariants of the impedance tensor, avoids the need to decide on best angles of rotation for identifying TE and TM modes. Field data from a given profile can thus be fed directly into the algorithm without much processing. The solutions offered by the regularized Hopfield neural network correspond to spatial averages computed through rectangular windows that can be chosen at will. Applications of the algorithm to simple synthetic models and to the standard COPROD2 data set illustrate the performance of the approximation.
A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red-Green-Blue
(Institute of Electrical and Electronics Engineers, 2017) Jesus Dario Romero; Marı́a J. Lado; Arturo J. Méndez
This paper presents an algorithm for background modeling and foreground detection that uses scaling coefficients, which are defined with a new color model called lightness-red-green-blue (LRGB). They are employed to compare two images by finding pixels with scaled lightness. Three backgrounds are used: 1) verified background with pixels that are considered as background; 2) testing background with pixels that are tested several times to check if they belong to the background; and 3) final background that is a combination of the testing and verified background (the testing background is used in places, where the verified background is not defined). If a testing background pixel matches pixels from previous frames (the match is tested using scaling coefficients), it is copied to the verified background, otherwise the pixel is set as the weighted average of the corresponding pixels of the last input images. After the background is computed, foreground objects are detected by using the scaling coefficients and additional criteria. The algorithm was evaluated using the SABS data set, Wallflower data set and a subset of the CDnet 2014 data set. The average F measure and sensitivity with the SABS Data set were 0.7109 and 0.8725, respectively. In the Wallflower data set, the total number of errors was 5280 and the total F-measure was 0.9089. In the CDnet 2014 data set, the F-measure for the baseline test case was 0.8887 and for the shadow test case was 0.8300.
A COMPARATIVE OF CURVE-FITTING ALGORITHMS FOR THE EXTRACTION OF MODAL PARAMETERS FROM RESPONSE MEASUREMENTS
(2004) Robert B. Randall; Grover Zurita; T. Wardrop
The main objective of this paper is to perform a comparison of several curve-fitting methods for extraction of the modal parameters from response vibration measurements, and in particular the best damping estimates. Measurements were carried out on a steel beam to which a constrained layer had been added to make the damping more similar to that of vehicle structural components. Two shakers with different excitation signals, a periodic impulsive and a random signal, respectively, excited the structure, but after separation, only the random part was analysed for the results of this paper. This study compares a number of common curve fitting methods, viz: The Rational Fraction Polynomial Method, the Complex Exponential Method, the Complex Cepstrum Method, the Hilbert Envelope Method and the Ibrahim Time Domain method. The most accurate results for detection of the damping and natural frequencies were obtained by using the Ibrahim Time Domain Method, with the Rational Fraction Polynomial method very similar. The Hilbert Envelope method gave comparable damping estimates. The Cepstrum and Complex Exponential methods gave reasonable results for the frequencies, but not for the damping.
A COMPARATIVE OF CURVE-FITTING ALGORITHMS FOR THE EXTRACTION OF MODAL PARAMETERS FROM RESPONSE MEASUREMENTS
(2005) Robert B. Randall; Grover Zurita; T. Wardrop
The main objective of this paper is to perform a comparison of several curve-fitting methods for extraction of the modal parameters from response vibration measurements, and in particular the best damping estimates. Measurements were carried out on a steel beam to which a constrained layer had been added to make the damping more similar to that of vehicle structural components. Two shakers with different excitation signals, a periodic impulsive and a random signal, respectively, excited the structure, but after separation, only the random part was analysed for the results of this paper. This study compares a number of common curve fitting methods, viz: The Rational Fraction Polynomial Method, the Complex Exponential Method, the Complex Cepstrum Method, the Hilbert Envelope Method and the Ibrahim Time Domain method. The most accurate results for detection of the damping and natural frequencies were obtained by using the Ibrahim Time Domain Method, with the Rational Fraction Polynomial method very similar. The Hilbert Envelope method gave comparable damping estimates. The Cepstrum and Complex Exponential methods gave reasonable results for the frequencies, but not for the damping.
A Correlation-Based Distance Function for Nearest Neighbor Classification
(Springer Science+Business Media, 2008) Yanet Rodríguez Sarabia; Bernard De Baets; María M. García; Carlos Morell; Ricardo Grau
A Customized Machine Learning Algorithm for Discovering the Shapes of Recovery: Was the Global Financial Crisis Different?
(Springer Science+Business Media, 2022) Gonzalo Castañeda; Luis Castro Peñarrieta
A GENETIC ALGORITHM FOR THE RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM (RCPSP)
(2007) Édgar Gutiérrez Franco; Fernando La Torre Zurita; Gonzalo Mejía
This paper proposes a Genetic Algorithm (GA) for the Resource Constrained Project Scheduling Problem (RCPSP). Resources are renewable and there is a unique way to perform the activities. This work employs Genetics Algorithms to schedule project activities to minimize makespan subject to precedence constraints and resources availability. A serial generation scheme is used to obtain the schedule. The algorithm was programmed using Object Oriented programming that allows generating individuals with their own attributes such as activity sequence and makespan. A Genetic Algorithm is proposed which uses a novel chromosome representation. The issues of the GA parameter tuning are also discussed in this paper. A computer tool that allows the user to define activities, precedence constraints and resource capacity was developed.
A graph clustering algorithm for detection and genotyping of structural variants from long reads
(University of Oxford, 2023) Nicolás Gaitán; Jorge Duitama
The results show that our approach outperformed state-of-the-art tools on germline SV calling and genotyping, especially at low depths, and in error-prone repetitive regions. We believe this work significantly contributes to the development of bioinformatic strategies to maximize the use of long-read sequencing technologies.
A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization
(Institute of Advanced Engineering and Science (IAES), 2021) Olief Ilmandira Ratu Farisi; Budi Setiyono; R. Imbang Danandjojo
<span>This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.</span>
A Memetic Algorithm for a Pick-Up and Delivery Problem by Helicopter
(Springer Nature, 2008) Nubia Velasco; Philippe Castagliola; Pierre Dejax; Christelle Guéret; Christian Prins
A new algorithm for the integration of exponential and logarithmic functions
(1977) Michael Rothstein
An algorithm for symbolic integration of functions built up from the rational functions by repeatedly applying either the exponential or logarithm functions is discussed. This algorithm does not require polynomial factorization nor partial fraction decomposition and requires solutions of linear systems with only a small number of unknowns. It is proven that if this algorithm is applied to rational functions over the integers, a computing time bound for the algorithm can be obtained which is a polynomial in a bound on the integer length of the coefficients, and in the degrees of the numerator and denominator of the rational function involved.
A polynomial time algorithm for solving the word-length optimization problem
(2013) Karthick N. Parashar; Daniel Ménard; Olivier Sentieys
Trading off accuracy to the system costs is popularly addressed as the word-length optimization (WLO) problem. Owing to its NP-hard nature, this problem is solved using combinatorial heuristics. In this paper, a novel approach is taken by relaxing the integer constraints on the optimization variables and obtain an alternate noise-budgeting problem. This approach uses the quantization noise power introduced into the system due to fixed-point word-lengths as optimization variables instead of using the actual integer valued fixed-point word-lengths. The noise-budgeting problem is proved to be convex in the rounding mode quantization case and can therefore be solved using analytical convex optimization solvers. An algorithm with linear time complexity is provided in order to realize the actual fixed-point word-lengths from the noise budgets obtained by solving the convex noise-budgeting problem.
A Prediction Algorithm based on Markov Chains for finding the Minimum Cost Path in a Mobile WSNs
(Agora University, 2019) Germán A. Montoya; Yezid Donoso
In this paper we propose the usage of a prediction technique based on Markov Chains to predict nodes positions with the aim of obtain short paths at minimum energy consumption. Specifically, the valuable information from the mobility prediction method is provided to our distributed routing algorithm in order to take the best network decisions considering future states of network resources. In this sense, in each network node, the mobility method employed is based on a Markov model to forecast future RSSI states of neighboring nodes for determining if they will be farther or closer within the next steps. The approach is evaluated considering different algorithms such as: Distance algorithm, Distance Away algorithm and Random algorithm. In addition, with the aim of performing comparisons against optimal values, we present a mathematical optimization model for finding the minimum cost path between a source and a destination node considering all network nodes are mobile. This paper is an extended variant of [8].
A tree-matching algorithm: Application to airways in CT images of subjects with the acute respiratory distress syndrome
(Elsevier BV, 2016) Alfredo Pinzón; Marcela Hernández Hoyos; Jean‐Christophe Richard; Leonardo Flórez-Valencia; Maciej Orkisz
Acoustic signal characterization of a ball milling machine model
(IOP Publishing, 2011) J. Alexis Andrade-Romero; Jesus Franklin Andrade Romero; Mauricio Améstegui
Los Angeles machine is used both for mining process and for standard testing covering strength of materials. As the present work is focused on the latter application, an improvement in the estimation procedure for the resistance percentage of small-size coarse aggregate is presented. More precisely, is proposed a pattern identification strategy of the vibratory signal for estimating the resistance percentage using a simplified chaotic model and the continuous wavelet transform.
Algorithm 972
(Association for Computing Machinery, 2017) Juan F. Pérez; Daniel F. Silva; Julio C. Góez; Andrés Sarmiento; Andrés Sarmiento-Romero; Raha Akhavan‐Tabatabaei; Germán Riaño
Markov chains (MC) are a powerful tool for modeling complex stochastic systems. Whereas a number of tools exist for solving different types of MC models, the first step in MC modeling is to define the model parameters. This step is, however, error prone and far from trivial when modeling complex systems. In this article, we introduce jMarkov, a framework for MC modeling that provides the user with the ability to define MC models from the basic rules underlying the system dynamics. From these rules, jMarkov automatically obtains the MC parameters and solves the model to determine steady-state and transient performance measures. The jMarkov framework is composed of four modules: (i) the main module supports MC models with a finite state space; (ii) the jQBD module enables the modeling of Quasi-Birth-and-Death processes, a class of MCs with infinite state space; (iii) the jMDP module offers the capabilities to determine optimal decision rules based on Markov Decision Processes; and (iv) the jPhase module supports the manipulation and inclusion of phase-type variables to represent more general behaviors than that of the standard exponential distribution. In addition, jMarkov is highly extensible, allowing the users to introduce new modeling abstractions and solvers.
Algorithms with low computational cost for monitoring and analysis of Colombia soundscapes
(2015) Luis Alfredo Quiroz; Luis Tobòn; Paula Caycedo; Oscar Laverde-R.
Studies focused on soundscape are important on biological conservation, because natural sounds are permanent and with dynamic properties, they have been linked to the welfare of the environment and the structure of the landscape. These studies usually analyze the sound in time and frequency domains, with computationally heavy and centralized algorithms. However, new technologies for real time analysis requires distributed algorithms with low computational cost. Hence, the present work evaluates the computational cost of alternative methods with potential applicability in analysis of time-varying signals. The analyzed methods are short time Fourier transform, harmonic expansion, wavelet transform (analytical and non-analytical Morlet, Mexican hat, and Paul) and orthogonal polynomial expansion (Legendre, Chebyshev, and Hermite). A comparison between these methods is presented, in which processing time, memory consumption, quality of reconstruction and grouping index are some of the features selected, resulting in a useful computational cost ranking. The methods are applied to several signals generated with different procedures, such as artificial modulated signals and natural recorded sounds (provided by The Alexander von Humboldt Institute). In conclusion, Harmonic expansion, Chebyshev expansion, Legendre expansion and Short Time Fourier Transform are the best methods with excellent performance in all features.
Algoritmo para el uso de ImageJ: aplicación en la detección de fragilidad osmótica en eritrocitos humanos
(J. Selva Andina Res. Soc., 2026) Agramont Morales, Natalia; Quenta Álvarez, Daira Beth; Flores Botello, Belén Araceli; Carrasco Rosso, Marcia Olga; Chavez Lizárraga, Georgina
El estudio de las características de los eritrocitos se realiza mediante distintas pruebas, entre ellas la microscopía y el análisis de imágenes. El propósito de este artículo es desarrollar un algoritmo funcional en el software ImageJ para identificar las características morfológicas de los eritrocitos y determinar la variación de la fragilidad osmótica cuando estos son expuestos a distintas condiciones. Las imágenes utilizadas fueron observadas mediante un microscopio Leica Aristoplan y capturadas por una cámara Leica DMC6200 conectada al equipo, para posteriormente ser procesadas en el software ImageJ. El algoritmo describe de forma ordenada el proceso que se realiza desde la calibración de la imagen hasta la obtención de resultados cuantitativos. Este proceso incluye la calibración según el aumento del microscopio, la corrección de posibles errores en la imagen y la identificación de las células para la obtención de parámetros como el área, el perímetro, la redondez y el diámetro de Feret. La aplicación del algoritmo en ImageJ permite un procesamiento de imágenes que facilita la cuantificación morfológica de eritrocitos. El algoritmo presentado es eficiente y reproducible, además puede ser adaptado según las necesidades del usuario. Tras el análisis, se concluye que el algoritmo puede ser modificado según los requerimientos del usuario. En conjunto, el algoritmo facilita el uso de ImageJ para la cuantificación morfológica de eritrocitos.
Algoritmo SCDO en el Principio de Mínimo y la programación dinámica definida sobre dominios finitos
(2004) Juan José Cardillo Albarrán; Ferenc Szigeti; Jean-Claude Hennet; Jean-Louis Calvet
In this paper we show an algorithm based on formal calculus, which obtains the explicit form of the minimum on functions defined over finite domain. This algorithm is used to obtain expressions of the optimal control (minimum) when the methods: the principle of minimum for processes on finite domain and the parametric dynamic programming are used.
Algoritmos Wavenet con Aplicaciones en la Aproximación de Señales: un Estudio Comparativo
(Technical University of Valencia, 2012) C.R. Domínguez Mayorga; María Angélica Espejel Rivera; Luis Enrique Ramos‐Velasco; Julio C. Ramos Fernández; Enrique Escamilla-Hernández
In this paper adaptable methods for computational algorithms are presented. These algorithms use neural networks and wavelet series to build neuro wavenets approximators. The algorithms obtained are applied to the approximation of signals that represent algebraic functions and random functions, as well as a medical EKG signal. It shows how wavenets can be combined with auto-tuning methods for tracking complex signals that are a function of time. Results are shown in numerical simulation of two architectures of neural approximators wavenets: the first is based on a wavenet with which they approach the signals under study where the parameters of the neural network are adjusted online, the other neuro approximator scheme uses an IIR filter to the output of wavenet, which serves to filter the out- put, in this way discriminate contributions of neurons that are less important in the approximation of the signal, which helps reduce the convergence time to a desired minimum error.