Combinatorial fusion analysis is a paradigm that seeks to provide methods and workflows for combining multiple scoring systems in computational learning and modeling, informatics, and intelligent systems.
Combinatorial fusion analysis is a paradigm that seeks to provide methods and workflows for combining multiple scoring systems in computational learning and modeling, informatics, and intelligent systems.
An overview of the main methods, models, and results of Descriptive Image Analysis is given. Descriptive Image Analysis is a logically organized set of descriptive methods and models designed for image analysis and ev...
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An overview of the main methods, models, and results of Descriptive Image Analysis is given. Descriptive Image Analysis is a logically organized set of descriptive methods and models designed for image analysis and evaluation. The state of the art and trends in the development of Descriptive Image Analysis are determined by the methods, models, and results of the Descriptive Approach to image analysis and understanding. As the methods and apparatus of the Descriptive Approach to the analysis and understanding of images were developed and refined, its interpretation was proposed, defined as Descriptive Image Analysis. The main goal of Descriptive Image Analysis is to structure and standardize the various methods, processes, and concepts used in image analysis and recognition. Descriptive Image Analysis solves the fundamental problems of formalizing and systematizing methods and forms of information representation in image analysis, recognition, and understanding problems, in particular, associated with automating the extraction of information from images to make intelligent decisions (diagnosis, prediction, detection, assessment, and identification patterns of objects, events and processes). Descriptive Image Analysis makes it possible to solve both problems related to constructing formal descriptions of images as recognition objects and problems of synthesizing procedures for recognizing and understanding images. It is suggested that the processes of analysis and evaluation of information represented in the form of images (problem solution trajectories) can generally be considered a sequence/combination of transformations and calculations of a set of intermediate and final (determining the solution) estimates. These transformations are defined by equivalence classes of images and their representations. The latter are defined descriptively, i.e., using a basic set of prototypes and corresponding generating transformations that are functionally complete with respect t
The accurate estimation of the health (reliability) index is important to estimate probability of failure in the reliability assessment of structures. The conventional first-order reliability methods including the Has...
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The accurate estimation of the health (reliability) index is important to estimate probability of failure in the reliability assessment of structures. The conventional first-order reliability methods including the Hasofer-Lind, Rackwitz-Fiessler, and Monte Carlo could lead to unstable, fluctuating, and distorted solutions to nonlinear problems, featuring complicated structural performance functions. The present study aimed to propose a new method by combining the particle swarm optimization and differential evolution algorithms in order to calculate the reliability index. The performance of the proposed method was evaluated by 10 examples from different studies, and the convergence results were compared to the results of studies such as Hasofer-Lind, Rackwitz-Fiessler, Monte Carlo and some other methods. To verify the accuracy of the proposed method, to verify the accuracy of the proposed method, a reliability index chart was applied. The comparisons indicated the high accuracy and speed of the proposed method. Accordingly, in higher order nonlinear problems, the proposed method successfully calculated the reliability index while some failed to solve these problems. (C) 2019 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.
Working with different optimisation algorithms leads to the observation that different types of solutions are generated, disclosing their different nature, their pros and cons. We investigated the question whether or ...
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In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction...
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ISBN:
(纸本)9781467365963
In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction algorithms, each of which is characterized by a small number of adjustable parameters. Adaptability means that parameters of the constructed combination depend on a number of control parameters of the model, which includes the following factors: weather conditions, traffic density, driving dynamics, prediction horizon, and others. Adaptability is achieved by the use of a hierarchical regression (similar to a regression tree). The proposed arrival prediction algorithm has been tested with the data of all the public transport routes in Samara, Russia.
In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction...
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ISBN:
(纸本)9781467365970
In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction algorithms, each of which is characterized by a small number of adjustable parameters. Adaptability means that parameters of the constructed combination depend on a number of control parameters of the model, which includes the following factors: weather conditions, traffic density, driving dynamics, prediction horizon, and others. Adaptability is achieved by the use of a hierarchical regression (similar to a regression tree). The proposed arrival prediction algorithm has been tested with the data of all the public transport routes in Samara, Russia.
By providing a detailed analysis of the particle swarm optimization (PSO) principle and job-shop scheduling problems, this paper presents a new hybrid discrete GAPSO combining the genetic strategy. Adjusting factors a...
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ISBN:
(纸本)9781424467129
By providing a detailed analysis of the particle swarm optimization (PSO) principle and job-shop scheduling problems, this paper presents a new hybrid discrete GAPSO combining the genetic strategy. Adjusting factors are introduced to regulate the generation of convergence;the proposed algorithm is tested by a set of benchmark problems. The results obtained show good convergence of the algorithm. On this basis, a new event-driven strategy for dynamic JSP is proposed, with regard to some uncertain dynamic events like inserting new jobs and machine failures, the proposed algorithm can reschedule once there occur uncertain dynamic events. The results of simulation have confirmed the effectiveness and feasibility of the improved hybrid discrete GAPSO algorithm.
Early accurate detection of ventricular fibrillation (VF) is crucial for inducing a proper electrical therapy (e.g. defibrillation). For automatic defibrillation, several detection algorithms have been developed to di...
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ISBN:
(纸本)9783540892076
Early accurate detection of ventricular fibrillation (VF) is crucial for inducing a proper electrical therapy (e.g. defibrillation). For automatic defibrillation, several detection algorithms have been developed to discriminate between VF and non-VF rhythms. However, many of them are not very accurate or at least very complex for real-time implementation. The present study investigates the performance of five VF detection techniques by using a ventricular tachyarrhythmia ECG database annotated on a beat-to-beat-level. The algorithms were selected mainly for their accuracy and low computational cost. In addition, the authors propose a method based on a combination of two algorithms into one detection system. For each algorithm, the sensitivity and specificity were computed by comparing the decisions given by the algorithm with those found in the annotation files associated with the ECG database. The obtained results confirm that 1. each of the presented methods is capable of identifying VF (however, certain improvements are still required) and 2. that the use of two algorithms in series combines their advantages and reduces the error committed by each of them.
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