1,447 research outputs found

    Layout Optimization of Planar Braced Frames Using Modified Dolphin Monitoring Operator

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    Determining the optimum placement of braces in steel frames has always been one of the most challenging issues in structural engineering. In this paper, the size and placement of the X-braces in planar frame structures is determined in a way that the total weight of the braced frames becomes minimum, while satisfying the design requirements and constraints. Variables of the optimization contain the cross sections for beams, columns, and X-braces as well as the placement of these braces in the frames. Attempt has also been made to consider all the constraints of an actual design problem. One of the other objectives of this study is to investigate the effect of including or excluding some of the constraints affecting the optimization of the planar frame design. For this purpose, the Colliding Bodies Optimization (CBO) and CBO-MDM algorithms have been utilized. Modified Dolphin Monitoring (MDM) operator is recently developed for improving the performance of the metaheuristic algorithms. Here, this operator is utilized to enhance the performance of the CBO algorithm to optimize the weight of the frames. For additional comparison of the results, the particle swarm optimization (PSO) algorithm and imperialist competitive algorithm (ICA) are used

    Radar-based Feature Design and Multiclass Classification for Road User Recognition

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    The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different features are extracted from the measurement data and tested on their performance. From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large. To overcome this issue, classifier binarization is used among other techniques in order to better account for underrepresented classes. A new method to couple the resulting probabilities is proposed and compared to others with great success. Final results show substantial improvements when compared to ordinary multiclass classificationComment: 8 pages, 6 figure

    A ‘Best-of-Breed’ approach for designing a fast algorithm for computing fixpoints of Galois Connections

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    The fixpoints of Galois Connections form patterns in binary relational data, such as object-attribute relations, that are important in a number of data analysis fields, including Formal Concept Analysis (FCA), Boolean factor analysis and frequent itemset mining. However, the large number of such fixpoints present in a typical dataset requires efficient computation to make analysis tractable, particularly since any particular fixpoint may be computed many times. Because they can be computed in a canonical order, testing the canonicity of fixpoints to avoid duplicates has proven to be a key factor in the design of efficient algorithms. The most efficient of these algorithms have been variants of the Close-By-One (CbO) algorithm. In this article, the algorithms CbO, FCbO, In-Close, In-Close2 and a new variant, In-Close3, are presented together for the first time, with in-Close2 and In-Close3 being the results of breeding In-Close with FCbO. To allow them to be easily compared, the algorithms are presented in the same style and notation. The important advances in CbO are described and compared graphically using a simple example. For the first time, the algorithms are implemented using the same structures and techniques to provide a level playing field for evaluation. Their performance is tested and compared using a range of data sets and the most important features identified for a CbO ‘Best-of-Breed’. This article also presents, for the first time, the ‘partial-closure’ canonicity test

    Modified Dolphin Monitoring Operator for Weight Optimization of Frame Structures

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    In this article, a modified dolphin monitoring (MDM) operatoris introduced and used to improve the performance of the collidingbodies optimization (CBO) algorithm for optimal designof steel structures (CBO-MDM). The performance of the CBO,enhanced colliding bodies optimization (ECBO) and CBOMDMare compared through three well-established structuralbenchmarks. The optimized designs obtained by thesealgorithms are compared, and the results show that the performanceof CBO-MDM is superior to those of the other twoalgorithms. The MDM is found to be a suitable tool to enhancethe performance of the CBO algorithm
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