This article describes an efficient convexhull algorithm for finite point sets in 3D based on the idea of the Method of Orienting Curves (introduced by Phu in Optimization, 18 (1987) pp. 65-81, for solving optimal co...
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This article describes an efficient convexhull algorithm for finite point sets in 3D based on the idea of the Method of Orienting Curves (introduced by Phu in Optimization, 18 (1987) pp. 65-81, for solving optimal control problems with state constraints). The actual run times of our algorithm and known gift-wrapping algorithm on the set of random points (in uniform distribution) show that our algorithm runs significantly faster than the gift-wrapping one.
The performance of data-driven models such as Artificial Neural Networks and Support Vector Machines relies to a good extent on selecting proper data throughout the design phase. This paper addresses a comparison of f...
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The performance of data-driven models such as Artificial Neural Networks and Support Vector Machines relies to a good extent on selecting proper data throughout the design phase. This paper addresses a comparison of f...
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The performance of data-driven models such as Artificial Neural Networks and Support Vector Machines relies to a good extent on selecting proper data throughout the design phase. This paper addresses a comparison of four unsupervised data selection methods including random, convexhull based, entropy based and a hybrid data selection method. These methods were evaluated on eight benchmarks in classification and regression problems. For classification, Support Vector Machines were used, while for the regression problems, Multi-Layer Perceptrons were employed. Additionally, for each problem type, a non-dominated set of Radial Basis Functions Neural Networks were designed, benefiting from a Multi Objective Genetic Algorithm. The simulation results showed that the convexhull based method and the hybrid method involving convexhull and entropy, obtain better performance than the other methods, and that MOGA designed RBFNNs always perform better than the other models. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substa...
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Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convexhull algorithm. Results of the proposed framework on residential house data demonstrate the designed models' ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.
Using the two ray coherence theorems of Ohta and Maekawa the computation time of ray tracing algorithms for scenes of spheres and convex polyhedra can be reduced considerably. This paper presents further theorems whic...
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