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Titlebook: Computational Science – ICCS 2018; 18th International C Yong Shi,Haohuan Fu,Peter M. A. Sloot Conference proceedings 2018 Springer Internat

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樓主: Harrison
51#
發(fā)表于 2025-3-30 10:48:41 | 只看該作者
52#
發(fā)表于 2025-3-30 16:26:39 | 只看該作者
Detecting Wildlife in Unmanned Aerial Systems Imagery Using Convolutional Neural Networks Trained wiy true in imagery provided by an Unmanned Aerial System (UAS), where the relative size of wildlife is small and visually similar to its background. This work presents an automated feedback loop which can be used to train convolutional neural networks with extremely unbalanced class sizes, which alle
53#
發(fā)表于 2025-3-30 19:55:11 | 只看該作者
54#
發(fā)表于 2025-3-30 22:21:31 | 只看該作者
Hybrid Genetic Algorithm for an On-Demand First Mile Transit System Using Electric Vehiclesged as a preferable solution to first/last mile problem. However, existing work requires significant computation time or advance bookings. Hence, we propose a public transit system linking the neighborhoods to a rapid transit node using a fleet of demand responsive electric vehicles, which reacts to
55#
發(fā)表于 2025-3-31 02:32:02 | 只看該作者
Comprehensive Learning Gene Expression Programming for Automatic Implicit Equation Discoverycit equation discovery is based on calculating derivatives. However, this derivative-based mechanism requires high time consumption and it is difficult to solve problems with sparse data. To solve these deficiencies, this paper proposes a new mechanism named Comprehensive Learning Fitness Evaluation
56#
發(fā)表于 2025-3-31 05:42:57 | 只看該作者
Multi-population Genetic Algorithm for Cardinality Constrained Portfolio Selection Problemsa given amount of investment fund across a set of assets in such a way that the return is maximised and the risk is minimised. To solve PS more effectively and more efficiently, this paper introduces a Multi-population Genetic Algorithm (MPGA) methodology. The proposed MPGA decomposes a large popula
57#
發(fā)表于 2025-3-31 12:24:53 | 只看該作者
Recognition and Classification of Rotorcraft by Micro-Doppler Signatures Using Deep Learningtill difficult for the traditional radar signal processing methods to detect and distinguish rotorcraft targets from various types of moving objects. Moreover, it is even more challenging to classify different types of helicopters. As the development of high-precision radar, classification of moving
58#
發(fā)表于 2025-3-31 14:57:30 | 只看該作者
59#
發(fā)表于 2025-3-31 20:12:50 | 只看該作者
60#
發(fā)表于 2025-3-31 23:21:26 | 只看該作者
An Innovative Heuristic for Planning-Based Urban Traffic Controly. In this scenario, optimising the exploitation of urban road network is a pivotal challenge, particularly in the case of unexpected situations. In order to tackle this challenge, approaches based on mixed discrete-continuous planning have been recently proposed and although their feasibility has b
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