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Titlebook: Intelligent Science and Intelligent Data Engineering; Second Sino-foreign- Yanning Zhang,Zhi-Hua Zhou,Ying Li Conference proceedings 2012 S

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樓主: mobility
11#
發(fā)表于 2025-3-23 09:52:43 | 只看該作者
0302-9743 hange Workshop on Intelligence Science and Intelligent Data Engineering, IScIDE 2011, held in Xi‘a(chǎn)n, China, in October 2011. The 97 papers presented were carefully peer-reviewed and selected from 389 submissions. The IScIDE papers in this volume are organized in topical sections on machine learning
12#
發(fā)表于 2025-3-23 14:17:30 | 只看該作者
A Path Prediction Method for Human-Accompanying Mobile Robot Based on Neural NetworkBack propagation neural network predicts future positions of the accompanying human from the human track. Based on the future positions, a cubic spline generates a future path of the accompanying human. The experimental result validates the feasibility of the path prediction method.
13#
發(fā)表于 2025-3-23 21:30:17 | 只看該作者
14#
發(fā)表于 2025-3-24 00:02:00 | 只看該作者
Generalized Kernel Density Estimation Based Robust Estimator and Its Applicationeed. Compared to ASKC, pbM and other Kernel Density Estimation based robust estimator which do not have locality, GKDE has higher resolution for inliers, and experiments show that it has higher precision than traditional robust estimator such as RANSAC, LMeds. We also applied GKDE based estimator to image mosaic for homography estimation.
15#
發(fā)表于 2025-3-24 06:08:51 | 只看該作者
Multiple Feature Fusion for Object Trackinglihood evaluation, and the distance between foreground and background histograms is used for weight estimation. We evaluate our tracking algorithm on some popular benchmark videos and achieve competitive results compared with some state-of-art algorithms.
16#
發(fā)表于 2025-3-24 09:11:38 | 只看該作者
17#
發(fā)表于 2025-3-24 12:10:42 | 只看該作者
Orthogonal Projection Analysisnd discuss how to discretize it by using tangent space. We also show the geometric meaning of our approach, which is requiring the gradient field as orthogonal as possible to the tangent spaces. The experimental results have demonstrated the effectiveness of our proposed approach.
18#
發(fā)表于 2025-3-24 16:59:35 | 只看該作者
Remarks on Multi-layer Quantum Neural Network Controller Trained by Real-Coded Genetic Algorithmonducted for controlling a discrete-time system and a nonholonomic system - in this study a two-wheeled robot. Experimental results confirm the effectiveness of the real-coded genetic algorithm for the training of the quantum neural networks and show both feasibility and robustness of the direct quantum neural control system.
19#
發(fā)表于 2025-3-24 19:29:44 | 只看該作者
20#
發(fā)表于 2025-3-25 00:59:38 | 只看該作者
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