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Titlebook: Applied Intelligence; First International De-Shuang Huang,Prashan Premaratne,Changan Yuan Conference proceedings 2024 The Editor(s) (if ap

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樓主: 信賴
51#
發(fā)表于 2025-3-30 08:15:29 | 只看該作者
Visual Servo Control System for AUV Stabilization placed on the bottom of AUV, which takes pictures of the seabed under the device. A special visual marker, represented by an ArUco(Augmented Reality University of Cordoba) marker, is pre-installed on the seabed. The proposed method makes it possible to stabilize the control object in the hover mode
52#
發(fā)表于 2025-3-30 15:23:05 | 只看該作者
53#
發(fā)表于 2025-3-30 19:50:05 | 只看該作者
Multi-scale Texture Network for Industrial Surface Defect Detectionhat addresses this challenge by effectively analyzing textures at various scales. The proposed network incorporates a “Multi-Scale Texture Feature Processing” module to generate multi-scale texture tokens for comprehensive surface analysis. Additionally, a “Multi-Head Feature Encoding” mechanism cap
54#
發(fā)表于 2025-3-30 21:55:11 | 只看該作者
55#
發(fā)表于 2025-3-31 04:33:34 | 只看該作者
Advancing Short-Term Traffic Congestion Prediction: Navigating Challenges in Learning-Based Approachros and cons among different approaches with test results. In addition, this paper develops a perspective synthesis of the current status quo that could be the next steps for a more accurate, more efficient prediction. In the end, the paper yields conclusions about possible future research endeavors
56#
發(fā)表于 2025-3-31 05:08:46 | 只看該作者
Transformer-Based Multi-industry Electricity Demand Forecastingd forecasting model that utilizes transformer networks and fully connected neural networks (FC) for electricity demand forecasting in different industries within a city. The model employs the encoder part of the transformer to capture the dependencies between different influencing factors and uses F
57#
發(fā)表于 2025-3-31 11:55:07 | 只看該作者
58#
發(fā)表于 2025-3-31 14:00:36 | 只看該作者
A Broader Study of Spectral Missing in Multi-spectral Vehicle Re-identification and multi-stream learning in spectral missing. The result shows that the most advanced multi-stream learning performed better than the one-stream learning models. In some cases, the performance of multi-stream learning is even worse than that of one-stream learning methods in Siamese spectral missi
59#
發(fā)表于 2025-3-31 21:22:10 | 只看該作者
60#
發(fā)表于 2025-4-1 00:56:31 | 只看該作者
Design and Utilization of an Auto-Visual-Inspection Composite System for Suspension Cables with Fastspension cables of arch bridges strongly evidence the effectiveness of the proposed robot, and the utilization of YOLOv7 demonstrates the rapid, autonomous, and accurate identification of flaw features.
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