找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復(fù)制鏈接]
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.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-24 11:42
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
怀来县| 承德县| 宜章县| 福鼎市| 宣武区| 遵义市| 沾益县| 福安市| 梁河县| 广昌县| 兴安县| 松江区| 杭锦后旗| 海林市| 四平市| 久治县| 湘潭县| 海淀区| 凤城市| 汝城县| 南溪县| 肃宁县| 咸宁市| 定西市| 宝丰县| 个旧市| 浑源县| 巴南区| 元谋县| 日喀则市| 佳木斯市| 钦州市| 南澳县| 德格县| 武功县| 公安县| 合水县| 安阳县| 新安县| 监利县| 七台河市|