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: Cyber-Physical Systems and Control II; Dmitry G. Arseniev,Nabil Aouf Conference proceedings 2023 The Editor(s) (if applicable) and The Aut

[復(fù)制鏈接]
樓主: otitis-externa
31#
發(fā)表于 2025-3-26 22:56:44 | 只看該作者
Gro?rechner und die Zipfsche Regeltics independent on the parameters of the?parent distribution. If analytical construction is not possible, the distribution function of such statistics can be determined as a result of statistical modeling.
32#
發(fā)表于 2025-3-27 03:45:42 | 只看該作者
33#
發(fā)表于 2025-3-27 08:53:28 | 只看該作者
Der Soziabele und der Solit?re Menschentypusoned approaches into a single working flow proves its efficiency due to significant reduction of efforts on dataset preparation and high accuracy of the detection and recognition: the implemented system recognizes license plates with 94,8% accuracy on a test sample of 20,000 images.
34#
發(fā)表于 2025-3-27 12:13:05 | 只看該作者
35#
發(fā)表于 2025-3-27 17:33:44 | 只看該作者
36#
發(fā)表于 2025-3-27 20:11:20 | 只看該作者
License Plates Detection and Recognition Based on Semi-supervised Learningoned approaches into a single working flow proves its efficiency due to significant reduction of efforts on dataset preparation and high accuracy of the detection and recognition: the implemented system recognizes license plates with 94,8% accuracy on a test sample of 20,000 images.
37#
發(fā)表于 2025-3-28 00:38:07 | 只看該作者
Physics-Informed Radial Basis Function Networks: Solving Inverse Problems for Partial Differential Ermed neural networks to solve direct and inverse boundary value problems is presented. It is proposed to use radial basis function neural networks (RBFNNs) as physically-informed neural networks, which have a simple structure and the?ability to adjust the non-linear parameters of the basis functions
38#
發(fā)表于 2025-3-28 04:14:42 | 只看該作者
On a Method for Identifying Failure Models of Complex Systemsication of the failure model is conducted in conditions of insufficient information using small ordered samples. When applying the methods of the theory of stochastic indication, the features of a limited volume and rapid aging of information are taken into account. Expansion of the range of possibi
39#
發(fā)表于 2025-3-28 08:08:47 | 只看該作者
40#
發(fā)表于 2025-3-28 11:25:56 | 只看該作者
Method of Expansion of Mathematical Tools of the Reliability Theory Due to the Properties of Stochasrties of the stochastic theory of similarity are proposed and justified in the article. Such an?approach to creating mathematical “symbiosis” of two large theories is aimed at improving the efficiency of solving the problem of identification of the quality indicators (reliability) of complex technic
 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|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-7 14:40
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
涿鹿县| 延吉市| 巫山县| 洛隆县| 娱乐| 泰顺县| 蒙自县| 沙雅县| 上思县| 彩票| 新邵县| 钦州市| 广平县| 寿阳县| 竹北市| 施秉县| 祁阳县| 信丰县| 娄烦县| 越西县| 石门县| 南陵县| 巴塘县| 阿图什市| 蕲春县| 三河市| 彭州市| 古浪县| 丰顺县| 宣武区| 沅江市| 丽江市| 安平县| 洛隆县| 望奎县| 平塘县| 鸡西市| 台前县| 蓝田县| 黑龙江省| 延寿县|