找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Intelligent Systems Modeling and Simulation II; Machine Learning, Ne Samsul Ariffin Abdul Karim Book 2022 The Editor(s) (if applicable) and

[復(fù)制鏈接]
樓主: DEIGN
31#
發(fā)表于 2025-3-26 22:10:55 | 只看該作者
Data Interpolation Using Rational Cubic Ball with Three Parameters,rocessing Unit (CPU) time in second, the proposed scheme is better than existing schemes. Notably, we achieved . 0.01331105, . 0.91398405, . 0.00448758 and CPU time?=?0.00009958 by using the new derivative estimation. Thus, the proposed scheme with construction of a new derivative estimation has improved the existing rational interpolant.
32#
發(fā)表于 2025-3-27 03:55:08 | 只看該作者
Thermal Analysis of VLSI System using Successive Over Relaxation (SOR) Method,etween these two methods will be analysed based on iteration counts, computer proses time and the maximum temperature. The numerical results show that SOR is more efficient compared to GS. Therefore, the results for this study may be beneficial for the future research in solving numerical iterative method.
33#
發(fā)表于 2025-3-27 08:42:15 | 只看該作者
,Solution of Peak Junction Temperature with Crank-Nicolson?and SOR Approach,e method. The Successive Over Relaxation (SOR) and Gauss–Seidel (GS) methods will be used to solve the generated system of linear equations iteratively. The SOR method uses fewer iterations and takes less time to compute than the traditional GS iterative method, based on the results. In terms of PTJ accuracy, however, both methods are comparable.
34#
發(fā)表于 2025-3-27 12:13:40 | 只看該作者
Heat Transfer Modelling with Physics-Informed Neural Network (PINN),he number of parameters. In this chapter, we will explore the application of the Physics-Informed Neural Network (PINN) in solving heat equation with distinct types of materials. To leverage the GPU performance and cloud computing, we perform the simulations on the Google Cloud Platform.
35#
發(fā)表于 2025-3-27 14:23:43 | 只看該作者
36#
發(fā)表于 2025-3-27 20:07:08 | 只看該作者
Data-Driven Ordinary Differential Equations Model for Predicting Missing Data and Forecasting Cruderedicting missing data and forecasting future data applying the data-driven ordinary differential model using Runge-Kutta-Fehlberg produce good missing data prediction and the best future data forecasting among compared methods.
37#
發(fā)表于 2025-3-27 22:10:30 | 只看該作者
New Norm Disease Resilient Air-Conditioning Control Module,ng system. In this chapter, an integrated system with the use of Industrial Internet of Things (IIoT) that are part of the future units for monitor, control, and operational performance both from the health perspective and increases system performance is presented.
38#
發(fā)表于 2025-3-28 03:27:50 | 只看該作者
Using Intelligent Systems in Enterprises and Organizations in Russian Regions,tion in the study was paid to the regional peculiarities of the use of the technologies in question. The results of the study showed that despite the relatively recent start of the introduction of the technologies in question, they used in enterprises and organizations of all regions of Russia.
39#
發(fā)表于 2025-3-28 09:25:15 | 只看該作者
40#
發(fā)表于 2025-3-28 13:33:47 | 只看該作者
 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(ài)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(diǎn)評(píng) 投稿經(jīng)驗(yàn)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(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-1-28 15:50
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
扬州市| 柳州市| 舞钢市| 赤水市| 昭苏县| 鹤岗市| 康马县| 鸡泽县| 昌吉市| 石门县| 宝清县| 宜都市| 库伦旗| 和田县| 乃东县| 肃宁县| 平凉市| 威海市| 太保市| 内江市| 突泉县| 江川县| 辛集市| 邵东县| 阿勒泰市| 玉溪市| 会宁县| 霞浦县| 手机| 宁波市| 普安县| 密云县| 安徽省| 紫金县| 江北区| 壤塘县| 江油市| 墨玉县| 龙川县| 尤溪县| 建德市|