<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Stream Processing on Jaehyeon Kim</title><link>https://jaehyeon.me/categories/stream-processing/</link><description>Recent content in Stream Processing on Jaehyeon Kim</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>Copyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.</copyright><lastBuildDate>Tue, 21 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jaehyeon.me/categories/stream-processing/index.xml" rel="self" type="application/rss+xml"/><item><title>Building a Real-Time Industrial Digital Twin with Apache Flink and Online Machine Learning</title><link>https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/</guid><description>Overview Imagine using a rolling pin to flatten out a thick piece of dough. A Hot Strip Mill does the exact same thing, but with glowing red-hot steel slabs (often heated over 1000°C) and massive mechanical rollers. The steel is passed through a series of these rollers, crushing it down from a thick block into a long, thin sheet.
Calculating the exact Rolling Force required to crush the steel is critical.</description><enclosure url="https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/featured.png" length="130846" type="image/png"/></item></channel></rss>