CCF WISA 2022 | 唐立新院士将作大会特邀报告

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2022-12-01

第十九届CCF中国信息系统及应用大会  

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会议简介

Introduction

      第十九届中国信息系统及应用大会(WISA  2022)是由中国计算机学会(CCF)主办、CCF信息系统专业委员会、大连海洋大学、大连市大数据产业发展研究院共同承办的旗舰会议。

      大会将围绕“面向数字化转型的信息系统”主题,关注智慧信息系统、智慧城市、智慧政务、智慧医疗健康与信息系统安全等领域,聚焦区块链、知识图谱、数据融合与共享治理等关键问题,搭建学术、企业、政府交流平台。

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报告信息

Infomation

报告人:唐立新

报告题目:Quality Analytics and Optimization for Manu-facture-Circulation Industrial System

日期:12月04日

时间:08:30 ~ 09:20

腾讯会议:190-916-263(密码:221203)

B站直播:

https://live.bilibili.com/24655078

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报告摘要

Abstract

      Data analytics is the frontier basic research direction of industrial intelligence and one of the driving forces to promote scientific development. Systems optimization is the core basic theory of decision-making in smart industry, as well as the heart and engine of data analytics. This talk will discuss some systems modeling methods and optimization solution methods we have been working on. The systems modeling methods are to quantitatively describe different practical problems with proper formulations, including set-packing model, space-time network model, and continuous-time based model. The optimization solution methods include integer optimization, convex optimization, intelligent optimization, and dynamic optimization. This talk will also introduce systems optimization and data analytics of production, logistics, and energy in the steel industry, including: 1) production batching and scheduling in steelmaking/continuous casting, and hot/cold rolling operations; 2) logistics scheduling in loading operations, shuffling/reshuffling, and stowage; 3) data analytics-based energy optimization, including dynamic energy allocation and scheduling, energy analytics covering energy description, diagnosis and prediction; 4) data analytics, including temperature prediction of blast furnace, dynamic analytics of BOF steelmaking process based on multi-stage modeling, temperature prediction of reheat furnace based on mechanism and machine learning, and strip quality analytics of continuous annealing based on multi-objective ensemble learning.

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会议报名

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