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Selected Colleen Hoover Books at €9.99c | In-store & Online
Selected Colleen Hoover Books at €9.99c | In-store & Online
A01=Bin Liang
A01=Fang Chen
A01=Hongda Tian
A01=Ting Guo
A01=Yang Wang
A01=Zhidong Li
Age Group_Uncategorized
Age Group_Uncategorized
Author_Bin Liang
Author_Fang Chen
Author_Hongda Tian
Author_Ting Guo
Author_Yang Wang
Author_Zhidong Li
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Category1=Non-Fiction
Category=GT
Category=PBT
Category=PBW
Category=RPC
Category=UB
Category=UNF
Category=UYA
COP=United Kingdom
Delivery_Pre-order
Language_English
PA=Not yet available
Price_€50 to €100
PS=Forthcoming
softlaunch

Data Analytics for Smart Infrastructure: Asset Management and Network Performance

This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decades experience working with industry and demonstrating the capabilities of data analytics for infrastructure and asset management.

The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks.

This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.

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Current price €50.39
Original price €55.99
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A01=Bin LiangA01=Fang ChenA01=Hongda TianA01=Ting GuoA01=Yang WangA01=Zhidong LiAge Group_UncategorizedAuthor_Bin LiangAuthor_Fang ChenAuthor_Hongda TianAuthor_Ting GuoAuthor_Yang WangAuthor_Zhidong Liautomatic-updateCategory1=Non-FictionCategory=GTCategory=PBTCategory=PBWCategory=RPCCategory=UBCategory=UNFCategory=UYACOP=United KingdomDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€50 to €100PS=Forthcomingsoftlaunch

Will deliver when available. Publication date 31 Jan 2025

Product Details
  • Dimensions: 156 x 234mm
  • Publication Date: 31 Jan 2025
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: United Kingdom
  • Language: English
  • ISBN13: 9781032754154

About Bin LiangFang ChenHongda TianTing GuoYang WangZhidong Li

Yang Wang is a professor at UTS Data Science Institute leading advanced data analytics for smart infrastructure. Yang keeps actively engaged with industry partners and delivers innovative data-driven solutions for critical infrastructures including supply water and transport network structural health monitoring etc. Yang has received various research and innovation awards including Eureka Prize iAwards and AWA water awards. Associate Professor Zhidong Li at UTS is an award-winning expert in data science and machine learning with a notable tenure at Data61 CSIRO and a history of significant contributions to translate machine learning into industrial fields including infrastructure finance environment and agriculture. Ting Guo is a senior research fellow in the Data Science Institute at UTS. He has years of experience in collaborative research with industry partners in infrastructure failure prediction and proactive maintenance. His research interests include deep learning graph learning and data mining. Bin Liang a senior lecturer at UTS is an accomplished data scientist with extensive industry and research experience. With publications in top venues and successful industry project deliverables his expertise in data analytics AI and computer vision has driven significant academic social and economic advancements. Hongda Tian is a research and innovation focused Senior Lecturer at the UTS Data Science Institute. By leveraging the power of artificial intelligence he has been focusing on research translation through working with government and industry partners and providing data-driven solutions to real-world problems.Professor Fang Chen is the Executive Director at the UTS Data Science Institute. She is an award-winning internationally recognised leader in AI and data science having won the Australian Museum Eureka Prize 2018 for Excellence in Data Science NSW Premier's Prize of Science and Engineering and the Australia and New Zealand Women in AI Award in Infrastructure in 2021. Her extensive expertise is centered around developing data-driven innovations that address complex challenges across large-scale networks in different industry sectors.

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