Data Analytics for Smart Infrastructure: Asset Management and Network Performance
暫譯: 智慧基礎設施的數據分析：資產管理與網絡性能
Wang, Yang, Li, Zhidong, Liang, Bin
- 出版商: CRC
- 出版日期: 2025-01-31
- 售價: $2,510
- 貴賓價: 9.5 折 $2,385
- 語言: 英文
- 頁數: 184
- 裝訂: Quality Paper - also called trade paper
- ISBN: 103275415X
- ISBN-13: 9781032754154
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相關分類:
Data Science
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decade's 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.
商品描述(中文翻譯)
本書首次介紹智慧基礎設施的數據分析。作者基於十多年與業界合作的經驗，展示了數據分析在基礎設施和資產管理中的能力。
本書提供數據驅動的解決方案，以涵蓋基礎設施和資產管理的三個關鍵能力領域：1) 情境感知 2) 預測分析 3) 決策支持。讀者將從各種數據分析技術中受益，包括異常檢測、性能評估、故障預測、趨勢分析、資產優先排序、智慧感測以及實時/在線系統。這些數據分析技術對於解決基礎設施和資產管理中的問題至關重要。讀者將受益於來自關鍵基礎設施的案例研究，例如水資源管理、結構健康監測和鐵路網絡。
這部開創性的著作將是那些在智慧基礎設施背景下學習和實踐分析的讀者必讀的資料。
作者簡介
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.
作者簡介(中文翻譯)
楊旺是UTS數據科學研究所的教授，負責智慧基礎設施的先進數據分析。楊教授積極與產業夥伴合作，提供創新的數據驅動解決方案，應用於關鍵基礎設施，包括供水和交通網絡、結構健康監測等。楊教授曾獲得多項研究和創新獎項，包括尤里卡獎（Eureka Prize）、iAwards和AWA水獎。
李志東副教授是UTS的獲獎數據科學和機器學習專家，曾在Data61和CSIRO任職，對於將機器學習轉化為工業領域的貢獻顯著，涵蓋基礎設施、金融、環境和農業等領域。
郭婷是UTS數據科學研究所的高級研究員。他在基礎設施故障預測和主動維護方面與產業夥伴有多年合作研究經驗。他的研究興趣包括深度學習、圖學習和數據挖掘。
梁彬是UTS的高級講師，是一位成就卓越的數據科學家，擁有豐富的產業和研究經驗。他在頂尖期刊上發表過多篇論文，並成功交付多個產業項目，其在數據分析、人工智慧和計算機視覺方面的專業知識推動了顯著的學術、社會和經濟進步。
田洪達是UTS數據科學研究所專注於研究和創新的高級講師。他利用人工智慧的力量，專注於通過與政府和產業夥伴合作，將研究成果轉化為解決現實問題的數據驅動解決方案。
陳芳教授是UTS數據科學研究所的執行主任。她是一位獲獎的國際知名AI和數據科學領導者，曾於2018年獲得澳大利亞博物館尤里卡獎（Eureka Prize）數據科學卓越獎、新南威爾士州總理科學與工程獎，以及2021年澳大利亞和新西蘭「女性在AI」基礎設施獎。她的專業知識集中於開發數據驅動的創新，以應對不同產業領域大規模網絡中的複雜挑戰。
