Wind Turbine Icing Physics and Anti-/De-Icing Technology gives a comprehensive update of research on the underlying physics pertinent to wind turbine icing and the development of various effective and robust anti-/de-icing technology for wind turbine icing mitigation. The book introduces the most recent research results derived from both laboratory studies and field experiments. Specifically, the research results based on field measurement campaigns to quantify the characteristics of the ice structures accreted over the blades surfaces of utility-scale wind turbines by using a Supervisory Control and Data Acquisition (SCADA) system and an Unmanned-Aerial-Vehicle (UAV) equipped with a high-resolution digital camera are also introduced. In addition, comprehensive lab experimental studies are explored, along with a suite of advanced flow diagnostic techniques, a detailed overview of the improvements, and the advantages and disadvantages of state-of-the-art ice mitigation strategies. This new addition to the Wind Energy Engineering series will be useful to all researchers and industry professionals who address icing issues through testing, research and industrial innovation.

Wind Turbine Icing Physics and Anti-/De-Icing Technology (Wind Energy Engineering)

Science & Math

Liu, Yang

Hu, Hui

Gao, Linyue

Technology

Academic Press

Showcasing more than thirty ancient bronzes from the exceptional holdings of the Shanghai Museum, this generously illustrated book offers a compelling overview of the beauty of Chinese bronzes and the fascinating traditions surrounding them. These important objects, many of which have never before appeared in an English-language publication, date from the 18th to the 1st century B.C.E. and span numerous dynasties. Highlights of the exhibition include an early thin-wall cast three-legged food vessel (ding) from the Erlitou period, a set of nine bells (bianzhong) from the early Spring and Autumn period, and a beast-shaped wine vessel (he) from the early Warring States period. An accessible essay serves as an introduction to these masterpieces, and sumptuous, newly commissioned photography makes this publication a standout addition to the literature on Asian bronze sculpture.

Cast for Eternity: Ancient Ritual Bronzes from the Shanghai Museum

Arts & Photography

History & Criticism

Clark Art Institute

Book by Yang, Liu, Capon, Edmund, Liu, Yang

Masks of Mystery: Ancient Chinese Bonzes from Sanxingdui (English and Chinese Edition)

Capon, Edmund

Art Gallery of New South Wales

杨柳

Collections, Catalogs & Exhibitions

New South Wales Government

FANTASTIC MOUNTAINS CHINESE LANDSCAPE /ANGLAIS (THE ART GALLERY)

Edmund Capon

Stephen Little

Thames&Hudson

The Poetic Mandarin: Chinese Calligraphy from the James Hayes Collection = Huan Hai Mo Yun: Xu Shu Cang Zhongguo Qing Dai Shu Fa

State Estimation and Fault Diagnosis under Imperfect Measurements

Engineering & Transportation

Wang, Zidong

Zhou, Donghua

Engineering

CRC-Press

Electronic Structure and Properties of Transition Metal Compounds: Theory and Applications

Bersuker, Isaac B.

Chemistry

wiley

The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more.This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems.Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)

Computers & Technology

Hutchinson, Ben

Raskar, Ramesh

Mohri, Mehryar

Yang, Qiang

Yu, Han

Bennis, Mehdi

Evans,David

Song, Dawn

Joshi, Gauri

Kairouz, Peter

McMahan, H Brendan

Avent, Brendan

Bellet, Aurélien

Bhagoji, Arjun Nitin

Bonawit, Kallista

Charles, Zachary

Cormode, Graham

Cummings, Rachel

D'Oliveira, Rafael G L

Eichner, Hubert

El Rouayheb, Salim

Gardner, Josh

Garrett, Zachary

Gascón, Adrià

Ghazi, Badih

Gibbons, Phillip B

Gruteser, Marco

Harchaoui, Zaid

He, Chaoyang

He, Lie

Huo, Zhouyuan

Hsu, Justin

Jäggi, Martin

Javidi, Tara

Khodak, Mikhail

Konecný, Jakub

Korolova, Aleksandra

Koushanfar, Farinaz

Koyejo, Sanmi

Lepoint, Tancrède

Mittal, Prateek

Nock, Richard

Özgür, Ayfer

Pagh, Rasmus

Qi, Hang

Ramage, Daniel

Raykova, Mariana

Song, Weikang

Stich, Sebastian U

Sun, Ziteng

Theertha Suresh, Ananda

Tramèr, Florian

Vepakomma, Praneeth

Wang, Jianyu

Xiong, Li

Xu, Zheng

Yu, Felix X

Zhao, Sen

Computer Science

Now Publishers

Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private?
Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Cheng, Yong

Kang, Yan

Chen, Tianjian

Morgan Claypool

Morgan & Claypool Publishers

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Books by Liu, Yang