Wei Wang

Jun Zhao

Chunyang Sheng

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

Data-Driven Prediction for Industrial Processes and Their Applications

Kangyin Dong

Xiucheng Dong

Databook of Surface Modification Additives, Second Edition contains data on ten groups of additives, including anti-scratch and mar-preventing additives, additives for surface tension reduction and wetting, hydrophobization additives, gloss enhancement and surface matting additives, additives for the formation of tack-free surface and tackifiers, and stain inhibiting additives. The information on each is divided into five sections, including General Information, Physical-Chemical Properties, Health and Safety, Ecological Properties, and Use and Performance. This data is provided for approximately 360 of the most important surface modification additives produced and used today. This databook will be an extremely useful resource for engineers, researchers and technicians interested in using additives to modify and improve the surface properties of materials. Provides detailed, up-to-date, essential data on 10 groups of additives Covers general information, physical-chemical properties, health and safety, ecological properties, and use and performance Includes recommendations for specific products, applications, processing methods and concentrations used

Evaluation and Alleviation

Energy Poverty in China

Valentina Presutti

Francesco Poggi

Paolo Ciancarini

Mathieu d'Aquin

Matthew Horridge

Tudor Groza

Mari Carmen Suarez-Figueroa

This book contains the best selected papers of two Satellite Events held at the 20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016, in November 2016 in Bologna, Italy: The Second International Workshop on Educational Knowledge Management, EKM 2016, and the First Workshop: Detection, Representation and Management of Concept Drift in Linked Open Data, Drift-an-LOD 2016. The 6 revised full papers included in this volume were carefully reviewed and selected from the 13 full papers that were accepted for presentation at the conference from the initial 82 submissions. This volume also contains the 37 accepted contributions for the EKAW 2016 tutorials, demo and poster sessions, and the doctoral consortium. The special focus of this year's EKAW was "evolving knowledge", which concerns all aspects of the management and acquisition of knowledge representations of evolving, contextual, and local models. This includes change management, trend detection, model evolution, streaming data and stream reasoning, event processing, time-and space dependent models, contextual and local knowledge representations with a special emphasis on the evolvability and localization of knowledge and the correct usage of these limits.

EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, Italy, November 19–23, 2016, Revised Selected Papers

Knowledge Engineering and Knowledge Management

Jun Zhao

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