爱学术-智能检索

智能城市的数据分析

ISBN：9781138308770，出版年：2018，中图分类号：TP3

The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications.

Urban analytics Data analysis Data envelopment analysis Exploratory data analysis Social data analysis Social data analytics Symbolic data analysis Logical analysis of data Big data data analysis Data-flow analysis

Principles and Applications of Thermal Analysis

热分析原理与应用

Wiley Paul Gabbott

ISBN：9780470697702，出版年：2008，中图分类号：TK

Thermal Analysis techniques are used in a wide range of disciplines, from pharmacy and foods to polymer science, materials and glasses; in fact any field where changes in sample behaviour are observed under controlled heating or controlled cooling conditions. The wide range of measurements possible provide fundamental information on the material properties of the system under test, so thermal analysis has found increasing use both in basic characterisation of materials and in a wide range of applications in research, development and quality control in industry and academia. Principles and Applications of Thermal Analysis is written by manufacturers and experienced users of thermal techniques. It provides the reader with sound practical instruction on how to use the techniques and gives an up to date account of the principle industrial applications. By covering basic thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) including the new approach of Fast Scanning DSC, together with dynamic mechanical analysis (DMA /TMA) methods, then developing the discussion to encompass industrial applications, the book serves as an ideal introduction to the technology for new users. With a strong focus on practical issues and relating the measurements to the physical behaviour of the materials under test, the book will also serve as an important reference for experienced analysts.

Thermogravimetric analysis Prescriptive analytics Analysis of algorithms Applied ethics Requirements analysis Logical analysis of data Bivariate analysis Principal component analysis Climate analysis Mathematical analysis