84,363 research outputs found
Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems
Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
Structural Material Property Tailoring Using Deep Neural Networks
Advances in robotics, artificial intelligence, and machine learning are
ushering in a new age of automation, as machines match or outperform human
performance. Machine intelligence can enable businesses to improve performance
by reducing errors, improving sensitivity, quality and speed, and in some cases
achieving outcomes that go beyond current resource capabilities. Relevant
applications include new product architecture design, rapid material
characterization, and life-cycle management tied with a digital strategy that
will enable efficient development of products from cradle to grave. In
addition, there are also challenges to overcome that must be addressed through
a major, sustained research effort that is based solidly on both inferential
and computational principles applied to design tailoring of functionally
optimized structures. Current applications of structural materials in the
aerospace industry demand the highest quality control of material
microstructure, especially for advanced rotational turbomachinery in aircraft
engines in order to have the best tailored material property. In this paper,
deep convolutional neural networks were developed to accurately predict
processing-structure-property relations from materials microstructures images,
surpassing current best practices and modeling efforts. The models
automatically learn critical features, without the need for manual
specification and/or subjective and expensive image analysis. Further, in
combination with generative deep learning models, a framework is proposed to
enable rapid material design space exploration and property identification and
optimization. The implementation must take account of real-time decision cycles
and the trade-offs between speed and accuracy
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