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

Advanced telemetry systems for payloads. Technology needs, objectives and issues

The current trends in advanced payload telemetry are the new developments in advanced modulation/coding, the applications of intelligent techniques, data distribution processing, and advanced signal processing methodologies. Concerted efforts will be required to design ultra-reliable man-rated software to cope with these applications. The intelligence embedded and distributed throughout various segments of the telemetry system will need to be overridden by an operator in case of life-threatening situations, making it a real-time integration issue. Suitable MIL standards on physical interfaces and protocols will be adopted to suit the payload telemetry system. New technologies and techniques will be developed for fast retrieval of mass data. Currently, these technology issues are being addressed to provide more efficient, reliable, and reconfigurable systems. There is a need, however, to change the operation culture. The current role of NASA as a leader in developing all the new innovative hardware should be altered to save both time and money. We should use all the available hardware/software developed by the industry and use the existing standards rather than inventing our own

Conclusions and implications of automation in space

Space facilities and programs are reviewed. Space program planning is discussed

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

The Evolution of Transport Networks

Between 1900 and 2000, the length of paved roads in the United States increased from 240 km to 6,400,000 km (Peat 2002, BTS 2002) with virtually 100% of the U.S. population having almost immediate access to paved roadways. Similarly, in 1830 there were 37 km of railroad in the United States, but by 1920 total track mileage had increased more than ten-thousand times to 416,000 km miles, however since then, rail track mileage has shrunk to about 272,000 km (Garrison 1996, BTS 2002). The growth (and decline) of transport networks obviously affects the social and economic activities that a region can support; yet the dynamics of how such growth occurs is one of the least understood areas in transport, geography, and regional science. This is revealed time and again in the long-range planning efforts of metropolitan planning organizations (MPOs), where transport network changes are treated exclusively as the result of top-down decision-making. Changes to the transport network are rather the result of numerous small decisions (and some large ones) by property owners, firms, developers, towns, cities, counties, state department of transport districts, MPOs, and states in response to market conditions and policy initiatives. Understanding how markets and policies translate into facilities on the ground is essential for scientific understanding and improving forecasting, planning, policy-making, and evaluation.Transportation Network Growth, Transportation-Land Use Interaction, Markov Chain