181,484 research outputs found
Smart Asset Management for Electric Utilities: Big Data and Future
This paper discusses about future challenges in terms of big data and new
technologies. Utilities have been collecting data in large amounts but they are
hardly utilized because they are huge in amount and also there is uncertainty
associated with it. Condition monitoring of assets collects large amounts of
data during daily operations. The question arises "How to extract information
from large chunk of data?" The concept of "rich data and poor information" is
being challenged by big data analytics with advent of machine learning
techniques. Along with technological advancements like Internet of Things
(IoT), big data analytics will play an important role for electric utilities.
In this paper, challenges are answered by pathways and guidelines to make the
current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on
Engineering Asset Management (WCEAM) 201
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Nigerian Stock Exchange and Economic Development
The need to critically analyze the efficiency of capital market on the Nigerian economy for
the period between 1979 and 2008 as a reference point for developing economies is the bedrock of this work.
The results indicate that the stock market indeed contributes to economic growth as all variables conformed
to expectation. The Nigerian Stock Exchange has not been having the best of times as an aftermath of the
global financial crisis after an unprecedented surge in returns on investment which has resulted in a continuous
downturn in market capitalization. Multiple regression method of econometric analysis was used
for the work. The major findings revealed a negative relationship between the market capitalization and the
Gross Domestic Product as well as a negative relationship between the turnover ratio and the Gross Domestic
Product while a positive relationship was observed between the all-share index and the Gross Domestic
Product. These findings led to some policy formulations aimed at an improved and developed market for
potential gain to the benefit of rational investors even across national borders
Machine Learning Applications in Estimating Transformer Loss of Life
Transformer life assessment and failure diagnostics have always been
important problems for electric utility companies. Ambient temperature and load
profile are the main factors which affect aging of the transformer insulation,
and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a
model for calculating the transformer loss of life based on ambient temperature
and transformer's loading. In this paper, this standard is used to develop a
data-driven static model for hourly estimation of the transformer loss of life.
Among various machine learning methods for developing this static model, the
Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical
simulations demonstrate the effectiveness and the accuracy of the proposed
ANFIS method compared with other relevant machine learning based methods to
solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201
An integrated core competence evaluation framework for portfolio management in the oil industry
Drawing upon resource-based theory, this paper presents a core competence evaluation framework for managing the competence portfolio of an oil company. It introduces a network typology to illustrate how to form different types of strategic alliance relations with partnering firms to manage and grow the competence portfolio. A framework is tested using a case study approach involving face-to-face structured interviews. We identified purchasing, refining and sales and marketing as strong candidates to be the core competencies. However, despite the company's core business of refining oil, the core competencies were identified to be their research and development and performance management (PM) capabilities. We further provide a procedure to determine different kinds of physical, intellectual and cultural resources making a dominant impact on company's competence portfolio. In addition, we provide a comprehensive set of guidelines on how to develop core competence further by forging a partnership alliance choosing an appropriate network topology
Risks and remedies in e-learning system
One of the most effective applications of Information and Communication
Technology (ICT) is the emergence of E-Learning. Considering the importance and
need of E-Learning, recent years have seen a drastic change of learning
methodologies in Higher Education. Undoubtedly, the three main entities of
E-Learning system can be considered as Student, Teacher & Controlling Authority
and there will be different level, but a good E-Learning system needs total
integrity among all entities in every level. Apart from integrity enforcement,
security enforcement in the whole system is the other crucial way to organize
the it. As internet is the backbone of the entire system which is inherently
insecure, during transaction of message in E-Learning system, hackers attack by
utilising different loopholes of technology. So different security measures are
required to be imposed on the system. In this paper, emphasis is given on
different risks called e-risks and their remedies called e-remedies to build
trust in the minds of all participants of E-Learning system
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de EconomĂa y Competitividad DPI2015-70842-
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