Abstract: High-voltage power transmission lines are the back
bone of electrical power utilities. The stability and continuous
monitoring of this critical infrastructure is pivotal. Nine-Sigma
representing Eskom Holding SOC limited, South Africa has a major
problem on proactive detection of fallen power lines and real time
sagging measurement together with slipping of such conductors. The
main objective of this research is to innovate RFID technology to
solve this challenge. Various options and technologies such as GPS,
PLC, image processing, MR sensors and etc., have been reviewed
and draw backs were made. The potential of RFID to give precision
measurement will be observed and presented. The future research
will look at magnetic and electrical interference as well as corona
effect on the technology.
Abstract: The majority of existing predictors for time series are
model-dependent and therefore require some prior knowledge for the
identification of complex systems, usually involving system
identification, extensive training, or online adaptation in the case of
time-varying systems. Additionally, since a time series is usually
generated by complex processes such as the stock market or other
chaotic systems, identification, modeling or the online updating of
parameters can be problematic. In this paper a model-free predictor
(MFP) for a time series produced by an unknown nonlinear system or
process is derived using tracking theory. An identical derivation of the
MFP using the property of the Newton form of the interpolating
polynomial is also presented. The MFP is able to accurately predict
future values of a time series, is stable, has few tuning parameters and
is desirable for engineering applications due to its simplicity, fast
prediction speed and extremely low computational load. The
performance of the proposed MFP is demonstrated using the
prediction of the Dow Jones Industrial Average stock index.