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Notice of Violation of IEEE Publication Principles
"A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market"
by Phatchakorn Areekul, Tomonobu Senjyu, Hirofumi Toyama, and Atsushi Yona
in IEEE Transactions on Power Systems, Vol 25, No 1, February 2010
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains large portions of original text from the paper cited below. The original text was copied without insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
"Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model"
by G. Peter Zhang,
in Neurocomputing, Vol 50, Elsevier, 2003, pp. 159-175
A novel two-dimensional readout scheme for gas detectors is presented that uses small metal pads with 2.54-mm pitch as an anode. The pads are read out via 128-channel VLSI low-noise preamplifier/multiplexer chips. These chips are mounted on 2.8-cm*2.8-cm modules, which are directly plugged onto the detector backplane, delay-chained with jumpers, and read out sequentially. The readout has been successfully tested with a low-pressure, two-step, TMAE-filled, ultraviolet-sensitive ring imaging Cerenkov counter. A single-electron efficiency of >90% was observed at moderate chamber gains (<10/sup 6/). The method offers high electronic amplification, low noise, and high readout speed with a very flexible and compact design that is suited for space-limited applications.<