3,367 research outputs found
Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device
A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario
Applying Deep Learning to Fast Radio Burst Classification
Upcoming Fast Radio Burst (FRB) surveys will search 10\, beams on
sky with very high duty cycle, generating large numbers of single-pulse
candidates. The abundance of false positives presents an intractable problem if
candidates are to be inspected by eye, making it a good application for
artificial intelligence (AI). We apply deep learning to single pulse
classification and develop a hierarchical framework for ranking events by their
probability of being true astrophysical transients. We construct a tree-like
deep neural network (DNN) that takes multiple or individual data products as
input (e.g. dynamic spectra and multi-beam detection information) and trains on
them simultaneously. We have built training and test sets using false-positive
triggers from real telescopes, along with simulated FRBs, and single pulses
from pulsars. Training of the DNN was independently done for two radio
telescopes: the CHIME Pathfinder, and Apertif on Westerbork. High accuracy and
recall can be achieved with a labelled training set of a few thousand events.
Even with high triggering rates, classification can be done very quickly on
Graphical Processing Units (GPUs). That speed is essential for selective
voltage dumps or issuing real-time VOEvents. Next, we investigate whether
dedispersion back-ends could be completely replaced by a real-time DNN
classifier. It is shown that a single forward propagation through a moderate
convolutional network could be faster than brute-force dedispersion; but the
low signal-to-noise per pixel makes such a classifier sub-optimal for this
problem. Real-time automated classification may prove useful for bright,
unexpected signals, both now and in the era of radio astronomy when data
volumes and the searchable parameter spaces further outgrow our ability to
manually inspect the data, such as for SKA and ngVLA
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
To develop an efficient variable speed compressor motor system
This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque
is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce
dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy
optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization
control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained
clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment
Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment
Partial discharge (PD) pattern recognition has been applied for identifying the types of insulation defects in high voltage (HV) equipment. This paper proposes a novel Bayesian neural network (BNN) and discrete wavelet transform (DWT) hybrid algorithm for PD pattern recognition. Laboratory experiments on a number of PD models have been conducted for evaluating the performance of the proposed algorithm
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