A converter compensator for power factor control in 3-phase induction machines

The advent of the harmonic neutralised shunt Converter Compensator as a practical means of reactive power compensation in power transmission systems has cleared ground for wider application of this type of equipment. An experimental 24-pulse voltage sourced convector has been successfully applied in controlling the terminal power factor of a 1.5kW, 240V three phase cage rotor induction motor, whose winding has been used in place of the usual phase shifting transformers. To achieve this, modifications have been made to the conventional stator winding of the induction machine. These include an unconventional phase spread and facilitation of compensator connections to selected tapping points between stator coils to give a three phase winding with a twelve phase connection to the twenty four pulse converter. Theoretical and experimental assessments of the impact of these modifications and attachment of the compensator have shown that there is a slight reduction in the torque developed at a given slip and in the combined system efficiency. There is also an increase in the noise level, also a consequence of the harmonics. The stator leakage inductance gave inadequate coupling reactance between the converter and the effective voltage source, necessitating the use of external inductors in each of the twelve phases. The terminal power factor is fully controllable when the induction machine is used either as a motor or as a generator.

Mixed-frequency testing of induction machines using inverters

This work has concentrated on the testing of induction machines to determine their temperature rise at full-load without mechanically coupling to a load machine. The achievements of this work are outlined as follows. 1. Four distinct categories of mixed-frequency test using an inverter have been identified by the author. The simulation results of these tests as well as the conventional 2-supply test have been analysed in detail. 2. Experimental work on mixed-frequency tests has been done on a small (4 kW) squirrel cage induction machine using a voltage source PWM inverter. Two out of the four categories of test suggested have been tested and the temperature rise results were found to be similar to the results of a direct loading test. Further, one of the categories of test proposed has been performed on a 3.3 kW slip-ring induction machine for the conformation of the rotor values. 3. A low current supply mixed-frequency test-rig has been proposed. For this purpose, a resonant bank was connected to the DC link of the inverter in order to maintain the exchange of power between the test machine and the resonant bank instead of between the main supply and the test machine. The resonant bank was then replaced with a special electro-mechanical energy storage unit. The current of the main power supply was then reduced in amplitude. 4. A variable inertia test for full load temperature rise testing of induction machines has been introduced. This test is purely mechanical in nature and does not require any electrical connection of the test machine to any other machine. It has the advantage of drawing very little net power from the supply.

Direct current ripple compensation for multi-phase fault-tolerant machines

A second-harmonic direct current (DC) ripple compensation technique is presented for a multi-phase, fault-tolerant, permanent magnet machine. The analysis has been undertaken in a general manner for any pair of phases in operation with the remaining phases inactive. The compensation technique determines the required alternating currents in the machine to eliminate the second-harmonic DC-link current, while at the same time minimising the total rms current in the windings. An additional benefit of the compensation technique is a reduction in the magnitude of the electromagnetic torque ripple. Practical results are included from a 70 kW, five-phase generator system to validate the analysis and illustrate the performance of the compensation technique.

Computational capabilities of multilayer committee machines

We obtained an analytical expression for the computational complexity of many layered committee machines with a finite number of hidden layers (L < 8) using the generalization complexity measure introduced by Franco et al (2006) IEEE Trans. Neural Netw. 17 578. Although our result is valid in the large-size limit and for an overlap synaptic matrix that is ultrametric, it provides a useful tool for inferring the appropriate architecture a network must have to reproduce an arbitrary realizable Boolean function.

Load losses in synchronous machines:a study of the pole face loss caused by armature reaction M.M.F. harmonics

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Learning in ultrametric committee machines

The problem of learning by examples in ultrametric committee machines (UCMs) is studied within the framework of statistical mechanics. Using the replica formalism we calculate the average generalization error in UCMs with L hidden layers and for a large enough number of units. In most of the regimes studied we find that the generalization error, as a function of the number of examples presented, develops a discontinuous drop at a critical value of the load parameter. We also find that when L>1 a number of teacher networks with the same number of hidden layers and different overlaps induce learning processes with the same critical points.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Steps and bumps:precision extraction of discrete states of molecular machines

We report statistical time-series analysis tools providing improvements in the rapid, precision extraction of discrete state dynamics from time traces of experimental observations of molecular machines. By building physical knowledge and statistical innovations into analysis tools, we provide techniques for estimating discrete state transitions buried in highly correlated molecular noise. We demonstrate the effectiveness of our approach on simulated and real examples of steplike rotation of the bacterial flagellar motor and the F1-ATPase enzyme. We show that our method can clearly identify molecular steps, periodicities and cascaded processes that are too weak for existing algorithms to detect, and can do so much faster than existing algorithms. Our techniques represent a step in the direction toward automated analysis of high-sample-rate, molecular-machine dynamics. Modular, open-source software that implements these techniques is provided.

The characterisation and emulation of rotary mechanical machines

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A study of stray load losses in core clamp plates of salient pole synchronous machines

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The use of electronic techniques in the construction of machines which can learn

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Storage capacity of ultrametric committee machines

The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inputs, and K units in the first hidden layer, is analyzed using techniques from statistical mechanics. We found that the storage capacity strongly depends on the network architecture αc ∼ (log K)1-1/2L and that the number of units K limits the number of possible hidden layers L through the relationship 2L - 1 < 2log K. © 2014 IOP Publishing Ltd.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

An exploration into measurement consistency on coordinate measuring machines

In high precision industry, the measurement of geometry is often performed using coordinate measuring machines (CMMs). Measurements on CMMs can occur at many places within a long and global supply chain. In this context it is a challenge to control consistency, so that measurements are applied with appropriate levels of rigour and achieve comparable results, wherever and whenever they are performed. In this paper, a framework is outlined in which consistency is controlled through measurement strategy, such as the number and location of measurement points. The framework is put to action in a case study, demonstrating the usefulness of the approach and highlighting the dangers of imposing rigid measurement strategies across the supply chain, even if linked to standardised manufacturing processes. Potential mitigations, and the requirements for future research, are outlined.