complexFuzzy: A novel clustering method for selecting training instances of cross-project defect prediction

Over the last decade, researchers have investigated to what extent cross-project defect prediction (CPDP) shows advantages over traditional defect prediction settings. These works do not take training and testing data of defect prediction from the same project. Instead, dissimilar projects are employed. Selecting proper training data plays an important role in terms of the success of CPDP. In this study, a novel clustering method named complexFuzzy is presented for selecting training data of CPDP. The method is developed by determining membership values with the help of some metrics which can be considered as indicators of complexity. First, CPDP combinations are created on 29 different data sets. Subsequently, complexFuzzy is evaluated by considering cluster centers of data sets and comparing some performance measures including area under the curve (AUC) and F-measure. The method is superior to other five comparison algorithms in terms of the distance of cluster centers and prediction performance

Neural networks and early fast Doppler for prediction in meteor-burst communications systems.

Thesis (Ph.D.)-University of Natal, Durban, 1994.In meteor-burst communications systems, the channel is bursty with a continuously fluctuating signal-to-noise ratio. Adaptive data rate systems attempt to use the channel more optimally by varying the bit rate. Current adaptive rate systems use a method of closed-loop decision-feedback to control the transmitted data rate. It is proposed that an open-loop adaptive data rate system without a decision feedback path may be possible using implicit channel information carried in the first few milliseconds of the link establishment probe signal. The system would have primary application in low-cost half-duplex telemetry systems. It is shown that the key elements in such a system would be channel predictors. The development of these predictors is the focus of this research. Two novel methods of predicting channel parameters are developed. The first utilises early fast Doppler information that precedes many long duration, large signal-to-noise-ratio overdense trails. The presence of early fast Doppler at the trail commencement is used as a toggle to operate at a higher data rate. Factors influencing the use of early fast Doppler for this purpose are also presented. The second method uses artificial neural networks. Data measured during trail formation is processed and presented to the neural networks for prediction of trail parameters. Several successful neural networks are presented which predict trail type, underdense or overdense, and peak trail amplitude from the first 50ms of the trail's lifetime. This method allows better estimation of the developing trail. This fact can be used to implement a multi-rate open-loop adaptive data rate system

Neural Network Fatigue Life Prediction in 7075-T6 Aluminum from Acoustic Emission Data

The objective of this research was to classify acoustic emission (AE) -data associated with fatigue cracks in aluminum fatigue specimens and to use early cycle life AE data to predict failure in such members. An AE data acquisition system coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used to classify the AE flaw growth signals. The signals were classified into three categories based on their AE parameters: plastic deformation, plane strain fracture and mixed mode (plane strain and plane stress) fracture. Acoustic emission amplitude data from the twenty-four low cycle fatigue tests were used to train and test a back propagation neural network for prediction of cycles to failure. The input data consisted of amplitude frequency histograms (30-100 dB) and the actual cycle lives. The output was the predicted cycles to failure or fatigue life. A network capable of predicting cycles to failure with a worst case error of- 9.30% was the final result

Importance of selecting research stimuli: a comparative study of the properties, structure and validity of both standard and novel emotion elicitation techniques

The principal aim of this doctoral research has been to investigate whether various popular methods of emotion elicitation perform differently in terms of self-reported participant affect - and if so, whether any of them is better able to mimic real-life emotional situations. A secondary goal has been to understand how continuous affect can be classified into discrete categories - whether by using clustering algorithms, or resorting to human participants for creating the classifications. A variety of research directions subserved these main goals: firstly, developing data-driven strategies for selecting 'appropriate' stimuli, and matching them across various stimulus modalities (i.e., words, sounds, images,films and virtual environments / VEs); secondly, comparing the chosen modalities on various self-report measures (with VEs assessed both with and without a head-mounted display / HMD); thirdly, comparing how humans classify emotional information vs. a clustering algorithm; and finally, comparing all five lab-based stimulus modalities to emotional data collected via an experience sampling phone app. Findings / outputs discussed will include a matched database of stimuli geared towards lab use, how the choice of stimulus modality may affect research results, the links (or discrepancies) between human and machine classification of emotional information, as well as range restriction affecting lab stimuli relative to `real-life' emotional phenomena

Model Parameter Calibration in Power Systems

In power systems, accurate device modeling is crucial for grid reliability, availability, and resiliency. Many critical tasks such as planning or even realtime operation decisions rely on accurate modeling. This research presents an approach for model parameter calibration in power system models using deep learning. Existing calibration methods are based on mathematical approaches that suffer from being ill-posed and thus may have multiple solutions. We are trying to solve this problem by applying a deep learning architecture that is trained to estimate model parameters from simulated Phasor Measurement Unit (PMU) data. The data recorded after system disturbances proved to have valuable information to verify power system devices. A quantitative evaluation of the system results is provided. Results showed high accuracy in estimating model parameters of 0.017 MSE on the testing dataset. We also provide that the proposed system has scalability under the same topology. We consider these promising results to be the basis for further exploration and development of additional tools for parameter calibration

Aspects of multi-resolutional foveal images for robot vision

Imperial Users onl