9 research outputs found

    Classification of movements of the rat based on intra-cortical signals using artificial neural network and support vector machine

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    A BCI aims at creating a communication pathway between the brain and an external device. This is possible by decoding signals from the primary motor cortex and translating them into commands for a prosthetic device. The experimental design was developed starting from intra-cortical signal recorded in the rat brain. The data pre-processing included denoising with wavelet technique, spike detection, and feature extraction. Artificial neural network and support vector machine were applied to classify the rat movements into two possible classes, Hit or No Hit. The misclassification error rates from denoised and not denoised data were statistically different (p<0.05), proving the efficiency of the denoising technique. ANN and SVM gave comparable classification result

    Data-Augmented Structure-Property Mapping for Accelerating Computational Design of Advanced Material Systems

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    abstract: Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

    A generalised feedforward neural network architecture and its applications to classification and regression

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    Shunting inhibition is a powerful computational mechanism that plays an important role in sensory neural information processing systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks where the basic synaptic computations are based on shunting inhibition. SIANNs were designed to solve difficult machine learning problems by exploiting the inherent non-linearity mediated by shunting inhibition. The aim was to develop powerful, trainable networks, with non-linear decision surfaces, for classification and non-linear regression tasks. This work enhances and extends the original SIANN architecture to a more general form called the Generalised Feedforward Neural Network (GFNN) architecture, which contains as subsets both SIANN and the conventional Multilayer Perceptron (MLP) architectures. The original SIANN structure has the number of shunting neurons in the hidden layers equal to the number of inputs, due to the neuron model that is used having a single direct excitatory input. This was found to be too restrictive, often resulting in inadequately small or inordinately large network structures

    On the training of feedforward neural networks.

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    by Hau-san Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [178-183]).Chapter 1 --- INTRODUCTIONChapter 1.1 --- Learning versus Explicit Programming --- p.1-1Chapter 1.2 --- Artificial Neural Networks --- p.1-2Chapter 1.3 --- Learning in ANN --- p.1-3Chapter 1.4 --- Problems of Learning in BP Networks --- p.1-5Chapter 1.5 --- Dynamic Node Architecture for BP Networks --- p.1-7Chapter 1.6 --- Incremental Learning --- p.1-10Chapter 1.7 --- Research Objective and Thesis Organization --- p.1-11Chapter 2 --- THE FEEDFORWARD MULTILAYER NEURAL NETWORKChapter 2.1 --- The Perceptron --- p.2-1Chapter 2.2 --- The Generalization of the Perceptron --- p.2-4Chapter 2.3 --- The Multilayer Feedforward Network --- p.2-5Chapter 3 --- SOLUTIONS TO THE BP LEARNING PROBLEMChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- Attempts in the Establishment of a Viable Hidden Representation Model --- p.3-5Chapter 3.3 --- Dynamic Node Creation Algorithms --- p.3-9Chapter 3.4 --- Concluding Remarks --- p.3-15Chapter 4 --- THE GROWTH ALGORITHM FOR NEURAL NETWORKSChapter 4.1 --- Introduction --- p.4-2Chapter 4.2 --- The Radial Basis Function --- p.4-6Chapter 4.3 --- The Additional Input Node and the Modified Nonlinearity --- p.4-9Chapter 4.4 --- The Initialization of the New Hidden Node --- p.4-11Chapter 4.5 --- Initialization of the First Node --- p.4-15Chapter 4.6 --- Practical Considerations for the Growth Algorithm --- p.4-18Chapter 4.7 --- The Convergence Proof for the Growth Algorithm --- p.4-20Chapter 4.8 --- The Flow of the Growth Algorithm --- p.4-21Chapter 4.9 --- Experimental Results and Performance Analysis --- p.4-21Chapter 4.10 --- Concluding Remarks --- p.4-33Chapter 5 --- KNOWLEDGE REPRESENTATION IN NEURAL NETWORKSChapter 5.1 --- An Alternative Perspective to Knowledge Representation in Neural Network: The Temporal Vector (T-Vector) Approach --- p.5-1Chapter 5.2 --- Prior Research Works in the T-Vector Approach --- p.5-2Chapter 5.3 --- Formulation of the T-Vector Approach --- p.5-3Chapter 5.4 --- Relation of the Hidden T-Vectors to the Output T-Vectors --- p.5-6Chapter 5.5 --- Relation of the Hidden T-Vectors to the Input T-Vectors --- p.5-10Chapter 5.6 --- An Inspiration for a New Training Algorithm from the Current Model --- p.5-12Chapter 6 --- THE DETERMINISTIC TRAINING ALGORITHM FOR NEURAL NETWORKSChapter 6.1 --- Introduction --- p.6-1Chapter 6.2 --- The Linear Independency Requirement for the Hidden T-Vectors --- p.6-3Chapter 6.3 --- Inspiration of the Current Work from the Barmann T-Vector Model --- p.6-5Chapter 6.4 --- General Framework of Dynamic Node Creation Algorithm --- p.6-10Chapter 6.5 --- The Deterministic Initialization Scheme for the New Hidden NodesChapter 6.5.1 --- Introduction --- p.6-12Chapter 6.5.2 --- Determination of the Target T-VectorChapter 6.5.2.1 --- Introduction --- p.6-15Chapter 6.5.2.2 --- Modelling of the Target Vector βQhQ --- p.6-16Chapter 6.5.2.3 --- Near-Linearity Condition for the Sigmoid Function --- p.6-18Chapter 6.5.3 --- Preparation for the BP Fine-Tuning Process --- p.6-24Chapter 6.5.4 --- Determination of the Target Hidden T-Vector --- p.6-28Chapter 6.5.5 --- Determination of the Hidden Weights --- p.6-29Chapter 6.5.6 --- Determination of the Output Weights --- p.6-30Chapter 6.6 --- Linear Independency Assurance for the New Hidden T-Vector --- p.6-30Chapter 6.7 --- Extension to the Multi-Output Case --- p.6-32Chapter 6.8 --- Convergence Proof for the Deterministic Algorithm --- p.6-35Chapter 6.9 --- The Flow of the Deterministic Dynamic Node Creation Algorithm --- p.6-36Chapter 6.10 --- Experimental Results and Performance Analysis --- p.6-36Chapter 6.11 --- Concluding Remarks --- p.6-50Chapter 7 --- THE GENERALIZATION MEASURE MONITORING SCHEMEChapter 7.1 --- The Problem of Generalization for Neural Networks --- p.7-1Chapter 7.2 --- Prior Attempts in Solving the Generalization Problem --- p.7-2Chapter 7.3 --- The Generalization Measure --- p.7-4Chapter 7.4 --- The Adoption of the Generalization Measure to the Deterministic Algorithm --- p.7-5Chapter 7.5 --- Monitoring of the Generalization Measure --- p.7-6Chapter 7.6 --- Correspondence between the Generalization Measure and the Generalization Capability of the Network --- p.7-8Chapter 7.7 --- Experimental Results and Performance Analysis --- p.7-12Chapter 7.8 --- Concluding Remarks --- p.7-16Chapter 8 --- THE ESTIMATION OF THE INITIAL HIDDEN LAYER SIZEChapter 8.1 --- The Need for an Initial Hidden Layer Size Estimation --- p.8-1Chapter 8.2 --- The Initial Hidden Layer Estimation Scheme --- p.8-2Chapter 8.3 --- The Extension of the Estimation Procedure to the Multi-Output Network --- p.8-6Chapter 8.4 --- Experimental Results and Performance Analysis --- p.8-6Chapter 8.5 --- Concluding Remarks --- p.8-16Chapter 9 --- CONCLUSIONChapter 9.1 --- Contributions --- p.9-1Chapter 9.2 --- Suggestions for Further Research --- p.9-3REFERENCES --- p.R-1APPENDIX --- p.A-

    Robust text independent closed set speaker identification systems and their evaluation

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    PhD ThesisThis thesis focuses upon text independent closed set speaker identi cation. The contributions relate to evaluation studies in the presence of various types of noise and handset e ects. Extensive evaluations are performed on four databases. The rst contribution is in the context of the use of the Gaussian Mixture Model-Universal Background Model (GMM-UBM) with original speech recordings from only the TIMIT database. Four main simulations for Speaker Identi cation Accuracy (SIA) are presented including di erent fusion strategies: Late fusion (score based), early fusion (feature based) and early-late fusion (combination of feature and score based), late fusion using concatenated static and dynamic features (features with temporal derivatives such as rst order derivative delta and second order derivative delta-delta features, namely acceleration features), and nally fusion of statistically independent normalized scores. The second contribution is again based on the GMM-UBM approach. Comprehensive evaluations of the e ect of Additive White Gaussian Noise (AWGN), and Non-Stationary Noise (NSN) (with and without a G.712 type handset) upon identi cation performance are undertaken. In particular, three NSN types with varying Signal to Noise Ratios (SNRs) were tested corresponding to: street tra c, a bus interior and a crowded talking environment. The performance evaluation also considered the e ect of late fusion techniques based on score fusion, namely mean, maximum, and linear weighted sum fusion. The databases employed were: TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3,600 speech utterances. The third contribution is based on the use of the I-vector, four combinations of I-vectors with 100 and 200 dimensions were employed. Then, various fusion techniques using maximum, mean, weighted sum and cumulative fusion with the same I-vector dimension were used to improve the SIA. Similarly, both interleaving and concatenated I-vector fusion were exploited to produce 200 and 400 I-vector dimensions. The system was evaluated with four di erent databases using 120 speakers from each database. TIMIT, SITW and NIST 2008 databases were evaluated for various types of NSN namely, street-tra c NSN, bus-interior NSN and crowd talking NSN; and the G.712 type handset at 16 kHz was also applied. As recommendations from the study in terms of the GMM-UBM approach, mean fusion is found to yield overall best performance in terms of the SIA with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings. However, in the I-vector approach the best SIA was obtained from the weighted sum and the concatenated fusion.Ministry of Higher Education and Scienti c Research (MoHESR), and the Iraqi Cultural Attach e, Al-Mustansiriya University, Al-Mustansiriya University College of Engineering in Iraq for supporting my PhD scholarship

    Sliding Mode Control

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    The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

    A Cognitive Information Theory of Music: A Computational Memetics Approach

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    This thesis offers an account of music cognition based on information theory and memetics. My research strategy is to split the memetic modelling into four layers: Data, Information, Psychology and Application. Multiple cognitive models are proposed for the Information and Psychology layers, and the MDL best-fit models with published human data are selected. Then, for the Psychology layer only, new experiments are conducted to validate the best-fit models. In the information chapter, an information-theoretic model of musical memory is proposed, along with two competing models. The proposed model exhibited a better fit with human data than the competing models. Higher-level psychological theories are then built on top of this information layer. In the similarity chapter, I proposed three competing models of musical similarity, and conducted a new experiment to validate the best-fit model. In the fitness chapter, I again proposed three competing models of musical fitness, and conducted a new experiment to validate the best-fit model. In both cases, the correlations with human data are statistically significant. All in all, my research has shown that the memetic strategy is sound, and the modelling results are encouraging. Implications of this research are discussed
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