Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals

The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion. Here we apply wavelet packet decomposition to extract features from acoustic emission signals. We then use the extracted wavelet packet coefficients for distinguishing between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking. The local discriminant basis selection algorithm can be considered as a standard for the selection of the most discriminative wavelet coefficients. However, it does not take the statistical dependencies between wavelet coefficients into account. We show that, when these dependencies are ignored, a lower accuracy is obtained in predicting the corrosion type. We compare several mutual information filters to take these dependencies into account in order to arrive at a more accurate prediction

Oscillatory activity in the medial prefrontal cortex and nucleus accumbens correlates with impulsivity and reward outcome.

Actions expressed prematurely without regard for their consequences are considered impulsive. Such behaviour is governed by a network of brain regions including the prefrontal cortex (PFC) and nucleus accumbens (NAcb) and is prevalent in disorders including attention deficit hyperactivity disorder (ADHD) and drug addiction. However, little is known of the relationship between neural activity in these regions and specific forms of impulsive behaviour. In the present study we investigated local field potential (LFP) oscillations in distinct sub-regions of the PFC and NAcb on a 5-choice serial reaction time task (5-CSRTT), which measures sustained, spatially-divided visual attention and action restraint. The main findings show that power in gamma frequency (50-60 Hz) LFP oscillations transiently increases in the PFC and NAcb during both the anticipation of a cue signalling the spatial location of a nose-poke response and again following correct responses. Gamma oscillations were coupled to low-frequency delta oscillations in both regions; this coupling strengthened specifically when an error response was made. Theta (7-9 Hz) LFP power in the PFC and NAcb increased during the waiting period and was also related to response outcome. Additionally, both gamma and theta power were significantly affected by upcoming premature responses as rats waited for the visual cue to respond. In a subgroup of rats showing persistently high levels of impulsivity we found that impulsivity was associated with increased error signals following a nose-poke response, as well as reduced signals of previous trial outcome during the waiting period. Collectively, these in-vivo neurophysiological findings further implicate the PFC and NAcb in anticipatory impulsive responses and provide evidence that abnormalities in the encoding of rewarding outcomes may underlie trait-like impulsive behaviour.RCUK, Wellcome, OtherThis is the final version of the article. It first appeared at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111300

Probabilistic identification of cerebellar cortical neurones across species.

Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equi-probable decision boundaries between cell classes. Firing frequency statistics were useful for separating Purkinje cells from granular layer units, whilst firing irregularity measures proved most useful for distinguishing cells within granular layer cell classes. Considered as single statistics, we achieved classification accuracies of 72.5% and 92.7% for granular layer and molecular layer units respectively. Combining statistics to form twin-variate GPC models substantially improved classification accuracies with the combination of mean spike frequency and log-interval entropy offering classification accuracies of 92.7% and 99.2% for our molecular and granular layer models, respectively. A cross-species comparison was performed, using data drawn from anaesthetised mice and decerebrate cats, where our models offered 80% and 100% classification accuracy. We then used our models to assess non-identified data from awake monkeys and rabbits in order to highlight subsets of neurones with the greatest degree of similarity to identified cell classes. In this way, our GPC-based approach for tentatively identifying neurones from their spontaneous activity signatures, in the absence of an established ground-truth, nonetheless affords the experimenter a statistically robust means of grouping cells with properties matching known cell classes. Our approach therefore may have broad application to a variety of future cerebellar cortical investigations, particularly in awake animals where opportunities for definitive cell identification are limited

Genome sequencing and analysis of the versatile cell factory Aspergillus niger CBS 513.88

The filamentous fungus Aspergillus niger is widely exploited by the fermentation industry for the production of enzymes and organic acids, particularly citric acid. We sequenced the 33.9-megabase genome of A. niger CBS 513.88, the ancestor of currently used enzyme production strains. A high level of synteny was observed with other aspergilli sequenced. Strong function predictions were made for 6,506 of the 14,165 open reading frames identified. A detailed description of the components of the protein secretion pathway was made and striking differences in the hydrolytic enzyme spectra of aspergilli were observed. A reconstructed metabolic network comprising 1,069 unique reactions illustrates the versatile metabolism of A. niger. Noteworthy is the large number of major facilitator superfamily transporters and fungal zinc binuclear cluster transcription factors, and the presence of putative gene clusters for fumonisin and ochratoxin A synthesis

Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals

The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion. Here we apply wavelet packet decomposition to extract features from acoustic emission signals. We then use the extracted wavelet packet coefficients for distinguishing between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking. The local discriminant basis selection algorithm can be considered as a standard for the selection of the most discriminative wavelet coefficients. However, it does not take the statistical dependencies between wavelet coefficients into account. We show that, when these dependencies are ignored, a lower accuracy is obtained in predicting the corrosion type. We compare several mutual information filters to take these dependencies into account in order to arrive at a more accurate prediction

Information Theoretic Approach to Feature Selection and Redundancy Assessment (Informatietheoretische benadering voor selectie van kenmerken en inschatting van redundantie)

Preface III Abstract V Beknopte samenvatting VII Contents XIII List of symbols XVII 1 Introduction 1 1.1 A Unifying Information Theoretic Framework for Information Relevance and Redundancy Detection 4 1.2 Optimal Information Theoretic Feature Relevance Filtering 4 1.3 Mutual Information based Feature Selection in Wavelet Packet Decomposition for Acoustic Emission Corrosion Signals 5 1.4 Posterior Probability Profiles for the Automated Assessment of the Recovery of Stroke Patients 5 2 A Unifying Information Theoretic Framework for Information Relevance and Redundancy Detection 9 2.1 Introduction 9 2.2 Formal relationship between conditional relative entropy framework for feature selection and mutual information 9 2.2.1 Derivation of the relationships 10 2.2.2 Traditional motivation for the use of mutual information in feature selection and extraction 14 2.2.3 Markov blanket and mutual information 14 2.2.4 Assuming feature independence and class conditional feature independence 19 2.3 Reformulation of feature definitions in the MI framework 26 2.3.1 Irrelevant features 26 2.3.2 Weakly relevant features 27 2.3.3 Strongly relevant features 27 2.3.4 Redundant features 29 2.4 Derivation of a mutual information test statistic for causality detection based on the conditional relative entropy framework 31 2.4.1 Introduction 31 2.4.2 The conditional relative entropy framework for causality detection 32 2.4.3 Conditional relative entropy and mutual information for time series 33 2.4.4 Techniques for causality detection 34 2.4.5 Computation of conditional relative entropy 35 2.4.6 Validation 36 2.5 Conclusion 39 3 Optimal Information Theoretic Feature Relevance Filtering 43 3.1 Introduction 43 3.2 Relevance filtering 43 3.2.1 Optimality of marginal feature relevance 43 3.3 Permutation testing 46 3.3.1 Accuracy of permutation testing 46 3.4 Feature subset selection 48 3.4.1 Search procedures 48 3.4.2 Sequential forward selection(SFS) 51 3.4.3 Sequential forward floating selection (SFFS)52 3.4.4 Hybrid genetic algorithm (HGA) 55 3.4.4.1 Initialization of GA parameters 55 3.4.4.2 Initialization of the population 55 3.4.4.3 Select two parents from the population P 56 3.4.4.4 Crossover and mutation 56 3.4.4.5 Local improvement 57 3.4.4.6 Replacement 57 3.4.5 Criterion function and induction algorithm 59 3.4.6 Hybrid filter - wrapper 59 3.4.7 Computational complexity of the filter and wrapper 61 3.5 Experiments 62 3.5.1 SFS experiments 63 3.5.2 SFFS experiments 66 3.5.3 HGA experiments 68 3.5.4 Comparison between SFS, SFFS, HGA 69 3.6 Conclusion 72 4 Mutual Information based Feature Selection in Wavelet Packet Decomposition for Acoustic Emission Corrosion Signals 77 4.1 Introduction 77 4.2 Corrosion acoustic emission signals 77 4.2.1 Sources of AE signals in corrosion 79 4.2.2 Influence of wave propagation and sensor 81 4.2.3 Excluding system and environmental distortions 82 4.3 Materials and experimental conditions 83 4.4 Wavelet packet decomposition 84 4.4.1 Theoretical background of WPD 84 4.4.2 Exponential number of bases 91 4.4.3 No preferential basis theorem 91 4.5 Motivation for feature extraction 94 4.6 Comparison between Local Discriminant Basis algorithm and mutual information selection 96 4.6.1 Local Discriminant Basis algorithm 97 4.6.2 Mutual information based selection of wavelet coefficients 100 4.6.2.1 Diminishing returns theorem in SFS 101 4.6.2.2 Increasing loss theorem in SBS 102 4.7 Experiments with LDB and MI feature selection 104 4.7.1 Classification algorithms 104 4.7.2 Feature selection approaches 105 4.7.3 Acoustic emission 4 class problem 106 4.7.4 Acoustic emission 3 class problem 109 4.7.5 Acoustic emission two class problem 113 4.7.6 CBF problem 116 4.7.7 Orthonormality of the basis does not imply independence of features 121 4.8 Interpretation and visualization of the wavelet coefficients 121 4.9 Conclusion 125 5 Posterior Probability Profiles for the Automated Assessment of the Recovery of Stroke Patients 133 5.1 Introduction 133 5.1.1 Types of stroke 134 5.1.2 Stroke statistics 134 5.1.3 Costs related to stroke 134 5.2 Overview of the data processing 135 5.3 Measurements from ADL tasks 137 5.4 Movement initiation onset detection 138 5.4.1 Change in variance 138 5.4.2 Continuous wavelet transform 140 5.4.3 Information redundancy as onset detection criterion 140 5.5 Feature construction 143 5.6 Class posterior probabilities and dimensionality reduction 145 5.6.1 Rationale of class posterior probabilities 146 5.6.2 Feature subset selection 146 5.6.2.1 Filter based feature selection 147 5.6.2.2 Wrapper based feature selection 147 5.7 Validation 148 5.8 Conclusion 153 6 Conclusion 157 6.1 A Unifying Information Theoretic Framework for Information Relevance and Redundancy Detection 157 6.2 Optimal Information Theoretic Feature Relevance Filtering 157 6.3 Mutual Information based Feature Selection in Wavelet Packet Decomposition for Acoustic Emission Corrosion Signals 158 6.4 Posterior Probability Profiles for the Automated Assessment of the Recovery of Stroke Patients 159 6.5 Future Research 159 Appendix i Curriculum Vitae xi Publication list xiiinrpages: 160 + xivstatus: publishe

Een theoretisch gefundeerde benadering van het begrip informatie maakt het mogelijk systemen goedkoper en sneller te maken

Ingenieurs worden overladen met ‘informatie’ uit de omgeving, afkomstig van bijvoorbeeld beelden of signalen uit meerdere sensoren. Na de geschikte datatransformaties zoals principaal componentanalyse of wavelettransformaties kunnen de beelden en signalen soms met behulp van een beperkt aantal kenmerken, ook wel features genaamd, voorgesteld worden.Populariserend artikel over het doctoraatsonderzoekstatus: publishe

Joint Markov Blankets in Feature Sets Extracted from Wavelet Packet Decompositions

Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the Markov blanket filtering theory. We introduce the concept of joint Markov blankets. It is shown that joint Markov blankets are a natural extension of Markov blankets, which are defined for single features, to a set of features. We show that these joint Markov blankets exist in feature sets consisting of the wavelet coefficients. Furthermore, we prove that wavelet energy features from the highest frequency resolution level form a joint Markov blanket for all other wavelet energy features. The joint Markov blanket theory indicates that one can expect an increase of classification accuracy with the increase of the frequency resolution level of the energy features