Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Stop and revive? The effectiveness of nap and active rest breaks for reducing drivers sleepiness

The purpose of this study was to compare the effects of two commonly utilised sleepiness countermeasures: a nap break and an active rest break. The effects of the countermeasures were evaluated by physiological (EEG), subjective, and driving performance measures. Participants completed two hours of simulated driving, followed by a 15 minute nap break or a 15 minute active rest break then completed the final hour of simulated driving. The nap break reduced EEG and subjective sleepiness. The active rest break did not reduce EEG sleepiness, with sleepiness levels eventually increasing, and resulted in an immediate reduction of subjective sleepiness. No difference was found between the two breaks for the driving performance measure. The immediate reduction of subjective sleepiness after the active rest break could leave drivers with erroneous perceptions of their sleepiness, particularly with increases of physiological sleepiness after the break.

Classifying natural aerial scenery for autonomous aircraft emergency landing

In this paper, we present an approach for image-based surface classification using multi-class Support Vector Machine (SVM). Classifying surfaces in aerial images is an important step towards an increased aircraft autonomy in emergency landing situations. We design a one-vs-all SVM classifier and conduct experiments on five data sets. Results demonstrate consistent overall performance figures over 88% and approximately 8% more accurate to those published on multi-class SVM on the KTH TIPS data set. We also show per-class performance values by using normalised confusion matrices. Our approach is designed to be executed online using a minimum set of feature attributes representing a feasible and ready-to-deploy system for onboard execution.

Flying supercapacitors as power smoothing elements in wind generation

This paper presents a capacitor-clamped three-level inverter-based supercapacitor direct integration scheme for wind energy conversion systems. The idea is to increase the capacitance of clamping capacitors with the use of supercapacitors and allow their voltage to vary within a defined range. Even though this unique approach eliminates the need of interfacing dc-dc converters for supercapacitors, the variable voltage operation brings about several challenges. The uneven distribution of space vectors is the major modulation challenge. A space vector modulation method is proposed in this paper to address this issue and to generate undistorted currents even in the presence of dynamic changes in supercapacitor voltages. A supercapacitor voltage equalization algorithm is also presented. Moreover, control strategies of the proposed system are discussed in detail. Simulation and experimental results are presented to verify the efficacy of the proposed system in suppressing short-term wind power fluctuations.

Large scale read classification for next generation sequencing

Next Generation Sequencing (NGS) has revolutionised molecular biology, resulting in an explosion of data sets and an increasing role in clinical practice. Such applications necessarily require rapid identification of the organism as a prelude to annotation and further analysis. NGS data consist of a substantial number of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. Highly accurate results have been obtained for restricted sets using SVM classifiers, but such methods are difficult to parallelise and success depends on careful attention to feature selection. This work examines the problem at very large scale, using a mix of synthetic and real data with a view to determining the overall structure of the problem and the effectiveness of parallel ensembles of simpler classifiers (principally random forests) in addressing the challenges of large scale genomics.

A direct integration scheme for battery-supercapacitor hybrid energy storage systems with the use of grid side inverter

Battery-supercapacitor hybrid energy storage systems are becoming popular in the renewable energy sector due to their improved power and energy performances. These hybrid systems require separate dc-dc converters, or at least one dc-dc converter for the supercapacitor bank, to connect them to the dc-link of the grid interfacing inverter. These additional dc-dc converters increase power losses, complexity and cost. Therefore, possibility of their direct connection is investigated in this paper. The inverter system used in this study is formed by cascading two 3-level inverters, named as the “main inverter” and the “auxiliary inverter”, through a coupling transformer. In the test system the main inverter is connected with the rectified output of a wind generator while the auxiliary inverter is directly attached to a battery and a supercapacitor bank. The major issues with this approach are the dynamic changes in dc-link voltages and inevitable imbalances in the auxiliary inverter voltages, which results in unevenly distributed space vectors. A modified SVM technique is proposed to solve this issue. A PWM based time sharing method is proposed for power sharing between the battery and the supercapacitor. Simulation results are presented to verify the efficacy of the proposed modulation and control techniques.

Stop and revive? The effectiveness of nap and active rest breaks for reducing driver sleepiness

Objectives The purpose for this study was to determine the relative benefit of nap and active rest breaks for reducing driver sleepiness. Methods Participants were 20 healthy young adults (20-25 years), including 8 males and 12 females. A counterbalanced within-subjects design was used such that each participant completed both conditions on separate occasions, a week apart. The effects of the countermeasures were evaluated by established physiological (EEG theta and alpha absolute power), subjective (Karolinska Sleepiness Scale), and driving performance measures (Hazard Perception Task). Participants woke at 5am, and undertook a simulated driving task for two hours; each participant then had either a 15-minute nap opportunity or a 15-minute active rest break that included 10 minutes of brisk walking, followed by another hour of simulated driving. Results The nap break reduced EEG theta and alpha absolute power and eventually reduced subjective sleepiness levels. In contrast, the active rest break did not reduce EEG theta and alpha absolute power levels with the power levels eventually increasing. An immediate reduction of subjective sleepiness was observed, with subjective sleepiness increasing during the final hour of simulated driving. No difference was found between the two breaks for hazard perception performance. Conclusions Only the nap break produced a significant reduction in physiological sleepiness. The immediate reductions of subjective sleepiness following the active rest break could leave drivers with erroneous perceptions of their sleepiness, particularly as physiological sleepiness continued to increase after the break.

Space vector modulated cascade multi-level inverter for PMSG wind generation systems

Modulation and control of a cascade multilevel inverter, which has a high potential in future wind generation applications, are presented. The inverter is a combination of a high power, three level “bulk inverter” and a low power “conditioning inverter”. To minimize switching losses, the bulk inverter operates at a low frequency producing square wave outputs while high frequency conditioning inverter is used to suppress harmonic content produced by the bulk inverter output. This paper proposes an improved Space Vector Modulation (SVM) algorithm and a neutral point potential balancing technique for the inverter. Furthermore, a maximum power tracking controller for the Permanent Magnet Synchronous Generator (PMSG) is described in detail. The proposed SVM technique eliminates most of the computational burdens on the digital controller and renders a greater controllability under varying DC-link voltage conditions. The DC-link capacitor voltage balancing of both bulk and conditioning inverters is carried out using Redundant State Selection (RSS) method and is explained in detail. Experimental results are presented to verify the proposed modulation and control techniques.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

The N170 observed ‘in the wild’: robust event-related potentials to faces in cluttered dynamic visual scenes

As a social species in a constantly changing environment, humans rely heavily on the informational richness and communicative capacity of the face. Thus, understanding how the brain processes information about faces in real-time is of paramount importance. The N170 is a high temporal resolution electrophysiological index of the brain's early response to visual stimuli that is reliably elicited in carefully controlled laboratory-based studies. Although the N170 has often been reported to be of greatest amplitude to faces, there has been debate regarding whether this effect might be an artifact of certain aspects of the controlled experimental stimulation schedules and materials. To investigate whether the N170 can be identified in more realistic conditions with highly variable and cluttered visual images and accompanying auditory stimuli we recorded EEG 'in the wild', while participants watched pop videos. Scene-cuts to faces generated a clear N170 response, and this was larger than the N170 to transitions where the videos cut to non-face stimuli. Within participants, wild-type face N170 amplitudes were moderately correlated to those observed in a typical laboratory experiment. Thus, we demonstrate that the face N170 is a robust and ecologically valid phenomenon and not an artifact arising as an unintended consequence of some property of the more typical laboratory paradigm.

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

Examining the neural correlates of choice behavior in a gambling task using steady state topography

The present study investigated the behavioral and neuropsychological characteristics of decision-making behavior during a gambling task as well as how these characteristics may relate to the Somatic Marker Hypothesis and the Frequency of Gain model. The applicability to intertemporal choice was also discussed. Patterns of card selection during a computerized interpretation of the Iowa Gambling Task were assessed for 10 men and 10 women. Steady State Topography was employed to assess cortical processing throughout this task. Results supported the hypothesis that patterns of card selection were in line with both theories. As hypothesized, these 2 patterns of card selection were also associated with distinct patterns of cortical activity, suggesting that intertemporal choice may involve the recruitment of right dorsolateral prefrontal cortex for somatic labeling, left fusiform gyrus for object representations, and the left dorsolateral prefrontal cortex for an analysis of the associated frequency of gain or loss. It is suggested that processes contributing to intertemporal choice may include inhibition of negatively valenced options, guiding decisions away from those options, as well as computations favoring frequently rewarded options.