Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features.

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert's knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring.

Imaging Time Series for the Classification of EMI Discharge Sources.

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome.

Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation.

This work exploits four entropy measures known as Sample, Permutation, Weighted Permutation, and Dispersion Entropy to extract relevant information from Electromagnetic Interference (EMI) discharge signals that are useful in fault diagnosis of High-Voltage (HV) equipment. Multi-class classification algorithms are used to classify or distinguish between various discharge sources such as Partial Discharges (PD), Exciter, Arcing, micro Sparking and Random Noise. The signals were measured and recorded on different sites followed by EMI expert's data analysis in order to identify and label the discharge source type contained within the signal. The classification was performed both within each site and across all sites. The system performs well for both cases with extremely high classification accuracy within site. This work demonstrates the ability to extract relevant entropy-based features from EMI discharge sources from time-resolved signals requiring minimal computation making the system ideal for a potential application to online condition monitoring based on EMI.

Survey on the Performance of Source Localization Algorithms.

The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton-Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.

Multimodality imaging of peripheral neuropathies of the upper limb and brachial plexus.

The peripheral nerves of the upper limb are affected by a number of entrapment and compression neuropathies. These discrete syndromes involve the brachial plexus as well as the musculocutaneous, axillary, suprascapular, ulnar, radial, and median nerves. Clinical examination and electrophysiologic studies are the traditional mainstay of diagnostic work-up; however, ultrasonography and magnetic resonance imaging provide spatial information regarding the affected nerve and its surroundings, often assisting in narrowing the differential diagnosis and guiding treatment. Imaging is particularly valuable in complex cases with discrepant nerve function test results. Familiarity with the clinical features of various peripheral neuropathies of the upper extremity, the relevant anatomy, and the most common sites and causes of nerve entrapment assists in diagnosis and treatment.

Potentiating the benefit of vascular-supplied glutamine during small bowel storage: importance of buffering agent.

BACKGROUND: Glutamine (gln)-supplemented University of Wisconsin (UW) solution improves overall small bowel (SB) preservation. Sustained gln metabolism in a system devoid of hepatic detoxification will necessarily result in the accumulation of pH active end products leading to nonphysiologic pH shifts. We hypothesized that simultaneous addition of N,N-bis[2-hydroxyethyl]-2-aminoethane sulfonic acid (BES), a known buffering agent, would potentiate the beneficial effect of gln supplementation by addressing the fundamental metabolic principle of pH homeostasis. METHODS: Sprague-Dawley SB rats were administered a vascular flush with one of four solutions: UW; UW+90 mM BES (UWB); UW+2% gln (UWG); or UW+2% gln+90 mM BES (UWBG). Indices of energetics, barrier function, gln catabolism, and histology (light and electron microscopy) were assessed over a 10-hr cold storage time course. RESULTS: Superior gln utilization in the UWBG group was indicated by elevated levels of key catabolites (glutamate, aspartate, glycine, ammonia). The addition of BES and gln resulted in significantly higher levels of all energetic parameters (ATP, total adenylates) at 10 hr compared with UW, UWB, and/or UWG. Barrier function was markedly improved after 10 hr storage in the UWBG group; mannitol permeability was 169 nmol/cm2/hr versus 572 and 445 nmol/cm(2)/hr (for UW and UWG, respectively). Histologic injury at 10 hr was 5.5, 7.5, and 8 (Park's grade) for UWBG, UWG, and UW. Ultrastructural damage was markedly reduced with UWBG, as assessed by grade of mitochondria damage. CONCLUSION: This study strongly supports that the beneficial effects of gln-enriched UW solution can be amplified when combined with an effective buffering agent such as BES.

Body-weight-support treadmill training improves blood glucose regulation in persons with incomplete spinal cord injury.

The impact of a 6-mo body-weight-supported treadmill training program on glucose homeostasis and muscle metabolic characteristics was investigated. Nine individuals (31 +/- 3 yr, 8.1 +/- 2.5 yr postinjury; means +/- SE) with incomplete spinal cord injury trained three times weekly for a total of 6 mo. Training session duration and intensity (velocity) increased by 54 +/- 10% (P < 0.01) and 135 +/- 20%, respectively. Muscle biopsies and a modified glucose tolerance test (100 g glucose with [U-(13)C]glucose) were performed before (Pre) and after training (Post). Training resulted in a reduction in area under the curve of glucose x time (-15 +/- 4%) and insulin x time (-33 +/- 8%; both P < 0.05). Oxidation of exogenous (ingested) glucose increased as a result of training (Pre = 4.4 +/- 0.7 g/h, Post = 7.4 +/- 0.6 g/h; P < 0.05), as did oxidation of endogenous (liver) glucose (Pre = 3.8 +/- 0.3 g/h, Post = 5.2 +/- 0.3 g/h; P < 0.05). Training resulted in increased muscle glycogen (80 +/- 23%; P < 0.05) and GLUT-4 content and hexokinase II enzyme activity (126 +/- 34 and 49 +/- 4%, respectively, both P < 0.01). Resting muscle phosphocreatine content also increased after training (Pre = 62.1 +/- 4.3, Post = 78.7 +/- 3.8, both mmol/kg dry wt and P < 0.05). Six months of thrice-weekly body-weight-supported treadmill training in persons with an incomplete spinal cord injury improved blood glucose regulation by increasing oxidation and storage of an oral glucose load. Increases in the capacity for transport and phosphorylation glucose in skeletal muscle likely play a role in these adaptations.

Bcl-2 expression decreases cadherin-mediated cell-cell adhesion.

Bcl-2, a member of the apoptosis-regulating family of proteins confers a survival advantage on cells by inhibiting apoptosis. Bcl-2 expression is estrogen-responsive and high in various tumors. Overexpression of Bcl-2 has been associated with the loss of contact inhibition, unregulated growth and foci formation in culture. In this study, we have examined the effects of bcl-2 overexpression and expression on cell-cell adhesion in MCF-7 and MDCK epithelial cell lines respectively. Overexpression of Bcl-2 in estrogen receptor-positive MCF-7 mammary carcinoma cells led to decreased cell surface E-cadherin and the disruption of junctional complexes concurrent with intracellular redistribution of their components. Particularly noticeable, was the partial nuclear localization of the tight junction-associated protein ZO-1 which coincided with upregulation of ErbB2. The expression of this EGF co-receptor is regulated by the ZO-1-associated transcription factor ZONAB. Growth in estrogen-depleted media led to downregulation of Bcl-2 expression and upregulation and membrane localization of all junctional proteins. Similar disruption in junctions, accompanied by decreased transepithelial resistance, was observed when Bcl-2 was expressed in MDCK cells. These results strongly suggest that Bcl-2 expression decreases the level of functional E-cadherin thereby interfering with junction formation. The inhibition of junction formation decreases cell-cell adhesion leading to the loss of contact inhibition, which, in vivo, can lead to unregulated growth and tumorigenesis.

Treadmill training-induced adaptations in muscle phenotype in persons with incomplete spinal cord injury.

Body weight-supported treadmill (BWST) training has been shown to improve ambulatory capacity in persons with a spinal cord injury (SCI); however, the effect that BWST training has on skeletal muscle phenotype is unknown. We aimed to determine whether 6 months (three sessions/week) of BWST training in neurologically stable persons with a traumatic spinal cord injury (ASIA C) alters skeletal muscle phenotype, ambulatory capacity, and blood lipid profile. Externally supported body weight decreased, and walking velocity and duration of the training sessions increased (all P < 0.05) as a result of training. Muscle biopsies revealed increases in the mean muscle-fiber area of type I and IIa fibers. Training induced a reduction in type IIax/IIx fibers, as well as a decrease in IIX myosin heavy chain, and an increase in type IIa fibers. Maximal citrate synthase and 3-hydroxy-acyl-CoA dehydrogenase activity also increased following training. BWST training brought about reductions in plasma total (-11%) and low-density lipoprotein (-13%) cholesterol. We conclude that, in patients with a spinal cord injury, BWST training is able to induce an increase in muscle fiber size and bring about increases in muscle oxidative capacity. In addition, BWST training can bring about improvements in ambulatory capacity and antiatherogenic changes in blood lipid profile.