Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Src Homology 3-interacting Domain of Rv1917c of Mycobacterium tuberculosis Induces Selective Maturation of Human Dendritic Cells by Regulating PI3K-MAPK-NF-kappa B Signaling and Drives Th2 Immune Responses

Mycobacterium tuberculosis, an etiological agent of pulmonary tuberculosis, causes significant morbidity and mortality worldwide. Pathogenic mycobacteria survive in the host by subverting host innate immunity. Dendritic cells (DCs) are professional antigen-presenting cells that are vital for eliciting immune responses to infectious agents, including pathogenic mycobacteria. DCs orchestrate distinct Th responses based on the signals they receive. In this perspective, deciphering the interactions of the proline-glutamic acid/proline-proline-glutamic acid (PE/PPE) family of proteins of M. tuberculosis with DCs assumes significant pathophysiological attributes. In this study, we demonstrate that Rv1917c (PPE34), a representative member of the proline-proline-glutamic-major polymorphic tandem repeat family, interacts with TLR2 and triggers functional maturation of human DCs. Signaling perturbations implicated a critical role for integrated cross-talk among PI3K-MAPK and NF-kappa B signaling cascades in Rv1917c-induced maturation of DCs. However, this maturation of DCs was associated with a secretion of high amounts of anti-inflammatory cytokine IL-10, whereas Th1-polarizing cytokine IL-12 was not induced. Consistent with these results, Rv1917c-matured DCs favored secretion of IL-4, IL-5, and IL-10 from CD4(+) T cells and contributed to Th2-skewed cytokine balance ex vivo in healthy individuals and in patients with pulmonary tuberculosis. Interestingly, the Rv1917c-skewed Th2 immune response involved induced expression of cyclooxygenase-2 (COX-2) in DCs. Taken together, these results indicate that Rv1917c facilitates a shift in the ensuing immunity toward the Th2 phenotype and could aid in immune evasion by mycobacteria.

Characterization and immune response of a protein produced by a cDNA clone of foot and mouth disease virus, type Asia 1 63/72

A 0.9 kb double stranded cDNA of foot and mouth disease virus (FMDV) Type Asia 1, 63/72 was cloned in an expression vector, pUR222. A protein of 38 kd was produced by the clone which reacted with the antibodies raised against the virus. A 20 kd protein which may be derived from the 38 kd protein contained the antigenic epitopes of the protein VP1 of the virus. Injection of 10-20 micrograms of the partially purified 38 and 20 kd proteins or a lysate of cells containing 240 micrograms of the proteins elicited high titers of FMDV specific antibodies in guinea pigs and cattle respectively. Also, at these concentrations, the proteins protected 5 of 8 guinea pigs and 3 of 8 cattle when challenged with a virulent virus.

Impaired conflict monitoring in Parkinson's disease patients during an oculomotor redirect task

Fallibility is inherent in human cognition and so a system that will monitor performance is indispensable. While behavioral evidence for such a system derives from the finding that subjects slow down after trials that are likely to produce errors, the neural and behavioral characterization that enables such control is incomplete. Here, we report a specific role for dopamine/basal ganglia in response conflict by accessing deficits in performance monitoring in patients with Parkinson's disease. To characterize such a deficit, we used a modification of the oculomotor countermanding task to show that slowing down of responses that generate robust response conflict, and not post-error per se, is deficient in Parkinson's disease patients. Poor performance adjustment could be either due to impaired ability to slow RT subsequent to conflicts or due to impaired response conflict recognition. If the latter hypothesis was true, then PD subjects should show evidence of impaired error detection/correction, which was found to be the case. These results make a strong case for impaired performance monitoring in Parkinson's patients.

Monitoring Fatigue Crack Propagation in Compact Tension Specimens via Remote Sensing of Back Face Strain

Back face strain (BFS) measurement is now well-established as an indirect technique to monitor crack length in compact tension (CT) fracture specimens [1,2]. Previous work [2] developed empirical relations between fatigue crack propagation (FCP) parameters. BFS, and number of cycles for CT specimens subjected to constant amplitude fatigue loading. These predictions are experimentally validated in terms of the variations of mean values of BFS and load as a function of crack length. Another issue raised by this study concerns the validity of assigning fixed values for the Paris parameters C and n to describe FCP in realistic materials.

Fiber Bragg Grating Sensors for Real Time Monitoring of Evacuation Process

Fiber bragg grating (FBG) sensors have been widely used for number of sensing applications like temperature, pressure, acousto-ultrasonic, static and dynamic strain, refractive index change measurements and so on. Present work demonstrates the use of FBG sensors in in-situ measurement of vacuum process with simultaneous leak detection capability. Experiments were conducted in a bell jar vacuum chamber facilitated with conventional Pirani gauge for vacuum measurement. Three different experiments have been conducted to validate the performance of FBG sensor in monitoring vacuum creating process and air bleeding. The preliminary results of FBG sensors in vacuum monitoring have been compared with that of commercial Pirani gauge sensor. This novel technique offers a simple alternative to conventional method for real time monitoring of evacuation process. Proposed FBG based vacuum sensor has potential applications in vacuum systems involving hazardous environment such as chemical and gas plants, automobile industries, aeronautical establishments and leak monitoring in process industries, where the electrical or MEMS based sensors are prone to explosion and corrosion.

Composite material and piezoelectric coefficient uncertainty effects on structural health monitoring using feedback control gains as damage indicators

This article addresses uncertainty effect on the health monitoring of a smart structure using control gain shifts as damage indicators. A finite element model of the smart composite plate with surface-bonded piezoelectric sensors and actuators is formulated using first-order shear deformation theory and a matrix crack model is integrated into the finite element model. A constant gain velocity/position feedback control algorithm is used to provide active damping to the structure. Numerical results show that the response of the structure is changed due to matrix cracks and this change can be compensated by actively tuning the feedback controller. This change in control gain can be used as a damage indicator for structural health monitoring. Monte Carlo simulation is conducted to study the effect of material uncertainty on the damage indicator by considering composite material properties and piezoelectric coefficients as independent random variables. It is found that the change in position feedback control gain is a robust damage indicator.

Immune responses to hemagglutinin-neuraminidase protein of peste des petits ruminants virus expressed in transgenic peanut plants in sheep

Peste des petits ruminants (PPR) is an acute, highly contagious disease of small ruminants caused by a morbillivirus, Peste des petits ruminants virus (PPRV). The disease is prevalent in equatorial Africa, the Middle East, and the Indian subcontinent. A live attenuated vaccine is in use in some of the countries and has been shown to provide protection for at least three years against PPR. However, the live attenuated vaccine is not robust in terms of thermotolerance. As a step towards development of a heat stable subunit vaccine, we have expressed a hemagglutinin-neuraminidase (HN) protein of PPRV in peanut plants (Arachis hypogea) in a biologically active form, possessing neuraminidase activity. Importantly. HN protein expressed in peanut plants retained its immunodominant epitopes in their natural conformation. The immunogenicity of the plant derived HN protein was analyzed in sheep upon oral immunization. Virus neutralizing antibody responses were elicited upon oral immunization of sheep in the absence of any mucosal adjuvant. In addition, anti-PPRV-HN specific cell-mediated immune responses were also detected in mucosally immunized sheep. (C) 2010 Elsevier B.V. All rights reserved.

Comparison of electrical resistivity by geophysical method and neutron probe logging for soil moisture monitoring in a forested watershed

Geophysical methods are becoming more popular nowadays in the field of hydrology due to their time and space efficiency. So an attempt has been made here to relate electrical resistivity with soil moisture content in the field. The experiments were carried out in an experimental watershed `Mulehole' in southern India, which is a forested watershed with approximately 80% red soil. Five auger holes were drilled to perform the soil moisture and electrical resistivity measurements in a toposequence having red and black soils, with sandy weathered soil at the bottom. Soil moisture was measured using neutron probe and electrical resistivity was measured using electrical logging tool. The results indicate that electrical resistivity measurements can be used to measure soil moisture content for red soils only.

In-situ power monitoring scheme and its application in dynamic voltage and threshold scaling for digital CMOS integrated circuits

An in-situ power monitoring technique for Dynamic Voltage and Threshold scaling (DVTS) systems is proposed which measures total power consumed by load circuit using sleep transistor acting as power sensor. Design details of power monitor are examined using simulation framework in UMC 90nm CMOS process. Experimental results of test chip fabricated in AMS 0.35µm CMOS process are presented. The test chip has variable activity between 0.05 and 0.5 and has PMOS VTH control through nWell contact. Maximum resolution obtained from power monitor is 0.25mV. Overhead of power monitor in terms of its power consumption is 0.244 mW (2.2% of total power of load circuit). Lastly, power monitor is used to demonstrate closed loop DVTS system. DVTS algorithm shows 46.3% power savings using in-situ power monitor.