A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

A low-noise low-power noise-adaptive neural amplifier in 0.13um CMOS technology

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and in elucidating human neurophysiology. The advent of multichannel microelectrode arrays has driven the need for electronics to record neural signals from many neurons. The dynamic range of the system is limited by background system noise which varies over time. We propose a neural amplifier in UMC 130 nm, 2P8M CMOS technology. It can be biased adaptively from 200 nA to 2 uA, modulating input referred noise from 9.92 uV to 3.9 uV. We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. The amplifier can pass signal from 5 Hz to 7 kHz while rejecting input DC offsets at electrode-electrolyte interface. The bandwidth of the amplifier can be tuned by the pseudo-resistor for selectively recording low field potentials (LFP) or extra cellular action potentials (EAP). The amplifier achieves a mid-band voltage gain of 37 dB and minimizes the attenuation of the signal from neuron to the gate of the input transistor. It is used in fully differential configuration to reject noise of bias circuitry and to achieve high PSRR.

IGF-1 stimulated upregulation of cyclin D1 is mediated via STAT5 signaling pathway in neuronal cells

Signal Transducer and Activator of Transcription (STATs) regulate various target genes such as cyclin D1, MYC, and BCL2 in nonneuronal cells which contribute towards progression as well as prevention of apoptosis and are involved in differentiation and cell survival. However, in neuronal cells, the role of STATs in the activation and regulation of these target genes and their signaling pathways are still not well established. In this study, a robust cyclin D1 expression was observed following IGF-1 stimulation in SY5Y cells as well as neurospheres. JAK/STAT pathway was shown to be involved in this upregulation. A detailed promoter analysis revealed that the specific STAT involved was STAT5, which acted as a positive regulatory element for cyclin D1 expression. Overexpression studies confirmed increase in cyclin D1 expression in response to STAT5a and STAT5b constructs when compared to dominant-negative STAT5. siRNA targeting STAT5, diminished the cyclin D1 expression, further confirming that STAT5 specifically regulated cyclin D1 in neuronal cells. Together, these findings shed new light on the mechanism of IGF-1 mediated upregulation of cyclin D1 expression in neural cell lines as well as in neural stem cells via the JAK/STAT5 signaling cascade.

Processing of DNA double-stranded breaks and intermediates of recombination and repair by saccharomyces cerevisiae Mre11 and its stimulation by Rad50, Xrs2, and Sae2 proteins

Saccharomyces cerevisiae RAD50, MRE11, and XRS2 genes are essential for telomere length maintenance, cell cycle checkpoint signaling, meiotic recombination, and DNA double-stranded break (DSB) repair via nonhomologous end joining and homologous recombination. The DSB repair pathways that draw upon Mre11-Rad50-Xrs2 subunits are complex, so their mechanistic features remain poorly understood. Moreover, the molecular basis of DSB end resection in yeast mre11-nuclease deficient mutants and Mre11 nuclease-independent activation of ATM in mammals remains unknown and adds a new dimension to many unanswered questions about the mechanism of DSB repair. Here, we demonstrate that S. cerevisiae Mre11 (ScMre11) exhibits higher binding affinity for single-over double-stranded DNA and intermediates of recombination and repair and catalyzes robust unwinding of substrates possessing a 3' single-stranded DNA overhang but not of 5' overhangs or blunt-ended DNA fragments. Additional evidence disclosed that ScMre11 nuclease activity is dispensable for its DNA binding and unwinding activity, thus uncovering the molecular basis underlying DSB end processing in mre11 nuclease deficient mutants. Significantly, Rad50, Xrs2, and Sae2 potentiate the DNA unwinding activity of Mre11, thus underscoring functional interaction among the components of DSB end repair machinery. Our results also show that ScMre11 by itself binds to DSB ends, then promotes end bridging of duplex DNA, and directly interacts with Sae2. We discuss the implications of these results in the context of an alternative mechanism for DSB end processing and the generation of single-stranded DNA for DNA repair and homologous recombination.

GYROSONICS: SIGNATURE ANALYSIS AND REDUCED-ORDER MODELS

In this paper, the authors study the structure of a novel binaural sound with a certain phase and amplitude modulation and the response to this excitation when it is applied to natural rewarding circuit of human brain through auditory neural pathways. This novel excitation, also referred to as gyrosonic excitation in this work, has been found to have interesting effects such as stabilization effects on the left and right hemispheric brain signaling as captured by Galvanic Skin Resistance (GSR) measurements, control of cardiac rhythms (observed from ECG signals), mitigation of psychosomatic syndrome, and mitigation of migraine pain. Experimental data collected from human subjects are presented, and these data are examined to categorize the extent of systems disorder and reinforcement reward due to the gyrosonic stimulus. A multi-path reduced-order model has been developed to analyze the GSR signals. The filtered results are indicative of complicated reinforcing reward patterns due to the gyrosonic stimulation when it is used as a control input for patients with psychosomatic and cardiac disorders.

An 8-to-1 bit 1-MS/s SAR ADC With VGA and Integrated Data Compression for Neural Recording

Low power consumption per channel and data rate minimization are two key challenges which need to be addressed in future generations of neural recording systems (NRS). Power consumption can be reduced by avoiding unnecessary processing whereas data rate is greatly decreased by sending spike time-stamps along with spike features as opposed to raw digitized data. Dynamic range in NRS can vary with time due to change in electrode-neuron distance or background noise, which demands adaptability. An analog-to-digital converter (ADC) is one of the most important blocks in a NRS. This paper presents an 8-bit SAR ADC in 0.13-mu m CMOS technology along with input and reference buffer. A novel energy efficient digital-to-analog converter switching scheme is proposed, which consumes 37% less energy than the present state-of-the-art. The use of a ping-pong input sampling scheme is emphasized for multichannel input to alleviate the bandwidth requirement of the input buffer. To reduce the data rate, the A/D process is only enabled through the in-built background noise rejection logic to ensure that the noise is not processed. The ADC resolution can be adjusted from 8 to 1 bit in 1-bit step based on the input dynamic range. The ADC consumes 8.8 mu W from 1 V supply at 1 MS/s speed. It achieves effective number of bits of 7.7 bits and FoM of 42.3 fJ/conversion-step.

Patterned growth and differentiation of neural cells on polymer derived carbon substrates with micro/nano structures in vitro

The growth of neuroblastoma (N2a) and Schwann cells has been explored on polymer derived carbon substrates of varying micro and nanoscale geometries: resorcinol-formaldehyde (RE) gel derived carbon films and electrospun nanofibrous (similar to 200 nm diameter) mat and SU-8 (a negative photoresist) derived carbon micro-patterns. MTT assay and complementary lactate dehydrogenase (LDH) assay established cytocompatibility of RE derived carbon films and fibers over a period of 6 days in culture. The role of length scale of surface patterns in eliciting lineage-specific adaptive response along, across and on the interspacing between adjacent micropatterns (i.e., ``on'', ``across'' and ``off'') has been assayed. Textural features were found to affect 3',5'-cyclic AMP sodium salt-induced neurite outgrowth, over a wide range of length scales: from similar to 200 nm (carbon fibers) to similar to 60 mu m (carbon patterns). Despite their innate randomness, carbon nanofibers promoted preferential differentiation of N2a cells into neuronal lineage, similar to ordered micro-patterns. Our results, for the first time, conclusively demonstrate the potential of RE-gel and SU-8 derived carbon substrates as nerve tissue engineering platforms for guided proliferation and differentiation of neural cells in vitro. (C) 2013 Elsevier Ltd. All rights reserved.

Effect of choice of basis functions in neural network for capturing unknown function for dynamic inversion control

The basic requirement for an autopilot is fast response and minimum steady state error for better guidance performance. The highly nonlinear nature of the missile dynamics due to the severe kinematic and inertial coupling of the missile airframe as well as the aerodynamics has been a challenge for an autopilot that is required to have satisfactory performance for all flight conditions in probable engagements. Dynamic inversion is very popular nonlinear controller for this kind of scenario. But the drawback of this controller is that it is sensitive to parameter perturbation. To overcome this problem, neural network has been used to capture the parameter uncertainty on line. The choice of basis function plays the major role in capturing the unknown dynamics. Here in this paper, many basis function has been studied for approximation of unknown dynamics. Cosine basis function has yield the best response compared to any other basis function for capturing the unknown dynamics. Neural network with Cosine basis function has improved the autopilot performance as well as robustness compared to Dynamic inversion without Neural network.

Pulsed Electrical Stimulation and Surface Charge Induced Cell Growth on Multistage Spark Plasma Sintered Hydroxyapatite-Barium Titanate Piezobiocomposite

The primary purpose of the present work was to illustrate whether cell proliferation can be enhanced on electroactive bioceramic composite, when the cells are cultured in the presence of external electrical stimulation. The two different aspects of the influence of electric field (E-field) application toward stimulating the growth/proliferation of bone/connective tissue cells in vitro, (a) intermittent delivery of extremely low strength pulsed electrical stimulation (0.5-4V/cm, 400s DC pulse) and (b) surface charge generated by electrical poling (10kV/cm) of hydroxyapatite (HA)-BaTiO3 piezobiocomposite have been demonstrated. The experimental results establish that the cell growth can be enhanced using the new culture protocol of the intermittent delivery of electrical pulses within a narrow range of stimulation parameters. The optimal E-field strength for enhanced cellular response for mouse fibroblast L929 and osteogenic cells is in the range of 0.5-1V/cm. The MTT 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide] assay results suggested the increased viability of E-field treated cells over 7d in culture, implicating the positive impact of electrical pulses on proliferation behavior. The alizarin red assay results showed noticeable increase in Ca-deposition on the E-field treated samples in comparison to their untreated counterparts. The negatively charged surfaces of developed piezocomposite stimulated the cell growth in a statistically noticeable manner as compared with the uncharged or positively charged surfaces of similar composition.

Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a `footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by similar to 50% in generator potentials, to similar to 3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.