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

Temperature dependence of spin lifetime of conduction electrons in bulk germanium

Optically generated spin polarized electrons in bulk n-type Ge samples have been detected by using a radio-frequency modulation technique. Using the Hanle effect in an external magnetic field, the spin lifetime was measured as a function of temperature in the range 90 K to 180 K. The lifetime decreases with increasing temperature from similar to 5 ns at 100 K to similar to 2 ns at 180 K. We show that the temperature dependence is consistent with the Elliott-Yafet spin relaxation mechanism R. J. Elliot, Phys. Rev. 96, 266 (1954)]. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4772500]

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.

Effect of Temperature Cycling on Conduction Mechanisms in CdTe Thin Films

CdTe thin films of 500 thickness prepared by thermal evaporation technique were analyzed for leakage current and conduction mechanisms. Metal-insulator-metal (MIM) capacitors were fabricated using these films as a dielectric. These films have many possible applications, such as passivation for infrared diodes that operate at low temperatures (80 K). Direct-current (DC) current-voltage (I-V) and capacitance-voltage (C-V) measurements were performed on these films. Furthermore, the films were subjected to thermal cycling from 300 K to 80 K and back to 300 K. Typical minimum leakage currents near zero bias at room temperature varied between 0.9 nA and 0.1 mu A, while low-temperature leakage currents were in the range of 9.5 pA to 0.5 nA, corresponding to resistivity values on the order of 10(8) a''broken vertical bar-cm and 10(10) a''broken vertical bar-cm, respectively. Well-known conduction mechanisms from the literature were utilized for fitting of measured I-V data. Our analysis indicates that the conduction mechanism in general is Ohmic for low fields < 5 x 10(4) V cm(-1), while the conduction mechanism for fields > 6 x 10(4) V cm(-1) is modified Poole-Frenkel (MPF) and Fowler-Nordheim (FN) tunneling at room temperature. At 80 K, Schottky-type conduction dominates. A significant observation is that the film did not show any appreciable degradation in leakage current characteristics due to the thermal cycling.

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.

Proton Conduction in Metal-Organic Frameworks and Related Modularly Built Porous Solids

Proton-conducting materials are an important component of fuel cells. Development of new types of proton-conducting materials is one of the most important issues in fuel-cell technology. Herein, we present newly developed proton-conducting materials, modularly built porous solids, including coordination polymers (CPs) or metalorganic frameworks (MOFs). The designable and tunable nature of the porous materials allows for fast development in this research field. Design and synthesis of the new types of proton-conducting materials and their unique proton-conduction properties are discussed.

Heat conduction mechanisms in hot pressed ZrB2 and ZrB2-SiC composites

Thermal diffusivity and conductivity of hot pressed ZrB2 with different amounts of B4C (0-5 wt%) and ZrB2-SiC composites (10-30 vol% SiC) were investigated experimentally over a wide range of temperature (25-1500 degrees C). Both thermal diffusivity and thermal conductivity were found to decrease with increase in temperature for all the hot pressed ZrB2 and ZrB2-SiC composites. At around 200 degrees C, thermal conductivity of ZrB2-SiC composites was found to be composition independent. Thermal conductivity of ZrB2-SiC composites was also correlated with theoretical predictions of the Maxwell Eucken relation. The dominated mechanisms of heat transport for all hot pressed ZrB2 and ZrB2-SiC composites at room temperature were confirmed by Wiedemann Franz analysis by using measured electrical conductivity of these materials at room temperature. It was found that electronic thermal conductivity dominated for all monolithic ZrB2 whereas the phonon contribution to thermal conductivity increased with SiC contents for ZrB2-SiC composites.

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.

Vertical electric field stimulated neural cell functionality on porous amorphous carbon electrodes

We demonstrate the efficacy of amorphous macroporous carbon substrates as electrodes to support neuronal cell proliferation and differentiation in electric field mediated culture conditions. The electric field was applied perpendicular to carbon substrate electrode, while growing mouse neuroblastoma (N2a) cells in vitro. The placement of the second electrode outside of the cell culture medium allows the investigation of cell response to electric field without the concurrent complexities of submerged electrodes such as potentially toxic electrode reactions, electro-kinetic flows and charge transfer (electrical current) in the cell medium. The macroporous carbon electrodes are uniquely characterized by a higher specific charge storage capacity (0.2 mC/cm(2)) and low impedance (3.3 k Omega at 1 kHz). The optimal window of electric field stimulation for better cell viability and neurite outgrowth is established. When a uniform or a gradient electric field was applied perpendicular to the amorphous carbon substrate, it was found that the N2a cell viability and neurite length were higher at low electric field strengths (<= 2.5 V/cm) compared to that measured without an applied field (0 V/cm). While the cell viability was assessed by two complementary biochemical assays (MTT and LDH), the differentiation was studied by indirect immunostaining. Overall, the results of the present study unambiguously establish the uniform/gradient vertical electric field based culture protocol to either enhance or to restrict neurite outgrowth respectively at lower or higher field strengths, when neuroblastoma cells are cultured on porous glassy carbon electrodes having a desired combination of electrochemical properties. (C) 2013 Elsevier Ltd. All rights reserved.

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.

Self-assembled multicomponent Pd-6 aggregates showing low-humidity proton conduction

Two Pd-6 molecular aggregates (1 and 2), self-sorted via a template-free three-component self-assembly process, represent new examples of discrete architectures exhibiting very high proton conductivity 0.78 x 10(-3) S cm(-1) (1) and 0.22 X 10(-3) S cm(-1) (2)] at 300 K at low relative humidity (B46%) with low activation energy comparable to that of currently used Nafion in fuel cells.