Snow cover models in operational watershed forecasting

Yhteenveto: Lumimallit vesistöjen ennustemalleissa

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.

WOMM: A Weak Operational Memory Model

Memory models of shared memory concurrent programs define the values a read of a shared memory location is allowed to see. Such memory models are typically weaker than the intuitive sequential consistency semantics to allow efficient execution. In this paper, we present WOMM (abbreviation for Weak Operational Memory Model) that formally unifies two sources of weak behavior in hardware memory models: reordering of instructions and weakly consistent memory. We show that a large number of optimizations are allowed by WOMM. We also show that WOMM is weaker than a number of hardware memory models. Consequently, if a program behaves correctly under WOMM, it will be correct with respect to those hardware memory models. Hence, WOMM can be used as a formally specified abstraction of the hardware memory models. Moreover; unlike most weak memory models, WOMM is described using operational semantics, making it easy to integrate into a model checker for concurrent programs. We further show that WOMM has an important property - it has sequential consistency semantics for datarace-free programs.

Operational experience on a grid connected 100 kWe biomass gasification power plant in Karnataka, India

The paper reports the operational experience from a 100 kWe gasification power plant connected to the grid in Karnataka. Biomass Energy for Rural India (BERI) is a program that implemented gasification based power generation with an installed capacity of 0.88 MWe distributed over three locations to meet the electrical energy needs in the district of Tumkur. The operation of one 100 kWe power plant was found unsatisfactory and not meeting the designed performance. The Indian Institute of Science, Bangalore, the technology developer, took the initiative to ensure the system operation, capacity building and prove the designed performance. The power plant connected to the grid consists of the IISc gasification system which includes reactor, cooling, cleaning system, fuel drier and water treatment system to meet the producer gas quality for an engine. The producer gas is used as a fuel in Cummins India Limited, GTA 855 G model, turbo charged engine and the power output is connected to the grid. The system has operated for over 1000 continuous hours, with only about 70 h of grid outages. The total biomass consumption for 1035 h of operation was 111 t at an average of 107 kg/h. Total energy generated was 80.6 MWh reducing over loot of CO(2) emissions. The overall specific fuel consumption was about 1.36 kg/kWh, amounting to an overall efficiency from biomass to electricity of about 18%. The present operations indicate that a maintenance schedule for the plant can be at the end of 1000 h. The results for another 1000 h of operation by the local team are also presented. (C) 2011 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

An Area-Efficient Noise-Adaptive Neural Amplifier in 130 nm CMOS Technology

Chronic recording of neural signals is indispensable in designing efficient brain–machine interfaces and to elucidate human neurophysiology. The advent of multichannel micro-electrode arrays has driven the need for electronics to record neural signals from many neurons. The dynamic range of the system can vary over time due to change in electrode–neuron distance and background noise. We propose a neural amplifier in UMC 130 nm, 1P8M complementary metal–oxide–semiconductor (CMOS) technology. It can be biased adaptively from 200 nA to 2 $mu{rm A}$, modulating input referred noise from 9.92 $mu{rm V}$ to 3.9 $mu{rm V}$. We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. Optimum sizing of the input transistors minimizes the accentuation of the input referred noise of the amplifier and obviates the need of large input capacitance. The amplifier achieves a noise efficiency factor of 2.58. The amplifier can pass signal from 5 Hz to 7 kHz and the bandwidth of the amplifier can be tuned for rejecting low field potentials (LFP) and power line interference. The amplifier achieves a mid-band voltage gain of 37 dB. In vitro experiments are performed to validate the applicability of the neural low noise amplifier in neural recording systems.

A High‐Gain Audiofrequency Amplifier

The design of a three‐stage high‐gain amplifier for laboratory use in audiofrequency investigations is described. Four‐electrode tubes are used as screen‐grid amplifiers and an amplification of the order of 200 per stage is obtained. The inaccuracy of McDonald's formula for calculation of stage‐gain has been pointed out. The gain‐frequency characteristics are given for power as well as voltage amplification. It is shown that extreme care is necessary in the design of shielding to obtain high‐voltage amplification of the order of 120 decibels as obtained in this three‐stage amplifier.

Direct ultrafast laser written C-band waveguide amplifier in Er-doped chalcogenide glass

This paper reports the fabrication and characterization of an ultrafast laser written Er-doped chalcogenide glass buried waveguide amplifier; Er-doped GeGaS glass has been synthesized by the vacuum sealed melt quenching technique. Waveguides have been fabricated inside the 4 mm long sample by direct ultrafast laser writing. The total passive fiber-to-fiber insertion loss is 2.58 +/- 0.02 dB at 1600 nm, including a propagation loss of 1.6 +/- 0.3 dB. Active characterization shows a relative gain of 2.524 +/- 0.002 dB/cm and 1.359 +/- 0.005 dB/cm at 1541 nm and 1550 nm respectively, for a pump power of 500 mW at a wavelength of 980 nm. (C) 2012 Optical Society of America

Arithmetic coding based lossless compression schemes for power system steady state operational data

Introduction of processor based instruments in power systems is resulting in the rapid growth of the measured data volume. The present practice in most of the utilities is to store only some of the important data in a retrievable fashion for a limited period. Subsequently even this data is either deleted or stored in some back up devices. The investigations presented here explore the application of lossless data compression techniques for the purpose of archiving all the operational data - so that they can be put to more effective use. Four arithmetic coding methods suitably modified for handling power system steady state operational data are proposed here. The performance of the proposed methods are evaluated using actual data pertaining to the Southern Regional Grid of India. (C) 2012 Elsevier Ltd. All rights reserved.

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.

Operational inferences on VANETs in 802.16e and 802.11p with improved performance by Congestion alert

Mobile WiMAX is a burgeoning network technology with diverse applications, one of them being used for VANETs. The performance metrics such as Mean Throughput and Packet Loss Ratio for the operations of VANETs adopting 802.16e are computed through simulation techniques. Next we evaluated the similar performance of VANETs employing 802.11p, also known as WAVE (Wireless Access in Vehicular Environment). The simulation model proposed is close to reality as we have generated mobility traces for both the cases using a traffic simulator (SUMO), and fed it into network simulator (NS2) based on their operations in a typical urban scenario for VANETs. In sequel, a VANET application called `Street Congestion Alert' is developed to assess the performances of these two technologies. For this application, TraCI is used for coupling SUMO and NS2 in a feedback loop to set up a realistic simulation scenario. Our inferences show that the Mobile WiMAX performs better than WAVE for larger network sizes.

Influence of operational and geometrical parameters on the performance of twin thermoacoustic prime mover

Thermoacoustics is the interaction between heat and sound, which are useful in designing heat engines and heat pumps. Research in the field of thermoacoustics focuses on the demand to improve the performance which is achieved by altering operational, geometrical and fluid parameters. The present study deals with improving the performance of twin thermoacoustic prime mover, which has gained the significant importance in the recent years for the production of high amplitude sound waves. The performance of twin thermoacoustic prime mover is evaluated in terms of onset temperature difference, resonance frequency and pressure amplitude of the acoustic waves by varying the resonator length and charge pressures of fluid medium nitrogen. DeltaEC, the free simulation software developed by LANL, USA is employed in the present study to simulate the performance of twin thermoacoustic prime mover. Experimental and simulated results are compared and the deviation is found to be within 10%.

Assessing impact of material transition and thermal comfort models on embodied and operational energy in vernacular dwellings (India)

Vernacular dwellings are well-suited climate-responsive designs that adopt local materials and skills to support comfortable indoor environments in response to local climatic conditions. These naturally-ventilated passive dwellings have enabled civilizations to sustain even in extreme climatic conditions. The design and physiological resilience of the inhabitants have coevolved to be attuned to local climatic and environmental conditions. Such adaptations have perplexed modern theories in human thermal-comfort that have evolved in the era of electricity and air-conditioned buildings. Vernacular local building elements like rubble walls and mud roofs are given way to burnt brick walls and reinforced cement concrete tin roofs. Over 60% of Indian population is rural, and implications of such transitions on thermal comfort and energy in buildings are crucial to understand. Types of energy use associated with a buildings life cycle include its embodied energy, operational and maintenance energy, demolition and disposal energy. Embodied Energy (EE) represents total energy consumption for construction of building, i.e., embodied energy of building materials, material transportation energy and building construction energy. Embodied energy of building materials forms major contribution to embodied energy in buildings. Operational energy (OE) in buildings mainly contributed by space conditioning and lighting requirements, depends on the climatic conditions of the region and comfort requirements of the building occupants. Less energy intensive natural materials are used for traditional buildings and the EE of traditional buildings is low. Transition in use of materials causes significant impact on embodied energy of vernacular dwellings. Use of manufactured, energy intensive materials like brick, cement, steel, glass etc. contributes to high embodied energy in these dwellings. This paper studies the increase in EE of the dwelling attributed to change in wall materials. Climatic location significantly influences operational energy in dwellings. Buildings located in regions experiencing extreme climatic conditions would require more operational energy to satisfy the heating and cooling energy demands throughout the year. Traditional buildings adopt passive techniques or non-mechanical methods for space conditioning to overcome the vagaries of extreme climatic variations and hence less operational energy. This study assesses operational energy in traditional dwelling with regard to change in wall material and climatic location. OE in the dwellings has been assessed for hot-dry, warm humid and moderate climatic zones. Choice of thermal comfort models is yet another factor which greatly influences operational energy assessment in buildings. The paper adopts two popular thermal-comfort models, viz., ASHRAE comfort standards and TSI by Sharma and Ali to investigate thermal comfort aspects and impact of these comfort models on OE assessment in traditional dwellings. A naturally ventilated vernacular dwelling in Sugganahalli, a village close to Bangalore (India), set in warm - humid climate is considered for present investigations on impact of transition in building materials, change in climatic location and choice of thermal comfort models on energy in buildings. The study includes a rigorous real time monitoring of the thermal performance of the dwelling. Dynamic simulation models validated by measured data have also been adopted to determine the impact of the transition from vernacular to modern material-configurations. Results of the study and appraisal for appropriate thermal comfort standards for computing operational energy has been presented and discussed in this paper. (c) 2014 K.I. Praseeda. Published by Elsevier Ltd.