A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Reinforcement learning controllers for automatic generation control in power systems having re-heat units with GRC and dead-band

A new automatic generation controller (AGC) design approach, adopting reinforcement learning (RL) techniques, was recently pro- posed [1]. In this paper we demonstrate the design and performance of controllers based on this RL approach for automatic generation control of systems consisting of units having complex dynamics—the reheat type of thermal units. For such systems, we also assess the capabilities of RL approach in handling realistic system features such as network changes, parameter variations, generation rate constraint (GRC), and governor deadband.

Analytical Field-Effect Method for Extraction of Subgap States in Thin-Film Transistors

We present an analytical field-effect method to extract the density of subgap states (subgap DOS) in amorphous semiconductor thin-film transistors (TFTs), using a closed-form relationship between surface potential and gate voltage. By accounting the interface states in the subthreshold characteristics, the subgap DOS is retrieved, leading to a reasonably accurate description of field-effect mobility and its gate voltage dependence. The method proposed here is very useful not only in extracting device performance but also in physically based compact TFT modeling for circuit simulation.

A full-length bifunctional protein involved in c-di-GMP turnover is required for long-term survival under nutrient starvation in Mycobacterium smegmatis

The bacterial second messenger cyclic diguanosine monophosphate (c-di-GMP) plays an important role in a variety of cellular functions, including biofilm formation, alterations in the cell surface, host colonization and regulation of bacterial flagellar motility, which enable bacteria to survive changing environmental conditions. The cellular level of c-di-GMP is regulated by a balance between opposing activities of diguanylate cyclases (DGCs) and cognate phosphodiesterases (PDE-As). Here, we report the presence and importance of a protein, MSDGC-1 (an orthologue of Rv1354c in Mycobacterium tuberculosis), involved in c-di-GMP turnover in Mycobacterium smegmatis. MSDGC-1 is a multidomain protein, having GAF, GGDEF and EAL domains arranged in tandem, and exhibits both c-di-GMP synthesis and degradation activities. Most other proteins containing GGDEF and EAL domains have been demonstrated to have either DGC or PDE-A activity. Unlike other bacteria, which harbour several copies of the protein involved in c-di-GMP turnover, M. smegmatis has a single genomic copy, deletion of which severely affects long-term survival under conditions of nutrient starvation. Overexpression of MSDGC-1 alters the colony morphology and growth profile of M. smegmatis. In order to gain insights into the regulation of the c-di-GMP level, we cloned individual domains and tested their activities. We observed a loss of activity in the separated domains, indicating the importance of full-length MSDGC-1 for controlling bifunctionality.

Automatic energy-momentum conserving time integrators for hyperelastic waves

An energy-momentum conserving time integrator coupled with an automatic finite element algorithm is developed to study longitudinal wave propagation in hyperelastic layers. The Murnaghan strain energy function is used to model material nonlinearity and full geometric nonlinearity is considered. An automatic assembly algorithm using algorithmic differentiation is developed within a discrete Hamiltonian framework to directly formulate the finite element matrices without recourse to an explicit derivation of their algebraic form or the governing equations. The algorithm is illustrated with applications to longitudinal wave propagation in a thin hyperelastic layer modeled with a two-mode kinematic model. Solution obtained using a standard nonlinear finite element model with Newmark time stepping is provided for comparison. (C) 2012 Elsevier B.V. All rights reserved.

Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: An update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal

This paper discusses the use of Jason-2 radar altimeter measurements to estimate the Ganga-Brahmaputra surface freshwater flux into the Bay of Bengal for the period mid-2008 to December 2011. A previous estimate was generated for 1993-2008 using TOPEX-Poseidon, ERS-2 and ENVISAT, and is now extended using Jason-2. To take full advantages of the new availability of in situ rating curves, the processing scheme is adapted and the adjustments of the methodology are discussed here. First, using a large sample of in situ river height measurements, we estimate the standard error of Jason-2-derived water levels over the Ganga and the Brahmaputra to be respectively of 0.28 m and 0.19 m, or less than similar to 4% of the annual peak-to-peak variations of these two rivers. Using the in situ rating curves between water levels and river discharges, we show that Jason-2 accurately infers Ganga and Brahmaputra instantaneous discharges for 2008-2011 with mean errors ranging from similar to 2180 m(3)/s (6.5%) over the Brahmaputra to similar to 1458 m(3)/s (13%) over the Ganga. The combined Ganga-Brahmaputra monthly discharges meet the requirements of acceptable accuracy (15-20%) with a mean error of similar to 16% for 2009-2011 and similar to 17% for 1993-2011. The Ganga-Brahmaputra monthly discharge at the river mouths is then presented, showing a marked interannual variability with a standard deviation of similar to 12500 m(3)/s, much larger than the data set uncertainty. Finally, using in situ sea surface salinity observations, we illustrate the possible impact of extreme continental freshwater discharge event on the northern Bay of Bengal as observed in 2008.

Precision and long-term stability of clumped-isotope analysis of CO2 using a small-sector isotope ratio mass spectrometer

RATIONALE The ratio of the measured abundance of 13C18O bonding CO2 to its stochastic abundance, prescribed by the delta 13C and delta 18O values from a carbonate mineral, is sensitive to its growth temperature. Recently, clumped-isotope thermometry, which uses this ratio, has been adopted as a new tool to elucidate paleotemperatures quantitatively. METHODS Clumped isotopes in CO2 were measured with a small-sector isotope ratio mass spectrometer. CO2 samples digested from several kinds of calcium carbonates by phosphoric acid at 25 degrees C were purified using both cryogenic and gas-chromatographic separations, and their isotopic composition (delta 13C, delta 18O, Delta 47, Delta 48 and Delta 49 values) were then determined using a dual-inlet Delta XP mass spectrometer. RESULTS The internal precisions of the single gas Delta 47 measurements were 0.005 and 0.02 parts per thousand (1 SE) for the optimum and the routine analytical conditions, respectively, which are comparable with those obtained using a MAT 253 mass spectrometer. The long-term variations in the Delta 47 values for the in-house working standard and the heated CO2 gases since 2007 were close to the routine, single gas uncertainty while showing seasonal-like periodicities with a decreasing trend. Unlike the MAT 253, the Delta XP did not show any significant relationship between the Delta 47 and delta 47 values. CONCLUSIONS The Delta XP gave results that were approximately as precise as those of the MAT 253 for clumped-isotope analysis. The temporal stability of the Delta XP seemed to be lower, although an advantage of the Delta XP was that no dependency of delta 47 on Delta 47 was found. Copyright (c) 2012 John Wiley & Sons, Ltd.

Short- and long-term temporal variations in salinity and the oxygen, carbon and hydrogen isotopic compositions of the Hooghly Estuary water, India

The Hooghly River estuary provides a unique experimental site to understand the effect of monsoonal river discharge on freshwater and seawater mixing. Water samples collected bi-weekly for a duration of 17 months were analyzed for salinity, delta O-18,delta C-13(DIC), as well as delta D to investigate the differential mixing of freshwater and seawater. The differences in salinity and delta O-18 of samples collected during low and high tides on the same day are strongly correlated suggesting a well mixed water column at our sampling site. Low salinity and depleted delta O-18 during monsoon is consistent with increased river discharge as well as high rainfall. We identified different slopes in a delta O-18 versus salinity plot for the estuary water samples collected during monsoon and non-monsoon seasons. This is driven by composition of the freshwater source which is dominated by rainwater during monsoon and rivers during non-monsoon months. Selected delta D analyses of samples indicate that groundwater contributes significantly to the Hooghly Estuary during low rainfall times of the year. delta C-13(DIC) measured in the water recorded low values towards the end of monsoon indicating low productivity (i.e. increased organic respiration) while progressively increasing delta C-13(DIC) values from October till January as well as during some of the pre-monsoon months can be explained by increasing productivity. Very low delta C-13(DIC) (similar to-20%0) suggests involvement of carbon derived from anaerobic oxidation of organics and/or methane with potential contribution from increased anthropogenic water supply. An estimate of seawater incursion into the Hooghly Estuary at different times of the year is obtained by using salinity data in a two-component mixing model. Presence of seawater was found maximum (31-37%) during February till July and lowest (less than or equal to 6%) from September till November. We notice a temporal offset between Ganges River discharge farther upstream at Farakka and salinity variation at the Hooghly Estuary. We believe that this time lag is a result of the physical distance between Farakka and Kakdweep (our sampling location) and put constraints on the travel time of river water during early monsoon. (c) 2012 Published by Elsevier B.V.

An adaptive reliable transport protocol based on automatic reSend reQuest(ASQ) technique for wireless sensor networks

There are many wireless sensor network(WSN) applications which require reliable data transfer between the nodes. Several techniques including link level retransmission, error correction methods and hybrid Automatic Repeat re- Quest(ARQ) were introduced into the wireless sensor networks for ensuring reliability. In this paper, we use Automatic reSend request(ASQ) technique with regular acknowledgement to design reliable end-to-end communication protocol, called Adaptive Reliable Transport(ARTP) protocol, for WSNs. Besides ensuring reliability, objective of ARTP protocol is to ensure message stream FIFO at the receiver side instead of the byte stream FIFO used in TCP/IP protocol suite. To realize this objective, a new protocol stack has been used in the ARTP protocol. The ARTP protocol saves energy without affecting the throughput by sending three different types of acknowledgements, viz. ACK, NACK and FNACK with semantics different from that existing in the literature currently and adapting to the network conditions. Additionally, the protocol controls flow based on the receiver's feedback and congestion by holding ACK messages. To the best of our knowledge, there has been little or no attempt to build a receiver controlled regularly acknowledged reliable communication protocol. We have carried out extensive simulation studies of our protocol using Castalia simulator, and the study shows that our protocol performs better than related protocols in wireless/wire line networks, in terms of throughput and energy efficiency.

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Spectral zero-crossings: localization properties and application to epoch extraction in speech signals

We analyze the spectral zero-crossing rate (SZCR) properties of transient signals and show that SZCR contains accurate localization information about the transient. For a train of pulses containing transient events, the SZCR computed on a sliding window basis is useful in locating the impulse locations accurately. We present the properties of SZCR on standard stylized signal models and then show how it may be used to estimate the epochs in speech signals. We also present comparisons with some state-of-the-art techniques that are based on the group-delay function. Experiments on real speech show that the proposed SZCR technique is better than other group-delay-based epoch detectors. In the presence of noise, a comparison with the zero-frequency filtering technique (ZFF) and Dynamic programming projected Phase-Slope Algorithm (DYPSA) showed that performance of the SZCR technique is better than DYPSA and inferior to that of ZFF. For highpass-filtered speech, where ZFF performance suffers drastically, the identification rates of SZCR are better than those of DYPSA.

Scalar field visualization via extraction of symmetric structures

Identifying symmetry in scalar fields is a recent area of research in scientific visualization and computer graphics communities. Symmetry detection techniques based on abstract representations of the scalar field use only limited geometric information in their analysis. Hence they may not be suited for applications that study the geometric properties of the regions in the domain. On the other hand, methods that accumulate local evidence of symmetry through a voting procedure have been successfully used for detecting geometric symmetry in shapes. We extend such a technique to scalar fields and use it to detect geometrically symmetric regions in synthetic as well as real-world datasets. Identifying symmetry in the scalar field can significantly improve visualization and interactive exploration of the data. We demonstrate different applications of the symmetry detection method to scientific visualization: query-based exploration of scalar fields, linked selection in symmetric regions for interactive visualization, and classification of geometrically symmetric regions and its application to anomaly detection.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.