Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

Direct evidence of redox interaction between metal ion and support oxide in Ce(0.98)Pd(0.02)O(2-delta) by a combined electrochemical and XPS study

A combined electrochemical method and X-ray photo electron spectroscopy (XPS) has been utilized to understand the Pd(2+)/CeO(2) interaction in Ce(1-x)Pd(x)O(2-delta) (x = 0.02). A constant positive potential (chronoamperometry) is applied to Ce(0.98)Pd(0.02)O(2-delta) working electrode which causes Ce(4+) to reduce to Ce(3+) to the extent of similar to 35%, while Pd remains in the +2 oxidation state. Electrochemically cycling this electrode between 0.0-1.2 V reverts back to the original state of the catalyst. This reversibility is attributed to the reversible reduction of Ce(4+) to Ce(3+) state. CeO(2) electrode with no metal component reduces to CeO(2-y) (y similar to 0.4) after applying 1.2 V which is not reversible and the original composition of CeO(2) cannot be brought back in any electrochemical condition. During the electro-catalytic oxygen evolution reaction at a constant 1.2 V for 1000 s, Ce(0.98)Pd(0.02)O(2-delta) reaches a steady state composition with Pd in the +2 states and Ce(4+) : Ce(3+) in the ratio of 0.65 : 0.35. This composition can be denoted as Ce(0.63)(4+)Ce(0.35)(4+)Pd(0.02)O(2-delta-y) (y similar to 0.17). When pure CeO(2) is put under similar electrochemical condition, it never reaches the steady state composition and reduces almost to 85%. Thus, Ce(0.98)Pd(0.02)O(2-delta) forms a stable electrode for the electro-oxidation of H(2)O to O(2) unlike CeO(2) due to the metal support interaction.

Spatial Decision Support System for Assessing Micro, Mini and Small Hydel Potential

This study describes the design and implementation of DSS for assessment of Mini, Micro and Small Schemes. The design links a set of modelling, manipulation, spatial analyses and display tools to a structured database that has the facility to store both observed and simulated data. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in less time and can be used by decision-making bodies to assess the impacts of various scenarios (e.g.: changes in land use pattern) and to review, cost and benefits of decisions to be made. It also offers means of entering, accessing and interpreting the information for the purpose of sound decision making. Thus, the overall objective of this DSS is the development of set of tools aimed at transforming data into information and aid decisions at different scales.

Decision support system for regional electricity planning

Electricity appears to be the energy carrier of choice for modern economics since growth in electricity has outpaced growth in the demand for fuels. A decision maker (DM) for accurate and efficient decisions in electricity distribution requires the sector wise and location wise electricity consumption information to predict the requirement of electricity. In this regard, an interactive computer-based Decision Support System (DSS) has been developed to compile, analyse and present the data at disaggregated levels for regional energy planning. This helps in providing the precise information needed to make timely decisions related to transmission and distribution planning leading to increased efficiency and productivity. This paper discusses the design and implementation of a DSS, which facilitates to analyse the consumption of electricity at various hierarchical levels (division, taluk, sub division, feeder) for selected periods. This DSS is validated with the data of transmission and distribution systems of Kolar district in Karnataka State, India.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

Optimal maintenance policies for machines subject to deterioration and intermittent breakdowns

Optimal preventive maintenance policies, for a machine subject to deterioration with age and intermittent breakdowns and repairs, are derived using optimal control theory. The optimal policies are shown to be of bang-bang nature. The extension to the case when there are a large number of identical machines and several repairmen in the system is considered next. This model takes into account the waiting line formed at the repair facility and establishes a link between this problem and the classical ``repairmen problem.''

Time Series Classification for Online Tamil Handwritten Character Recognition – A Kernel Based Approach

In this paper, we consider the problem of time series classification. Using piecewise linear interpolation various novel kernels are obtained which can be used with Support vector machines for designing classifiers capable of deciding the class of a given time series. The approach is general and is applicable in many scenarios. We apply the method to the task of Online Tamil handwritten character recognition with promising results.

Torque Angle Loop Analysis of Synchronous Machines

This paper reports the dynamic stability analysis of a single machine infinite bus system through torque angle loop analysis and forms an extension of the work on Block diagrams and torque angle loop analysis of synchronous machines reported by I. Nagy [3]. It aims to incorporate in the machine model, the damper windings (one on each axis) and to compare the dynamic behaviour of the system with and without damper windings. The effect of using different stabilizing signals (viz. active power and speed deviations) on the dynamic performance is analysed and the significant effect of damper windings on the dynamic behaviour of the system is highlighted.

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.