G. V. Uma

Bio: G. V. Uma is an academic researcher from Anna University. The author has contributed to research in topics: Ćuk converter & Ontology (information science). The author has an hindex of 20, co-authored 108 publications receiving 1357 citations. Previous affiliations of G. V. Uma include Maharishi Markandeshwar University, Mullana & KCG College of Technology.

A new approach in development of distributed framework for automated software testing using agents

Abstract: Software testing is the technical kernel of software quality engineering, and to develop critical and complex software systems not only requires a complete, consistent and unambiguous design, and implementation methods, but also a suitable testing environment that meets certain requirements, particularly, to face the complexity issues. Traditional methods, such as analyzing each requirement and developing test cases to verify correct implementation, are not effective in understanding the software's overall complex behavior. In that respect, existing approaches to software testing are viewed as time-consuming and insufficient for the dynamism of the modern business environment. This dynamics requires new tools and techniques, which can be employed in tandem with innovative approaches to using and combining existing software engineering methods. This work advocates the use of a recently proposed software engineering paradigm, which is particularly suited to the construction of complex and distributed software-testing systems, which is known as Agent-Oriented Software Engineering. This methodology is a new one, which gives the basic approach to agent-based frameworks for testing.

Adaptive and optimal control of a non-linear process using intelligent controllers

Abstract: A time-optimal control for set point changes and an adaptive control for process parameter variations using neural network for a non-linear conical tank level process are proposed in this work. Time-optimal level control was formulated using dynamic programming algorithm and basic properties of the solutions were analysed. It was found that the control is of bang-bang type and there is only one switching. In this method, a mathematical step-by-step procedure is used to obtain the optimal valve position path with one switching and is trained by neural network, based on the back-propagation algorithm. The dynamic programming procedure allows the set point to be reached as fast as possible without overshoot. An adaptive system is also designed and proved to be useful in adjusting the trained parameter of the dynamic programming based neural network for the process parameter variations. A prototype of conical tank level system has been built and implementation of dynamic programming based neural network control algorithm for set point changes and implementation of adaptive control for process parameter variations are performed. Finally, the performance is compared with conventional control. The results prove the effectiveness of the proposed optimal and adaptive control schemes.

Automatic construction of object oriented design models [UML diagrams] from natural language requirements specification

Abstract: Application of natural language understanding to requirements gathering remains a field that has only limited explorations so far. This paper presents an approach to extract the object oriented elements of the required system. This approach starts with assigning the parts of speech tags to each word in the given input document. Further, to resolve the ambiguity posed by the pronouns, the pronoun resolutions are performed before normalizing the text. Finally the elements of the object-oriented system namely the classes, the attributes, methods and relationships between the classes, sequence of actions, the use-cases and actors are identified by mapping the 'parts of speech- tagged' words onto the Object Oriented Modeling Language elements using mapping rules which is the key to a successful implementation of user requirements.

Experimental Verification of Hopf Bifurcation in DC--DC Luo Converter

Abstract: DC-DC converters have been reported as exhibiting a wide range of bifurcations and chaos under certain conditions. This paper analyzes the bifurcations in current-controlled Luo topology operating in continuous conduction mode using continuous-time model. The stability of the system is analyzed by studying the locus of the complex eigenvalues, and the characteristic multipliers locate the onset of Hopf bifurcation. The 1-periodic orbit loses its stability via Hopf bifurcation, and the resulting attractor is a quasi-periodic orbit. This later bifurcates to chaos via border collision bifurcation. A computer simulation using MATLAB/SIMULINK confirms the predicted bifurcations. It has also been inferred from the experimental results that the margin of system stability decreases as the load decreases.

Design of PID controller for boost converter with RHS zero

Abstract: This paper gives a new solution for controlling boost converter that exhibits inverse response (IR) due to the presence of right hand side (RHS) zero. The RHS Zero /spl eta/ imposes a limit on the attainable closed-loop bandwidth of the controlled converter thereby reducing the controller gain K/sub c/. A comparative study is made by introducing different controller tuning methods such as internal model control (IMC), synthesis, equating coefficient and Ziegler-Nichol's (Z-N) techniques applicable to voltage control of dc-dc boost converter. The paper also presents closed loop simulation results of the converter for load regulation and line fluctuation. The results reveal that, synthesis method gives superior performance in terms of lowest ISE value.

Privacy preserving association rule mining in vertically partitioned data

Abstract: A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

Abstract: . In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test. For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.

Fuzzy-Logic-Controller-Based SEPIC Converter for Maximum Power Point Tracking

Abstract: This paper presents a fuzzy logic controller (FLC)-based single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) operation of a photovoltaic (PV) system. The FLC proposed presents that the convergent distribution of the membership function offers faster response than the symmetrically distributed membership functions. The fuzzy controller for the SEPIC MPPT scheme shows high precision in current transition and keeps the voltage without any changes, in the variable-load case, represented in small steady-state error and small overshoot. The proposed scheme ensures optimal use of PV array and proves its efficacy in variable load conditions, unity, and lagging power factor at the inverter output (load) side. The real-time implementation of the MPPT SEPIC converter is done by a digital signal processor (DSP), i.e., TMS320F28335. The performance of the converter is tested in both simulation and experiment at different operating conditions. The performance of the proposed FLC-based MPPT operation of SEPIC converter is compared to that of the conventional proportional-integral (PI)-based SEPIC converter. The results show that the proposed FLC-based MPPT scheme for SEPIC can accurately track the reference signal and transfer power around 4.8% more than the conventional PI-based system.