Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains

Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures.

Experimental and Numerical Studies in a Compact Trapped Vortex Combustor: Stability Assessment and Augmentation

Fundamental studies on a compact trapped vortex combustor indicate that cavity injection strategies play a major role on flame stability. Detailed experiments indicate that blow-out occurs for a certain range of cavity air flow velocities. An unsteady RANS-based reacting flow simulation tool has been utilized to study the basic dynamics of cavity vortex for various flow conditions. The phenomenon of flame blow-out at certain intermediate cavity air velocities is explained on the basis of transition from a cavity-stabilized mode to an opposed flow stagnation mode. A novel strategy is proposed for achieving flame stability at all conditions. This involves using a flow guide vane in the path of the main flow to direct a portion of the main flow into the cavity. This seems to result in a desirable dual vortex structure, i.e., a small clockwise vortex behind the vane and large counterclockwise vortex in the cavity. Experimental results show stable flame at all flow conditions with the flow guide vane, and pressure drop is estimated to be within acceptable limits. Cold flow simulations show self-similar velocity profiles for a range of main inlet velocities, and high reverse velocity ratios (-0.3) are observed. Such a high-velocity ratio in the reverse flow shear layer profile leads to enhanced production of turbulence imperative to compact combustors. Reacting flow simulations show even higher reverse velocity ratios (above -0.7) due to flow acceleration. The flame is observed to be stable, even though minor shear layer oscillations are present in the form of vortex shedding. Self-similarity is also observed in reacting flow temperature profiles at combustor exit over the entire range of the mainstream velocity. This indicates that the present configuration holds a promise of delivering robust performance invariant of the flow operating conditions.

Interference Alignment Algorithms for the K User Constant MIMO Interference Channel

This paper considers the degrees of freedom (DOF) for a K user multiple-input multiple-output (MIMO) M x N interference channel using interference alignment (IA). A new performance metric for evaluating the efficacy of IA algorithms is proposed, which measures the extent to which the desired signal dimensionality is preserved after zero-forcing the interference at the receiver. Inspired by the metric, two algorithms are proposed for designing the linear precoders and receive filters for IA in the constant MIMO interference channel with a finite number of symbol extensions. The first algorithm uses an eigenbeamforming method to align sub-streams of the interference to reduce the dimensionality of the interference at all the receivers. The second algorithm is iterative, and is based on minimizing the interference leakage power while preserving the dimensionality of the desired signal space at the intended receivers. The improved performance of the algorithms is illustrated by comparing them with existing algorithms for IA using Monte Carlo simulations.

Specification Based Regression Testing Using Explicit State Space Enumeration

This paper presents a method of partial automation of specification based regression testing, which we call ESSE (Explicit State Space Enumeration). The first step in ESSE method is the extraction of a finite state model of the system making use of an already tested version of the system under test (SUT). Thereafter, the finite state model thus obtained is used to compute good test sequences that can be used to regression test subsequent versions of the system. We present two new algorithms for test sequence computation - both based on our finite state model generated by the above method. We also provide the details and results of the experimental evaluation of ESSE method. Comparison with a practically used random-testing algorithm has shown substantial improvements.

Discrete control algorithms for a tubular ammonia reactor

The specific objective of this paper is to develop direct digital control strategies for an ammonia reactor using quadratic regulator theory and compare the performance of the resultant control system with that under conventional PID regulators. The controller design studies are based on a ninth order state-space model obtained from the exact nonlinear distributed model using linearization and lumping approximations. The evaluation of these controllers with reference to their disturbance rejection capabilities and transient response characteristics, is carried out using hybrid computer simulation.

Simulation and experimental study on compositional evolution of Li-Co in LiCoO2 thin films during sputter deposition

The compositional evolution in sputter deposited LiCoO2 thin films is influenced by process parameters involved during deposition. The electrochemical performance of these films strongly depends on their microstructure, preferential orientation and stoichiometry. The transport process of sputtered Li and Co atoms from the LiCoO2 target to the substrate, through Ar plasma in a planar magnetron configuration, was investigated based on the Monte Carlo technique. The effect of sputtering gas pressure and the substrate-target distance (dst) on Li/Co ratio, as well as, energy and angular distribution of sputtered atoms on the substrate were examined. Stable Li/Co ratios have been obtained at 5 Pa pressure and dst in the range 5−11 cm. The kinetic energy and incident angular distribution of Li and Co atoms reaching the substrate have been found to be dependent on sputtering pressure. Simulations were extended to predict compositional variations in films prepared at various process conditions. These results were compared with the composition of films determined experimentally using x-ray photoelectron spectroscopy (XPS). Li/Co ratio calculated using XPS was in moderate agreement with that of the simulated value. The measured film thickness followed the same trend as predicted by simulation. These studies are shown to be useful in understanding the complexities in multicomponent sputtering.

Experimental and computational studies on a gasifier based stove

The work reported here is concerned with a detailed thermochemical evaluation of the flaming mode behaviour of a gasifier based stove. Determination of the gas composition over the fuel bed, surface and gas temperatures in the gasification process constitute principal experimental features. A simple atomic balance for the gasification reaction combined with the gas composition from the experiments is used to determine the CH(4) equivalent of higher hydrocarbons and the gasification efficiency (eta g). The components of utilization efficiency, namely, gasification-combustion and heat transfer are explored. Reactive flow computational studies using the measured gas composition over the fuel bed are used to simulate the thermochemical flow field and heat transfer to the vessel; hither-to-ignored vessel size effects in the extraction of heat from the stove are established clearly. The overall flaming mode efficiency of the stove is 50-54%; the convective and radiative components of heat transfer are established to be 45-47 and 5-7% respectively. The efficiency estimates from reacting computational fluid dynamics (RCFD) compare well with experiments. (C) 2011 Elsevier Ltd. All rights reserved.

Experimental implementation of quantumUlam’s problem in a nuclear magnetic resonance quantum information processor

The Ulam’s problem is a two person game in which one of the player tries to search, in minimum queries, a number thought by the other player. Classically the problem scales polynomially with the size of the number. The quantum version of the Ulam’s problem has a query complexity that is independent of the dimension of the search space. The experimental implementation of the quantum Ulam’s problem in a Nuclear Magnetic Resonance Information Processor with 3 quantum bits is reported here.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Determinant: Old algorithms, new insights (Extended Abstract)

In this paper we approach the problem of computing the characteristic polynomial of a matrix from the combinatorial viewpoint. We present several combinatorial characterizations of the coefficients of the characteristic polynomial, in terms of walks and closed walks of different kinds in the underlying graph. We develop algorithms based on these characterizations, and show that they tally with well-known algorithms arrived at independently from considerations in linear algebra.

Synthesis of configurable architectures for DSP algorithms

ASICs offer the best realization of DSP algorithms in terms of performance, but the cost is prohibitive, especially when the volumes involved are low. However, if the architecture synthesis trajectory for such algorithms is such that the target architecture can be identified as an interconnection of elementary parameterized computational structures, then it is possible to attain a close match, both in terms of performance and power with respect to an ASIC, for any algorithmic parameters of the given algorithm. Such an architecture is weakly programmable (configurable) and can be viewed as an application specific instruction-set processor (ASIP). In this work, we present a methodology to synthesize ASIPs for DSP algorithms.

Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques

A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.