Saroj Biswas

Optimal temperature tracking for accelerated cooling processes in hot rolling of steel

This paper is concerned with the development of a control system for tracking of a desired temperature-time profile for accelerated cooling processes in hot rolling of steel. Control of temperature during cooling is essential for achieving desired mechanical and metallurgical properties of steel. The cooling process is represented by the heat equation in Lagrangian coordinates which is approximated by a set of ordinary differential equations. The control variable is the velocity of coolant water flow. Motivated by the fact that in many steel industries water flow is adjusted to some fixed velocity based on experience and heuristics, we assume that the control variable, water jet velocity, be a constant time invariant parameter. Gamkrelidze minimum principle is used to determine the optimal jet velocity for the desired temperature tracking. The results are illustrated by simulation examples.

Association of DOA Estimation From Two ULAs

In this paper, we consider the problem of associating estimated direction-of-arrival (DOA) angles of multiple targets using two uniform linear arrays (ULAs) of sensors. It is categorized as a multisource information aggregation problem, in which we devise our algorithm in the following two steps: (1) Estimate two sets of DOA angles from two ULAs, respectively, and (2) associate estimated DOA angles with the targets. Note, however, that the primary focus of this paper is the second step, i.e., association of DOA angles with the corresponding targets, whereas for the first step, we use the well-known improved polynomial rooting method. Generally speaking, if there are q targets, we have q! possible association pairs, among which, there is only one set corresponding to the correct association. Since target signals must be correlated in the observations of two ULAs, we can determine how to associate the DOA angles computed from step 1 by evaluating the covariance matrix of the data received at the two ULAs. The algorithm is effective, robust, and accurate in classifying the DOA angles into the correct association.

Stochastic Hopfield artificial neural network for electric power production costing

The paper presents a stochastic Hopfield artificial neural network for unit commitment and economic power dispatch. Because of uncertainties in both the system load demand and unit availability, the unit commitment and economic power dispatch problem is stochastic, in this paper we model forced unit outages as independent Markov processes, and load demand as a normal Gaussian random variable. The (0,1) unit commitment-status variables and the hourly unit loading are modelled as sample functions of appropriate random processes. They are solutions of appropriately derived stochastic differential equations which describe the dynamics of a stochastic system for which the operating cost function is a stochastic Lyapunov function. Once the unit commitment and economic power dispatch have been done, the corresponding production costs are computed. >

How to Update Dependable Secure Computing Systems from a Survivability Assessment Perspective

All practical applications contain some degree of non- determinism. When such applications are replicated to achieve Byzantine fault tolerance (BFT), their nondeterministic operations must be controlled to ensure replica consistency. To the best of our knowledge, only the most simplistic types of replica nondeterminism have been dealt with. Furthermore, there lacks a systematic approach to handling common types of nondeterminism. In this paper, we propose a classification of common types of replica nondeterminism with respect to the requirement of achieving Byzantine fault tolerance, and describe the design and implementation of the core mechanisms necessary to handle such nondeterminism within a Byzantine fault tolerance framework.In this paper, we investigate how to compute an updating time for a dependable secure computing (DSC) system from a survivability assessment perspective. Generally speaking, to prevent adversaries from learning secret information in a DSC system, the secret information has to be updated in the system. The proposed method is based on a novel concept of survivability of reconfigurable systems presented earlier in [1]. It is shown that secret updating time increases with k, which is the number of secret shares for reconstructing the secret. The proposed method presents a solution for an important problem in the DSC system which is how to determine an appropriate updating time.

Stabilization of systems governed by the wave equation in the presence of distributed white noise

It is shown that by application of velocity feedback systems governed by the wave equation perturbed by distributed white noise can be stabilized with respect to a ball centered at the origin in the energy space. The radius of this attractor depends on the noise strength and the damping. It is further shown that by approriate choice of damping, it is possible to minimize the size of the attractor (i.e., reduced vibration amplitude) and maximize the decay rate.

Dynamic modeling and optimal control of flexible robotic manipulators

A methodology for mathematical modeling and control of flexible robotic manipulators is presented. For the sake of simple exposition of the basic methology, a single-axis robot consisting of a flexible arm driven by a servomotor is considered. The complete dynamics of the robot is given in terms of a coupled system of ordinary and partial differential equations. A finite-dimensional model is also developed from this model using the technique of modal expansion. A control scheme is presented for optimal regulation of the flexible manipulator to achieve a desired angular rotation of the link while simultaneously suppressing structural vibrations.<<ETX>>

Simultaneous solution of unit commitment and dispatch problems using artificial neural networks

Abstract In this paper a combination of Hopfield-Tank type and Chua-Lin type artificial neural networks is applied to solve simultaneously the unit commitment and the associated economic unit dispatch problems. The approach is based on imbedding the various economic and electrical constraints of the unit commitment and dispatch problems in a generalized energy function, and then defining the network dynamics in such a way that the generalized energy function is a Lyapunov function of the artificial neural network. The novel feature of the proposed approach is that the non-linear programming problem for unit dispatch and the combinatorial optimization problem for unit commitment are solved simultaneously by one network. The method is illustrated by an example.

Application of artificial neural networks to unit commitment

Artificial neural networks are currently being applied to a variety of complex combinatorial optimization and nonlinear programming problems. In this paper, a combination of Hopfield Tank type, and Chua-Lin type artificial neural networks is applied to solve simultaneously the unit commitment and the associated economic unit dispatch problems. The approach is based on imbedding the various constraints in a generalized energy function, and then defining the network dynamics in such a way that the generalized energy function is a Lyapunov function of the artificial neural network. The novel feature of the proposed approach is that the nonlinear programming and the combinatorial optimization problems are solved simultaneously by one network. An illustrative example is also presented.<<ETX>>

Performance analysis of mobile agent-based wireless sensor network

In this paper, we describe a reliability model which can be used to analyze the performance and power consumption in resource constrained, data rate scarce, mobile agent-based wireless sensor network (WSN) systems. The primary model is referred to as a generalize access structure congestion (GGC) system [3] which is an extended model from a circular sequential k-out-of-n congestion (CSknC) [2]. There are many other reliability models which can be used to study WSN systems, but they are not suitable to analyze and address mobile agent-based multisensory WSN systems. These systems are not based on a centralized architecture because they use mobile agent technologies to distribute decision tasks at local nodes. By employing mobile agent technologies, the systems can make accurate decisions quickly and reduce data rate and data redundancy problems. An important research problem is to determine how to maintain efficient duty cycle by using multiple types of sensors without centralized architecture and with mobile agent technologies. From the GGC model, we can develop a method to determine an optimal power management scheme by computing an efficient duty cycle in mobile agent-based multisensory WSN systems.

Optimal control of large space structures governed by a coupled system of ordinary and partial differential equations

In this paper we consider the problem of optimal regulation of large space structures in the presence of flexible appendages. For simplicity of presentation, we consider a spacecraft consisting of a rigid bus and a flexible beam. The complete dynamics of the system is given by a coupled set of ordinary and partial differential equations. We show that the solution of this hybrid system is defined in a product space of appropriate finite- and infinite-dimensional spaces. We develop necessary conditions for determining the control torque and forces for optimal regulation of attitude maneuvers of the satellite along with simultaneous suppression of elastic vibrations of the flexible beam.