Location and Sizing of Defects in Coated Metallic Pipes Using Limited View Scattered Data in Frequency Domain

The inverse problem of reconstructing the location and size of defects in a coated metallic pipe from single frequency limited view electromagnetic scattered field is considered. Specifically, the paper addresses the problem of assessing shape changes in the shadow region entailed by limited view data in 2D by operating in an intermediate size parameter (ka) range. The inverse scattering problem is formulated as a non-linear optimization problem solved through genetic algorithm that seeks to minimize in the least-squared sense the difference between measured data and simulated data through iterations of the solution to a forward problem. The hybrid finite element boundary integral formulations were used to solve the forward problem of coated metallic pipe with defects. The proposed inversion methodology was applied for shadow region shape change assessment to determine whether a metal pipe is coated or not, and to reconstruct the location and size of corrosion-like defect, over a range of size parameters. The study is carried out using transverse electric and transverse magnetic polarized fields. To understand the effect of coating on backscattered fields, parametric studies are conducted using numerical data. A range for size parameter \(2.0\le ka\le 4\) was found to produce the highest contrast between defect free pipe and coated metallic pipes with defects in the deep shadow region. The experiments are carried out using vector network analyzer in an anechoic chamber. The inversion results obtained using measured data were found be in good agreement with inversion results obtained using synthetic data. Estimated extent of corrosion in deep shadow region of a coated metallic pipe was found to be within 9 % of actual extent of corrosion.     

Attenuated total reflection fourier transform infrared spectroscopy towards disclosing mechanism of bacterial adhesion on thermally stabilized titanium nano-interfaces

Titanium is widely used as medical implant material and as condenser material in the nuclear industry where its integrity is questioned due to its susceptibility to bacterial adhesion. A systematic investigation on the influence of thermally (50–800 °C) stabilized titanium (TS-Ti) nano oxide towards bacterial adhesion was carried out. The results showed that below 350 °C significant bacterio-phobicity was observed, while above 500 °C significant affinity towards bacterial cells was recorded. Conventional characterization tools such as HR-TEM and XRD did not provide much insight on the changes occurring on the oxide film with heat treatment, however, attenuated total reflection fourier transform infrared spectroscopy (ATR-FTIR) of the surface showed significant changes in the spectral pattern as a function of increasing heat treatment. It was observed that elevated OH, N–H and C=O groups and rutile titania on the TS-Ti oxide films led to higher affinity for bacterial adhesion. On the other hand low temperature TS-Ti nanooxide films (<350 °C) showed high C–H groups and decreased OH groups on their surface, which possibly contributed towards their bacterio-phobicity. The TS-Ti nanooxide film grown at 50 °C was observed to be the most efficient anti-bacterial adhesion interface, while the 800 °C interface was the one showing highest affinity towards bacterial adhesion. This study confirms the successful application of ATR-FTIR technique for nano-oxide film characterization and towards understanding the variations in bacterial interaction of such nano interfaces.     

Reduced one-against-all method for multiclass SVM classification

We present an improved version of one-against-all method for multiclass SVM classification based on subset sample selection, named reduced one-against-all, to achieve high performance in large multiclass problems. Reduced one-against-all drastically decreases the computing effort involved in training one-against-all classifiers, without any compromise in classification accuracy. Computational comparisons on publicly available datasets indicate that the proposed method has comparable accuracy to that of conventional one-against-all method, but with an order of magnitude faster. On the largest dataset considered, reduced one-against-all method achieved 50% reduction in computing time over one-against-all method for almost the same classification accuracy. We further investigated reduced one-against-all with linear kernel for multi-label text categorization applications. Computational results demonstrate the effectiveness of the proposed method on both the text corpuses considered.     

Least squares twin support vector machines for pattern classification

In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes. Here we attempt to solve two modified primal problems of TSVM, instead of two dual problems usually solved. We show that the solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TSVM. Classification using nonlinear kernel also leads to systems of linear equations. Our experiments on publicly available datasets indicate that the proposed least squares TSVM has comparable classification accuracy to that of TSVM but with considerably lesser computational time. Since linear least squares TSVM can easily handle large datasets, we further went on to investigate its efficiency for text categorization applications. Computational results demonstrate the effectiveness of the proposed method over linear proximal SVM on all the text corpuses considered.     

Some fixed point theorems in 1-M-complete fuzzy metric-like spaces

Very recently, in order to unify the notions of fuzzy metric space and metric-like space, Shukla and Abbas introduced the concept of fuzzy metric-like space and proved some fixed-point results in this setting. In this article, we modify the notion of Cauchy sequence and completeness to generalize their results. Thus, we extend their theorems to a more general framework, which is also appropriate to generalize some recent, well-known results in this line of research. Furthermore, several examples are presented to illustrate the significance of our results.     

Data structures for order-sensitive predicates in parallel nondeterministic systems

We study the problem of guaranteeing correct execution semantics in parallel implementations of logic programming languages in presence of built-in constructs that are sensitive to order of execution. The declarative semantics of logic programming languages permit execution of various goals in any arbitrary order (including in parallel). However, goals corresponding to extra-logical built-in constructs should respect the sequential order of execution to ensure correct semantics. Ensuring this correctness in presence of such built-in constructs, while efficiently exploiting maximum parallelism, is a difficult problem. In this paper, we propose a formalization of this problem in terms of operations on dynamic trees. This abstraction enables us to: (i) show that existing schemes to handle order-sensitive computations used in current parallel systems are sub-optimal; (ii) develop a novel, optimal scheme to handle order-sensitive goals that requires only a constant time overhead per operation. While we present our results in the context of logic programming, they will apply equally well to most parallel non-deterministic systems. Received: 20 April 1998 / 3 April 2000     

Hybrid Game Strategy in Fuzzy Markov-Game-Based Control

This paper proposes a novel control approach that incorporates a hybrid game strategy in Markov-game-based fuzzy control. Specifically, we aim at designing a “safe and universally consistent” controller that exhibits an ability to maintain performance against large disturbance and environment variations. The proposed hybrid control is a convex combination (based on experiential information) of “a variation of cautious fictitious play” approach and the “minimax” control approach implemented on a fuzzy Markov game platform. We show analytical convergence of Markov-game-based control in the presence of bounded external disturbances, and extend the analysis to show convergence of the proposed Markov-game-based hybrid control approach. Controller simulation and comparison against baseline Markov game fuzzy control and fuzzy $Q$ -learning control on a highly nonlinear two-link robot brings out the superiority of the approach in handling severe environment and disturbance variations over different desired trajectories. This paper illustrates the possibility of obtaining “universal consistency,” i.e., reasonable performance against severe environment and disturbance variations, by hybridizing “cautious fictitious play” with “minimax” approaches in Markov-game-based control.      

A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators

This paper develops an adaptive fuzzy controller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein the reinforcement signal is used to tune online the conclusion part of a fuzzy Markov game controller. The proposed Markov game-adaptive fuzzy controller uses a simple fuzzy inference system (FIS), is computationally efficient, generates a swift control, and requires no exact dynamics of the robot system. To illustrate the superiority of Markov game-adaptive fuzzy control, we compare the performance of the controller against a) the Markov game-based robust neural controller, b) the reinforcement learning (RL)-adaptive fuzzy controller, c) the FQL controller, d) the H_infin theory-based robust neural game controller, and e) a standard RL-based robust neural controller, on two highly nonlinear robot arm control problems of i) a standard two-link rigid robot arm and ii) a 2-DOF SCARA robot manipulator. The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories. The results also demonstrate the viability of FISs for accelerating learning in Markov games and extending Markov game-based control to continuous state-action space problems.