Mini-maximizing two qubit quantum computations

Two qubit quantum computations are viewed as two player, strictly competitive games and a game-theoretic measure of optimality of these computations is developed. To this end, the geometry of Hilbert space of quantum computations is used to establish the equivalence of game-theoretic solution concepts of Nash equilibrium and mini-max outcomes in games of this type, and quantum mechanisms are designed for realizing these mini-max outcomes.     

Computationally efficient 2D beamspace matrix pencil method for direction of arrival estimation

In this paper, we propose a new 2-dimensional beamspace matrix pencil (2D BMP) method for direction of arrival (DOA) estimation of plane wave signals using a uniform rectangular array (URA). Based on some a priori information about DOA, the proposed method transforms the complex signal subspace in 2D matrix pencil (2D MP) method [Y. Hua, Estimating two-dimensional frequencies by matrix enhancement and matrix pencil, IEEE Trans. Signal Process. 40 (9) (1992) 2267–2280] into a real and reduced dimensional beamspace using the discrete Fourier transform (DFT) matrix transformation. Consequently, the computational complexity is reduced (several times) in comparison with 2D MP method. Computer simulations are provided to show that 2D BMP method gives comparable performance in terms of average mean square error of the estimated DOA with lesser floating point operations as compared to the existing (MP) methods.     

Bioaugmented membrane bioreactor (MBR) with a GAC-packed zone for high rate textile wastewater treatment

The long-term performance of a bioaugmented membrane bioreactor (MBR) containing a GAC-packed anaerobic zone for treatment of textile wastewater containing structurally different azo dyes was observed. A unique feeding strategy, consistent with the mode of evolution of separate waste streams in textile plants, was adopted to make the best use of the GAC-zone for dye removal. Dye was introduced through the GAC-zone while the rest of the colorless media was simultaneously fed through the aerobic zone. Preliminary experiments confirmed the importance of coupling the GAC-amended anaerobic zone to the aerobic MBR and also evidenced the efficacy of the adopted feeding strategy. Following this, the robustness of the process under gradually increasing dye-loading was tested. The respective average dye concentrations (mg/L) in the sample from GAC-zone and the membrane-permeate under dye-loadings of 0.1 and 1 g/L.d were as follows: GAC-zone (3, 105), permeate (0, 5). TOC concentration in membrane-permeate for the aforementioned loadings were 3 and 54 mg/L, respectively. Stable decoloration along with significant TOC removal during a period of over 7 months under extremely high dye-loadings demonstrated the superiority of the proposed hybrid process.     

A robust neural network model for fault detection in the presence of mislabelled data

Several data-driven methodologies for process monitoring and detection of faults or abnormalities have been developed for the safety of processing systems. The effectiveness of data-based models, however, is impacted by the volume and quality of training data. This work presents a robust neural network model for addressing the mislabelled and low-quality data in detecting faults and process abnormalities. The approach is based on harnessing data quality features along with supervisory labels in the network training. The data quality has been computed using the Mahalanobis distances and trusted centres of each class of data such as normal and faulty data. The method has been examined for detecting abnormalities in two case studies; a continuous stirred tank heater problem for detecting leaks and the Tennessee Eastman chemical process for detecting step and sticking faults. The performance of the proposed robust artificial neural networks (ANN) model is evaluated in terms of accuracy, fault detection rate, false alarm rate, and classification index at varying extents of mislabelling, namely, 1%, 5%, and 10% mislabelled data. The proposed model demonstrates higher detection performance, especially at increased labels of mislabelled data where the performance of the conventional ANN is severely impacted. The proposed methodology can be advantageous in handling mislabelled and low-quality data issues which are crucial in the data-driven modelling of processing systems.     

Stand-alone programmable controller for time-critical robotic systems

This paper proposes and describes a new technique in the design and implementation of stand-alone programmable controllers which implement complex control algorithms for complex systems in general and time-critical systems in particular. Unlike other existing commercially available controllers, the proposed controller is capable of implementing all kinds of simple and complex control algorithm techniques such as PIDs, adaptive, optimal, etc. The controller is designed to handle timed events accurately due to the utilization of a new software approach based on Petri net technique as well as the use of an optimized simulation language designed for control and data acquisition applications. The most significant aspects of the proposed controller are its low cost, high speed, and easy implementation. Using the new approach, it is possible to effortlessly and efficiently simulate any controller algorithm and controlled plant, verify results if they meet pre-defined system specifications and then immediately generate and save codes on EPROMs to be placed on the controller board. The proposed controller system consists of a hardware portion and a software package. The software package is written in C and assembly languages and consists of four different programs. A prototype of the proposed controller was designed, constructed and successfully tested to implement various control algorithms. The data obtained suggest that the proposed technique will significantly aid engineers to simplify the task of implementing complex algorithms industry such as robotics.     

A semi-Markovian model for low-cycle elastic-plastic fatigue crack growth

Elastic-plastic fatigue crack growth is described by a continuous time semi-Markovian stochastic process. The load on the member is considered to be deterministic and the resistance is assumed to be random. Crack growth resistance is described by an equation employing the J-integral. Quantities of interest that can be obtained from this model include emergency statistics and probability distributions on the time to reach a critical crack length. The model is tested with fatigue crack propagation data for A533B pressure vessel steel. The results indicate that the model underpredicts the mean first-passage time at the start of a test but predicts it well in the mid-ranges and towards the end of the test; the variance of the process is better predicted for specimens that undergo large plasticity than those with low plasticity.