Microstructure and High Temperature Impression Creep Properties of Mg–3Ca–xZr (x = 0.3, 0.6, 0.9 wt%) Alloys

The current study has investigated the influence of zirconium (Zr) addition to Mg–3Ca–xZr (x = 0.3, 0.6, 0.9 wt%) alloys prepared using argon arc melting on the microstructure and impression properties at 448–498 K under constant stress of 380 MPa. Microstructural analysis of as-cast Mg–3Ca–xZr alloys showed grain refinement with Zr addition. The observed grain refinement was attributed to the growth restriction effect of Zr in hypoperitectic Mg–3Ca–0.3 wt% Zr alloys. Heterogeneous nucleation of α-Mg in properitectic Zr during solidification resulted in grain refinement of hyperperitectic Mg–3Ca–0.6 wt% Zr and Mg–3Ca–0.9 wt% Zr alloys. The hardness of Mg–3Ca–xZr alloys increased as the amount of Zr increased due to grain refinement and solid solution strengthening of α-Mg by Zr. Creep resistance of Mg–3Ca–xZr alloys increased with the addition of Zr due to solid solution strengthening of α-Mg by Zr. The calculated activation energy (Q_a) for Mg–3Ca samples (131.49 kJ/mol) was the highest among all alloy compositions. The Q_a values for 0.3, 0.6 and 0.9 wt% Zr containing Mg–3Ca alloys were 107.22, 118.18 and 115.24 kJ/mol, respectively.     

Structural instability of carbon nanotubes embedded in viscoelastic medium and subjected to distributed tangential load

In this paper, the nonlocal Euler-Bernoulli beam model is used to predict the static and dynamic structural instability of carbon nanotubes (CNTs) subjected to a distributed tangential compressive load. The CNT is considered to be embedded in a Kelvin-Voigt viscoelastic medium. Equation of motion and boundary conditions are obtained using the extended Hamilton’s principle and the extended Galerkin’s method is applied in order to transform the resulting equations into a general eigenvalue problem. The derived equations are validated by comparing the results achieved from the new derivations with existing solutions in literature. Effects of several experimentally interesting boundary conditions are considered on the stability characteristics of the CNT. Moreover, the influences of small scale parameter and material properties of the surrounding viscoelastic medium on the stability boundaries are examined.     

An intrinsic semantic framework for recognizing image objects

The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification. The algorithm has been tested on a standard benchmark database and compared with existing approaches.     

Application of artificial intelligence to improve quality of service in computer networks

Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.     

Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.     

Process Discovery and Refinement of an Enterprise Management System

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data. This data can be extremely valuable for executing organizations because the data allows constant monitoring, analyzing, and improving the underlying processes, which leads to the reduction of cost and the improvement of the quality. Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours. This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints. By investigating and analyzing their order handling processes, this study aims at learning a model that gives insight inspection of the processes and performance analysis. Furthermore, the animation is also performed for the better inspection, diagnostics, and compliance-related questions to specify the system. The configuration of the system and the conformance checking for further enhancement is also addressed in this research. To achieve the objectives, this research uses process mining techniques, i.e. process discovery in the form of formal Petri nets models with the help of process maps, and process refinement through conformance checking and enhancement. Initially, the identified executed process is reconstructed by using the process discovery techniques. Following the reconstruction, we perform a deep analysis for the underlying process to ensure the process improvement and redesigning. Finally, some recommendations are made to improve the enterprise management system processes.     

FPGA-based IP cores implementation for face recognition using dynamic partial reconfiguration

This paper presents a combination of novel feature vectors construction approach for face recognition using discrete wavelet transform (DWT) and field programmable gate array (FPGA)-based intellectual property (IP) core implementation of transform block in face recognition systems. Initially, four experiments have been conducted including the DWT feature selection and filter choice, features optimisation by coefficient selections and feature threshold. To examine the most suitable method of feature extraction, different wavelet quadrant and scales have been evaluated, and it is followed with an evaluation of different wavelet filter choices and their impact on recognition accuracy. In this study, an approach for face recognition based on coefficient selection for DWT is presented, and the significant of DWT coefficient threshold selection is also analysed. For the hardware implementation, two architectures for two-dimensional (2-D) Haar wavelet transform (HWT) IP core with transpose-based computation and dynamic partial reconfiguration (DPR) have been synthesised using VHDL and implemented on Xilinx Virtex-5 FPGAs. Experimental results and comparisons between different configurations using partial and non-partial reconfiguration processes and a detailed performance analysis of the area, power consumption and maximum frequency are also discussed in this paper.     

Implementation of genetic algorithm in an embedded microcontroller-based polarization control system

Implementation of genetic algorithm in a PIC32MX microcontroller-based polarization control system is proposed and demonstrated. The controller measures the signal intensity that is used to estimate the genetic value. This process is controlled by the genetic algorithm rather than referring to the Look-Up-Table as implemented in existing solutions. To reach optimum performance, the code is optimized by using the best genetic parameters so that the fastest execution time can be achieved. An ability of genetic algorithm to work efficiently in polarization control system possesses many advantages including easy code construction, low memory consumption and fast control speed. Genetic algorithm is intelligent enough to be used for endless polarization stabilization and in the worst case, able to stabilize the polarization changes in 300 μs. In the best case the response time can reach 17 μs.     

Stabilized active camera tracking system

We present a robust and real-time stabilized active camera tracking system (ACTS), which consists of three algorithmic modules: visual tracking, pan-tilt control, and digital video stabilization. We propose an efficient correlation-based framework for visual tracking module that is designed to handle the problems which severely deteriorate the performance of a traditional tracker. The problems that it handles are template drift, noise, object fading (obscuration), background clutter, intermittent occlusion, varying illumination in the scene, high computational complexity, and varying shape, scale, and velocity of the manoeuvring object during its motion. The pan-tilt control module is a predictive open-loop car-following control strategy, which moves the camera efficiently and smoothly so that the target being tracked is always at the center of the video frame. Video stabilization module is required to eliminate the vibration in the video, when the system is mounted on a vibratory platform such as truck, helicopter, ship, etc. We present a very efficient video stabilization method that adds no extra computational overhead to the overall system. It exploits the coordinates of the target, computed by the tracker module, to sense the amount of vibration and then filters it out of the video. The proposed system works at full frame rate (30 fps), and has been successfully used in uncontrolled real-world environment. Experimental results show the efficiency, precision, and robustness of the proposed stabilized ACTS.