Adaptive output feedback tracking control of robot manipulators using position measurements only

In this paper, a new adaptive neuro controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of joint positions only. The system uncertainty, which may include payload variation, unknown nonlinearities and torque disturbances is estimated by a Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to generate pseudo filtered tracking error signals (for the elimination of velocity measurements) and the theory of function approximation using CNN. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload variation but also to unstructured one such as disturbances. Moreover the computational complexity of the proposed controller is reduced as compared to the multilayered neural network controller. The validity of the control scheme is shown by simulation results of a two-link robot manipulator. Simulation results are also provided to compare the proposed controller with a controller where velocity is estimated by finite difference methods using position measurements only.     

Extranasal Staphylococcus aureus colonization predisposes to bloodstream infections in patients on hemodialysis with noncuffed internal jugular vein catheters

Introduction: Staphylococcal infection of endogenous origin is an important cause of morbidity and mortality in patients who receive hemodialysis (HD). The risk of such infections in nasal carriers of the organism is well defined. Extranasal carriage of the organism at extranasal sites may pose similar risks. Methods: A total of 70 patients about to undergo internal jugular vein catheterization for HD were enrolled in this prospective observational study. Swab cultures were obtained from anterior nares, posterior pharynx, axillae, toe web spaces, and vascular access sites at baseline and 1 week later. A patient was defined as a persistent carrier when the same organism was grown in both samples. Staphylococcus aureus bloodstream infections were assessed by blood and catheter tip cultures over a 90‐day period. Findings: The mean age of the patients was 43.71 ± 16.2 years. Persistent S. aureus carriage at anterior nares, throat, axilla, toe web spaces, vascular access site, and all sites was documented in 27.9%, 11.4%, 40%, 32.9%, 4.3%, and 64.2% of patients, respectively. Fifteen patients developed S. aureus infections. Catheter related S. aureus infections (CRI) were more likely in persistent carriers than nonpersistent carriers with odds ratios (95% CI) of 10.2 (2.8–37.1), 8.6 (1.7–42.2), 17.3 (3.4–86.0), 3.0 (0.9–9.8), and 1.9 (0.2–22.4) for anterior nares, throat, axilla, toe web spaces, and vascular access site carriers, respectively. The probability of developing CRI in persistent S. aureus carriers was 55% compared to none in noncarriers at 90 days (P = 0.04). Discussion: Extranasal S. aureus carriage is as significant a risk factor as nasal carriage for staphylococcal infections in patients on HD through catheters. The study is limited by lack of molecular phenotyping.     

Process and die profile design for tube extrusion of γ iron

This study is related to material modeling and die and process design of tube extrusion of γ iron. Strain dependent rate power law is used for material modeling whose coefficients are arrived at through genetic algorithm (GA). Die profile of the tube extrusion process is optimized to produce microstructurally sound product at maximum production speed and minimum left out material in the die. The design problem is formulated as a nonlinear programming problem which is solved using GA. Selection of the processing parameters is carried out using dynamic material modeling (DMM). Using this approach tube extrusion process of γ iron is successfully designed.     

Optimal testing resource allocation during module testing considering cost, testing effort and reliability

Software reliability is one of the most important quality attributes of commercial software. During software testing, software reliability growth models (SRGMs) are commonly used to describe the phenomenon of failure occurrence and/or fault removal which consequently enhancements software reliability. Large software systems are developed by integrating a number of relatively small and independent modules, which are tested independently during module testing phase. The amount of testing resource available is limited which is desired to be consumed judiciously so as to optimize the testing process. In this paper we formulate a resource allocation problem of minimizing the cost of software testing under available amount of testing resource, given a reliability constraint. We use a flexible SRGM considering testing effort which, depending upon the values of parameters, can describe either exponential or S-shaped failure pattern of software modules. A systematic and sequential Algorithm is proposed to solve the optimization problem formulated. Numerical examples are given to illustrate the formulation and solution procedures. Sensitivity analysis is performed to examine the behavior of some parameters of SRGM with most significant influence.     

Cross‐polarization reduction of microstrip antenna using microwave absorbers

In this article, a metamaterial inspired microwave absorber is used to reduce the cross‐polarization (XP) level of the radiated wave in microstrip antenna (MSA). A microwave absorber unit‐cell has been analyzed and implemented to reduce the cross polarization (XP) level in a single element and a 2 × 2 microstrip patch array antennas. The antennas have been designed on a FR‐4 substrate of thickness 0.8 mm at 10.1 GHz center frequency. The 2 × 2 patch array antenna with and without the absorbers have been experimentally verified for the S11 parameter, the radiation pattern, and the XP suppression in H‐plane and a good comparison has been found.     

A DEM‐based parallel computing hydrodynamic and transport model

The sudden and accidental water pollution response system (SAWPRS) for Yangtze River in central China required to develop a hydrodynamic and transport model, which is readily available and capable of simulating a large river system within GIS environment. This study facilitates such effort by developing a parallel computing method based on digital elevation model (DEM) using overlapping domain decomposition approach (ODDA) and message passing interface (MPI) protocol. The hydrodynamic and transport model was redesigned using finite volume method for hydrodynamic and transport model dispersion, the SIMPLEC method for solving the flow field, and the pressure weighted interpolating method for the flow field modification. This modelling approach was verified in two experiments using different sets of computer clusters. The model output was evaluated against the measured data collected for the year 1998 for Wanzhou, an upstream river segment of Yangtze River. The relative error was found to be less than 10%. The performance of parallel computation was found excellent as evident from the cost efficiency values greater than 0.81 in both experiments and increased computation speed while increasing the number of computer clusters. Overall, the parallel computing modelling system developed here was found to meet all requirements of SAWPRS. Copyright © 2010 John Wiley & Sons, Ltd.     

Perfect r-domination in the Kronecker product of two cycles, with an application to diagonal/toroidal mesh

If r?1, and m and n are each a multiple of (r+1)²+r², then each isomorphic component of C_m×C_n admits of a vertex partition into (r+1)²+r² perfect r-dominating sets. The result induces a dense packing of C_m×C_n by means of vertex-disjoint subgraphs, each isomorphic to a diagonal array. Areas of applications include efficient resource placement in a diagonal mesh and error-correcting codes.     

Failure-Oriented Path Restoration Algorithm for Survivable Networks

In this article, a new polynomial-time approximation algorithm called Service Path Local Optimization (SPLO) is proposed for the online restoration problem. SPLO is shown to perform competitively with existing offline heuristics algorithm in terms of spare capacity. SPLO is designed for online computation where only one request is computed at any one time, and the decision making does not depend on future requests. The polynomial-time and online nature of the algorithm makes SPLO suitable for use in real-time on-demand path request applications. SPLO can be combined with a non-polynomial post-processing component that re-optimizes the backup paths. Significant reductions in spare capacity requirements are achievable at the expense of higher computation time. Further, the potential for SPLO as an algorithm in traffic engineering applications is investigated by looking at the performance impact when source-destination-based traffic aggregation is applied. We also introduce a new concept called path intermix where the service path?s allocated bandwidth can be used by the backup paths protecting that particular service path.     

Knowledge-based systems using neural networks for electron beam welding process of reactive material (Zircaloy-4)

Bead-on-plate welding of zircaloy-4 (a reactive material) plates was conducted using electron beam according to central composite design of experiments. Its predictive models were developed in the form of knowledge-based systems in both forward and reverse directions using neural networks. Input parameters considered for this welding of reactive metals were accelerating voltage, beam current and weld speed. The responses of the welding process were measured in terms of bead width, depth of penetration and micro-hardness. Forward mapping of the welding process was conducted using regression analysis, back-propagation neural network (BPNN), genetic algorithm-tuned neural network (GANN) and particle swarm optimization algorithm-tuned neural network (PSONN). Reverse mapping of this process was also carried out using the BPNN, GANN and PSONN-based approaches. Neural network-based approaches could model this welding process of reactive material in both forward and reverse directions efficiently, which is required for the automation of the same. The performance of the neural network models was found to be data-dependent. The BPNN could outperform the other two approaches for most of the cases but not all in both the forward and reverse mappings.     

Physical modeling and numerical investigation of fluid flow and solidification behavior in a slab caster mold using hexa-furcated nozzle

Slag entrapment from metal–slag interface during continuous casting operations has been a major area of concern for steelmakers globally. The presence of inactive regions in the upper region of the mold poses another challenge. Proper flow behavior of the molten metal coming out of the nozzle in the mold is required to overcome these challenges. Nozzle design greatly affects the flow pattern of the molten steel inside the mold. The present investigation is an attempt to study the flow and solidification behavior in a slab caster mold with the use of a novel-designed hexa-furcated nozzle using numerical investigation results. The casting speed and submerged entry nozzle (SEN) depth are varied to study the effect of these parameters on minimizing the inactive zones in the mold and the steel/slag interface fluctuations. The results show that the interface fluctuation increases at higher casting speed and lower SEN depth. The residence time distribution (RTD) analysis was also performed for different cases to investigate the flow behavior. The validation of the fluid flow and RTD curve inside the computational domain is carried out with the use of physical modeling.