A memetic algorithm for the delineation of local labour markets

Given a territory composed of basic geographical units, the delineation of local labour market areas (LLMAs) can be seen as a problem in which those units are grouped subject to multiple constraints. In previous research, standard genetic algorithms were not able to find valid solutions, and a specific evolutionary algorithm was developed. The inclusion of multiple ad hoc operators allowed the algorithm to find better solutions than those of a widely-used greedy method. However, the percentage of invalid solutions was still very high. In this paper we improve that evolutionary algorithm through the inclusion of (i) a reparation process, that allows every invalid individual to fulfil the constraints and contribute to the evolution, and (ii) a hillclimbing optimisation procedure for each generated individual by means of an appropriate reassignment of some of its constituent units. We compare the results of both techniques against the previous results and a greedy method.

A parallel method for impulsive image noise removal on hybrid CPU/GPU systems

A parallel algorithm for image noise removal is proposed. The algorithm is based on peer group concept and uses a fuzzy metric. An optimization study on the use of the CUDA platform to remove impulsive noise using this algorithm is presented. Moreover, an implementation of the algorithm on multi-core platforms using OpenMP is presented. Performance is evaluated in terms of execution time and a comparison of the implementation parallelised in multi-core, GPUs and the combination of both is conducted. A performance analysis with large images is conducted in order to identify the amount of pixels to allocate in the CPU and GPU. The observed time shows that both devices must have work to do, leaving the most to the GPU. Results show that parallel implementations of denoising filters on GPUs and multi-cores are very advisable, and they open the door to use such algorithms for real-time processing.

Dynamic Visual Servoing With Chaos Control for Redundant Robots

This paper presents a new dynamic visual control system for redundant robots with chaos compensation. In order to implement the visual servoing system, a new architecture is proposed that improves the system maintainability and traceability. Furthermore, high performance is obtained as a result of parallel execution of the different tasks that compose the architecture. The control component of the architecture implements a new visual servoing technique for resolving the redundancy at the acceleration level in order to guarantee the correct motion of both end-effector and joints. The controller generates the required torques for the tracking of image trajectories. However, in order to guarantee the applicability of this technique, a repetitive path tracked by the robot-end must produce a periodic joint motion. A chaos controller is integrated in the visual servoing system and the correct performance is observed in low and high velocities. Furthermore, a method to adjust the chaos controller is proposed and validated using a real three-link robot.

Logic-Based Outer-Approximation Algorithm for Solving Discrete-Continuous Dynamic Optimization Problems

We present an extension of the logic outer-approximation algorithm for dealing with disjunctive discrete-continuous optimal control problems whose dynamic behavior is modeled in terms of differential-algebraic equations. Although the proposed algorithm can be applied to a wide variety of discrete-continuous optimal control problems, we are mainly interested in problems where disjunctions are also present. Disjunctions are included to take into account only certain parts of the underlying model which become relevant under some processing conditions. By doing so the numerical robustness of the optimization algorithm improves since those parts of the model that are not active are discarded leading to a reduced size problem and avoiding potential model singularities. We test the proposed algorithm using three examples of different complex dynamic behavior. In all the case studies the number of iterations and the computational effort required to obtain the optimal solutions is modest and the solutions are relatively easy to find.

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations.

FPGA implementation of a multi-population PBIL algorithm

Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations.

Genetic algorithm-based spinning reserve dispatching in a competitive electricity market

Power systems rely greatly on ancillary services in maintaining operation security. As one of the most important ancillary services, spinning reserve must be provided effectively in the deregulated market environment. This paper focuses on the design of an integrated market for both electricity and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the ISO's total payment while ensuring system security. Genetic algorithms are used in the finding of the global optimal solutions for this dispatching problem. Case studies and corresponding analyses haw been carried out to demonstrate and discuss the efficiency and usefulness of the proposed market.

Design and implementation of a windows-based parallel computing environment for large scale optimization

A parallel computing environment to support optimization of large-scale engineering systems is designed and implemented on Windows-based personal computer networks, using the master-worker model and the Parallel Virtual Machine (PVM). It is involved in decomposition of a large engineering system into a number of smaller subsystems optimized in parallel on worker nodes and coordination of subsystem optimization results on the master node. The environment consists of six functional modules, i.e. the master control, the optimization model generator, the optimizer, the data manager, the monitor, and the post processor. Object-oriented design of these modules is presented. The environment supports steps from the generation of optimization models to the solution and the visualization on networks of computers. User-friendly graphical interfaces make it easy to define the problem, and monitor and steer the optimization process. It has been verified by an example of a large space truss optimization. (C) 2004 Elsevier Ltd. All rights reserved.

Limitations of asymmetric parallel-beam geometry

Bragg diffraction peak profiles and intensities in asymmetric (Omega-2theta) diffraction using a mirror-based parallel-beam geometry were compared with symmetric parallel-beam (theta-2theta) and conventional Bragg - Brentano (theta-2theta) diffraction for a powdered quartz sample and the NIST standard reference material (SRM) 660a (LaB6, lanthanum hexaboride). A comparison of the intensities and line widths (full width at half-maximum, FWHM) of these techniques demonstrated that low incident angles (Omega < 5&DEG;) are preferable for the parallel-beam setup. For higher &UOmega; values, if 2θ < 2Omega, mass absorption reduces the intensities significantly compared with the Bragg - Brentano setup. The diffraction peak shapes for the mirror geometry are more asymmetric and have larger FWHM values than corresponding peaks recorded with a Bragg - Brentano geometry. An asymmetric mirror-based parallel-beam geometry offers some advantages in respect of intensity when compared with symmetric geometries, and hence may be well suited to quantitative studies, such as those involving Rietveld analysis. A trial Rietveld refinement of a 50% quartz - 50% corundum mixture was performed and produced adequate results.

Structural basis for control of secondary vessels in the long-finned eel, Anguilla reinhardtii

Histological sections of primary segmental arteries and associated interarterial anastomoses and secondary vessels from the long-finned eel Anguilla reinhardtii were examined by light and transmission electron microscopy. Interarterial anastomoses were found to originate from the primary vasculature as depressions through the tunica intima and media, from where they ran perpendicularly to the adventitial layer, before coiling extensively. From here the anastomoses travelled a relatively linear path in the outer margin of the adventitia to anastomose with a secondary vessel running in parallel with the primary counterpart. In contrast to findings from other species, secondary vessels had a structure quite similar to that of primary vessels; they were lined by endothelial cells on a continuous basement membrane, with a single layer of smooth muscle cells surrounding the vessel. Smooth muscle cells were also found in the vicinity of interarterial anastomoses in the adventitia, but these appeared more longitudinally orientated. The presence of smooth muscle cells on all aspects of the secondary circulation suggests that this vascular system is regulated in a similar manner as the primary vascular system. Because interarterial anastomoses are structurally integrated with the primary vessel from which they originate, it is anticipated that flow through secondary vessels to some extent is affected by the vascular tone of the primary vessel. Immunohistochemical studies showed that primary segmental arteries displayed moderate immunoreactivity to antibodies against 5-hydroxytryptamine and substance P, while interarterial anastomoses and secondary vessels showed dense immunoreactivity. No immunoreactivity was observed on primary or secondary arteries against neuropeptide Y or calcitonin gene-related peptide.

A double blind, randomized, multicenter, parallel group study of the effectiveness and tolerance of intraarticular hyaluronan in osteoarthritis of the knee

Objective. To investigate the efficacy and tolerability of a course of 5 injections of hyaluronan (HA) given at intervals of one week in patients with symptomatic, mild to moderate osteoarthritis (OA) of the knee. Methods: A double blind, randomized, parallel group, multicenter (17 centers), saline vehicle-controlled study was conducted over 18 weeks. Patients received either 25 mg (2.5 ml) HA in a phosphate buffered solution or 2.5 ml vehicle containing only the buffer by intraarticular injection. Five injections were given at one week intervals and the patients were followed for a further 13 weeks. The Western Ontario McMaster (WOMAC) OA instrument was used as the primary efficacy variable and repeated measures analysis of covariance was used to compare the 2 treatments over Weeks 6, 10, 14, and 18. Results. Of 240 patients randomized for inclusion in the study, 223 were evaluable for the modified intention to treat analysis. The active treatment and control groups were comparable for demographic details, OA history, and previous treatments. Scores for the pain and stiffness subscales of the WOMAC were modestly but significantly lower in the HA-treated group overall (Weeks 6 to 18; p < 0.05) and the statistically significant difference from the control was not apparent until after the series of injections was complete. The physical function subscale did not reach statistical significance (p = 0.064). Tolerability of the procedure was good and there were no serious adverse events that were considered to have a possible causal relationship with the study treatment. Conclusion. Intraarticular HA treatment was significantly more effective than saline vehicle in mild to moderate OA of the knee for the 13 week postinjection period of the study.

Seizure detection algorithm for neonates based on wave-sequence analysis

Objective: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. Methods: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. Results: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30 s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. Conclusions: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. Significance: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring. Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.

A lightweight encryption algorithm for mobile online multimedia devices

Online multimedia data needs to be encrypted for access control. To be capable of working on mobile devices such as pocket PC and mobile phones, lightweight video encryption algorithms should be proposed. The two major problems in these algorithms are that they are either not fast enough or unable to work on highly compressed data stream. In this paper, we proposed a new lightweight encryption algorithm based on Huffman error diffusion. It is a selective algorithm working on compressed data. By carefully choosing the most significant parts (MSP), high performance is achieved with proper security. Experimental results has proved the algorithm to be fast. secure: and compression-compatible.

Biologically inspired joint control for a humanoid robot

The GuRm is a 1.2m tall, 23 degree of freedom humanoid consuucted at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRw projcct is the development of appropriate learning strategies for control and coodinadon of the robot’s many joints. The development of learning strategies is Seen as a way to sidestep the inherent intricacy of modeling a multi-DOP biped robot. This paper outlines the approach taken to generate an appmpria*e control scheme for the joinis of the GuRoo. The paper demonsrrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-fonward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on thc CMAC architecture. Results from tats on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.

Neural control of a batch distillation

The purpose of the work reported here was to investigate the application of neural control to a common industrial process. The chosen problem was the control of a batch distillation. In the first phase towards deployment, a complex software simulation of the process was controlled. Initially, the plant was modelled with a neural emulator. The neural emulator was used to train a neural controller using the backpropagation through time algorithm. A high accuracy was achieved with the emulator after a large number of training epochs. The controller converged more rapidly, but its performance varied more widely over its operating range. However, the controlled system was relatively robust to changes in ambient conditions.