Selection of industrial robots using compromise ranking and outranking methods

Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is ‘VIsekriterijumsko KOmpromisno Rangiranje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.     

A new social and momentum component adaptive PSO algorithm for image segmentation

In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu’s method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting ‘social’ and ‘momentum’ components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935–952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653–666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337–344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85–95) and the basic PSO variant employing linearly decreasing inertia weight factor.     

User-input driven QoS management in ad hoc networks

Though the network quality-of-service (QoS) metrics are defined in terms of technical parameters (e.g., delay, jitter, bandwidth), they are rather subjective when it comes to the end user. Oftentimes, the end user finds it difficult to express his desired QoS in such technical parameters, though he has a fair idea of what QoS he desires. In this paper, we show how translation functions can be devised and used to translate the user inputs to networking parameters that are used by various layers of the protocol stack. In particular, we consider an ad hoc network and show an interface design that uses translation functions to map user supplied inputs to parameters at the medium access control (MAC) and routing layers. These parameters, in turn, choose the right strategies that particular layer functionality can adopt, such that the QoS desired by the user is achieved. We also compute the associated costs due to the different strategies adopted. We implement the interface on ns-2 and conduct simulation experiments with randomly scattered IEEE 802.11 enabled nodes. Results show the functionality of the interface and demonstrate how delay, throughput, and network lifetime are affected when the end user seeks different levels of QoS.     

Experimental characterization of piezoelectrically actuated micromachined silicon valveless micropump

In this paper, performance of piezoelectrically actuated pyramidal valveless micropumps is studied experimentally in detail. Valveless micropumps based on silicon and glass substrate are fabricated using MEMS technology. Two different sizes of micropumps having overall dimensions of 5 mm × 5 mm × 1 mm and 10 mm × 10 mm × 1 mm are fabricated and characterized. In the fabricated micropumps, the thickness of silicon diaphragm is <20 µm which gives the advantage of operating pump at low voltage with excellent stability and consistency. The performance of micropumps in terms of flowrate and backpressure is evaluated for a wide range of driving frequency and actuating voltages. The maximum flowrate of water in the 10-mm micropump is 355 µl/min and backpressure of 3.1 kPa at zero flowrate for an applied voltage of 80 V at frequency 1.05 kHz. The reported micropumps have low footprint, high flowrate and backpressure. Thus, these micropumps are especially suited for biological applications as these can withstand adequate amount of backpressure. Comparative study of the performance of these micropumps with those available in the literature brings out the efficacy of these micropumps.     

Complexity of points-to analysis of Java in the presence ofexceptions

At each program point, points-to analysis for statically typed object oriented programming languages (e.g., Java, C++) determines those objects to which a reference may refer (or a pointer may point) during execution. Points-to analysis is necessary for any semantics based software tools for object oriented systems. Our new complexity results for points-to analysis distinguish the difficulty of intraprocedural and interprocedural points-to analyses for languages with combinations of single-level types (i.e., types with data members only of primitive type), exceptions with or without subtyping, and dynamic dispatch. Our results include: 1) the first polynomial-time algorithm for points-to analysis in the presence of exceptions that handles a robust subset of Java without threads and can be applied to C++; 2) proof that the above algorithm is safe, in general, and provably precise on programs with single-level types and exceptions without subtyping, but not dynamic dispatch, thus, this case is in P; 3) proof that an interprocedural points-to analysis problem with single-level types and exceptions with subtyping, but without dynamic dispatch, is PSPACE-hard, while the intraprocedural problem is PSPACE-complete. Other complexity characterizations of points-to analysis in programs without exceptions are presented, including an algorithm with worst-case bound of O(n⁵ ), which improves over the O(n⁷) worst-case bound achievable from previous approaches of T. Reps et al. (1995) and W.A. Landi and B.G. Ryder (1991)     

Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.     

Cashing in on Caching: An Architecture for Time-Bounded Knowledge-Based Problem Solving

Knowledge-based computing, in general, suffers from an inherent open-endedness that precludes its application in time-bounded domains where an answer must be computed within a stipulated time limit. We examine a two-way improvement of the shortcomings: a knowledge representation scheme that provides easy access to relevant knowledge and thereby reduces search time, and a reasoning scheme that is algorithmic in nature and thus makes computational requirements meaningfully estimable.In this work, we offer a cache-based architecture that is capable of both storing knowledge in different formats (e.g. rules, cases), and invoking an appropriate reasoning scheme to fit the available computing time. The cache helps in retrieving the most relevant pieces of knowledge (not only exact matches) required for solving a given problem. This cache relies on a reasoning tactic, knowledge interpolation, that can generate a solution from two near-matches in an algorithmic way, to generate time-bounded solutions. We illustrate the design of such a cache for solving resource allocation problems in the domain of shortwave radio transmission and evaluate its performance in observing imposed temporal bounds.     

Influential rule search scheme (IRSS) - a new fuzzy pattern classifier

Automatic generation of fuzzy rule base and membership functions from an input-output data set, for reliable construction of an adaptive fuzzy inference system, has become an important area of research interest. We propose a new robust, fast acting adaptive fuzzy pattern classification scheme, named influential rule search scheme (IRSS). In IRSS, rules which are most influential in contributing to the error produced by the adaptive fuzzy system are identified at the end of each epoch and subsequently modified for satisfactory performance. This fuzzy rule base adjustment scheme is accompanied by an output membership function adaptation scheme for fine tuning the fuzzy system architecture. This iterative method has shown a relatively high speed of convergence. Performance of the proposed IRSS is compared with other existing pattern classification schemes by implementing it for Fisher's iris data problem and Wisconsin breast cancer data problems.     

Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization

The particle swarm optimization (PSO) is a relatively new generation of combinatorial metaheuristic algorithms which is based on a metaphor of social interaction, namely bird flocking or fish schooling. Although the algorithm has shown some important advances by providing high speed of convergence in specific problems it has also been reported that the algorithm has a tendency to get stuck in a near optimal solution and may find it difficult to improve solution accuracy by fine tuning. The present paper proposes a new variation of PSO model where we propose a new method of introducing nonlinear variation of inertia weight along with a particle's old velocity to improve the speed of convergence as well as fine tune the search in the multidimensional space. The paper also presents a new method of determining and setting a complete set of free parameters for any given problem, saving the user from a tedious trial and error based approach to determine them for each specific problem. The performance of the proposed PSO model, along with the fixed set of free parameters, is amply demonstrated by applying it for several benchmark problems and comparing it with several competing popular PSO and non-PSO combinatorial metaheuristic algorithms.