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.     

GPU-based Acceleration of System-level Design Tasks

Many system-level design tasks (e.g., high-level timing analysis, hardware/software partitioning and design space exploration) involve computational kernels that are intractable (usually NP-hard). As a result, they involve high running times even for mid-sized problems. In this paper we explore the possibility of using commodity graphics processing units (GPUs) to accelerate such tasks that commonly arise in the electronic design automation (EDA) domain. We demonstrate this idea via two detailed case studies. The first explores the possibility of using GPUs to speedup standard schedulability analysis problems. The second proposes a GPU-based engine for a general hardware/software design space exploration problem. Not only do these problems commonly arise in the embedded systems domain, their computational kernels turn out to be variants of a combinatorial optimization problem—viz., the knapsack problem—that lies at the heart of several EDA applications. Experimental results show that our GPU-based implementations offer very attractive speedups for the computational kernels (up to 100×), and speedups of up to 17× for the full problem. In contrast to ASIC/FPGA-based accelerators—given that even low-end desktop and notebook computers are now equipped with GPUs—our solution involves no extra hardware cost. Although recent research has shown the benefits of using GPUs for a variety of non-graphics applications (e.g., in databases and bioinformatics), harnessing the parallelism of GPUs to accelerate problems from the EDA domain has not been sufficiently explored so far. We believe that our results and the generality of the core problem that we address will motivate researchers from this community to explore the possibility of using GPUs for a wider variety of problems from the EDA domain.     

An experimental analysis of electrostatically vibrated array of polysilicon cantilevers

Micromechanical cantilevers are one of the most fundamental and widely studied structures in micro-electromechanical systems. Dynamic response of such cantilevers has long been an interesting subject to researchers and different analytical and experimental approaches have been reported to determine it. Theoretical estimation of different damping mechanisms have been reported over years which are relevant particularly in studying the dynamics of the micro-mechanical structures. Most properties and functionalities of the MEMS devices are invariably dependant on the dynamic response of the devices, which in turn depends on the quality factor of the devices or in other words the overall damping present in the system. This paper presents a thorough experimental analysis of vibration characteristics of micro-mechanical cantilevers of different dimensions. Arrays of polysilicon micro-cantilevers of different dimensions have been designed and fabricated using surface micromachining process. The beams are resonated by electrostatic actuation and their vibration characteristics have been observed using Laser Doppler Vibrometer. Also a thorough analysis of modal behaviour of the beams is presented using analytical approach and finite element method based simulation. Different damping mechanisms have been critically reviewed and a semi-analytical estimation of the overall damping is presented. The results are compared with experimental values.     

Bayesian binary kernel probit model for microarray based cancer classification and gene selection

With the arrival of gene expression microarrays a new challenge has opened up for identification or classification of cancer tissues. Due to the large number of genes providing valuable information simultaneously compared to very few available tissue samples the cancer staging or classification becomes very tricky.In this paper we introduce a hierarchical Bayesian probit model for two class cancer classification. Instead of assuming a linear structure for the function that relates the gene expressions with the cancer types we only assume that the relationship is explained by an unknown function which belongs to an abstract functional space like the reproducing kernel Hilbert space. Our formulation automatically reduces the dimension of the problem from the large number of covariates or genes to a small sample size. We incorporate a Bayesian gene selection scheme with the automatic dimension reduction to adaptively select important genes and classify cancer types under an unified model. Our model is highly flexible in terms of explaining the relationship between the cancer types and gene expression measurements and picking up the differentially expressed genes. The proposed model is successfully tested on three simulated data sets and three publicly available leukemia cancer, colon cancer, and prostate cancer real life data sets.     

A layered algorithm for quantifier elimination from linear modular constraints

Linear equalities, disequalities and inequalities on fixed-width bit-vectors, collectively called linear modular constraints, form an important fragment of the theory of fixed-width bit-vectors. We present a practically efficient and bit-precise algorithm for quantifier elimination from conjunctions of linear modular constraints. Our algorithm uses a layered approach, whereby sound but incomplete and cheaper layers are invoked first, and expensive but complete layers are called only when required. We then extend this algorithm to work with arbitrary Boolean combinations of linear modular constraints as well. Experiments on an extensive set of benchmarks demonstrate that our techniques significantly outperform alternative quantifier elimination techniques based on bit-blasting and linear integer arithmetic.     

Different Involvement of Vimentin during Invasion by Listeria monocytogenes at the Blood–Brain and the Blood–Cerebrospinal Fluid Barriers In Vitro

The human central nervous system (CNS) is separated from the blood by distinct cellular barriers, including the blood–brain barrier (BBB) and the blood–cerebrospinal fluid (CFS) barrier (BCSFB). Whereas at the center of the BBB are the endothelial cells of the brain capillaries, the BCSFB is formed by the epithelium of the choroid plexus. Invasion of cells of either the BBB or the BCSFB is a potential first step during CNS entry by the Gram-positive bacterium Listeria monocytogenes (Lm). Lm possesses several virulence factors mediating host cell entry, such as the internalin protein family—including internalin (InlA), which binds E-cadherin (Ecad) on the surface of target cells, and internalin B (InlB)—interacting with the host cell receptor tyrosine kinase Met. A further family member is internalin (InlF), which targets the intermediate filament protein vimentin. Whereas InlF has been shown to play a role during brain invasion at the BBB, its function during infection at the BCSFB is not known. We use human brain microvascular endothelial cells (HBMEC) and human choroid plexus epithelial papilloma (HIBCPP) cells to investigate the roles of InlF and vimentin during CNS invasion by Lm. Whereas HBMEC present intracellular and surface vimentin (besides Met), HIBCPP cells do not express vimentin (except Met and Ecad). Treatment with the surface vimentin modulator withaferin A (WitA) inhibited invasion of Lm into HBMEC, but not HIBCPP cells. Invasion of Lm into HBMEC and HIBCPP cells is, however, independent of InlF, since a deletion mutant of Lm lacking InlF did not display reduced invasion rates.     

Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear

This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice.     

A review of power electronics interfaces for distributed energy systems towards achieving low-cost modular design

Due to increased attention towards clean and sustainable energy, distributed energy (DE) systems are gaining popularity all over the world. Power electronics are an integral part of these energy systems being able to convert generated electricity into consumer usable and utility compatible forms. But the addition of power electronics adds costs to the DE capital investments along with some reliability issues. Therefore, widespread use of distributed energy requires power electronics topologies that are less expensive and more dependable. Use of modular power electronics is a way to address these issues. Adoption of functional building blocks that can be used for multiple applications results in high volume production and reduced engineering effort, design testing, onsite installation and maintenance work for specific customer applications. In this paper, different power electronics topologies are reviewed that are typically used with distributed energy systems. The integrated power electronics module (IPEM) based back-to-back converter topologies are found to be most suitable interface that can operate with different DE systems with small or no modifications. Also the requirements for a hierarchical control structure with standardized power and communication interfaces are addressed in the paper along with some discussion on future design possibilities for the IPEM-based power electronics topologies. It is expected that modular and flexible power electronics and standardized controls and interfaces, will provide commonality in hardware and software for the power electronics interfaces, thus will enable their volume production and decrease their cost share in distributed energy applications.     

Effects of alkalization and fiber loading on the mechanical properties and morphology of bamboo fiber composites. II. Resol matrix

Resol resin composites reinforced with alkali‐treated bamboo strips were fabricated with a hand‐lay‐up technique. This study was aimed at the evaluation of the influence of the caustic concentration on the mechanical properties of bamboo‐strip‐reinforced resol composites with a constant 50% loading of the reinforcement. The treatment of bamboo fiber in a solution of sodium hydroxide with increasing concentration percentages resulted in more and more rigid composites; as a result, the strength and modulus values exhibited improvements. The maximum improvement in the properties was possibly achieved with 20% caustic treated reinforcements. An infrared study indicated the formation of aryl alkyl ether with ? OH groups of cellulose and methylol groups of resol. Beyond 20%, there was degradation in all the strength properties due to the failure of the mechanical properties of the reinforcement itself. A correlation was found to exist between the mechanical properties and the morphology that developed. Another set of composites with variable loadings of 20% alkali treated fiber (40, 50, and 60%) was fabricated, and a 60% fiber loading showed the best mechanical properties. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009