FMS scheduling with knowledge based genetic algorithm approach

In this paper a complex scheduling problem in flexible manufacturing system (FMS) has been addressed with a novel approach called knowledge based genetic algorithm (KBGA). The literature review indicates that meta-heuristics may be used for combinatorial decision-making problem in FMS and simple genetic algorithm (SGA) is one of the meta-heuristics that has attracted many researchers. This novel approach combines KB (which uses the power of tacit and implicit expert knowledge) and inherent quality of SGA for searching the optima simultaneously. In this novel approach, the knowledge has been used on four different stages of SGA: initialization, selection, crossover, and mutation. Two objective functions known as throughput and mean flow time, have been taken to measure the performance of the FMS. The usefulness of the algorithm has been measured on the basis of number of generations used for achieving better results than SGA. To show the efficacy of the proposed algorithm, a numerical example of scheduling data set has been tested. The KBGA was also tested on 10 different moderate size of data set to show its robustness for large sized problems involving flexibility (that offers multiple options) in FMS.     

A virtual RV-M1 robot system

Exploring a virtual model under simulated environments is the best way to learn about a real system. This is particularly true in robotics where it is quite expensive to provide the system to each individual. The interdisciplinary area of robotics is being studied commonly in various fields like electrical, computer, mechanical engineering, nanotechnology, etc. A virtual robot system can help one fully understand the controls and working of a robot. The system may also be helpful to design the path and plan the trajectory of a robot in an industrial environment or other robotics application. Virtual model of RV-M1 robot has been developed in the MATLAB environment. The virtual system performs forward kinematics and inverse kinematics in addition to providing a simulation of the robot teachbox.     

Automated process planning method to machine A B-Spline free-form feature on a mill–turn center

In this paper, we present a methodology for automating the process planning and NC code generation for a widely encountered class of free-form features that can be machined on a 3-axis mill–turn center. The free-form feature family that is considered is that of extruded protrusions whose cross-section is a closed, periodic B-Spline curve. In this methodology, for machining a part with B-Spline protrusion located at the free end, the part is first rough turned to the maximum profile diameter of the B-Spline, followed by rough profile cutting and finish profiling with axially mounted end mill tools. The identification and sequencing of machining volumes is completely automated, as is the generation of actual NC code. The approach supports both convex and non-convex profiles. In the case of non-convex profiles, the process planning algorithm ensures that there is no gouging of the work piece by the tool. The algorithm also identifies when sections of the tool path lie outside the work piece and utilizes rapid traverses in these regions to reduce cutting time. This methodology presents an integrated turn–mill process planning where by making the process fully automated from design with no user intervention making the overall process planning efficient. The algorithm was tested on several examples and test parts using the unmodified NC code obtained from the implementation were run on a Moriseiki mill–turn center. The parts that were produced met the dimensional specifications of the desired part.     

Worst-case optimal algorithm for XPath evaluation over XML streams

We consider the XPath evaluation problem: Evaluate an XPath query Q on a streaming XML document D; i.e., determine the set Q(D) of document elements selected by Q. We mainly consider Conjunctive XPath queries that involve only the child and descendant axes. Previously known in-memory algorithms for this problem use O(|D|) space and O(|Q||D|) time. Several previously known algorithms for the streaming version use Ω(dn) space and Ω(dn|D|) time in the worst case; d denotes the depth of D, and n denotes the number of location steps in Q. Their exponential space requirement could well exceed the O(|D|) space used by the in-memory algorithms. We present an efficient algorithm that uses O(d|Q|+nc) space and O((|Q|+dn)|D|) time in the worst case; c denotes the maximum number of elements of D that can be candidates for output, at any one instant. For some worst case Q and D, the memory space used by our algorithm matches our lower bound proved in a different paper; so, our algorithm uses optimal memory space in the worst case.     

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.     

A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures

We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers.     

Tri‐band composite cylindrical dielectric resonator antenna with hybrid mode excitation and cross‐polarization suppression

This communication investigates composite cylindrical dielectric resonator antenna (CDRA) for various wireless applications. Three important features of proposed antenna design are (i) realization of two different hybrid modes, that is, HEM_11δ and HEM_12δ mode in CDRA with the help of modified annular ring printed line (work as both magnetic dipole and electric dipole), both the hybrid modes support broadside radiation characteristics (ii) suppression of HEM_21δ mode, in order to reduce the cross‐polarization level in H‐plane of other hybrid modes (HEM_11δ and HEM_12δ mode) by an amount of 8‐10 dB (iii) creation of triple‐band attribute using the concept of composite antenna. The proposed antenna design has been fabricated and practically tested. Simulated outcomes show good agreement with measured outcomes. It works in three frequency bands, that is, 2.25‐2.79 GHz, 3.1‐4.0 GHz, and 5.05‐5.6 GHz. The designed antenna structure is appropriate for WLAN and WiMAX applications.