Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems.     

Spline interpolation with optimal frequency spectrum for vibration avoidance

A major source of inaccuracy in CNC machines is unwanted vibrations induced by the frequency spectra of reference motion trajectory. This paper presents a novel approach where instead of filtering techniques, axis motion commands are generated with optimal frequency spectra in the first place. Tangential feedrate profile is defined as parametric spline, and its frequency spectrum is optimized with respect to structural dynamics of the machine. The optimization problem is solved efficiently using Quadratic Programming. Experimental results confirm that proposed technique can greatly improve surface finish during machining spline tool-paths without sacrificing from cycle time and contouring performance.     

Sol–gel deposition of multiply doped thermographic phosphor coatings Al2O3:(Cr,M) (M = Dy,Tm) for wide range surface temperature measurement application

A promising method of measuring surface temperatures in harsh environments is the use of thermographic phosphor coatings. There, the surface temperature is evaluated from the phosphorescence decay lifetime following a pulsed laser or flash lamp light excitation. Depending on the used dopant, single doped M³⁺:α-Al₂O₃ (M = Cr, Dy, Tm) emit at 694 nm (Cr³⁺), 488 nm (Dy³⁺), 584 nm (Dy³⁺), and 459 nm (Tm³⁺), respectively. However, the accessible temperature range with a single dopant is limited: for the Cr³⁺-transition from 293 K up to 900 K, and for the Dy³⁺ and Tm³⁺-transitions both from 1073 K up to 1473 K. In the present study a new approach is followed to extend these limitations by co-doping two dopants using the sol–gel method and dip coating of α-Al₂O₃ thin films. For that application (Dy³⁺ + Cr³⁺) co-doped thin α-Al₂O₃ films and (Tm³⁺ + Cr³⁺) co-doped α-Al₂O₃ films with thicknesses of 4–6 μm were prepared, and the temperature-dependent luminescence properties (emission spectra and lifetimes) were analysed after pulsed laser excitation in the UV (355 nm). The phosphorescence lifetime as a function of temperature were measured between 293 K and 1473 K. A considerably extended range for surface temperature evaluation was established following this new approach by combining different dopants on the molecular level.     

Design and evaluation of a mechanical nanomanufacturing system for nanomilling

This paper presents design and evaluation of a mechanical nanomanufacturing system for performing the nanomilling process. The nanomilling process uses a nanotool (an atomic force microscope probe tip) that is rotated at high speeds to fabricate three-dimensional (3D) nano-scale features on a sample surface. After explaining the kinematics of the two nanomilling process configurations, the nanomilling system, including the 3D piezoelectric actuator that rotates the nanotool, the nanopositioning stage that provides the feeding and depth motions, and the software program that controls the nanomilling motions are described. A measurement system is then constructed to measure the dynamic nanomilling motions. A compensation algorithm is developed to enable obtaining desired nanotool motions in the presence of frequency and amplitude-dependent nonlinearities of the 3D piezoelectric actuator. The nanomilling system is then evaluated directly by measuring the nanotool motions, and indirectly by assessing the accuracy of the fabricated nanoscale features. It was shown that the nanomilling system facilitates fabrication of complex nano-scale features with high accuracy through the high-stiffness nanotool assembly and high-frequency (compensated) nanotool motions.     

Multipath channel identification by using global optimization in ambiguity function domain

A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.     

Detection of interdependent primary systems using wideband cognitive radios

Cognitive radios (CRs) may be sharing multiple frequency bands with primary systems if the CR is a wideband or an ultra wideband (UWB) system. In that case, the CR should ensure all the coexisting primary systems in these bands are detected before it can start data transmission. In this work, we study the primary system detection performance of a wideband CR assuming that there are multiple coexisting primary systems and that these primary systems may be jointly active. Accordingly, we consider the implementation of energy detection scheme in multiple bands followed by two detection methods: (i) a maximum-a-posteriori (MAP) based detection (i.e., joint detection) that takes into account the statistics of simultaneously operating systems in independent bands and (ii) a Neyman–Pearson (NP) test based detection that optimizes the threshold values independently in each band (i.e., independent detection). For a simpler implementation of the independent detection, we show that the threshold values obtained from joint detection can be used in order to achieve the optimum NP test based independent detection results. In addition to quantifying the gain of joint detection over independent detection in terms of probabilities of false alarm and detection for practical scenarios, we also present the operation capability of CRs in terms of the fractions of time the CR can access the channel without interfering with the primary systems. The results are important for the practical implementation of multiband detection when the primary systems are known to be interdependent.     

Effects of porosity and related interstitial phase morphology difference on the grindability of clinkers

The effects of regional clinker porosity and interstitial phase morphology differences on grindability of clinkers have been investigated within the scope of this study. Porosity and phase characteristics of two clinkers from different furnaces which are using the same raw materials were determined by the application of image processing techniques on SEM images of polished surfaces. Comparison of interstitial phase compositions of two clinkers showed that, clinkers with similar phase compositions may have different grindability due to the porosity and phase morphology differences. Porous clinkers usually exhibit a dendritic interstitial phase morphology with an improved grindability. Increasing porosity also leads to greater incidence of the dendritic morphology in porous regions and lower incidence of alkali in the aluminate in dendritic regions.     

From engineering diagrams to engineering models: Visual recognition and applications

We present a computational recognition approach to convert network-like, image-based engineering diagrams into engineering models with which computations of interests, such as CAD modeling, simulation, information retrieval and semantic-aware editing, are enabled. The proposed approach is designed to work on diagrams produced using computer-aided drawing tools or hand sketches, and does not rely on temporal information for recognition. Our approach leverages a Convolutional Neural Network (CNN) as a trainable engineering symbol recognizer. The CNN is capable of learning the visual features of the defined symbol categories from a few user-supplied prototypical diagrams and a set of synthetically generated training samples. When deployed, the trained CNN is applied either to the entire input diagram using a multi-scale sliding window or, where applicable, to each isolated pixel cluster obtained through Connected Component Analysis (CCA). Then the connectivity between the detected symbols are analyzed to obtain an attributed graph representing the engineering model conveyed by the diagram. We evaluate the performance of the approach with benchmark datasets and demonstrate its utility in different application scenarios, including the construction and simulation of control system or mechanical vibratory system models from hand-sketched or camera-captured images, content-based image retrieval for resonant circuits and sematic-aware image editing for floor plans.     

A Survey of Handover Techniques for Wireless ATM Networks

This paper provides a review and comprehensivecomparison of existing handover schemes proposed forwireless ATM networks. Existing schemes include thoseproposed by NEC, Olivetti, Bell Labs, Cambridge, Stanford, CMU, Berkeley, Michigan, VTT, andColumbia. We highlight the methodologies employed by thevarious schemes and reveal their differences. We discussissues related to the effect of ATM switch architectures on mobility implementation and on futurehandover paradigms.