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Automation Science and Engineering, IEEE Transactions on

Issue 4 • Date Oct. 2012

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Displaying Results 1 - 22 of 22
  • Table of Contents

    Page(s): C1
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  • IEEE Transactions on Automation Science and Engineering publication information

    Page(s): C2
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  • Editorial [intro. to Guest Editorial by Prof. Raff D'Andrea]

    Page(s): 637
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  • Guest Editorial: A Revolution in the Warehouse: A Retrospective on Kiva Systems and the Grand Challenges Ahead

    Page(s): 638 - 639
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  • A Hybrid Model of Complex Automated Warehouse Systems—Part I: Modeling and Simulation

    Page(s): 640 - 653
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2502 KB) |  | HTML iconHTML  

    An automated warehouse system has two main components: an automated storage and retrieval subsystem consisting of a number of aisles, each one served by a crane, and a picking area which is formed by bays where stock units coming from the aisles are partially emptied by human operators. These two components are connected via an interface area consisting of carousels, conveyors, and buffers. This area is usually modeled as a discrete event system, while the overall system performance depends also on continuous time phenomena. Part I presents a hybrid model based on a new Petri net formalism that merges the concepts of Hybrid Petri Nets and Colored Petri Nets to obtain modular and compact models for these systems. An example is discussed in detail to motivate the introduction of a new formalism. A control oriented simulation tool is also presented. Part II will focus on the application of this formalism to automated warehouse systems analysis and performance evaluation. Finally, a real case study is considered to show the effectiveness of the approach. View full abstract»

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  • A Hybrid Model of Complex Automated Warehouse Systems—Part II: Analysis and Experimental Results

    Page(s): 654 - 668
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2877 KB) |  | HTML iconHTML  

    An automated warehouse system has two main components: an automated storage and retrieval subsystem consisting of a number of aisles, each one served by a crane, and a picking area which is formed by bays where stock units coming from the aisles are partially emptied by human operators. These two components are connected via an interface area consisting of carousels, conveyors and buffers. This area is usually modeled as a discrete event system, while the overall system performance depends also on continuous time phenomena. In Part I, a hybrid modeling approach based on a new Petri net formalism and a freeware simulation tool have been presented. The concepts of Hybrid Petri Nets and Colored Petri Nets are merged to obtain modular and compact models for automated warehouse systems. Part II now focuses on the application of this formalism to automated warehouse systems analysis and performance evaluation. Liveness analysis is performed by means of a hybrid automaton obtained from the net model. A deadlock prevention policy is synthesized working on an aggregated model. Finally, a real case study is considered to show the effectiveness of the approach. View full abstract»

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  • Real-Time Path Planning for Coordinated Transport of Multiple Particles Using Optical Tweezers

    Page(s): 669 - 678
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1496 KB) |  | HTML iconHTML  

    Automated transport of multiple particles using optical tweezers requires real-time path planning to move them in coordination by avoiding collisions among themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized path planning approach by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. We use an iterative version of a maximum bipartite graph matching algorithm to assign given goal locations to such particles. We then employ a three-step method consisting of clustering, classification, and branch and bound optimization to determine the final collision-free paths. We demonstrate the effectiveness of the developed approach via experiments using silica beads in a holographic tweezers setup. We also discuss the applicability of our approach and challenges in manipulating biological cells indirectly by using the transported particles as grippers. View full abstract»

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  • Nanorobotic Assembly and Focused Ion Beam Processing of Nanotube-Enhanced AFM Probes

    Page(s): 679 - 686
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1415 KB) |  | HTML iconHTML  

    In this paper, a focused ion beam processing technique is presented that facilitates the modification of carbon nanotubes (CNTs) in terms of length, diameter, and orientation. The CNTs are mounted onto an atomic force microscope (AFM) probe by using a nanorobotic microgripper-based pick-and-place handling strategy. Such CNT-enhanced AFM probes are needed for metrology measurements of nanostructures with critical dimensions and high aspect ratios. The complete process of assembly and processing is realized inside a nanorobotic dual beam scanning electron microscope (SEM) and focused ion beam (FIB) machine. View full abstract»

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  • Modeling and Optimization of Building Emergency Evacuation Considering Blocking Effects on Crowd Movement

    Page(s): 687 - 700
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    In building emergency evacuation, the perception of hazards can stress crowds, evoke their competitive behaviors, and trigger disorder and blocking as they pass through narrow passages (e.g., a small exit). This is a serious concern threatening evacuees' survivability and egress efficiency. How to optimize crowd guidance while considering such effects is an important problem. Based on advanced microscopic pedestrian models and simulations, this paper establishes a new macroscopic network-flow model where fire, smoke, and psychological factors can evoke a crowd's desire to escape—the desired flow rate. Disorder and blocking occur when the desired flow rate exceeds the passage capacity, resulting in a drastic decrease of crowd movement in a nonlinear and random fashion. To effectively guide crowds, a divide-and-conquer approach is developed based on groups to reduce computational complexity and to reflect psychological findings. Egress routes for individual groups are optimized by using a novel combination of stochastic dynamic programming and the rollout scheme. These routes are then coordinated so that limited passage capacities are shared to meet the total need for joint movement. Numerical testing and simulation demonstrate that, compared with a strategy of merely using nearest exits, our solution can evacuate more people more rapidly by preventing or mitigating potential disorder and blocking at bottleneck passages. View full abstract»

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  • An Efficient Outpatient Scheduling Approach

    Page(s): 701 - 709
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    Outpatient scheduling is considered as a complex problem. Efficient solutions to this problem are required by many health care facilities. This paper proposes an efficient approach to outpatient scheduling by specifying a bidding method and converting it to a group role assignment problem. The proposed approach is validated by conducting simulations and experiments with randomly generated patient requests for available time slots. The major contribution of this paper is an efficient outpatient scheduling approach making automatic outpatient scheduling practical. The exciting result is due to the consideration of outpatient scheduling as a collaborative activity and the creation of a qualification matrix in order to apply the group role assignment algorithm. View full abstract»

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  • Iterative Deepening A* Algorithms for the Container Relocation Problem

    Page(s): 710 - 722
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1887 KB) |  | HTML iconHTML  

    The container relocation problem, where containers that are stored in bays are retrieved in a fixed sequence, is a crucial port operation. Existing approaches using branch and bound algorithms are only able to optimally solve small cases in a practical time frame. In this paper, we investigate iterative deepening A* algorithms (rather than branch and bound) using new lower bound measures and heuristics, and show that this approach is able to solve much larger instances of the problem in a time frame that is suitable for practical application. We also examine a more difficult variant of the problem that has been largely ignored in existing literature. View full abstract»

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  • Automated Detection of Influential Patents Using Singular Values

    Page(s): 723 - 733
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2115 KB) |  | HTML iconHTML  

    Centrality measures such as degree centrality have been utilized to identify influential and important patents in a citation network. However, no existing centrality measures take into consideration information from the change of the similarity matrix. This paper presents a new centrality measure based on the change of a node similarity matrix. The proposed approach gives more intuitive understanding of the finding of the influential nodes. The present study starts off with the assumption that the change of matrix that may result from removing a given node would assess the importance of the node since each node make a contribution to the given similarity matrix between nodes. The various matrix norms using the singular values such as nuclear norm which is the sum of all singular values, are used for calculating the contribution of a given node to a node similarity matrix. In other words, we can obtain the change of matrix norms for a given node after we calculate the singular values for the case of the nonexistence and the case of existence of the node. Then, the node resulting in the largest change (i.e., decrease) of matrix norms can be considered as the most important node. Computation of singular values can be computationally intensive when the similarity matrix size is large. Therefore, the singular value update technique is also developed for the case of the network with large nodes. We compare the performance of our proposed approach with other widely used centrality measures using U.S. patents data in the area of information and security. Experimental results show that our proposed approach is competitive or even performs better compared to existing approaches. View full abstract»

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  • Vision-Based Tactile Sensing and Shape Estimation Using a Fluid-Type Touchpad

    Page(s): 734 - 744
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4367 KB) |  | HTML iconHTML  

    In this paper, we propose a new method to estimate the shape and irregularity of objects by a vision-based tactile sensor, which consists of a CCD camera, LED lights, transparent acrylic plate, and a touchpad which consists of an elastic membrane filled with translucent red water. Intensities of red, green and blue bands of the traveling light in the touchpad are analyzed in this study to estimate the shape/irregularity of the object. The LED light traveling in the touchpad is scattered and absorbed by the red pigment in the fluid. The depth of the touchpad is estimated by using the intensity of the light obtained from the red-green-blue (RGB) values of the image, in consideration of the scattering and reflection effects. The reflection coefficient that depends on the shape of the membrane, was decoupled in the proposed formulation. The intensity of the traveling light is represented with the geometrical parameters of the touchpad surface. In order to reduce the approximation error caused by unmodeled factors, we compensate the error by using a function of the deformation of the membrane. The validation of the proposed method is confirmed through experimental results. View full abstract»

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  • Modeling the Interactions Among Neighboring Nanostructures for Local Feature Characterization and Defect Detection

    Page(s): 745 - 754
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2876 KB) |  | HTML iconHTML  

    Since properties of nanomaterials are determined by their structures, characterizing nanostructure feature variability and diagnosing structure defects are of great importance for quality control in scale-up nanomanufacturing. It is known that nanostructure interactions such as competing for source materials during growth contribute strongly to nanostructure uniformity and defect formation. However, there is a lack of rigorous formulation to describe nanostructure interactions and their effects on nanostructure variability. In this work, we develop a method to relate local nanostructure variability (quality measure) to nanostructure interactions under the framework of Gaussian Markov random field. With the developed modeling and estimation approaches, we are able to extract nanostructure interactions for any local region with or without defects based on its feature measurement. The established connection between nanostructure variability and interactions not only provides a metric for assessing nanostructure quality, but also enables a method to automatically detect defects and identify their patterns based on the underlying interaction patterns. Both simulation and real case studies are conducted to demonstrate the developed methods. The insights obtained from real case study agree with physical understanding. View full abstract»

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  • Drift Compensation in AFM-Based Nanomanipulation by Strategic Local Scan

    Page(s): 755 - 762
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1131 KB) |  | HTML iconHTML  

    The drift distorts the atomic force microscopy (AFM) images as the time taken to acquire a complete AFM image is relatively long (a few minutes). As the AFM image is used as a reference for most manipulation mechanisms, the image distorted by drift will cause problems for AFM-based manipulation because the displayed positions of the objects under nanomanipulation do not match their actual locations. The drift during manipulation, similarly, will further exacerbate the mismatch between the displayed positions and the actual locations. Such mismatch is a major hurdle to achieve automation in AFM-based nanomanipulation. Without proper compensation, manipulation based on a wrong displayed location of the object often fails. In this paper, we present an algorithm to identify and eliminate the drift-induced distortion in the AFM image by applying a strategic local scan method. Briefly, after an AFM image is captured, the entire image is divided into several parts along vertical direction. A quick local scan is performed in each part of the image to measure the drift value in that very part. In this manner, the drift value is calculated in a small local area instead of the global image. Thus, the drift can be more precisely estimated and the actual position of the objects can be more accurately identified. In this paper, we also present the strategy to constantly compensate the drift during manipulation. By applying local scan on a single fixed feature in the AFM image frequently, the most current positions of all objects can be displayed in the augmented reality for real-time visual feedback. View full abstract»

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  • Constructing Multiple Kernel Learning Framework for Blast Furnace Automation

    Page(s): 763 - 777
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2428 KB) |  | HTML iconHTML  

    This paper constructs the framework of the reproducing kernel Hilbert space for multiple kernel learning, which provides clear insights into the reason that multiple kernel support vector machines (SVM) outperform single kernel SVM. These results can serve as a fundamental guide to account for the superiority of multiple kernel to single kernel learning. Subsequently, the constructed multiple kernel learning algorithms are applied to model a nonlinear blast furnace system only based on its input-output signals. The experimental results not only confirm the superiority of multiple kernel learning algorithms, but also indicate that multiple kernel SVM is a kind of highly competitive data-driven modeling method for the blast furnace system and can provide reliable indication for blast furnace operators to take control actions. View full abstract»

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  • List of Reviewers for 2011/2012

    Page(s): 778 - 781
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  • IEEE Xplore Digital Library [advertisement]

    Page(s): 782
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  • IEEE Copyright Form

    Page(s): 783 - 784
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  • 2012 Index IEEE Transactions on Automation Science and Engineering Vol. 9

    Page(s): 785 - 797
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  • IEEE Robotics and Automation Society Information

    Page(s): C3
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  • IEEE Transactions on Automation Science and Engineering information for authors

    Page(s): C4
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T-ASE will publish foundational research on Automation: scientific methods and technologies that improve efficiency, productivity, quality, and reliability, specifically for methods, machines, and systems operating in structured environments over long periods, and the explicit structuring of environments.

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Meet Our Editors

Editor-in-Chief
Ken Goldberg
University of California, Berkeley