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Automation Science and Engineering, IEEE Transactions on

Issue 3 • Date July 2014

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Displaying Results 1 - 25 of 35
  • Table of contents

    Page(s): C1 - B646
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    Freely Available from IEEE
  • IEEE Transactions on Automation Science and Engineering publication information

    Page(s): C2
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    Freely Available from IEEE
  • Guest Editorial Can Drones Deliver?

    Page(s): 647 - 648
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  • Rapidly Exploring Random Tree Algorithm-Based Path Planning for Robot-Aided Optical Manipulation of Biological Cells

    Page(s): 649 - 657
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1346 KB) |  | HTML iconHTML  

    In numerous cellular applications, cells are transported to specific positions or extracted from complex cell solutions. Therefore, an efficient cell transportation path planner for these applications is important for avoiding collisions with other cells or obstacles. In this paper, a path planning approach to transporting cells using a robot-aided optical manipulation system is presented. Optical tweezers functions as a special end-effector in transporting a target cell to the desired position along the generated path. The path planner is designed based on the rapidly exploring random trees (RRT) algorithm for calculating a collision-free path for cell transportation. Both static and dynamic path planners are developed. For the dynamic path planner, an online monitoring strategy is employed to dynamically avoid collisions with randomly appeared obstacles caused by environmental influence such as the Brownian movement of microparticles. Experiments of transporting yeast cells are performed to demonstrate the effectiveness of the proposed approach. Note to Practitioners - Manipulations of cells and other microparticles represent an essential process for most cell-based bioengineering applications, such as cytopathology, cell sociology, and cytotaxonomy. Cell transportation, which is treated as a typical cell manipulation task, has recently received considerable attention because of its wide applications. This paper presents a novel approach to applying RRT-based path planner to cell transportation with a robot-aided optical manipulation system. The research outcome provides a unique solution to achieving cell transportation automatically and efficiently. View full abstract»

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  • Estimating Part Pose Statistics With Application to Industrial Parts Feeding and Shape Design: New Metrics, Algorithms, Simulation Experiments and Datasets

    Page(s): 658 - 667
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2035 KB) |  | HTML iconHTML  

    Part feeders take an unsorted bulk of identical parts and output them in a uniform orientation. Common feeders sort out those items that settle initially on one specific face, and further reorient them as desired. Quick estimators of the probability of settling on a given face facilitate the design of parts for efficient feeding and of the feeding lines themselves. Nevertheless, the evaluation and the development of such estimators have been hindered by the lack of data. Here, I create and analyze a large, simulated dataset; evaluate estimators available in the literature by comparing their predictions to simulation results with the help of a custom-made metric; and propose new estimation algorithms. The new estimators offer viable alternative to the direct dynamic simulation of parts due to their low average errors. View full abstract»

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  • An Assembly Automation Approach to Alignment of Noncircular Projections in Electron Microscopy

    Page(s): 668 - 679
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2420 KB) |  | HTML iconHTML  

    In single-particle electron microscopy (EM), multiple micrographs of identical macromolecular structures or complexes are taken from various viewing angles to obtain a 3D reconstruction. A high-quality EM reconstruction typically requires several thousand to several million images. Therefore, an automated pipeline for performing computations on many images becomes indispensable. In this paper, we propose a modified cross-correlation method to align a large number of images from the same class in single-particle electron microscopy of highly nonspherical structures, and show how this method fits into a larger automated pipeline for the discovery of 3D structures. Our modification uses a probability density in full planar position and orientation, akin to the pose densities used in Simultaneous Localization and Mapping (SLAM) and Assembly Automation. Using this alignment and a subsequent averaging process, high signal-to-noise ratio (SNR) images representing each class of viewing angles are obtained for reconstruction algorithms. In the proposed method, first we coarsely align projection images, and then realign the resulting images using the cross correlation (CC) method. The coarse alignment is obtained by matching the centers of mass and the principal axes of the images. The distribution of misalignment in this coarse alignment is estimated using the statistical properties of the additive background noise. As a consequence, the search space for realignment in the CC method is reduced. Additionally, in order to overcome the false peak problems in the CC, we use artificially blurred images for the early stage of the iteration and segment the intermediate result from every iteration step. The proposed approach is demonstrated on synthetic noisy images of GroEL/ES. View full abstract»

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  • Dynamic Surgery Assignment of Multiple Operating Rooms With Planned Surgeon Arrival Times

    Page(s): 680 - 691
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1925 KB) |  | HTML iconHTML  

    This paper addresses the dynamic assignment of a given set of surgeries to multiple identical operating rooms (ORs). Surgeries have random durations and planned surgeon arrival times. Surgeries are assigned dynamically to ORs at surgery completion events. The goal is to minimize the total expected cost incurred by surgeon waiting, OR idling, and OR overtime. We first formulate the problem as a multistage stochastic programming model. An efficient algorithm is then proposed by combining a two-stage stochastic programming approximation and some look-ahead strategies. A perfect information-based lower bound of the optimal expected cost is given to evaluate the optimality gap of the dynamic assignment strategy. Numerical results show that the dynamic scheduling and optimization with the proposed approach significantly improve the performance of static scheduling and First Come First Serve (FCFS) strategy. View full abstract»

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  • A Multiobjective Hybrid Genetic Algorithm for TFT-LCD Module Assembly Scheduling

    Page(s): 692 - 705
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2017 KB) |  | HTML iconHTML  

    The thin-film transistor-liquid crystal display (TFT-LCD) module assembly production is a flexible job-shop scheduling problem that is critical to satisfy the customer demands on time. On the module assembly shop floor, each workstation has identical and non-identical parallel machines that access the jobs at various processing velocities depending on the product families. To satisfy the various jobs, the machines need to be set up as the numerous tools to conduct consecutive products. This study aims to propose a novel approach to address the TFT-LCD module assembly scheduling problem by simultaneously considering the following multiple and often conflicting objectives such as the makespan, the weighted number of tardy jobs, and the total machine setup time, subject to the constraints of product families, non-identical parallel machines, and sequence-dependent setup times. In particular, we developed a multiobjective hybrid genetic algorithm (MO-HGA) that hybridizes with the variable neighborhood descent (VND) algorithm as a local search and TOPSIS evaluation technique to derive the best compromised solution. To estimate the validity of the proposed MO-HGA, experiments based on empirical data were conducted to compare the results with conventional approaches. The results have shown the validity of this approach. This study concludes with a discussion of future research directions. View full abstract»

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  • Integral Reinforcement Learning for Linear Continuous-Time Zero-Sum Games With Completely Unknown Dynamics

    Page(s): 706 - 714
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1753 KB) |  | HTML iconHTML  

    In this paper, we develop an integral reinforcement learning algorithm based on policy iteration to learn online the Nash equilibrium solution for a two-player zero-sum differential game with completely unknown linear continuous-time dynamics. This algorithm is a fully model-free method solving the game algebraic Riccati equation forward in time. The developed algorithm updates value function, control and disturbance policies simultaneously. The convergence of the algorithm is demonstrated to be equivalent to Newton's method. To implement this algorithm, one critic network and two action networks are used to approximate the game value function, control and disturbance policies, respectively, and the least squares method is used to estimate the unknown parameters. The effectiveness of the developed scheme is demonstrated in the simulation by designing an H state feedback controller for a power system. View full abstract»

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  • Energy-Responsive Aggregate Context for Energy Saving in a Multi-Resident Environment

    Page(s): 715 - 729
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2420 KB) |  | HTML iconHTML  

    Human activity is among the critical information for a context-aware energy saving system since knowing what activities are undertaken is important for judging if energy is well spent. Most of the prior works on energy saving do not make the best of context-awareness especially in a multiuser environment to assist the energy saving system. In addition, they often ignore whether appliances are operating implicitly or explicitly related to the context. These factors may compromise the practicality and acceptability of most of the currently available energy saving systems, thus failing to meet real user needs. Therefore, we propose Energy-Responsive Aggregate Context (ERAC) to model multi-resident activities and their associated energy consumption. Based on the relationship, implicit or explicit, between a given appliance and its associated context, an energy saving system and its users can better determine whether the power consumed by the appliance is wasted. Our experimental results demonstrate the effectiveness of the proposed approach. View full abstract»

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  • Real-Time Planner in the Operational Space for the Automatic Handling of Kinematic Constraints

    Page(s): 730 - 739
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2123 KB) |  | HTML iconHTML  

    Planning problems in the operational space are characterized by implementation issues that do not occur in the joint space. For example, depending on the manipulator pose, relatively slow trajectories in the operational space could require unfeasible joint speeds, thus causing the degeneration of the system performances: Path tracking errors certainly increase but, in the worst situations, the manipulator must be stopped in order to prevent the system instability. This paper proposes a real-time planner in the operational space that is able to generate trajectories subject to dynamic constraints and devised according to the path-velocity decomposition approach. The feasibility is achieved by means of an automatic scaling system that, starting from a possibly unfeasible trajectory, modifies its longitudinal velocity in order to fulfill a given set of kinematic constraints, thus preserving an accurate path tracking. The scaling system promptly reacts to critical configurations through minimum-time transients. The proposed approach has been tested on an actual anthropomorphic manipulator by executing 6D trajectories. Note to Practitioners - The accurate path tracking must be guaranteed especially when trajectories are planned in the operational space. Unfortunately, path tracking worsens every time system limits are exceeded. The trajectory generator proposed in this paper is specifically designed for non-redundant manipulators and it is equipped with a scaling system that automatically modifies the speed of the end effector in order to guarantee an accurate path tracking. Several kinematic constraints are handled at the same time. Joint velocities are kept below the manufacturer's limits, while joint accelerations are bounded in order to achieve smooth movements. The system is also able to constrain the kinematics of the end effector. For example, in order to reduce the mechanical stress on the payload and to avoid the excitation of elastic modes, additional bounds - n the velocities and accelerations of the end effector are considered and managed. The planner can also be used to generate minimum-time constrained trajectories in real-time. To this purpose, further constraints on the longitudinal velocities and accelerations have been introduced. Differently from alternative approaches, the proposed planning scheme does not require any interaction with the controller. This is an advantage, since controllers of industrial manipulators are typically not accessible or modifiable, while, in turn, proprietary trajectory planners can normally be replaced with ad-hoc implementations. The scaling system can be easily expanded in order to handle additional constraints. The trajectory smoothness, for example, can be improved by managing the jerk bounds, so that the ongoing research activity is currently focused on that target. In the same way, it could also be possible to handle some dynamic constraints, but this would impose the introduction of mutual interactions between the scaling system and the central control unit. View full abstract»

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  • Guaranteed Cost Control for Uncertain Networked Control Systems With Predictive Scheme

    Page(s): 740 - 748
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2168 KB) |  | HTML iconHTML  

    The problem of guaranteed cost control for a class of networked control systems possessing uncertainties, network delays, and packet dropouts is solved in this paper. By means of introducing an auxiliary variable, a newly coupled, switched system model is derived first. Then, based on a predictive network control scheme, the conditions for guaranteed control performance of the overall system in terms of linear matrix inequalities are given. Next, a novel control design method, involving convex optimization technique to find solutions for the controllers that vary according to network delays and data-dropouts, is developed. It is shown from theory that the obtained criteria are much less conservative than existing ones. Finally, two illustrative examples, the second one being a laboratory-scale rig, are elaborated on to demonstrate the effectiveness of the proposed design method. Both numerical and simulation results appear favorable to this novel network control system synthesis. View full abstract»

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  • An Adaptable Robot Vision System Performing Manipulation Actions With Flexible Objects

    Page(s): 749 - 765
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3289 KB) |  | HTML iconHTML  

    This paper describes an adaptable system which is able to perform manipulation operations (such as Peg-in-Hole or Laying-Down actions) with flexible objects. As such objects easily change their shape significantly during the execution of an action, traditional strategies, e.g, for solve path-planning problems, are often not applicable. It is therefore required to integrate visual tracking and shape reconstruction with a physical modeling of the materials and their deformations as well as action learning techniques. All these different submodules have been integrated into a demonstration platform, operating in real-time. Simulations have been used to bootstrap the learning of optimal actions, which are subsequently improved through real-world executions. To achieve reproducible results, we demonstrate this for casted silicone test objects of regular shape. Note to Practitioners - The aim of this work was to facilitate the setup of robot-based automation of delicate handling of flexible objects consisting of a uniform material. As examples, we have considered how to optimally maneuver flexible objects through a hole without colliding and how to place flexible objects on a flat surface with minimal introduction of internal stresses in the object. Given the material properties of the object, we have demonstrated in these two applications how the system can be programmed with minimal requirements of human intervention. Rather than being an integrated system with the drawbacks in terms of lacking flexibility, our system should be viewed as a library of new technologies that have been proven to work in close to industrial conditions. As a rather basic, but necessary part, we provide a technology for determining the shape of the object when passing on, e.g., a conveyor belt prior to being handled. The main technologies applicable for the manipulated objects are: A method for real-time tracking of the flexible objects during manipulation, a method for model-based offline pr- diction of the static deformation of grasped, flexible objects and, finally, a method for optimizing specific tasks based on both simulated and real-world executions. View full abstract»

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  • Spatial Density Patterns for Efficient Change Detection in 3D Environment for Autonomous Surveillance Robots

    Page(s): 766 - 774
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1635 KB) |  | HTML iconHTML  

    The ability to detect changes is an essential competence that robots should possess for increased autonomy. In several applications, such as surveillance, a robot needs to detect relevant changes in the environment by comparing current sensory data with previously acquired information from the environment. We present an efficient method for point cloud comparison and change detection in 3D environments based on spatial density patterns. Our method automatically segments 3D data corrupted by noise and outliers into an implicit volume bounded by a surface, making it possible to efficiently apply Boolean operations in order to detect changes and to update existing maps. The method has been validated on several trials using mobile robots operating in real environments and its performance was compared to state-of-the-art algorithms. Our results demonstrate the performance of the proposed method, both in greater accuracy and reduced computational cost. View full abstract»

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  • A Robust Surface Coding Method for Optically Challenging Objects Using Structured Light

    Page(s): 775 - 788
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3581 KB) |  | HTML iconHTML  

    Though the structured light measurement system has been successfully applied to the profile measurement of diffuse objects, it is still a challenge to measure shiny objects due to the mix of both specular and diffuse reflections. To this end, we propose a robust encoding and decoding method in this paper. First, the monochromatic stripe patterns are utilized to eliminate the effect of texture and color of objects. Second, an intensity mask, dynamically adjusting the intensity of a projected pattern, is applied to avoid overexposure without any pre-knowledge of the workpiece. Thus, it is more flexible and efficient, compared with the existing methods. Third, to solve the internal reflection of the shiny part, an extrapolation model, combined with the intensity mask, is developed to detect the stripe edge for pattern decoding, resulting in accurate and robust 3D reconstruction. Compared with traditional polarization based methods, it does not need to readjust for a new part. The experimental results show that the proposed method is capable of measuring various parts without surface pretreatment. View full abstract»

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  • Energy Efficiency Management of an Integrated Serial Production Line and HVAC System

    Page(s): 789 - 797
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1910 KB) |  | HTML iconHTML  

    Modern manufacturing facilities waste many energy savings opportunities (ESO) due to the lack of integration between the facility and the production system. To explore the energy savings opportunities, this paper combines the two largest energy consumers in a manufacturing plant: the production line and the heating, ventilation, and air conditioning (HVAC) system. The concept of the energy opportunity window (OW) is utilized, which allows each machine to be turned off at set periods of time without any throughput loss. The recovery time of each machine is the minimum amount of time a machine must be operational between opportunity windows to guarantee zero production loss and it is explored both analytically and numerically. The opportunity window for the production line is synced with the peak periods of energy demand for the HVAC system to create a heuristic rule to optimize the energy cost savings. This integrated system is modeled and tested using simulation studies. View full abstract»

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  • Energy Efficient Use of Multirobot Production Lines in the Automotive Industry: Detailed System Modeling and Optimization

    Page(s): 798 - 809
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2578 KB) |  | HTML iconHTML  

    This paper quantitatively reports about potential energy savings on robotic assembly lines for the automotive industry. At first, a detailed system model is described, which improves previously published results by explicitly considering both manipulator and electrical drive dynamics. The model closely captures experimental data in terms of actuation torques and servodrive voltages, which are directly used to derive the plant input power. Two practical methods are then evaluated for reducing the overall energy consumption. The methods rely on: 1) implementation of energy-optimal trajectories obtained by means of time scaling, concerning the robots' motion from the last process point to the home positions and 2) reduction of energy consumption by releasing the actuator brakes earlier when the robots are kept stationary. Simulation results, based on the production timing characteristics measured at a real plant, clearly shows that the system energy consumption can be effectively reduced without negative effects on the production rate. Note to Practitioners - The global industry trend towards sustainability demands energy optimization as a primary goal. Currently, industrial multirobot systems are not efficiently programmed as long as effective simulation tools are mostly lacking. In this context, a detailed model of the production line is proposed, which can be readily integrated into commercial tools for robot programming. Furthermore, the impact of practical optimization methods which are readily applicable to existing equipment is quantitatively highlighted. The key aspect of the proposed approach is that both the production rate of each cell and the robot hardware limitations are considered as strict constraints, so that no significant changes to the plant are required. View full abstract»

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  • Reaching Law Approach to the Sliding Mode Control of Periodic Review Inventory Systems

    Page(s): 810 - 817
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1617 KB) |  | HTML iconHTML  

    In this paper, a discrete-time sliding mode inventory management strategy based on a novel non-switching type reaching law is introduced. The proposed reaching law eliminates undesirable chattering, and ensures that the sliding variable rate of change is upper bounded by a design parameter which does not depend on the system initial conditions. This approach guarantees fast convergence with non-negative, upper limited supply orders, and ensures that the maximum stock level may be specified a priori by the system designer. Furthermore, a sufficient condition for 100% customers' demand satisfaction is derived. The inventory replenishment system considered in this paper involves multiple suppliers with different lead times and different transportation losses in the delivery channels. View full abstract»

    Open Access
  • Navigation of Magnetic Microrobots With Different User Interaction Levels

    Page(s): 818 - 827
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    Micro-technologies based on wirelessly powered and manoeuvred submillimeter device, i.e.,microrobots, are attracting growing attention. Their application in lab-on-a-chip systems, such as micromanipulation and in vitro cell sorting, is expected to steeply increase. However, the actuation, powering and control of microrobots are challenges that still need concrete solutions. Magnetic fields generally enable wireless navigation of microrobots, but proper control architectures and magnetic navigation systems are needed, depending on the specific task and on the level of interaction required to the user. Here we present a magnetic navigation platform intended for lab-on-a-chip applications and we address its usability with different levels of human involvement by using two control architectures: teleoperated and autonomous. We perform an experimental analysis to demonstrate that both architectures, enrolling different levels of interaction by the user, lead to reliable execution of the microrobotic task. First, we validate the open-loop response of the microrobotic system, and second, we evaluate the performance of the system by testing both control architectures with a standard mobility task. The results show that users can teleoperate the microrobot with 100% success rate, in 14.4±1.9s with a normalized spatial mean error of 0.60±0.13. Moreover, results show a fast decaying learning curve for the users involved in the study. Compared to this, when the navigation task is performed by the autonomous control, 100% success rate, a time of 8.0±0.5s and a normalized spatial mean error of 0.50±0.05 are obtained. Finally, we quantitatively demonstrate how both control methodologies enable very smooth movements of the microrobot, suggesting application for any task where repeatable and dexterous movements in liquid microenvironments are key requirements. View full abstract»

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  • Analysis of Multiproduct Manufacturing Systems With Homogeneous Exponential Machines

    Page(s): 828 - 838
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1922 KB) |  | HTML iconHTML  

    This paper is devoted to modeling and analysis of multiproduct manufacturing systems with homogeneous exponential machines and finite buffers. In such systems, each machine processes multiple product types with different speeds, but the processing time for each product type is the same on all machines. Buffers are finite, shared for all products. Analytical methods to evaluate the system performance are developed, and system-theoretic properties are investigated. It is shown from numerical experiments that such a method has a high accuracy in performance evaluation. To improve the performance of such systems, bottleneck analysis is carried out to identify the machine and/or product whose improvement will lead to the largest improvement in system throughput. View full abstract»

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  • Neural-Network-Based Constrained Optimal Control Scheme for Discrete-Time Switched Nonlinear System Using Dual Heuristic Programming

    Page(s): 839 - 849
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3877 KB) |  | HTML iconHTML  

    In this paper, a novel iterative two-stage dual heuristic programming (DHP) is proposed to solve the optimal control problems for a class of discrete-time switched nonlinear systems subject to actuators saturation. First, a novel nonquadratic performance functional is introduced to confront control constraints of the saturating actuator. Then, the iterative two-stage DHP algorithm is developed to solve the Hamilton-Jacobi-Bellman (HJB) equation of the switched system with the saturating actuator. Moreover, the convergence and optimality of the two-stage DHP algorithm are strictly proven. To implement this algorithm efficiently, there are two neural networks used as parametric structure to approximate the costate function and the corresponding control law, respectively. Finally, simulation results are given to verify the effectiveness of the proposed algorithm. View full abstract»

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  • Multifurnace Optimization in Electric Smelting Plants by Load Scheduling and Control

    Page(s): 850 - 862
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2179 KB) |  | HTML iconHTML  

    For large electricity users, such as smelting plants, their electric loads cannot exceed a concerted limit in production. Traditional single-furnace optimization methods aim to satisfy the electric demand of a furnace to improve its production, and hence cannot consider the maximum demand constraint in a smelting plant. Maximum demand (MD) control is often utilized to keep the total electric demand within the limit via shedding the electric loads of some furnaces once the demand approaches the limit. However, the control method will enlarge the fluctuation of electric loads, which does harm to the production and causes a decline in energy-efficiency. In this paper, we propose a multifurnace optimization strategy to improve the production targets of a whole plant instead of a single furnace. In the strategy, an offline multiobjective load scheduling is first performed to assign electric loads for furnaces in each sampling period, taking into account of the MD constraint and production constraints. A multiobjective particle swarm optimization algorithm, combined with population initialization and constraint-handing strategies, is proposed to search for the Pareto optimal set of the scheduling problem, from which decision-makers can select one solution as the load scheduling program. A double closed-loop control mechanism is used to change the scheduled load into detailed load setpoints of furnaces and keep the actual loads up with the load setpoints. In the outer loop, the detailed load setpoints of furnaces are dynamically adjusted based on the deviation of actual loads from the scheduled loads. Thereafter, the desired setpoints are sent to the automatic control mechanism of each furnace, which is in the inner loop and responsible to keep the actual load up with the setpoint via a proportional-integral-derivative (PID) controller. The case study on a typical magnesia-smelting plant shows that the proposed multifurnace optimization strategy can achieve an increase of- about 12.29% in the production output, an improvement of about 0.46% of the magnesia in the product, and a slight reduction of 2.35% in electricity cost over the results of MD control. Note to Practitioners - For large electricity users, such as smelting plants, they are subjected to the maximum electric demand constraint. The maximum demand control device is widely adopted to solve the problem, but it will cause a decline in production output and energy-efficiency. This paper was motivated by improving the multiple production targets (i.e, the total production output, the product quality, and the total electricity cost) of a plant via load scheduling and control. In contrast to the maximum demand control, the load scheduling and control approach is a beforehand strategy that can optimize the operation. A case study on a magnesia-smelting plant shows that the proposed approach performs better than the maximum demand control technique. View full abstract»

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  • Dynamic Capacity Planning and Location of Hierarchical Service Networks Under Service Level Constraints

    Page(s): 863 - 880
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3599 KB) |  | HTML iconHTML  

    This paper addresses the problem of joint facility location and capacity planning of hierarchical service networks in order to determine when and where to open/close service units, their capacity and the demand-to-facility allocation. We propose a new hierarchical service network model in which both the facilities and customers have nested hierarchies, i.e., a higher level facility provides all services provided by a lower level facility and a customer requiring a certain level of service will additionally require lower level services. Poisson customer arrivals and random service times are assumed. Each service unit is modeled as an Erlang-loss system and its service level, defined as its customer acceptance probability, is given by the so-called Erlang-loss function. A nonlinear programming model is proposed to minimize the total cost, while keeping the service level of all service units above some given level. Different linearization models of the Erlang-loss function and their properties are proposed. Linearization transforms the nonlinear model into compact mixed integer programs solvable to optimality with standard solvers. Application to a real-life perinatal network is then presented. View full abstract»

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  • Multitarget Hierarchical Negotiation for Distributed Design in Collaborative Product Development

    Page(s): 881 - 890
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2076 KB) |  | HTML iconHTML  

    Distributed design in collaborative product development involves interorganizational teams of different disciplines, often geographically separated, that work jointly for the interest of an overall goal. The teams must agree on the product design without fully disclosing individual design information. Price-based negotiation is an effective design methodology under such circumstances. This work improves the negotiation mechanism based on the price schedules decomposition algorithm (PSDA), which only allows bi-level negotiation with a single design target, in two regards. The augmented price schedules decomposition algorithm (APSDA) is proposed to simultaneously consider multiple design targets in negotiation. Decision models and negotiation protocols are developed for complex design tasks decomposed in a hierarchy. The resultant APSDA-based negotiation mechanism enables multi-target hierarchical distributed design. A test scenario implemented with multiagent technologies validates the effectiveness of this mechanism. This study solves automating engineering collaborations with secure information sharing. View full abstract»

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  • Top- {\rm k} Automatic Service Composition: A Parallel Method for Large-Scale Service Sets

    Page(s): 891 - 905
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1823 KB) |  | HTML iconHTML  

    Quality-of-Service (QoS)-aware web service composition is of great importance to assemble individual services into a composite one meeting functional and nonfunctional requirements. Given a large number of candidate services, automatic composition is essential so as to derive a composite service efficiently. Most existing methods return one solution that is optimal in some given criteria. This is somewhat rigid in terms of flexibility. In case some component service in the optimal composition becomes unavailable, the composition algorithm has to run again to find another optimal solution. Also, in a lot of circumstances users prefer multiple alternatives over a single one. Therefore, providing top- k service compositions according to their QoS is becoming more desirable. On another aspect, from the perspective of computation efficiency, due to the explosion of the searching space, single-threaded methods are usually not capable of handling a large number of candidate services. This paper tackles these two issues together, i.e., large-scale, QoS-based services composition yielding top- k solutions. The composition algorithm is based on the combination of backtrack search and depth-first search, which can be executed in a parallel way. Experiments are carried out based on the datasets provided by the WS-Challenge competition 2009 and China Web Service 2011. The results show that our approach can not only find the same optimal solution as the winning systems from these competitions, but also provide alternative solutions together with the optimal QoS. View full abstract»

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Aims & Scope

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