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Automation Science and Engineering, IEEE Transactions on

Popular Articles (February 2015)

Includes the top 50 most frequently downloaded documents for this publication according to the most recent monthly usage statistics.
  • 1. A Survey of Research on Cloud Robotics and Automation

    Publication Year: 2015 , Page(s): 1 - 12
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1543 KB)  

    The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/ View full abstract»

    Open Access
  • 2. A Sensor-Based Dual-Arm Tele-Robotic System

    Publication Year: 2015 , Page(s): 4 - 18
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2882 KB) |  | HTML iconHTML  

    We present a novel system to achieve coordinated task-based control on a dual-arm industrial robot for the general tasks of visual servoing and bimanual hybrid motion/force control. The industrial robot, consisting of a rotating torso and two seven degree-of-freedom arms, performs autonomous vision-based target alignment of both arms with the aid of fiducial markers, two-handed grasping and force control, and robust object manipulation in a tele-robotic framework. The operator uses hand motions to command the desired position for the object via Microsoft Kinect while the autonomous force controller maintains a stable grasp. Gestures detected by the Kinect are also used to dictate different operation modes. We demonstrate the effectiveness of our approach using a variety of common objects with different sizes, shapes, weights, and surface compliances. View full abstract»

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  • 3. Automatic Control System for Thermal Comfort Based on Predicted Mean Vote and Energy Saving

    Publication Year: 2015 , Page(s): 378 - 383
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1046 KB) |  | HTML iconHTML  

    For human-centered automation, this study presents a wireless sensor network using predicted mean vote (PMV) as a thermal comfort index around occupants in buildings. The network automatically controls air conditioning by means of changing temperature settings in air conditioners. Interior devices of air conditioners thus do not have to be replaced. An adaptive neurofuzzy inference system and a particle swarm algorithm are adopted for solving a nonlinear multivariable inverse PMV model so as to determine thermal comfort temperatures. In solving inverse PMV models, the particle swarm algorithm is more accurate than ANFIS according to computational results. Based on the comfort temperature, this study utilizes feedforward-feedback control and digital self-tuning control, respectively, to satisfy thermal comfort. The control methods are validated by experimental results. Compared with conventional fixed temperature settings, the present control methods effectively maintain the PMV value within the range of ± 0.5 and energy is saved more than 30% in this study. View full abstract»

    Open Access
  • 4. A Toolkit for Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center

    Publication Year: 2015 , Page(s): 153 - 161
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1522 KB) |  | HTML iconHTML  

    Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be predicted or controlled. Therefore, performance evaluation of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations and requirements is difficult. There is still lack of tools that enable developers to compare different resource scheduling algorithms in IaaS regarding both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudSched. CloudSched can help developers identify and explore appropriate solutions considering different resource scheduling algorithms. Unlike traditional scheduling algorithms considering only one factor such as CPU, which can cause hotspots or bottlenecks in many cases, CloudSched treats multidimensional resource such as CPU, memory and network bandwidth integrated for both physical machines and virtual machines (VMs) for different scheduling objectives (algorithms). In this paper, two existing simulation systems at application level for Cloud computing are studied, a novel lightweight simulation system is proposed for real-time VM scheduling in Cloud data centers, and results by applying the proposed simulation system are analyzed and discussed. View full abstract»

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  • 5. Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response

    Publication Year: 2015 , Page(s): 1 - 13
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1977 KB)  

    This paper presents the software framework established to facilitate cloud-hosted robot simulation. The framework addresses the challenges associated with conducting a task-oriented and real-time robot competition, the Defense Advanced Research Projects Agency (DARPA) Virtual Robotics Challenge (VRC), designed to mimic reality. The core of the framework is the Gazebo simulator, a platform to simulate robots, objects, and environments, as well as the enhancements made for the VRC to maintain a high fidelity simulation using a high degree of freedom and multisensor robot. The other major component used is the CloudSim tool, designed to enhance the automation of robotics simulation using existing cloud technologies. The results from the VRC and a discussion are also detailed in this work. View full abstract»

    Open Access
  • 6. On the Implementation of Industrial Automation Systems Based on PLC

    Publication Year: 2013 , Page(s): 990 - 1003
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1506 KB) |  | HTML iconHTML  

    Industrial automation is largely based on PLC-based control systems. PLCs are today mostly programmed in the languages of the IEC 61131 standard which are not ready to meet the new challenges of widely distributed automation systems. Currently, an extension of IEC 61131 which includes object oriented programming as well as the new standard IEC 61499 are available. Moreover, service-oriented paradigms where autonomous and interoperable resources provide their functionalities in the form of services that can be accessed externally by clients without knowing the underlining implementation have been presented in the literature. In the supervisory control theory, methodologies based on formal models have been developed to improve the coordination of concurrent and distributed systems. In this paper, an event-driven approach is proposed to improve the design of industrial control systems using commercial PLCs. At a lower level, basic sequences are coded in elementary software objects, called function blocks, providing their functionalities as services. At an upper level, a Petri Net (PN) controller forces the execution of such services according to desired sequences, while by a PN supervisor constraints on the sequences are satisfied. View full abstract»

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  • 7. Kinematic Control With Singularity Avoidance for Teaching-Playback Robot Manipulator System

    Publication Year: 2015 , Page(s): 1 - 14
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2062 KB)  

    A teaching-playback robot manipulator system whereby the user controls the manipulator through a teaching pendant has been used widely in industrial applications. Kinematic singularity issue becomes an important problem in the control of robot with a teaching-playback system. In this paper, we propose and investigate three singularity avoidance methods for a teaching-playback robot manipulator system. Nonredundancy singularity avoidance (NRSA) attempts to reduce both the position and orientation errors of the end-effector with the same priority. Redundancy singularity avoidance (RSA) attempts to reduce the position error of the end-effector with the first priority and reduce the orientation error of the end-effector with the second priority; Both NRSA and RSA are based on a modification of a Jacobian matrix. Point-to-point singularity avoidance (PTPSA) makes the end-effector pass through a singular region based on joint-interpolated control without maintaining the position and orientation of the end-effector. Experimental case studies are developed to investigate the manipulator performance when the end-effector approaches the wrist and shoulder singularity. The maximal end-effector trajectory error and users' feelings are statistically evaluated and analyzed in the experiment. The results of the experiment show the effectiveness and practice of the proposed methods. View full abstract»

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  • 8. Nonlinear Model Predictive Control for DFIG-Based Wind Power Generation

    Publication Year: 2014 , Page(s): 1046 - 1055
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2443 KB) |  | HTML iconHTML  

    Reliable control and optimal operation of the doubly fed induction generator (DFIG) is necessary to ensure high efficiency and high load-following capability in modern wind power plants. This is often difficult to achieve using conventional linear controllers, as wind power plants are nonlinear and contain many uncertainties. Furthermore, unbalanced conditions often exist on the power network, which can degrade DFIG system performance. Considering the nonlinear DFIG dynamics, this paper proposes a nonlinear modeling technique for DFIG, meanwhile taking into account unbalanced grid conditions. Then, a nonlinear model predictive controller is derived for power control of DFIG. The prediction is calculated based on the input-output feedback linearization (IOFL) scheme. The control is derived by optimization of an objective function that considers both economic and tracking factors under realistic constraints. The simulation results show that the proposed controller can effectively reduce wear and tear of generating units under normal grid conditions, and reduce the rotor over-current under unbalanced grid conditions, thereby improving the ability of grid-connected wind turbines to withstand grid voltage faults. View full abstract»

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  • 9. Adaptive Dynamic Programming for Optimal Tracking Control of Unknown Nonlinear Systems With Application to Coal Gasification

    Publication Year: 2014 , Page(s): 1020 - 1036
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (5948 KB) |  | HTML iconHTML  

    In this paper, we establish a new data-based iterative optimal learning control scheme for discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) approach and apply the developed control scheme to solve a coal gasification optimal tracking control problem. According to the system data, neural networks (NNs) are used to construct the dynamics of coal gasification process, coal quality and reference control, respectively, where the mathematical model of the system is unnecessary. The approximation errors from neural network construction of the disturbance and the controls are both considered. Via system transformation, the optimal tracking control problem with approximation errors and disturbances is effectively transformed into a two-person zero-sum optimal control problem. A new iterative ADP algorithm is then developed to obtain the optimal control laws for the transformed system. Convergence property is developed to guarantee that the performance index function converges to a finite neighborhood of the optimal performance index function, and the convergence criterion is also obtained. Finally, numerical results are given to illustrate the performance of the present method. View full abstract»

    Open Access
  • 10. Carbon Footprint and the Management of Supply Chains: Insights From Simple Models

    Publication Year: 2013 , Page(s): 99 - 116
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1803 KB) |  | HTML iconHTML  

    Using relatively simple and widely used models, we illustrate how carbon emission concerns could be integrated into operational decision-making with regard to procurement, production, and inventory management. We show how, by associating carbon emission parameters with various decision variables, traditional models can be modified to support decision-making that accounts for both cost and carbon footprint. We examine how the values of these parameters as well as the parameters of regulatory emission control policies affect cost and emissions. We use the models to study the extent to which carbon reduction requirements can be addressed by operational adjustments, as an alternative (or a supplement) to costly investments in carbon-reducing technologies. We also use the models to investigate the impact of collaboration among firms within the same supply chain on their costs and carbon emissions and study the incentives firms might have in seeking such cooperation. We provide a series of insights that highlight the impact of operational decisions on carbon emissions and the importance of operational models in evaluating the impact of different regulatory policies and in assessing the benefits of investments in more carbon efficient technologies. Note to Practitioners-Firms worldwide, responding to the threat of government legislation or to concerns raised by their own consumers or shareholders, are undertaking initiatives to reduce their carbon footprint. It is the conventional thinking that such initiatives will require either capital investments or a switch to more expensive sources of energy or input material. In this paper, we show that firms could effectively reduce their carbon emissions without significantly increasing their costs by making only operational adjustments and by collaborating with other members of their supply chain. We describe optimization models that can be used by firms to support operational decision making and supply chain collaboration, whil- taking into account carbon emissions. We analyze the effect of different emission regulations, including strict emission caps, taxes on emissions, cap-and-offset, and cap-and-trade, on supply chain management decisions. In particular, we show that the presence of emission regulation can significantly increase the value of supply chain collaboration. View full abstract»

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  • 11. Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

    Publication Year: 2015 , Page(s): 336 - 353
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2945 KB) |  | HTML iconHTML  

    In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms. View full abstract»

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  • 12. Energy Efficient Ethernet for Real-Time Industrial Networks

    Publication Year: 2015 , Page(s): 228 - 237
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1106 KB) |  | HTML iconHTML  

    To increase the energy efficiency of Ethernet networks, in 2010, the IEEE published the IEEE 802.3az amendment, known as Energy Efficient Ethernet (EEE). The amendment introduces a new operational mode, defined as Low Power Idle (LPI), that allows to considerably reduce the power consumption of inactive Ethernet links. In this paper, we address the application of EEE to Real-Time Ethernet (RTE) networks, the popular communication systems typically employed in factory automation, characterized by tight timing requirements. We start with a description of the EEE basics and, subsequently, focus on the introduction of EEE in the industrial communication scenario. Then, we specifically address the implementation of effective EEE strategies for some popular RTE networks. The analysis is carried out on configurations commonly deployed at low levels of factory automation systems. The obtained results show that considerable power savings can be achieved with very limited impact on network performance. View full abstract»

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  • 13. Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud

    Publication Year: 2014 , Page(s): 564 - 573
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2043 KB) |  | HTML iconHTML  

    Public clouds provide Infrastructure as a Service (IaaS) to users who do not own sufficient compute resources. IaaS achieves the economy of scale by multiplexing, and therefore faces the challenge of scheduling tasks to meet the peak demand while preserving Quality-of-Service (QoS). Previous studies proposed proactive machine purchasing or cloud federation to resolve this problem. However, the former is not economic and the latter for now is hardly feasible in practice. In this paper, we propose a resource allocation framework in which an IaaS provider can outsource its tasks to External Clouds (ECs) when its own resources are not sufficient to meet the demand. This architecture does not require any formal inter-cloud agreement that is necessary for the cloud federation. The key issue is how to allocate users' tasks to maximize the profit of IaaS provider while guaranteeing QoS. This problem is formulated as an integer programming (IP) model, and solved by a self-adaptive learning particle swarm optimization (SLPSO)-based scheduling approach. In SLPSO, four updating strategies are used to adaptively update the velocity of each particle to ensure its diversity and robustness. Experiments show that, SLPSO can improve a cloud provider's profit by 0.25%-11.56% compared with standard PSO; and by 2.37%-16.71% for problems of nontrivial size compared with CPLEX under reasonable computation time. View full abstract»

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

    Publication Year: 2014 , 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
  • 15. Stochastic Modeling and Quality Evaluation of Infrastructure-as-a-Service Clouds

    Publication Year: 2015 , Page(s): 162 - 170
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1839 KB) |  | HTML iconHTML  

    Cloud computing is a recently developed new technology for complex systems with massive service sharing, which is different from the resource sharing of the grid computing systems. In a cloud environment, service requests from users go through numerous provider-specific steps from the instant it is submitted to when the requested service is fully delivered. Quality modeling and analysis of clouds are not easy tasks because of the complexity of the automated provisioning mechanism and dynamically changing cloud environment. This work proposes an analytical model-based approach for quality evaluation of Infrastructure-as-a-Service cloud by considering expected request completion time, rejection probability, and system overhead rate as key quality metrics. It also features with the modeling of different warm-up and cool-down strategies of machines and the ability to identify the optimal balance between system overhead and performance. To validate the correctness of the proposed model, we obtain simulative quality-of-service (QoS) data and conduct a confidence interval analysis. The result can be used to help design and optimize industrial cloud computing systems. View full abstract»

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  • 16. Image-Based Process Monitoring Using Low-Rank Tensor Decomposition

    Publication Year: 2015 , Page(s): 216 - 227
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2395 KB) |  | HTML iconHTML  

    Image and video sensors are increasingly being deployed in complex systems due to the rich process information that these sensors can capture. As a result, image data play an important role in process monitoring and control in different application domains such as manufacturing processes, food industries, medical decision-making, and structural health monitoring. Existing process monitoring techniques fail to fully utilize the information of color images due to their complex data characteristics including the high-dimensionality and correlation structure (i.e., temporal, spatial and spectral correlation). This paper proposes a new image-based process monitoring approach that is capable of handling both grayscale and color images. The proposed approach models the high-dimensional structure of the image data with tensors and employs low-rank tensor decomposition techniques to extract important monitoring features monitored using multivariate control charts. In addition, this paper shows the analytical relationships between different low-rank tensor decomposition methods. The performance of the proposed method in quick detection of process changes is evaluated and compared with existing methods through extensive simulations and a case study in a steel tube manufacturing process. View full abstract»

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  • 17. Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1917 KB)  

    This paper considers the fusion of carrier-phase differential GPS (CP-DGPS), peer-to-peer ranging radios, and low-cost inertial navigation systems (INS) for the application of relative navigation of small unmanned aerial vehicles (UAVs) in close formation-flight. A novel sensor fusion algorithm is presented that incorporates locally processed tightly coupled GPS/INS-based absolute navigation solutions from each UAV in a relative navigation filter that estimates the baseline separation using integer-fixed relative CP-DGPS and a set of peer-to-peer ranging radios. The robustness of the dynamic baseline estimation performance under conditions that are typically challenging for CP-DGPS alone, such as a high occurrence of phase breaks, poor satellite visibility/geometry due to extreme UAV attitude, and heightened multipath intensity, amongst others, is evaluated using Monte Carlo simulation trials. The simulation environment developed for this work combines a UAV formation flight control simulator with a GPS constellation simulator, stochastic models of the inertial measurement unit (IMU) sensor errors, and measurement noise of the ranging radios. The sensor fusion is shown to offer improved robustness for 3-D relative positioning in terms of 3-D residual sum of squares (RSS) accuracy and increased percentage of correctly fixed phase ambiguities. Moreover, baseline estimation performance is significantly improved during periods in which differential carrier phase ambiguities are unsuccessfully fixed. View full abstract»

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  • 18. Dynamic Neuro-Fuzzy-Based Human Intelligence Modeling and Control in GTAW

    Publication Year: 2015 , Page(s): 324 - 335
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2635 KB) |  | HTML iconHTML  

    Human welder's experiences and skills are critical for producing quality welds in manual gas tungsten arc welding (GTAW) process. In this paper, a neuro-fuzzy-based human intelligence model is constructed and implemented as an intelligent controller in automated GTAW process to maintain a consistent desired full penetration. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc light interference. Experiments are designed to produce random changes in the welding speed and voltage resulting in fluctuations in the weld pool surface. Adaptive neuro-fuzzy inference system (ANFIS) is proposed to correlate the human welder's response to the 3D weld pool surface as characterized by its width, length and convexity. Closed-loop control experiments are conducted to verify the robustness of the proposed controller. It is found that the human intelligence model can adjust the current to robustly control the process to a desired penetration state despite different initial conditions and various disturbances. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding systems. View full abstract»

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  • 19. A Sampling Decision System for Virtual Metrology in Semiconductor Manufacturing

    Publication Year: 2015 , Page(s): 75 - 83
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1717 KB) |  | HTML iconHTML  

    In semiconductor manufacturing, metrology operations are expensive and time-consuming, for this reason only a certain sample of wafers is measured. With the need of highly reliable processes, the semiconductor industry aims at developing methodologies covering the gap of missing metrology information. Virtual Metrology turns out to be a promising method; it aims at predicting wafer and/or site fine metrology results in real time and free of costs. In this paper, we present a sampling decision system that relies on virtual measurements suggesting an efficient strategy for measuring productive wafers. Several methods for evaluating when a real measurement is needed (including the expected utility of measurement information, a two-stage sampling decision model and wafer quality risk values) are proposed. We further provide ideas on how to assess and update the reliability of the virtual measurements in a sampling decision system (whenever real measurements become available). In this context, we introduce equipment health factors and virtual trust factors for improving the reliability of the sampling decision system. Finally, the performance of the sampling decision system is demonstrated on a set of virtual and real metrology data from the semiconductor industry. It is shown that wafer measurements are efficiently performed when really needed. View full abstract»

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  • 20. RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

    Publication Year: 2015 , Page(s): 1 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2132 KB)  

    The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology. View full abstract»

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  • 21. Intensity-Based Visual Servoing for Instrument and Tissue Tracking in 3D Ultrasound Volumes

    Publication Year: 2015 , Page(s): 367 - 371
    Cited by:  Papers (1)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (643 KB) |  | HTML iconHTML  

    This paper presents a three dimensional ultrasound (3DUS)-based visual servoing technique for intraoperative tracking of the motion of both surgical instruments and tissue targets. In the proposed approach, visual servoing techniques are used to control the position of a virtual ultrasound probe so as to keep a target centered within the virtual probe's field-of-view. Multiple virtual probes can be servoed in parallel to provide simultaneous tracking of instruments and tissue. The technique is developed in the context of robotic beating-heart intracardiac surgery in which the goal of tracking is to both provide guidance to the operator as well as to provide the means to automate the surgical procedure. To deal with the low signal-to-noise ratio (SNR) of the 3DUS volumes, an intensity-based method is proposed that requires no primitive extraction or image segmentation since it directly utilizes the image intensity information as a visual feature. This approach is computationally efficient and can be applied to a wide range of tissue types and medical instruments. This paper presents the first validation of these techniques through offline robot and tissue tracking using actual in vivo cardiac volume sequences from a robotic beating-heart surgery. View full abstract»

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  • 22. Low-Dimensional Learning for Complex Robots

    Publication Year: 2015 , Page(s): 19 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1505 KB) |  | HTML iconHTML  

    This paper presents an algorithm for learning the switching policy and the boundaries conditions between primitive controllers that maximize the translational movements of a complex locomoting system. The algorithm learns an optimal action for each boundary condition instead of one for each discretized state-action pair of the system, as is typically done in machine learning. The system is modeled as a hybrid system because it contains both discrete and continuous dynamics. With this hybridification of the system and with this abstraction of learning boundary-action pairs, the “curse of dimensionality” is mitigated. The effectiveness of this learning algorithm is demonstrated on both a simulated system and on a physical robotic system. In both cases, the algorithm is able to learn the hybrid control strategy that maximizes the forward translational movement of the system without the need for human involvement. View full abstract»

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  • 23. A Novel Iterative \theta -Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems

    Publication Year: 2014 , Page(s): 1176 - 1190
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (4364 KB) |  | HTML iconHTML  

    This paper is concerned with a new iterative θ-adaptive dynamic programming (ADP) technique to solve optimal control problems of infinite horizon discrete-time nonlinear systems. The idea is to use an iterative ADP algorithm to obtain the iterative control law which optimizes the iterative performance index function. In the present iterative θ-ADP algorithm, the condition of initial admissible control in policy iteration algorithm is avoided. It is proved that all the iterative controls obtained in the iterative θ-ADP algorithm can stabilize the nonlinear system which means that the iterative θ-ADP algorithm is feasible for implementations both online and offline. Convergence analysis of the performance index function is presented to guarantee that the iterative performance index function will converge to the optimum monotonically. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative θ-ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the established method. View full abstract»

    Open Access
  • 24. Marker-Based Surgical Instrument Tracking Using Dual Kinect Sensors

    Publication Year: 2014 , Page(s): 921 - 924
    Cited by:  Papers (4)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (401 KB) |  | HTML iconHTML  

    This paper presents a passive-marker-based optical tracking system utilizing dual Kinect sensors and additional custom optical tracking components. To obtain sub-millimeter tracking accuracy, we introduce robust calibration of dual infrared sensors and point correspondence establishment in a stereo configuration. The 3D localization is subsequently accomplished using multiple back projection lines. The proposed system extends existing inexpensive consumer electronic devices, implements tracking algorithms, and shows the feasibility of applying the proposed low-cost system to surgical training for computer assisted surgeries. View full abstract»

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  • 25. Theory and Performance Evaluation of Group Coding of RFID Tags

    Publication Year: 2012 , Page(s): 458 - 466
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1431 KB) |  | HTML iconHTML  

    Radio frequency identification (RFID) is an automatic identification technology which identifies physical objects individually according to their unique identifiers (ID) recorded in each RFID tag. Many business processes require the integrity verification of a group of objects in addition to individual object identification. This paper proposes “group coding” of RFID tags with which we can verify the integrity of groups of objects by writing parity check data to the memory of RFID tags. It was revealed by simulations and experiments that we could determine the number of missing RFID tags up to 10 with accuracy over 99% when we write 96 bits of the checksum data to 20 RFID tags. The whole duration of group decoding measured in the experiment was approximately 2 to 3 s. The time to compute group encoding and decoding was in the order of several milliseconds and thus negligible. The RFID inventory accounts for the majority of the duration. Note to Practitioners-Current RFID features fast identification of many physical objects. However, the integrity check of a group of objects is usually done by looking up a packaging list or a shipment list in EDI, which requires a network connection. Our proposed “group coding” of RFID tags can perform the group integrity check without a network connection. In addition, when the integrity of the group is infringed, the group coding can determine the number of RFID tags missing from the group. These features of group coding can reduce the cost of looking up shipment lists and locate missing RFID tags. The accuracy of the determination is controlled by adjusting the size of data written in each RFID tag. Adopters of group coding can select the optimal performance of group coding from the requirements of the accuracy and constraints like memory consumption of RFID tags. View full abstract»

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  • 26. Motion Control, Planning and Manipulation of Nanowires Under Electric-Fields in Fluid Suspension

    Publication Year: 2015 , Page(s): 37 - 49
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3188 KB) |  | HTML iconHTML  

    Automated manipulation of nanowires and nanotubes would enable the scalable manufacturing of nanodevices for a variety of applications, including nanoelectronics and biological applications. In this paper, we present an electric-field-based method for motion control, planning, and manipulation of nanowires in liquid suspension with a simple, generic set of electrodes. We first present a dynamic model and a vision-based motion control of the nanowire motion in dilute suspension with a set of N×N controllable electrodes. Since the motion planning of a nanowire from one position to the target location is NP-hard, two heuristic algorithms are presented to generate near-optimal motion trajectories. We compare the heuristic motion planning algorithms with other existing algorithms such as the rapidly exploring random tree (RRT) and A* algorithms. The comparisons show that the proposed heuristic algorithms obtain near-optimal minimum time trajectories. Finally, we demonstrate a single, integrated process to position, orient, and deposit multiple nanowires onto the substrate. Extensive experimental and numerical results are presented to confirm the motion control and planning algorithms. View full abstract»

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  • 27. Brain Machine Interface System Automation Considering User Preferences and Error Perception Feedback

    Publication Year: 2014 , Page(s): 1275 - 1281
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (713 KB) |  | HTML iconHTML  

    This paper addresses the problem of mental fatigue caused by prolonged use of Brain Machine Interface (BMI) Systems. We propose a system that gradually becomes autonomous by learning user preferences and by considering error perception feedback. As a particular application, we show that our system allows patients to control electronic appliances in a hospital room, and learns the correlation of room sensor data, brain states, and user control commands. Moreover, error perception feedback based on a brain potential called error related negativity (ERN) - that spontaneously occurs when the user perceives an error made by the system - was used to correct system's mistakes and improve its learning performance. Experimental results with volunteers demonstrate that our system reduces the level of mental fatigue, and achieves over 90% overall learning performance when error perception feedback is considered. Note to Practitioners - This paper suggests a new approach for designing BMI systems that incorporate learning capabilities and error perception feedback in order to gradually become autonomous. This approach consists in learning the relationship between sensing data from the environment-brain and user actions when controlling robotic devices. After the system is trained, can predict control commands on behalf of the user under similar conditions. If the system makes a mistake, user's error perception feedback is considered to improve the learning performance the system. In this paper, we describe the methodologies to design and build hardware and software interfaces, acquire and process brain signals, and train the system using machine learning techniques. We then provide experimental evidence that demonstrates the effectiveness of this approach to design BMI systems that gradually become autonomous. View full abstract»

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  • 28. Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps

    Publication Year: 2015 , Page(s): 171 - 182
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1653 KB) |  | HTML iconHTML  

    In unstructured environments in people's homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a plate of food level to avoid spills or properly orienting a finger to push a button. We introduce a sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. Our time-dependent cost metric, learned from a set of demonstrations, encodes features of a task's motion that are consistent across the demonstrations and, hence, are likely required to successfully execute the task. Our sampling-based motion planner uses the learned cost metric to compute plans that simultaneously avoid obstacles and satisfy task constraints. The motion planner is asymptotically optimal and minimizes the Mahalanobis distance between the planned trajectory and the distribution of demonstrations in a feature space parameterized by the locations of task-relevant objects. The motion planner also leverages the distribution of the demonstrations to significantly reduce plan computation time. We demonstrate the method's effectiveness and speed using a small humanoid robot performing tasks requiring both obstacle avoidance and satisfaction of learned task constraints. View full abstract»

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  • 29. GridStore: A Puzzle-Based Storage System With Decentralized Control

    Publication Year: 2014 , Page(s): 429 - 438
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1276 KB) |  | HTML iconHTML  

    We describe a high-density storage system for physical goods in which identical conveyor modules can be plugged together to store and retrieve unit-loads or small containers. Material movement conforms to the “puzzle architecture” found in popular board games such as the 15-puzzle and Rush Hour. Control of the system is decentralized, meaning that each module contains identical operating logic that directs its behavior based on local conditions and message passing. We prove the system deadlock-free and show its performance under a wide variety of operating configurations. View full abstract»

    Open Access
  • 30. Trajectory Optimization for Well-Conditioned Parameter Estimation

    Publication Year: 2015 , Page(s): 28 - 36
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1575 KB) |  | HTML iconHTML  

    When attempting to estimate parameters in a dynamical system, it is often beneficial to strategically design experimental trajectories that facilitate the estimation process. This paper presents an optimization algorithm which improves conditioning of estimation problems by modifying the experimental trajectory. An objective function which minimizes the condition number of the Hessian of the least-squares identification method is derived and a least-squares method is used to estimate parameters of the nonlinear system. A software-simulated example demonstrates that an arbitrarily designed trajectory can lead to an ill-conditioned least-squares estimation problem, which in turn leads to slower convergence to the best estimate and, in the presence of experimental uncertainties, may lead to no convergence at all. A physical experiment with a robot-controlled suspended mass also shows improved estimation results in practice in the presence of noise and uncertainty using the optimized trajectory. View full abstract»

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  • 31. An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram

    Publication Year: 2015 , Page(s): 106 - 115
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1653 KB) |  | HTML iconHTML  

    Traditional approaches for obstructive sleep apnea (OSA) diagnosis are apt to using multiple channels of physiological signals to detect apnea events by dividing the signals into equal-length segments, which may lead to incorrect apnea event detection and weaken the performance of OSA diagnosis. This paper proposes an automatic-segmentation-based screening approach with the single channel of Electrocardiogram (ECG) signal for OSA subject diagnosis, and the main work of the proposed approach lies in three aspects: (i) an automatic signal segmentation algorithm is adopted for signal segmentation instead of the equal-length segmentation rule; (ii) a local median filter is improved for reduction of the unexpected RR intervals before signal segmentation; (iii) the designed OSA severity index and additional admission information of OSA suspects are plugged into support vector machine (SVM) for OSA subject diagnosis. A real clinical example from PhysioNet database is provided to validate the proposed approach and an average accuracy of 97.41% for subject diagnosis is obtained which demonstrates the effectiveness for OSA diagnosis. View full abstract»

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  • 32. Adaptive Fuzzy Control of a Class of MIMO Nonlinear System With Actuator Saturation for Greenhouse Climate Control Problem

    Publication Year: 2015 , Page(s): 1 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3835 KB)  

    This paper presents an indirect adaptive fuzzy control scheme for a class of MIMO non-affine nonlinear systems with unknown dynamics and actuator saturation for greenhouse climate control problems. The objective is to implement output tracking control on nonlinear systems. Using feedback linearization, control inputs with known control gains are first synthesized by well-modeled dynamics of the system, and Taylor series expansion is used to transform unknown non-affine dynamics into the corresponding affine forms. Fuzzy logic systems (FLS) are introduced to estimate the unknown nonlinearity of the transformed affine system and the saturation nonlinearity due to the actuator constraint. The control inputs corresponding to nonlinearity are constructed based on the estimations. By introducing a robust control term, estimation errors and external disturbances are well handled, so as to guarantee the stability when tracking the control process. The control gain estimation obtained by FLS is modified to avoid singularity. Lyapunov stability analysis is performed to derive the adaptive law. To validate the effectiveness of the proposed control scheme, we apply it to a greenhouse climate control problem. The ventilation rate in the greenhouse model is unknown; therefore, it is estimated by FLS. The simulation exhibits satisfactory results, in which the temperature and humidity inside the greenhouse are well tracked. View full abstract»

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  • 33. Virtual Battery: A Battery Simulation Framework for Electric Vehicles

    Publication Year: 2013 , Page(s): 5 - 15
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1941 KB) |  | HTML iconHTML  

    The battery is one of the most important components in electric vehicles. In this paper, a virtual battery model, which provides a framework of battery simulation for electric vehicles, is introduced. Using such a framework, we can model and simulate the performance of a battery during its usage, such as battery charge, discharge, and idle status, the impacts of internal and external temperature, the manufacturing quality on joints, the cell capacity and balance management, etc. Such a framework can provide a quantitative tool for design and manufacturing engineers to predict the battery performance, investigate the impacts of manufacturing process, and obtain feedback for improvement in battery design, control, and manufacturing processes. Note to Practitioners-Automotive battery manufacturing has become more and more important due to the need of alternative energy source to gasoline powered engines. Although substantial amount of attention has been paid to study both individual battery cells and the battery pack as a whole, a battery model which includes interactions of all its components (cells, joints, external inputs, etc.) is not available, and the impact of manufacturing quality on battery performance has not been investigated. In this paper, a virtual battery simulation framework is developed to evaluate battery performance under different circumstances, involving the issues of cell capacity, temperature, driving profile, the joint (manufacturing) quality, etc. Such a framework can help battery design and manufacturing engineers to evaluate battery performance, investigate the impacts of manufacturing practices, and provide feedback for improvement. View full abstract»

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  • 34. Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning

    Publication Year: 2015 , Page(s): 354 - 366
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1604 KB) |  | HTML iconHTML  

    Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology. View full abstract»

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  • 35. Energy-Efficient Control Strategies for Machine Tools With Stochastic Arrivals

    Publication Year: 2015 , Page(s): 50 - 61
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2242 KB) |  | HTML iconHTML  

    Energy saving in production plants is becoming more and more relevant due to the pressure from governments to contain the environmental impact of manufacturing and from companies to reduce costs. One of the measures for saving energy is the implementation of control strategies that reduce energy consumption during the machine idle periods. This paper proposes a framework that integrates different control policies for switching the machine off when production is not critical and on when the part flow has to be resumed. A general policy is formalized by modelling explicitly the energy consumed at each machine state. The policy parameters that minimize the requested machine expected energy are provided analytically for general distributions. Numerical results are based on data acquired with dedicated experimental measurements on a real machining centre, and a comparison with the most common practices in manufacturing is also reported. View full abstract»

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  • 36. Passive UHF RFID in Paper Industry: Challenges, Benefits and the Application Environment

    Publication Year: 2009 , Page(s): 66 - 79
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4795 KB) |  | HTML iconHTML  

    Radio frequency identification (RFID) systems for the paper industry is an emerging research topic due to the need for an automated identification system for the paper industry which would carry on the identification codes of paper and board reels throughout their life cycle. This paper discusses the application of passive ultra-high frequency (UHF) RFID systems to the paper industry. Challenges, benefits, and the application environment of using passive UHF RFID systems in the paper industry are presented and discussed. The major challenges are development of globally operable tag antenna designs and integration of reader units and reader antennas to paper handling machinery. To confront and solve these challenges, this paper presents novel tag antenna designs for paper and board reel identification and proposes solutions for reader and reader antenna integration to paper handling machinery. In addition, the identification locations within the paper reel supply chain and the effects of RFID systems to supply chain visibility are presented and discussed. In addition, test results of using passive UHF RFID systems in the paper industry environment are presented. View full abstract»

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  • 37. Building Energy Management: Integrated Control of Active and Passive Heating, Cooling, Lighting, Shading, and Ventilation Systems

    Publication Year: 2013 , Page(s): 588 - 602
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1985 KB) |  | HTML iconHTML  

    Buildings account for nearly 40% of global energy consumption. About 40% and 15% of that are consumed, respectively, by HVAC and lighting. These energy uses can be reduced by integrated control of active and passive sources of heating, cooling, lighting, shading and ventilation. However, rigorous studies of such control strategies are lacking since computationally tractable models are not available. In this paper, a novel formulation capturing key interactions of the above building functions is established to minimize the total daily energy cost. To obtain effective integrated strategies in a timely manner, a methodology that combines stochastic dynamic programming (DP) and the rollout technique is developed within the price-based coordination framework. For easy implementation, DP-derived heuristic rules are developed to coordinate shading blinds and natural ventilation, with simplified optimization strategies for HVAC and lighting systems. Numerical simulation results show that these strategies are scalable, and can effectively reduce energy costs and improve human comfort. View full abstract»

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  • 38. A Branch and Bound Algorithm for Cyclic Scheduling of Timed Petri Nets

    Publication Year: 2015 , Page(s): 309 - 323
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2321 KB) |  | HTML iconHTML  

    A timed Petri net (TPN) has been widely used for modeling, scheduling, and analyzing discrete event dynamic systems. This study examines cyclic scheduling problems of a TPN to minimize the cycle time especially for automated manufacturing systems. Appropriate token routing at each conflict place can make a TPN repeat an identical firing sequence. We propose a systematic procedure to transform a TPN with such cyclic token routing into an equivalent timed event graph (TEG) for which the cycle time and firing schedules can be evaluated by a linear programming (LP). Based on the transformation procedure, we develop an efficient branch and bound algorithm to solve the scheduling problem. A partial solution is defined as a partial token route that has only a subset of token routes for determining the complete schedule. The lower bound of a partial solution is determined by the cycle time of a TEG that has the partial token route. The cycle time of a TEG with an additional token route for a new partial solution is computed by a dual-simplex algorithm which avoids solving the LP completely again. A dynamic branching strategy that prevents unnecessary branching for the scheduling decision is also proposed. We demonstrate the computational efficiency through intensive experiments of cluster tools and robotic flow shops. Note to Practitioners-There are many systems which repeat an identical task sequence such as manufacturing systems, transportation systems, and robotic systems. Maximizing the throughput of such a system by optimizing the cyclic task sequence, which is called a cyclic scheduling problem, has been an important problem. In order to deal with cyclic scheduling problems, this paper uses a timed Petri net (TPN) which is a graphical modeling tool for discrete event dynamic systems. As the size of TPN model increases, the computational complexity of the cyclic scheduling problem exponentially increases due to the combinatorial nature of sequencing problems. Therefor- , an efficient algorithm for optimal cyclic scheduling of TPNs is needed. This study proposes an efficient branch and bound algorithm which is kind of a tree search algorithm. Several important techniques are developed. First, a transformation procedure from a TPN to timed event graph is developed. Second, an efficient branching rule which reduces the size of the search tree is proposed. Third, the lower bound which evaluates the cycle time of each node in the search tree is suggested. We verify the efficiency of the algorithm through the experiments on manufacturing systems such as a cluster tool and a robotic flow shop. View full abstract»

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  • 39. Mathematical Programming Models for Annual and Weekly Bloodmobile Collection Planning

    Publication Year: 2015 , Page(s): 96 - 105
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1633 KB) |  | HTML iconHTML  

    In this paper, we propose a two-step bloodmobile collection planning framework. The first step is the annual planning to determine weeks of collection at each mobile site in order to ensure regional self-sufficiency of blood supply. The second step is the detailed weekly planning to determine days of collections at each mobile site and to form corresponding transfusion teams. Only key resource requirements are considered for annual planning while detailed resource requirements and transportation times are considered for weekly planning. Two Mixed Integer Programming models are proposed for annual planning by assuming fixed or variable mobile collection frequencies. A new donation forecast model is proposed based on population demographics, donor generosity, and donor availability. A new concept of bloodmobile collection configurations is proposed for compact and efficient mathematical modeling of weekly planning in order to minimize the total working time. Field data from the French Blood Service (EFS) in the Auvergne-Loire Region are used to design numerical experiments and to assess the efficiency of the proposed models. View full abstract»

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  • 40. Precision Position/Force Interaction Control of a Piezoelectric Multimorph Microgripper for Microassembly

    Publication Year: 2013 , Page(s): 503 - 514
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2575 KB) |  | HTML iconHTML  

    Precision position and force control is a critical issue for automated microassembly systems to handle micro-objects delicately. This paper presents two new approaches to regulating both position and contact force of a piezoelectric multimorph microgripper dedicated to microassembly tasks. One of the advantages of the proposed approaches lies in that they are capable of controlling the position and contact force of a gripper arm simultaneously. The methodology is easy to implement since neither a state observer nor a hysteresis model of the system is required. The first approach is a position-based sliding mode impedance control which converts the target impedance into a desired position trajectory to be tracked, and the second one is established on the basis of a proportional-integral type of sliding function of the impedance measure error. Their tracking performances are guaranteed by two devised discrete-time sliding mode control algorithms, whose stabilities in the presence of model uncertainties and disturbances are proved in theory. The effectiveness of both schemes are validated by experimental investigations on a glass microbead gripping task. Results show that both approaches are capable of accomplishing promising interaction control accuracy. View full abstract»

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  • 41. Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

    Publication Year: 2014 , Page(s): 1 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3181 KB)  

    This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm. View full abstract»

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  • 42. Toward Welding Robot With Human Knowledge: A Remotely-Controlled Approach

    Publication Year: 2014 , Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1151 KB)  

    This paper presents a remotely controlled welding scheme that enables transformation of human welder knowledge into a welding robot. In particular, a 6-DOF UR-5 industrial robot arm is equipped with sensors to observe the welding process, including a compact 3D weld pool surface sensing system and an additional camera to provide direct view of the work-piece. Human welder operates a virtual welding torch, whose motion is tracked by a Leap sensor. To remotely operate the robot based on the motion information from the Leap sensor, a predictive control approach is proposed to accurately track the human motion by controlling the speed of the robot arm movement. Tracking experiments are conducted to track both simulated movement with varying speed and actual human hand movement. It is found that the proposed predictive controller is able to track human hand movement with satisfactory accuracy. A welding experiment has also been conducted to verify the effectiveness of the proposed remotely-controlled welding system. A foundation is thus established to realize teleoperation and help transfer human knowledge to the welding robot. View full abstract»

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  • 43. Dynamic Low-Power Reconfiguration of Real-Time Systems With Periodic and Probabilistic Tasks

    Publication Year: 2015 , Page(s): 258 - 271
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2903 KB) |  | HTML iconHTML  

    This paper deals with the dynamic low-power reconfiguration of a real-time system. It processes periodic and probabilistic tasks that have hard/soft deadlines corresponding to internal/external events. A runtime event-based reconfiguration scenario is a dynamic operation allowing the addition/removal of the assumed periodic/probabilistic tasks. Thereafter, some tasks may miss their hard deadlines and the power consumption may increase. In order to reconfigure the system to be feasible, i.e., satisfying its real-time constraints with low-power consumption, this research presents a software-agent-based architecture. An intelligent agent is developed, which provides four solutions to reconfigure the system at runtime. For these solutions, in order to reconfigure the probabilistic tasks to be feasible, the agent modifies their temporal parameters dynamically; moreover, in order to feasibly serve the probabilistic tasks and reduce the system's power consumption, the agent provides three virtual processors by dynamically extending the periods of the periodic tasks. A simulation study verifies the effectiveness of the agent. View full abstract»

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  • 44. Training Initialization of Hidden Markov Models in Human Action Recognition

    Publication Year: 2014 , Page(s): 394 - 408
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    Human action recognition in video is often approached by means of sequential probabilistic models as they offer a natural match to the temporal dimension of the actions. However, effective estimation of the models' parameters is critical if one wants to achieve significant recognition accuracy. Parameter estimation is typically performed over a set of training data by maximizing objective functions such as the data likelihood or the conditional likelihood. However, such functions are nonconvex in nature and subject to local maxima. This problem is major since any solution algorithm (expectation-maximization, gradient ascent, variational methods and others) requires an arbitrary initialization and can only find a corresponding local maximum. Exhaustive search is otherwise impossible since the number of local maxima is unknown. While no theoretical solutions are available for this problem, the only practicable mollification is to repeat training with different initializations until satisfactory cross-validation accuracy is attained. Such a process is overall empirical and highly time-consuming. In this paper, we propose two methods for one-off initialization of hidden Markov models achieving interesting tradeoffs between accuracy and training time. Experiments over three challenging human action video datasets (Weizmann, MuHAVi and Hollywood Human Actions) and with various feature sets measured from the frames (STIP descriptors, projection histograms, notable contour points) prove that the proposed one-off initializations are capable of achieving accuracy above the average of repeated random initializations and comparable to the best. In addition, the methods proposed are not restricted solely to human action recognition as they suit time series classification as a general problem. View full abstract»

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  • 45. Intelligent Component-Based Automation of Baggage Handling Systems With IEC 61499

    Publication Year: 2010 , Page(s): 337 - 351
    Cited by:  Papers (27)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2110 KB) |  | HTML iconHTML  

    Airport baggage handling is a field of automation systems that is currently dependent on centralized control systems and conventional automation programming techniques. In this and other areas of manufacturing and materials handling, these legacy automation technologies are increasingly limiting for the growing demand for systems that are reconfigurable, fault tolerant, and easy to maintain. IEC 61499 Function Blocks is an emerging architectural framework for the design of distributed industrial automation systems and their reusable components. A number of architectures have been suggested for multiagent and holonic control systems that incorporate function blocks. This paper presents a multiagent control approach for a baggage handling system (BHS) using IEC 61499 Function Blocks. In particular, it focuses on demonstrating a decentralized control system that is scalable, reconfigurable, and fault tolerant. The design follows the automation object approach, and produces a function block component representing a single section of conveyor. In accordance with holonic principles, this component is autonomous and collaborative, such that the structure and the behavior of a BHS can be entirely defined by the interconnection of these components within the function block design environment. Simulation is used to demonstrate the effectiveness of the agent-based control system and a utility is presented for real-time viewing of these systems. Tests on a physical conveyor test system demonstrated deployment to embedded control hardware. View full abstract»

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  • 46. A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

    Publication Year: 2011 , Page(s): 130 - 147
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (810 KB) |  | HTML iconHTML  

    Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) λ -coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The λ-coverage problem is concerned with finding a set of key nodes having minimal size that can influence a given percentage λ of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the λ -coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. View full abstract»

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  • 47. Data-Driven Soft-Sensor Modeling for Product Quality Estimation Using Case-Based Reasoning and Fuzzy-Similarity Rough Sets

    Publication Year: 2014 , Page(s): 992 - 1003
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3057 KB) |  | HTML iconHTML  

    Efficient operation of the integrated optimization or automation system in an industrial plant depends mainly on good measurement of product quality. However, measuring or estimating the product quality online in many industrial plants is usually not feasible using the available techniques. In this paper, a data-driven soft-sensor using case-based reasoning (CBR) and fuzzy-similarity rough sets is proposed for product quality estimation. Owning to the sustained learning ability, the modeling of a CBR soft-sensor does not need any additional model correction which is otherwise required by the neural network based methods to overcome the slow time-varying nature of industrial processes. Because the conventional k-nearest neighbor ( k-NN) algorithm is strongly influenced by the value of k, an improved k-NN algorithm with dynamic adjustment of case similarity threshold is proposed to retrieve sufficient matching cases for making a correct estimation. Moreover, considering that the estimation accuracy of the CBR soft-sensor system is closely related to the weights of case feature, a feature weighting algorithm using fuzzy-similarity rough sets is proposed in this paper. This feature weighting method does not require any transcendental knowledge, and its computation complexity is only linear with respect to the number of cases and attributes. The developed soft-sensor system has been successfully applied in a large grinding plant in China. And the application results show that the system has achieved satisfactory estimation accuracy and adaptation ability. View full abstract»

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  • 48. Robotic Cell Rotation Based on the Minimum Rotation Force

    Publication Year: 2014 , Page(s): 1 - 12
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3663 KB)  

    In this paper, a robotic cell rotation method based on the minimum rotation force is presented to adjust oocyte orientation in biological applications. In this method, the minimum rotation force, which can control the rotation angle (RA) of the oocyte quantitatively and generate minimum oocyte deformations, is derived through a force analysis on the oocyte in rotation. To exert this force on the oocyte, the moving trajectories (MT) of the injection micropipette (IM), are determined using mechanical properties of the oocytes. Further, by moving the IM along the designed MT, the rotation force control is achieved. To verify the feasibility of this method, a robotic rotation experiment for batch porcine oocytes are performed. Experimental results demonstrate that this system rotates the oocyte a10t an average speed of 28.6s/cell and with a success rate of 93.3%. More importantly, this method can generate much less oocyte deformations during cell rotation process compared with the manual method, while the average control error of RA in each step is only 1.2 ^{\circ} (versus averagely 8.3 ^{\circ} in manual operation), which demonstrates that our method can effectively reduce cell deformations and improve control accuracy of the RA. View full abstract»

    Open Access
  • 49. Simultaneous Vision-Based Shape and Motion Analysis of Cells Fast-Flowing in a Microchannel

    Publication Year: 2015 , Page(s): 204 - 215
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2833 KB) |  | HTML iconHTML  

    This paper proposes a novel concept for simultaneous cell shape and motion analysis in fast microchannel flows by implementing a multiobject feature extraction algorithm on a frame-straddling high-speed vision platform. The system can synchronize two camera inputs with the same view with only a tiny time delay on the sub-microsecond timescale. Real-time video processing is performed in hardware logic by extracting the moment features of multiple cells in 512 × 256 images at 4000 fps for the two camera inputs and their frame-straddling time can be adjusted from 0 to 0.25 ms in 9.9 ns steps. By setting the frame-straddling time in a certain range to avoid large image displacements between the two camera inputs, our frame-straddling high-speed vision platform can perform simultaneous shape and motion analysis of cells in fast microchannel flows of 1 m/s or greater. The results of real-time experiments conducted to analyze the deformabilities and velocities of sea urchin egg cells fast-flowing in microchannels verify the efficacy of our vision-based cell analysis system. View full abstract»

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  • 50. A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

    Publication Year: 2014 , Page(s): 367 - 378
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2649 KB) |  | HTML iconHTML  

    One of the important tasks for bridge maintenance is bridge deck crack inspection. Traditionally, a human inspector detects cracks using his/her eyes and marks the location of cracks manually. However, the accuracy of the inspection result is low due to the subjective nature of human judgement. We propose a crack inspection system that uses a camera-equipped mobile robot to collect images on the bridge deck. In this method, the Laplacian of Gaussian (LoG) algorithm is used to detect cracks and a global crack map is obtained through camera calibration and robot localization. To ensure that the robot collects all the images on the bridge deck, a path planning algorithm based on the genetic algorithm is developed. The path planning algorithm finds a solution which minimizes the number of turns and the traveling distance. We validate our proposed system through both simulations and experiments. 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