The Experts below are selected from a list of 4878 Experts worldwide ranked by ideXlab platform
Katinka Wolter - One of the best experts on this subject based on the ideXlab platform.
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ICPE - Reducing Task Completion Time in Mobile Offloading Systems through Online Adaptive Local Restart
Proceedings of the 6th ACM SPEC International Conference on Performance Engineering - ICPE '15, 2015Co-Authors: Qiushi Wang, Katinka WolterAbstract:Offloading is an advanced technique to improve the performance of mobile devices. In a mobile offloading system, heavy computations are migrated from resource constrained mobile devices to powerful cloud servers through a wireless network connection. The unreliable wireless network often disturbs system operation. Task Completion can be delayed or interrupted by congestion or packet loss in the network. To deal with this problem the offloaded jobs can be locally restarted and completed in the mobile device itself. In this paper, we propose a dynamic scheme to determine whether and when to locally restart a Task. First, we design an experiment to explore the impact of packet loss and delay in unreliable networks on the Completion Time of an offloading Task. Then, we mathematically derive the prerequisites for local restart and selection of the optimal Timeout. The analysis result confirms that local restart is beneficial when the distribution of Task Completion Time has high variance. Further, a dynamic local restart scheme is proposed for mobile applications. This scheme keeps track of the variance of the probability density function of the distribution of Task Completion Time. This is done using a dynamic histogram, which collects and updates data at run Time. The efficiency of the local restart scheme is confirmed by experimental results. The experiment shows that local restart at the right Time achieves better performance than always offloading.
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reducing Task Completion Time in mobile offloading systems through online adaptive local restart
International Conference on Performance Engineering, 2015Co-Authors: Qiushi Wang, Katinka WolterAbstract:Offloading is an advanced technique to improve the performance of mobile devices. In a mobile offloading system, heavy computations are migrated from resource constrained mobile devices to powerful cloud servers through a wireless network connection. The unreliable wireless network often disturbs system operation. Task Completion can be delayed or interrupted by congestion or packet loss in the network. To deal with this problem the offloaded jobs can be locally restarted and completed in the mobile device itself. In this paper, we propose a dynamic scheme to determine whether and when to locally restart a Task. First, we design an experiment to explore the impact of packet loss and delay in unreliable networks on the Completion Time of an offloading Task. Then, we mathematically derive the prerequisites for local restart and selection of the optimal Timeout. The analysis result confirms that local restart is beneficial when the distribution of Task Completion Time has high variance. Further, a dynamic local restart scheme is proposed for mobile applications. This scheme keeps track of the variance of the probability density function of the distribution of Task Completion Time. This is done using a dynamic histogram, which collects and updates data at run Time. The efficiency of the local restart scheme is confirmed by experimental results. The experiment shows that local restart at the right Time achieves better performance than always offloading.
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Task Completion Time
2010Co-Authors: Katinka WolterAbstract:As a reference model for the reliability enhancement techniques discussed later in this book we will first look at system performance, availability and reliability without restart, rejuvenation, or checkpointing. Task Completion Time is considered in general [15] in unreliable systems that are subject to failures. The three mechanisms can be evaluated by how much they improve the Task Completion Time at what cost. To be able to value the benefit obtained by reliability enhancement mechanisms knowledge of the system behaviour without those mechanisms necessary.
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Stochastic Models for Preventive Maintenance and Software Rejuvenation
Stochastic Models for Fault Tolerance, 2010Co-Authors: Katinka WolterAbstract:A number of stochastic models for preventive maintenance and specifically for software rejuvenation are presented and discussed in this chapter. Preventive maintenance is a method to enhance system reliability and availability. Even before a failure happens measures are taken to prevent system failure. Software rejuvenation is one such preventive action. Software rejuvenation restarts the process environment to counteract software aging. While the taken action is essentially the same as in the restart model the considered metrics are fundamentally different. In consequence, the developed models also differ from the restart model. While restart is exclusively carried out for the purpose of minimising Task Completion Time and the system state is not considered, software rejuvenation models are in both aspects the opposite. Software rejuvenation models do not explicitly model the Task Completion Time but instead focus on the operating environment processing the Task. Software rejuvenation models minimise system downTime as well as the downTime costs not considering individual Tasks. The restart model minimises Task Completion Time while not explicitly considering possible system breakage.
David Mease - One of the best experts on this subject based on the ideXlab platform.
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evaluating web search using Task Completion Time
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009Co-Authors: Ya Xu, David MeaseAbstract:We consider experiments to measure the quality of a web search algorithm based on how much total Time users take to complete assigned search Tasks using that algorithm. We first analyze our data to verify that there is in fact a negative relationship between a user's total search Time and a user's satisfaction for the types of Tasks under consideration. Secondly, we fit a model with the user's total search Time as the response to compare two different search algorithms. Finally, we propose an alternative experimental design which we demonstrate to be a substantial improvement over our current design in terms of variance reduction and efficiency.
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SIGIR - Evaluating web search using Task Completion Time
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09, 2009Co-Authors: Ya Xu, David MeaseAbstract:We consider experiments to measure the quality of a web search algorithm based on how much total Time users take to complete assigned search Tasks using that algorithm. We first analyze our data to verify that there is in fact a negative relationship between a user's total search Time and a user's satisfaction for the types of Tasks under consideration. Secondly, we fit a model with the user's total search Time as the response to compare two different search algorithms. Finally, we propose an alternative experimental design which we demonstrate to be a substantial improvement over our current design in terms of variance reduction and efficiency.
Tsuyoshi Ueyama - One of the best experts on this subject based on the ideXlab platform.
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selection of manipulator system for multiple goal Task by evaluating Task Completion Time and cost with computational Time constraints
Advanced Robotics, 2013Co-Authors: Yanjiang Huang, Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Tsuyoshi UeyamaAbstract:The focus of this study is on the problem of manipulator system selection for a multiple-goal Task by evaluating Task Completion Time and cost with computational Time constraints. An approach integrating system selection, structural configuration design, layout design, motion planning, and relative cost calculation is proposed to solve this problem within a reasonable computational Time. In the proposed approach, multiple-objective particle swarm optimization (MOPSO) is utilized to search for the appropriate manipulator system with appropriate structural configuration from a set of candidate systems. Particle swarm optimization (PSO) and the nearest neighborhood algorithm are employed in layout design and motion planning due to their high convergence speed. Three methods involving a random search algorithm are compared to the proposed approach through a simulation. The simulation is done with a set of Tasks and the result shows the effectiveness of the proposed approach.
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manipulator system selection based on evaluation of Task Completion Time and cost
Intelligent Robots and Systems, 2011Co-Authors: Yanjiang Huang, Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Tsuyoshi Ueyama, Masao SugiAbstract:Task Completion Time and cost are two significant criteria for the selection of manipulator system. For a given Task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation Time. In this paper, we propose a method that can select appropriate systems by evaluating Task Completion Time and cost within the desired calculation Time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the Task Completion Time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of Tasks and is proved to be effective and practical.
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IROS - Manipulator system selection based on evaluation of Task Completion Time and cost
2011 IEEE RSJ International Conference on Intelligent Robots and Systems, 2011Co-Authors: Yanjiang Huang, Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Tsuyoshi Ueyama, Masao SugiAbstract:Task Completion Time and cost are two significant criteria for the selection of manipulator system. For a given Task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation Time. In this paper, we propose a method that can select appropriate systems by evaluating Task Completion Time and cost within the desired calculation Time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the Task Completion Time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of Tasks and is proved to be effective and practical.
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ICRA - Multiple-goal Task realization utilizing redundant degrees of freedom of Task and tool attachment optimization
2011 IEEE International Conference on Robotics and Automation, 2011Co-Authors: Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Jia Cheng, Tsuyoshi UeyamaAbstract:Minimizing the Task Completion Time of manipulator systems is essential in order to achieve high productivity. In this paper, this problem is dealt with by utilizing the redundant degrees of freedom (DOF) of a given Task and the tool attachment optimization. For example, in a vision-based inspection where a camera is held by a manipulator, the extra DOF can be brought about by allowing the camera to be translated along its approach axis or rotated about this axis when capturing images. Furthermore, the manipulator end-effector position and orientation is optimized by designing an additional linkage at the manipulator end-effector which is called a tool attachment. A 7-DOF manipulator system is used in the simulations to verify the proposed approach. Results showed that this approach can minimize the Task Completion Time by about 17% compared to conducting only motion coordination.
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compact design of work cell with robot arm and positioning table under a Task Completion Time constraint
Intelligent Robots and Systems, 2009Co-Authors: Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Tsuyoshi UeyamaAbstract:A work cell is generally designed to achieve a high throughput and its size is typically viewed as contingent to component sizes. In this paper, we aim to design a compact work cell (spatial requirement) and to minimize its Task Completion Time (temporal requirement) to a value set as a constraint. By doing so, a work cell occupies a minimal space and achieves its desired throughput. The work cell size is evaluated based on the size and the swept volume of components. This evaluation is important since a robot arm can have a very large swept volume depending on a given Task. To satisfy the spatial and temporal requirements, we propose the integration of the base placement optimization, goal rearrangement, and motion coordination between the robot arm and the positioning table. Furthermore, we introduce two motion coordination schemes based on the spatial and temporal requirements. We showed the effectiveness of the proposed method through simulations.
Lounell B Gueta - One of the best experts on this subject based on the ideXlab platform.
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selection of manipulator system for multiple goal Task by evaluating Task Completion Time and cost with computational Time constraints
Advanced Robotics, 2013Co-Authors: Yanjiang Huang, Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Tsuyoshi UeyamaAbstract:The focus of this study is on the problem of manipulator system selection for a multiple-goal Task by evaluating Task Completion Time and cost with computational Time constraints. An approach integrating system selection, structural configuration design, layout design, motion planning, and relative cost calculation is proposed to solve this problem within a reasonable computational Time. In the proposed approach, multiple-objective particle swarm optimization (MOPSO) is utilized to search for the appropriate manipulator system with appropriate structural configuration from a set of candidate systems. Particle swarm optimization (PSO) and the nearest neighborhood algorithm are employed in layout design and motion planning due to their high convergence speed. Three methods involving a random search algorithm are compared to the proposed approach through a simulation. The simulation is done with a set of Tasks and the result shows the effectiveness of the proposed approach.
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manipulator system selection based on evaluation of Task Completion Time and cost
Intelligent Robots and Systems, 2011Co-Authors: Yanjiang Huang, Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Tsuyoshi Ueyama, Masao SugiAbstract:Task Completion Time and cost are two significant criteria for the selection of manipulator system. For a given Task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation Time. In this paper, we propose a method that can select appropriate systems by evaluating Task Completion Time and cost within the desired calculation Time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the Task Completion Time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of Tasks and is proved to be effective and practical.
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IROS - Manipulator system selection based on evaluation of Task Completion Time and cost
2011 IEEE RSJ International Conference on Intelligent Robots and Systems, 2011Co-Authors: Yanjiang Huang, Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Tsuyoshi Ueyama, Masao SugiAbstract:Task Completion Time and cost are two significant criteria for the selection of manipulator system. For a given Task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation Time. In this paper, we propose a method that can select appropriate systems by evaluating Task Completion Time and cost within the desired calculation Time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the Task Completion Time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of Tasks and is proved to be effective and practical.
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ICRA - Multiple-goal Task realization utilizing redundant degrees of freedom of Task and tool attachment optimization
2011 IEEE International Conference on Robotics and Automation, 2011Co-Authors: Lounell B Gueta, Ryosuke Chiba, Tamio Arai, Jia Cheng, Tsuyoshi UeyamaAbstract:Minimizing the Task Completion Time of manipulator systems is essential in order to achieve high productivity. In this paper, this problem is dealt with by utilizing the redundant degrees of freedom (DOF) of a given Task and the tool attachment optimization. For example, in a vision-based inspection where a camera is held by a manipulator, the extra DOF can be brought about by allowing the camera to be translated along its approach axis or rotated about this axis when capturing images. Furthermore, the manipulator end-effector position and orientation is optimized by designing an additional linkage at the manipulator end-effector which is called a tool attachment. A 7-DOF manipulator system is used in the simulations to verify the proposed approach. Results showed that this approach can minimize the Task Completion Time by about 17% compared to conducting only motion coordination.
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ROBIO - Rearrangement Task of multiple robots using Task assignment applicable to different environments
2010 IEEE International Conference on Robotics and Biomimetics, 2010Co-Authors: Naoki Oyama, Lounell B GuetaAbstract:This paper addresses a rearrangement problem by a group of mobile robots and proposes a method for Task assignment and path planning applicable to different kinds of environment. The method minimizes Task Completion Time considering complexity of robot's paths in achieving the goal state of a working environment. It minimizes the number of possible delivery Tasks between robots thereby reducing the Task Completion Time. The proposed method is compared with continuous transportation method and Territorial Approach through simulations. The results of simulations show the effectiveness of the proposed method. The proposed method can realize an efficient rearrangement Task by mobile robots in various working environments under feasible computation Time.
Osman Parlaktuna - One of the best experts on this subject based on the ideXlab platform.
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a genetic algorithm for Task Completion Time minimization for multi robot sensor based coverage
International Conference on Control Applications, 2009Co-Authors: Metin Ozkan, Ahmet Yazici, Muzaffer Kapanoglu, Osman ParlaktunaAbstract:Minimizing the coverage Task Time is important for many sensor-based coverage applications. The Completion Time of a sensor-based coverage Task is determined by the maximum Time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel Times. Most of the coverage algorithms results in sharp turns which require the robot to slow down, turn and accelerate. So the actual travel Time of a mobile robot is depending on the traveled distance and number of turns both. In this study, previously proposed hierarchical oriented genetic algorithm (HOGA) is extended to consider the travel Time rather than just the traveled distances. The HOGA consists of two phases. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. Then, in the second phase, a directed genetic algorithm is used to partition the route among robots considering actual travel Time costs. The algorithms are coded in C++ and simulations are conducted using P3-DX mobile robots in the MobileSim environment.
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CCA/ISIC - A genetic algorithm for Task Completion Time minimization for multi-robot sensor-based coverage
2009 IEEE International Conference on Control Applications, 2009Co-Authors: Metin Ozkan, Ahmet Yazici, Muzaffer Kapanoglu, Osman ParlaktunaAbstract:Minimizing the coverage Task Time is important for many sensor-based coverage applications. The Completion Time of a sensor-based coverage Task is determined by the maximum Time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel Times. Most of the coverage algorithms results in sharp turns which require the robot to slow down, turn and accelerate. So the actual travel Time of a mobile robot is depending on the traveled distance and number of turns both. In this study, previously proposed hierarchical oriented genetic algorithm (HOGA) is extended to consider the travel Time rather than just the traveled distances. The HOGA consists of two phases. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. Then, in the second phase, a directed genetic algorithm is used to partition the route among robots considering actual travel Time costs. The algorithms are coded in C++ and simulations are conducted using P3-DX mobile robots in the MobileSim environment.
