Task Execution Strategy Research Articles

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

FETES: a fast, emergency timeslot allocation, and three-tier energy saving-based task execution strategy for wireless body area network

Математическая модель распределения заданий между подъемно-транспортными машинами

Purpose: To determine task rational distribution between technics specialized in intra-terminal movements is to perform mathematical modeling of lifting transport machine operation in container terminal transport transshipment system given technological parameters and further multi-criteria optimization. Methods: Methods of probability theory and mathematical statistics as well as of expert estimates are used to discover the distribution. Results: Mathematical model of task distribution among lifting transport machines (LTM) has been developed. Total time dependences for completing LTM tasks using the current technology and task execution strategy at calculation of Pareto optimality towards six criteria, aimed at crane performance increase and empty LTM mileage minimization, are determined. Practical significance: Tasks rational distribution amongst techniques for container movement is proposed, which allows to increase processing capacity of a container terminal and to reduce technique operating costs.

Intelligent Service Deployment Policy for Next-Generation Industrial Edge Networks

Edge computing has appeared as a promising technology for realizing industrial computation data at the edge of the network. The fundamental challenge in edge-enabled industrial networks is how to deploy the service requests while utilizing the available edge resources efficiently. In this paper, we aim to design an intelligent service deployment strategy for simultaneously handling both Industrial Internet of Things (IIoT) generated dynamic service requests and edge resources in the next-generation industrial networks. Initially, we present the objective function as the mixed-integer nonlinear programming problem for optimizing the weighted energy-delay in the edge environment. To accomplish this objective, we model a heuristic-based task execution strategy and exploit the advantage of Deep Reinforcement Learning (DRL) to make accurate decisions in industrial networks. The proposed DRL-based strategy can learn well to control the industrial networks from its own experience and guarantees to handle as many service requests as possible using the set of available resource constraint edge servers. Experimental analysis reveals that the proposed strategy is robust to network changes and achieves better performance than existing algorithms in terms of energy consumption up to 13%, delay minimization by 23%, and other Quality of Service (QoS) parameters.

An Examination of the Effect of Perfectionism and Task Execution Strategy on Task Performance: From the Perspective of Regulatory Fit

An Examination of the Effect of Perfectionism and Task Execution Strategy on Task Performance: From the Perspective of Regulatory Fit

Performability Analysis of a Redundant Parallel Task in Network Systems

Recently, application of network systems ( e.g. , cloud computing systems) is increasingly prevalent for achieving integration, sharing and efficient utilization of various resources. A parallel task in a network system has multiple subtasks that can be executed in different servers in parallel. However, failures of any subtask inevitably result in that the entire task cannot be complete. To avoid such a situation, the network system can create some copies from a subtask and make them run on different servers simultaneously. This redundant parallel execution manner is an efficient approach to improve performance and guarantee reliability. However, it also brings complexity in modeling, evaluation and optimization. For example, link failures inevitably lead to inaccessibility of some servers, and server failures also result in that subtasks hosted on the server cannot be complete. This is the complicated failure correlation that cannot be ignored in modeling and evaluation. This paper first presents a reliability-performance correlation model for a redundant parallel task in the network system. The model captures precedence constraints of subtasks, multiple types of failures and complicated failure correlations to improve fidelity. This paper also design an algorithm that encompasses the Graph theory and the Bayesian theorem to evaluate a performability metric, which can be used to quantify important reliability-performance correlation. Finally, a heuristic algorithm is designed to search an optimal task execution strategy that maximizes the performability metric. Illustrative examples are presented.

Matching Participants for Triceps Surae Muscle Strength and Tendon Stiffness Does Not Eliminate Age-Related Differences in Mechanical Power Output During Jumping.

Reductions in muscular power output and performance during multi-joint motor tasks with aging have often been associated with muscle weakness. This study aimed to examine if matching younger and middle-aged adults for triceps surae (TS) muscle strength and tendon stiffness eliminates age-related differences in muscular power production during drop jump. The maximal ankle plantar flexion moment and gastrocnemius medialis tendon stiffness of 29 middle-aged (40–67 years) and 26 younger (18–30 years) healthy physically active male adults were assessed during isometric voluntary ankle plantar flexion contractions using simultaneous dynamometry and ultrasonography. The elongation of the tendon during the loading phase was assessed by digitizing the myotendinous junction of the gastrocnemius medialis muscle. Eight younger (23 ± 3 years) and eight middle-aged (54 ± 7 years) adults from the larger subject pool were matched for TS muscle strength and tendon stiffness (plantar flexion moment young: 3.1 ± 0.4 Nm/kg; middle-aged: 3.2 ± 0.5 Nm/kg; tendon stiffness: 553 ± 97 vs. 572 ± 100 N/mm) and then performed series of drop jumps from different box heights (13, 23, 33, and 39 cm) onto a force plate (sampling frequency 1000 Hz). The matched young and middle-aged adults showed similar drop jump heights for all conditions (from lowest to highest box height: 18.0 ± 3.7 vs. 19.7 ± 4.8 cm; 22.6 ± 4.2 vs. 22.9 ± 4.9 cm; 24.8 ± 3.8 vs. 23.5 ± 4.9 cm; 25.2 ± 6.2 vs. 22.7 ± 5.0 cm). However, middle-aged adults showed longer ground contact times (on average 36%), lower vertical ground reaction forces (36%) and hence lower average mechanical power (from lowest to highest box height: 2266 ± 563 vs. 1498 ± 545 W; 3563 ± 774 vs. 2222 ± 320 W; 4360 ± 658 vs. 2475 ± 528 W; 5008 ± 919 vs. 3034 ± 435 W) independent of box height. Further, leg stiffness was lower (48%) in middle-aged compared to younger adults for all jumping conditions and we found significant correlations between average mechanical power and leg stiffness (0.70 ≤ r ≤ 0.83; p < 0.01). Thus, while jumping performance appears to be unaffected when leg extensor muscle strength and tendon stiffness are maintained, the reduced muscular power output during lower limb multi-joint tasks seen with aging may be due to age-related changes in motor task execution strategy rather than due to muscle weakness.

When frequent media multitaskers perform worse and when they do not: The role of self-regulation ability and strategy manipulation

Ophir, Nass and Wagner (2009) showed that as multitasking frequency increases, multitasking performance decreases. Other studies, however, have not replicated this effect (e.g., Minear, Brasher, McCurdy, Lewis & Younggren, 2013). In this paper, we argue that the association between frequent media multitasking and poor multitasking performance depends on self-regulation ability and external factors, such as manipulation of the task execution strategy (sequential vs. free switching). In Study 1, we determined participants’ media multitasking frequency and measured their self-regulation ability. Then, participants performed a multiple media task in which they could freely switch between browser tabs. The results showed that high media multitasking levels were associated with more switches between tabs but only for participants with low (but not high) self-regulation ability. No differences in performance were observed. In Study 2, instead of measuring self-regulation ability, we manipulated task execution strategy (as an external form of regulation). As predicted, media multitasking frequency and performance on multiple tasks (overall score) were negatively related only in the free switching condition and not in the sequential condition. The results elucidate the relationship between media multitasking frequency and multitasking performance by showing its boundary conditions, and they help explain contradictory findings in the media multitasking literature.

Resource Virtualization Complex Scheduling and Task Execution Strategy Based on CloudSim Simulation Framework

Resource Virtualization Complex Scheduling and Task Execution Strategy Based on CloudSim Simulation Framework

Shared Mental Model Development During Technology-Mediated Collaboration

This study examines how collaboration mode – face-to-face and videoconferencing technology-mediated virtual teams - shapes negotiated shared interpretation of ideas needed for shared mental model construction. Social impact theory and group action theory provide a framework for explaining how technology-mediated collaboration constrains or enhances team shared mental model development. Social impact theory suggests that team member behavior is affected by 1) influential members, 2) number of members, and 3) proximity. Group action theory proposes that team member behavior is guided by 1) assessment of task requirements, 2) adopted task strategy, and 3) evaluation of task solution. This study argues that technology-mediated collaboration will exhibit lower participation rates and intra-team communication deficiencies while developing a shared mental model of task requirements, strategy and status. Partial least squares analysis revealed that technology-mediated collaboration does impact shared mental model development. Observers noted that decision making effectiveness and timeliness regarding task execution strategy and solution content was facilitated by a shared understanding of the task context. The study also confirmed the utility of direct observation for studying communication behaviors and social interaction in the development of shared mental model and teamwork.

Motion Planning and Coordination for Robot Systems Based on Representation Space

This paper proposes a general motion planning and coordination strategy for robot systems. The representation space (RS) of a robot system is constructed to describe the distributions of system attributes. The reachable area in the RS, denoting the attribute set that the system can be of, indicates the system's ability to accomplish tasks. Moreover, it also describes the influences of the internal and external constraints on the system's capability. Task realization is transformed to finding a trajectory in the RS for the system attributes to transit along under constraints. Meanwhile, the realizable conditions of a prescribed task by the robot system of specific configurations are discussed. If the task is realizable, the optimal strategy for task execution could further be figured out. Otherwise, it could be transformed to be realizable via task reassignment or system reconfigurations so that a connected path could be found for the transition of the system attributes from the starting point to the goal in the RS. The proposed scheme contributes to designing, planning, and coordination of the robotic tasks. Experiments on path planning of a robot manipulator and formation movement of a multirobot system, as well as coordination of a mobile manipulator system, are conducted to show the validity and generalization of the proposed method.

A new plan and coordination strategy for robot system based on state space

This paper proposes a general plan and coordination strategy for robot system. The state space for robot system is constructed according to the task requirement and system characteristic. Reachable state of the system is figured out by the system’s internal and external constraints. Task plan and coordination are then transformed as trajectory solving problem in the state space, by which the realizable conditions for the given task are discussed. If the task is realizable, the optimal strategy for task execution could be investigated and obtained in state space. Otherwise, it could be transformed to be realizable via adjusting the system configuration and/or task constraint, and the transformation condition could also be determined. This contributes to design, plan, and coordination of the robotic tasks. Experiments of the manipulator path planning and multi-robot formation movement are conducted to show the validity and generalization of the proposed method.