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Optimization of a crossbar parallel machine tool based on workspace and dexterity
Fang Xifeng,Zhang Sichong,Xu Qinhuan,Wang Tongyue,Liu Yuanwei,Chen Xiaogang 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.8
Increasing workspace and improving dexterity are important tasks for the design of parallel machine tools. The workspace of a crossbarparallel machine tool with constraints is obtained by using a 3D search method based on inverse kinematics. The new Jacobian matrixof the machine is also derived by using the natural coordinate method. Dexterity distribution of the machine tool is obtained on the basisof the workspace and the new Jacobian matrix. Influences of the structural parameters on the workspace volume index (WVI) and globaldexterity index (GDI) are analyzed. Structural optimization is conducted by treating the WVI and GDI as the global optimization goals. Unlike the initial data, the optimized results increased by 0.43 and 0.34 times.
Zhulin Li,Cuirong Wang,Haiyan Lv,Tongyu Xu 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.9
Hadoop uses a reliable, efficient and scalable way to process data. It provides a good solution for dealing with big data. The task scheduler is the core component of Hadoop, and it is responsible for the managing and allocating the cluster resources. Therefore, scheduling algorithm directly affects the overall performance of Hadoop platform and utilization of cluster resource. Based on this, the improved accelerate particle swarm algorithm (IAPSO) is introduced to the cloud environment, and to solve the cloud task scheduling problem in this article. When we use particle swarm algorithm for task scheduling, the tasks are considered as particles, the resource pool is seen as the search space, and the process of finding the optimal solution is considered as a process of task scheduling. If all the sub tasks find the appropriate resources, then stop the iteration and allocate sub asks to the resource nodes. Finally, we simulate the experiment by using CloudSim software. When a single type of task is committed, our algorithm and the other three algorithms can also be used to complete the task scheduling process, and our algorithm is more efficient. But in practice, the cloud computing environment is facing multiuser, and the types of tasks are also varied. With the increase in the number of tasks, the advantage of the other three algorithms decreases gradually, but algorithm in this paper has been exhibited higher efficiency. In addition, with the increase of the number of nodes, task completed time of the algorithm in this paper is significantly less than the other three algorithms, and it has a steady downward trend. Therefore, IAPSO algorithm which is proposed in this paper is applied to solve task scheduling problem in the cloud environment, and it can effectively improve the efficiency of task scheduling.