A multi-dimensional scheduling scheme in a Grid computing environment

In this paper, we propose a novel distributed resource-scheduling algorithm capable of handling multiple resource requirements for jobs that arrive in a Grid computing environment. In our proposed algorithm, referred to as multiple resource scheduling (MRS) algorithm, we take into account both the site capabilities and the resource requirements of jobs. The main objective of the algorithm is to obtain a minimal execution schedule through efficient management of available Grid resources. We first propose a model in which the job and site resource characteristics can be captured together and used in the scheduling algorithm. To do so, we introduce the concept of a n-dimensional virtual map and resource potential. Based on the proposed model, we conduct rigorous simulation experiments with real-life workload traces reported in the literature to quantify the performance. We compare our strategy with most of the commonly used algorithms in place on performance metrics such as job wait times, queue completion times, and average resource utilization. Our combined consideration of job and resource characteristics is shown to render high-performance with respect to above-mentioned metrics in the environment. Our study also reveals the fact that MRS scheme has a capability to adapt to both serial and parallel job requirements, especially when job fragmentation occurs. Our experimental results clearly show that MRS outperforms other strategies and we highlight the impact and importance of our strategy.     

Complete Boolean Satisfiability Solving Algorithms Based on Local Search

Boolean satisfiability (SAT) is a well-known problem in computer science, artificial intelligence, and operations research. This paper focuses on the satisfiability problem of Model RB structure that is similar to graph coloring problems and others. We propose a translation method and three effective complete SAT solving algorithms based on the characterization of Model RB structure. We translate clauses into a graph with exclusive sets and relative sets. In order to reduce search depth, we determine search order using vertex weights and clique in the graph. The results show that our algorithms are much more effective than the best SAT solvers in numerous Model RB benchmarks, especially in those large benchmark instances.     

A parallel genetic/neural network learning algorithm for MIMDshared memory machines

A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image recognition. The performance of the algorithm has been evaluated by applying it to three examples. The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated.     

Investigation of creep behavior of 316L stainless steel produced by selective laser melting with various processing parameters

Journal of Mechanical Science and Technology - Creep behavior of 316L stainless steel was experimentally investigated using small punch (SP) specimens produced by the selective laser melting (SLM)...     

Effects of process parameters on the high temperature strength of 17-4PH stainless steel produced by selective laser melting

Journal of Mechanical Science and Technology - In this study, the effects of process parameters on the high temperature strength of 17-4PH stainless steel manufactured by selective laser melting...     

Temperature-Aware Task Allocation and Scheduling for Embedded Multiprocessor Systems-on-Chip (MPSoC) Design

Temperature affects not only the performance but also the power, reliability, and cost of the embedded system. This paper proposes a temperature-aware task allocation and scheduling algorithm for MPSoC embedded systems. Thermal-aware heuristics are developed, and a temperature-aware floorplanning tool is used to reduce the peak temperature and achieve a thermally even distribution while meeting real time constraints. The paper investigates both power-aware and thermal-aware approaches to the task allocation and scheduling. The experimental results show that the thermal-aware approach outperforms the power-aware schemes in terms of maximal and average temperature reductions. To the best of our knowledge, this is the first MPSoC task allocation and scheduling algorithm that takes temperature into consideration.

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基于Deform 3D的高速车削加工仿真研究

基于有限元分析软件Deform 3D对高速车削加工进行仿真研究,通过实例分析了车削过程中切屑的形成过程,切削力大小、切削温度及应力应变的分布情况。该仿真结果对车削工艺效果的预测和优化具有现实的指导意义。     

Cooperative detection and protection for Interest flooding attacks in named data networking

Named data networking (NDN) is a new emerging architecture for future network, which may be a substitute of the current TCP/IP‐based network, for the content‐oriented data request mode becoming the future trend of development. The security of NDN has attracted much attention, as an implementation of next‐generation Internet architecture. Although NDN is immune to most current attack, it cannot resist the distributed denial of service like attack – Interest flooding attack (IFA) – effectively. IFA takes advantages of the forwarding mechanism of NDN, flooding a large number of malicious Interest packets at quite a high rate, and exploits the network resources, which may cause the paralysis of the network. Taking into account the severity of the destruction, we propose an algorithm to counter such new type of attack. We analyze three properties of IFA, and use them to judge and filter Interest packets. Vector space model and Markov model are used in our method to realize a cooperative detection. Meanwhile, we present the retransmission forwarding mechanism to ensure legitimate user request. The ndnSIM module of ns3 is used for the corresponding simulation, and results of the simulation will be given to show the effectiveness of our algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust CMAC control schemes for dynamic trajectory following

Abstract

Improved robust CMAC control schemes are proposed for tracing dynamic trajectories in this paper. There are two main structures in the proposed control schemes: one is the robust controller and the other is the improved CMAC network. The robust controller technique can achieve a certain goal without concern for instability of the controlled system in the presence of significant plant uncertainties if the nominal parameter is roughly estimated. Next, in order to reduce the tracing error, a suitable nominal parameter needs to be chosen. Thus, the improved CMAC learning approach under the robust control structure, using the concept of credit assignment, will be employed to determine control variables that can trace other states repeatedly during control processes. Finally, simulation results demonstrate the capability of the proposed control schemes to trace dynamic trajectories.     

Statistically optimised method for detecting adult image groups

Adult image recognition is an important technique for preventing children from accessing offensive material on the Internet. Most of the related works focus on single image recognition. However, adult images usually exist as a group and rarely stand alone. Therefore, considering the entire image group as a whole for classification should be more effective. This paper presents a new method of detecting adult image groups, which aims at achieving optimal recognition accuracy. Adult image group recognition generally includes two components: an adult image recogniser and a final decision rule for classifying the image group. We provide a theoretical analysis to clarify the correlation of the two components and use probability formulae to estimate the recognition rates for different settings of the adult image recogniser and the decision rule. Then, a set of optimal receiver-operating characteristic (ROC) curves for different image numbers is solved. To recognise an unknown image group, a desired recall rate for adult (or benign) image groups is specified and the system is set according to the parameters acquired from the optimal ROC curves. The proposed method can be dynamically adapted to the recall rates that the user expects. This advantage makes the proposed system more suitable for real applications. Our work can be viewed as an extension of single image recognition and the experimental results demonstrate that it can attain higher recognition accuracy than the earlier methods.