To build a large-scale computational grid resource model with realistic characteristics, this paper proposes a statistical remedy to approximate the distributions of computational and communicational abilities of resources. After fetching the source data of resources including computing devices and networks from the real world, the gamma and normal distributions are employed to approximate probabilities for the ability distribution of grid resources. With this method, researchers can supply the computational grid simulators with required parameters relating to computational and communicational abilities. The experiment result shows that proposed methodology can lead to a better precision according to the error measurement of the simulated data and its source. The proposed method can support existing modern grid simulators with a high-precision resource model which can consider the characteristics of distribution for its computational and communicational abilities in the grid computing environment.
Temperature detection technology plays an important role in the industrial automation systems, for example the production of the plastic container in the industrial manufacturing machines. To produce a high quality plastic product, blow molding machines need a high performance and a great precision non-contact temperature detecting system. In this paper, the authors custom design and implement a high performance distributed microcontroller system for non-contact temperature measurement. In practice, this system can detect the temperature of plastic preforms in the heating system of blow molding machines by using infrared temperature sensors and employing the network communication technologies, such as SMBus, UART and RS485 networks. Finally, some experiments are executed to evaluate the performance of the authors' system.
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