As the very large-scale integrated circuit designs enter the deep sub-micron era, many-core processors are regarded as promising architectures to keep up with the Moore's law. To provide effective communications between the on-chip components, network-on-chip was proposed as a new paradigm that exhibits better scalability than the traditional buses. There have been previous researches on application mappings to reduce the power consumption, the network latency and the network area overhead. However, some of the previous proposed algorithms such as the Kernighan-Lin algorithm (KL) and some genetic algorithms (GA) have the problem of finding the local best result instead of a global optimal solution. In this paper, we propose a novel application mapping algorithm for the mesh-of-tree network topology, called KL_GA algorithm. Our proposed algorithm takes the advantage of both the Kernighan-Lin algorithm and genetic algorithms to reduce the overall communication cost. Our KL_GA algorithm first generates a mapping solution using a KL-based method. In order to avoid the appearance of premature phenomena, we next apply a GA-based algorithm to get rid of the population trapped in the local optimum and re-generate a new population. Our evaluations show that, compared to the random mapping algorithm, our KL_GA algorithm saves the power by 21.6 % and reduces the network latency by 16.3 % on the average.

Lattice gas model is a kind of mature and convenient pedestrian simulation model. The original lattice gas model adopts discontinuous step length and finite moving directions to simulate crowd motion, which will lead to some unreasonable movements; besides, the transition probability used in this model is often manually designed and lacks the verification of realistic pedestrian trajectories. Based on an open pedestrian trajectory dataset, we first derived the relationship between local density and the distribution of pedestrian movements’ length and then proposed an extended lattice gas model considering the statistical characteristics of pedestrian movements, which extends the concept of transition probability in the original lattice gas model to distribution of pedestrian movements’ length in two perpendicular directions. The proposed model is applied to a scenario which is the same as the experiments of the open dataset, and the numerical results demonstrate that the proposed model can reproduce the fundamental diagrams and the transition probability of the experimental dataset well. This study is helpful to understand the statistical characteristics of pedestrian movements and can improve the applicability and accuracy of the lattice gas model.

The equipment monitoring brought by the smart grid big data is difficult to effectively supervise the operation status of the whole network substation, and the typical defects (familial defects) are difficult to classify and locate. This paper proposes the substation operation state evaluation algorithm and typical defect classification algorithm. The operating state evaluation algorithm of the substation is based on different operation data generated by the substation. By normalizing the data of different dimensions, the substation is divided into different operating state levels. The typical defect classification algorithm establishes and maintains the historical experience database, and calculates the conditional probability of each information item to realize the correlation between the signal and the defect, and finally judge whether the signal is from a typical defect. These two algorithms are effective means for equipment monitoring professionals to realize intelligent supervision of substations and equipment in the era of big data.

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