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Traditional hybrid scheduling method of partitioned processing and network communication restricts the schedulability of distributed integrated modular avionics (DIMA) systems according to strict constraints. This paper loosens these constraints by introducing harmonic partitions, converts the hybrid scheduling design into a nonlinear optimization problem, and proposes an improved method based on the tabu search metaheuristic. Comparative experiments demonstrate that this method adapts to a larger-scale system including 50 partitions and 1000 messages and guarantees lower processing-level end-to-end delay with an average optimization of 48%.

In this letter, we propose a novel robust transferable subspace learning (RTSL) method for cross-corpus facial expression recognition. In this method, on one hand, we present a novel distance metric algorithm, which jointly considers the local and global distance distribution measure, to reduce the cross-corpus mismatch. On the other hand, we design a label guidance strategy to improve the discriminate ability of subspace. Thus, the RTSL is much more robust to the cross-corpus recognition problem than traditional transfer learning methods. We conduct extensive experiments on several facial expression corpora to evaluate the recognition performance of RTSL. The results demonstrate the superiority of the proposed method over some state-of-the-art methods.