基于模型的系统工程(model-based systems engineering,MBSE)已被广泛应用于复杂系统设计之中。通过构建功能、行为和结构之间的关系,提出一种基于MBSE的多层级递进式架构设计流程。随后,以高度控制需求为导向,对民机飞行控制系统进行...基于模型的系统工程(model-based systems engineering,MBSE)已被广泛应用于复杂系统设计之中。通过构建功能、行为和结构之间的关系,提出一种基于MBSE的多层级递进式架构设计流程。随后,以高度控制需求为导向,对民机飞行控制系统进行了示例化建模。结果表明,基于MBSE的民机飞行控制系统多层级递进式架构设计能够充分发挥数字模型可重用的优势,保证需求与功能、逻辑和物理架构的紧密结合,提高系统设计的可追溯性,可为后续领域层阶段模型设计提架构参考。展开更多
In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive in...In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.展开更多
文摘基于模型的系统工程(model-based systems engineering,MBSE)已被广泛应用于复杂系统设计之中。通过构建功能、行为和结构之间的关系,提出一种基于MBSE的多层级递进式架构设计流程。随后,以高度控制需求为导向,对民机飞行控制系统进行了示例化建模。结果表明,基于MBSE的民机飞行控制系统多层级递进式架构设计能够充分发挥数字模型可重用的优势,保证需求与功能、逻辑和物理架构的紧密结合,提高系统设计的可追溯性,可为后续领域层阶段模型设计提架构参考。
文摘In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.
