针对民用直升机显控系统需求难以追溯、交互设计缺陷难以洞察以及早期系统设计验证难以实现等问题,提出基于MBSE(model-based system engineering)和VAPS的民用直升机显控系统设计与验证方法。捕获利益攸关者需求形成系统需求,将系统需...针对民用直升机显控系统需求难以追溯、交互设计缺陷难以洞察以及早期系统设计验证难以实现等问题,提出基于MBSE(model-based system engineering)和VAPS的民用直升机显控系统设计与验证方法。捕获利益攸关者需求形成系统需求,将系统需求分配给系统用例;构建黑盒活动图、顺序图自顶向下开展“需求–功能分析”描述显控系统级功能流,建立可运行的黑盒状态机验证功能逻辑设计的合理性;在黑盒功能架构的基础上进一步划分以构建显控系统架构,通过与飞行员交流迭代优化分配方案,将黑盒活动图中的活动分配到各显控子系统中实现功能向下传递,保证系统设计过程的连贯性。基于人机界面设计工具VAPS开发飞行员操作程序,验证了基于MBSE设计的显控系统需求、功能、逻辑的一致性和架构的合理性,实现了需求设计到验证的完全覆盖。展开更多
Visual Attention Prediction(VAP)is widely applied in GIS research,such as navigation task identification and driver assistance systems.Previous studies commonly took color information to detect the visual saliency of ...Visual Attention Prediction(VAP)is widely applied in GIS research,such as navigation task identification and driver assistance systems.Previous studies commonly took color information to detect the visual saliency of natural scene images.However,these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks.To better predict visual attention while driving tasks,in this paper,we firstly propose an Adaptive Feature Integration Fully Convolutional Network(AdaFI-FCN)using Scene-Adaptive Weights(SAW)to integrate RGB-D,motion and semantic features.The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models(AUC-Judd=0.971,CC=0.767,KL=1.046,SIM=0.579).In addition,the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes.The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes,such as AR-aided navigation,indoor navigation,and street-view image reading.展开更多
文摘针对民用直升机显控系统需求难以追溯、交互设计缺陷难以洞察以及早期系统设计验证难以实现等问题,提出基于MBSE(model-based system engineering)和VAPS的民用直升机显控系统设计与验证方法。捕获利益攸关者需求形成系统需求,将系统需求分配给系统用例;构建黑盒活动图、顺序图自顶向下开展“需求–功能分析”描述显控系统级功能流,建立可运行的黑盒状态机验证功能逻辑设计的合理性;在黑盒功能架构的基础上进一步划分以构建显控系统架构,通过与飞行员交流迭代优化分配方案,将黑盒活动图中的活动分配到各显控子系统中实现功能向下传递,保证系统设计过程的连贯性。基于人机界面设计工具VAPS开发飞行员操作程序,验证了基于MBSE设计的显控系统需求、功能、逻辑的一致性和架构的合理性,实现了需求设计到验证的完全覆盖。
基金supported by the National Natural Science Foundation of China(NSFC)under Grant No.42230103the State Key Laboratory of Geographic Information Engineering and the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of the Ministry of Natural Resources Jointly Funded Project under Grant No.2021-04-03.
文摘Visual Attention Prediction(VAP)is widely applied in GIS research,such as navigation task identification and driver assistance systems.Previous studies commonly took color information to detect the visual saliency of natural scene images.However,these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks.To better predict visual attention while driving tasks,in this paper,we firstly propose an Adaptive Feature Integration Fully Convolutional Network(AdaFI-FCN)using Scene-Adaptive Weights(SAW)to integrate RGB-D,motion and semantic features.The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models(AUC-Judd=0.971,CC=0.767,KL=1.046,SIM=0.579).In addition,the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes.The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes,such as AR-aided navigation,indoor navigation,and street-view image reading.
