【目的】提高传统的单一类别煤矸分选机器人在面对形状、尺寸差异较大的矸石时的适应性,分析异构机器人工作特性,实现异构机器人协同分选。【方法】基于深度Q值网络(deep Q network,DQN)提出异构机器人协同分选模型;分析协同工作分选流...【目的】提高传统的单一类别煤矸分选机器人在面对形状、尺寸差异较大的矸石时的适应性,分析异构机器人工作特性,实现异构机器人协同分选。【方法】基于深度Q值网络(deep Q network,DQN)提出异构机器人协同分选模型;分析协同工作分选流程制定决策框架,根据强化学习所需,设计交互环境,构建智能体连续的状态空间奖惩函数,长短期记忆网络(long short term memory,LTSM)和全连接网络相结合,构建DQN价值和目标网络,实现强化学习模型在工作过程中的任务分配。【结果】协同分选模型与传统顺序分配模型相比,在不同含矸率工作负载下,可提高分选效益0.49%~17.74%;在样本含矸率为21.61%,传送带速度为0.4~0.6 m/s的条件下,可提高分选效率2.41%~8.98%。【结论】异构机器人协同分选方法可以在不同的工作负载下获得稳定的分拣效益,避免单一分配方案无法适应动态变化的矸石流缺陷。展开更多
Visual quality is a significant issue in today’s modern world which needs to be evaluated. This assessment is based on the observer’s perception and sight of space and attempts to introduce the criteria of spatial d...Visual quality is a significant issue in today’s modern world which needs to be evaluated. This assessment is based on the observer’s perception and sight of space and attempts to introduce the criteria of spatial desirability in the landscape. Analysis of the relationships between visual quality and structural features of the landscape is an effective way of conducting cognitive research. For this purpose, the analysis of visual quality was considered by compiling measurement and evaluation criteria, based on the concept of user preferences, in the part of Tehran-Qom Freeway, near the northeastern side of Imam Khomeini International Airport (Iran). The main objective of this research is to present a strategic plan for preserving and restoring the ecological landscape, creating a sense of place in the region, emphasizing gradual changes in the landscape during the movement and considering its relation to landscape aesthetics. For this purpose, after the questionnaire was prepared, the classifying visual quality method (Q-Sort) was used. Finally, using the results obtained from landscape analysis, identifying the components which led to the lack of visibility of individuals, and by using the geographic information systems (GIS), the strategic design of the area was designed.展开更多
文摘【目的】提高传统的单一类别煤矸分选机器人在面对形状、尺寸差异较大的矸石时的适应性,分析异构机器人工作特性,实现异构机器人协同分选。【方法】基于深度Q值网络(deep Q network,DQN)提出异构机器人协同分选模型;分析协同工作分选流程制定决策框架,根据强化学习所需,设计交互环境,构建智能体连续的状态空间奖惩函数,长短期记忆网络(long short term memory,LTSM)和全连接网络相结合,构建DQN价值和目标网络,实现强化学习模型在工作过程中的任务分配。【结果】协同分选模型与传统顺序分配模型相比,在不同含矸率工作负载下,可提高分选效益0.49%~17.74%;在样本含矸率为21.61%,传送带速度为0.4~0.6 m/s的条件下,可提高分选效率2.41%~8.98%。【结论】异构机器人协同分选方法可以在不同的工作负载下获得稳定的分拣效益,避免单一分配方案无法适应动态变化的矸石流缺陷。
文摘Visual quality is a significant issue in today’s modern world which needs to be evaluated. This assessment is based on the observer’s perception and sight of space and attempts to introduce the criteria of spatial desirability in the landscape. Analysis of the relationships between visual quality and structural features of the landscape is an effective way of conducting cognitive research. For this purpose, the analysis of visual quality was considered by compiling measurement and evaluation criteria, based on the concept of user preferences, in the part of Tehran-Qom Freeway, near the northeastern side of Imam Khomeini International Airport (Iran). The main objective of this research is to present a strategic plan for preserving and restoring the ecological landscape, creating a sense of place in the region, emphasizing gradual changes in the landscape during the movement and considering its relation to landscape aesthetics. For this purpose, after the questionnaire was prepared, the classifying visual quality method (Q-Sort) was used. Finally, using the results obtained from landscape analysis, identifying the components which led to the lack of visibility of individuals, and by using the geographic information systems (GIS), the strategic design of the area was designed.
