There usually exist narrow-long-deep areas in mould needed to be machined in special machining. To identify the narrow-deep areas automatically, an automatic narrow-deep feature （NF） recognition method is put forwar...There usually exist narrow-long-deep areas in mould needed to be machined in special machining. To identify the narrow-deep areas automatically, an automatic narrow-deep feature （NF） recognition method is put forward accordingly. First, the narrow-deep feature is defined innovatively in this field and then feature hint is extracted from the mould by the characteristics of narrow-deep feature. Second, the elementary constituent faces （ECF） of a feature are found on the basis of the feature hint. By means of extending and clipping the ECF, the feature faces are obtained incrementally by geometric reasoning. As a result, basic narrow-deep features （BNF） related are combined heuristically. The proposed NF recognition method provides an intelligent connection between CAD and CAPP for machining narrow-deep areas in mould.展开更多
窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗广域网络(Low-Power Wide Area Network,LPWAN)技术,在实际应用中面临动态频谱需求、高设备密度和信道条件波动等复杂挑战。为此,提出一种基于深度强化学习的自适应资...窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗广域网络(Low-Power Wide Area Network,LPWAN)技术,在实际应用中面临动态频谱需求、高设备密度和信道条件波动等复杂挑战。为此,提出一种基于深度强化学习的自适应资源管理算法(Deep Reinforcement Learning-based Adaptive Resource Management Algorithm,DRL-ARMA)。该算法通过构建马尔可夫决策过程,对NB-IoT中的资源管理问题进行建模,并结合深度强化学习框架优化频谱分配、功率控制和调制方式选择。仿真实验结果表明,与传统的基于规则算法和随机分配算法相比,DRL-ARMA在吞吐量、延迟和能耗方面均展现出显著优势。所提出的算法有效地解决动态和复杂通信环境中的资源管理挑战,为NB-IoT中的智能资源分配提供一种新的技术方法和理论基础。展开更多
基金Supported by the National Natural Science Foundation of China under Grant No. 61073066the National High Technology Development 863 Program of China under Grant No. 2008AA04Z115
文摘There usually exist narrow-long-deep areas in mould needed to be machined in special machining. To identify the narrow-deep areas automatically, an automatic narrow-deep feature （NF） recognition method is put forward accordingly. First, the narrow-deep feature is defined innovatively in this field and then feature hint is extracted from the mould by the characteristics of narrow-deep feature. Second, the elementary constituent faces （ECF） of a feature are found on the basis of the feature hint. By means of extending and clipping the ECF, the feature faces are obtained incrementally by geometric reasoning. As a result, basic narrow-deep features （BNF） related are combined heuristically. The proposed NF recognition method provides an intelligent connection between CAD and CAPP for machining narrow-deep areas in mould.
文摘窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗广域网络(Low-Power Wide Area Network,LPWAN)技术,在实际应用中面临动态频谱需求、高设备密度和信道条件波动等复杂挑战。为此,提出一种基于深度强化学习的自适应资源管理算法(Deep Reinforcement Learning-based Adaptive Resource Management Algorithm,DRL-ARMA)。该算法通过构建马尔可夫决策过程,对NB-IoT中的资源管理问题进行建模,并结合深度强化学习框架优化频谱分配、功率控制和调制方式选择。仿真实验结果表明,与传统的基于规则算法和随机分配算法相比,DRL-ARMA在吞吐量、延迟和能耗方面均展现出显著优势。所提出的算法有效地解决动态和复杂通信环境中的资源管理挑战,为NB-IoT中的智能资源分配提供一种新的技术方法和理论基础。
