Leakage power optimization based on threshold voltage (V th ) assignment poses great challenge in circuit design due to its tremendous solution space. In this paper, a Reinforcement Learning-based Leakage Power Optimization framework (RL-LPO) is first-ever proposed to formulate V th assignment as a reinforcement learning (RL) process by learning timing and physical characteristics of each circuit instance with Graph Neural Networks (GNN). The proposed RL-LPO was validated by the IWLS2005 and Opencores benchmark circuits with TSMC 28nm technology and experimental results demonstrate that our work achieves better leakage power optimization by additional 3% reduction on average than the commercial tool PrimeTime with 6.7× speed up when being transferred to unseen circuits with negligible timing degradation.
Abstract To address many shortcomings of the current unmanned aerial vehicle (UAV) inspection mode of the transmission line high-voltage towers and improve the inspection efficiency, the UAV tower foreign matter detection technology based on Augmented Reality (AR) and You Only Look Once: Unified, Real-Time Object Detection (YOLOv7) has been developed. Firstly, the system architecture, including UAV, ground station, and AR glasses is established. Then, the AR scene is built by using the Unity engine, and the interactive control mode between AR glasses and UAV is designed. Subsequently, the mapping mode of head action and UAV command is devised to synchronize the UAV action and head action. Finally, the tower is detected for foreign objects by using YOLOv7 vision detection technology. The experimental results validate the efficacy of the built AR scene, demonstrating seamless functionality in completing the video streaming and attitude data transmission functions. The use of AR glasses can achieve interactive control of the UAV attitude. In addition, the YOLOv7 visual detection algorithm can accurately detect the attachment of foreign objects to the high-voltage tower.
针对相关电子设备在运行中故障率不确定导致可靠度难以识别的问题,本研究设计了一个改进型的电子设备可靠性评估系统.利用平均无故障运行时间模型对电力设备的时间特征进行监测,采用最小二乘拟合设备的MTBF-T曲线,通过对设备可靠性发展趋势分析推测设备的运行寿命和故障发生概率.利用AMSAA技术对MTBF-T模型进行融合,获取该设备处于整个周期的相应阶段.采取长短期记忆网络算法提高本研究模型对电子设备故障的判别与预测的正确度.试验结果表明,通过本算法进行数据质量核查的准确度高达90％以上,表明该系统对提升故障判别准确度具有较强的实用性.
随着导弹飞行器等设备的逐渐发展,对弹上数据采集设备的要求越来越高,数据传输应该更加快速高效,本设计采用FPGA+千兆以太网的数据传输方式,并且在数据传输过程中增加了数据重传机制,加快了数据传输速度,提高了传输效率,降低了数据远距离传输过程中容易出现的数据丢失和误码问题发生概率.试验在Xilinx公司的FPGA板卡上进行验证,证明FPGA+千兆以太网的数据传输是可行的,且数据传输速率更高,具有良好的可维护性和稳定性,能够在实际工程中应用.
0引 言 测试有时是一件单调和枯燥的事情,寻找故障也十分具有挑战性.那么,为什么不利用测试和测量仪器上的通信接口来简化测试过程呢?本文中我们将探索用Python进行控制,不需要昂贵的软件和授权的工具. 对于大多数开发者而言,测试和测量设备在工作台上只是静静地等待任务.不过你把设备转到背面时,往往会看到通信接口.只要有合适的软件,我们就可以控制测量过程,并收集结果以供事后分析.寻找偶发事件和故障,或者在测试应用的多个系统参数时,这会非常有帮助.产品下线测试和元件的分级也可以用类似方式进行自动化测试.
