An RF-UCard system is a contactless smartcard system with multiple chip operating systems and multiple applications. A multi-card collision occurs when more than one card within the reader’s read field and thus lower...An RF-UCard system is a contactless smartcard system with multiple chip operating systems and multiple applications. A multi-card collision occurs when more than one card within the reader’s read field and thus lowers the efficiency of the system. This paper presents a novel and enhanced algorithm to solve the multi-card collision problems in an RF-UCard system. The algorithm was originally inspired from framed ALOHA-based anti-collision algorithms applied in RFID systems. To maximize the system efficiency, a synchronous dynamic adjusting (SDA) scheme that adjusts both the frame size in the reader and the response probability in cards is developed and evaluated. Based on some mathematical results derived from the Poisson process and the occupancy problem, the algorithm takes the estimated card quantity and the new arriving cards in the current read cycle into consideration to adjust the frame size for the next read cycle. Also it changes the card response probability according to the request commands sent from the reader. Simulation results show that SDA outperforms other ALOHA-based anti-collision algorithms applied in RFID systems.展开更多
为实现柔性直流(voltage sourced converter-high voltage direct current,VSC-HVDC)换流阀冷却系统入阀水温的智能预测,文中提出一种基于随机森林(random forest,RF)和双向长短时记忆(bi-directional long short-term memory,BiLSTM)...为实现柔性直流(voltage sourced converter-high voltage direct current,VSC-HVDC)换流阀冷却系统入阀水温的智能预测,文中提出一种基于随机森林(random forest,RF)和双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络混合的柔直换流阀冷却系统入阀水温的预测模型,并以此为基础对柔直换流站阀冷系统的冷却能力进行评估。首先,采用RF算法对由阀冷系统监测变量组成的高维特征集进行重要性分析,筛选出影响入阀水温的重要特征,与历史入阀水温构成输入特征向量。然后,将特征向量输入到BiLSTM预测模型,对模型进行训练并实现对入阀水温的准确预测和冷却能力定量评估。最后,以广东电网某柔直换流站为实例对所提方法进行分析,验证了所提出的基于RF-BiLSTM的混合模型预测精度优于BiLSTM模型、RF模型、支持向量机(support vector machine,SVM)模型和自回归滑动平均模型(auto-regressive and moving average,ARMA)模型,并且实现了冷却能力的定量评估。结果表明该换流站冷却裕量达98%,存在过度冷却、能源浪费的问题,与换流站现场运行情况相符,验证了文中所提方法的有效性和准确性。展开更多
文摘An RF-UCard system is a contactless smartcard system with multiple chip operating systems and multiple applications. A multi-card collision occurs when more than one card within the reader’s read field and thus lowers the efficiency of the system. This paper presents a novel and enhanced algorithm to solve the multi-card collision problems in an RF-UCard system. The algorithm was originally inspired from framed ALOHA-based anti-collision algorithms applied in RFID systems. To maximize the system efficiency, a synchronous dynamic adjusting (SDA) scheme that adjusts both the frame size in the reader and the response probability in cards is developed and evaluated. Based on some mathematical results derived from the Poisson process and the occupancy problem, the algorithm takes the estimated card quantity and the new arriving cards in the current read cycle into consideration to adjust the frame size for the next read cycle. Also it changes the card response probability according to the request commands sent from the reader. Simulation results show that SDA outperforms other ALOHA-based anti-collision algorithms applied in RFID systems.
文摘为实现柔性直流(voltage sourced converter-high voltage direct current,VSC-HVDC)换流阀冷却系统入阀水温的智能预测,文中提出一种基于随机森林(random forest,RF)和双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络混合的柔直换流阀冷却系统入阀水温的预测模型,并以此为基础对柔直换流站阀冷系统的冷却能力进行评估。首先,采用RF算法对由阀冷系统监测变量组成的高维特征集进行重要性分析,筛选出影响入阀水温的重要特征,与历史入阀水温构成输入特征向量。然后,将特征向量输入到BiLSTM预测模型,对模型进行训练并实现对入阀水温的准确预测和冷却能力定量评估。最后,以广东电网某柔直换流站为实例对所提方法进行分析,验证了所提出的基于RF-BiLSTM的混合模型预测精度优于BiLSTM模型、RF模型、支持向量机(support vector machine,SVM)模型和自回归滑动平均模型(auto-regressive and moving average,ARMA)模型,并且实现了冷却能力的定量评估。结果表明该换流站冷却裕量达98%,存在过度冷却、能源浪费的问题,与换流站现场运行情况相符,验证了文中所提方法的有效性和准确性。
