Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operat...Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algorithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identification scheme for distribution networks.First,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.展开更多
Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate...Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.展开更多
针对目前各大型储物仓库货物数量多、质量大和人工分拣、搬运较为困难导致的一些仓库管理效率低下等弊端,文章设计了一款基于STM32的自动分拣式仓储物流机器人。该机器人采用射频识别(Radio Frequency Identification,RFID)技术自动识...针对目前各大型储物仓库货物数量多、质量大和人工分拣、搬运较为困难导致的一些仓库管理效率低下等弊端,文章设计了一款基于STM32的自动分拣式仓储物流机器人。该机器人采用射频识别(Radio Frequency Identification,RFID)技术自动识别货物信息;通过算法设计自动规划路径运行到相应的货架位置;采用超声波避障技术避开障碍完成智能寻路;利用物联网传感器技术进行称重、利用机械化结构技术进行减震、利用STM32单片机技术上下台阶和升降。该机器人在以上几个模块的协同配合下,能将货物安全、快速、准确地存放于货架上。与传统的人工分拣相比,该机器人使管理效率得到了显著提高,大大降低了人工成本和危险系数,在实现仓储物流的无人化管理方面具有广阔的发展空间与应用前景。展开更多
提供了一种用于便携式射频识别读写器的低功耗液晶实时显示系统,工作频率在125kHz国际通用频段。基于降低功耗的考虑,采用当前先进的超低功耗的片上系统(SOC-System On Chip) ,将外围电路尽可能地放置于SOC芯片内,运用了集成在控制器内...提供了一种用于便携式射频识别读写器的低功耗液晶实时显示系统,工作频率在125kHz国际通用频段。基于降低功耗的考虑,采用当前先进的超低功耗的片上系统(SOC-System On Chip) ,将外围电路尽可能地放置于SOC芯片内,运用了集成在控制器内部的液晶驱动电路。运用软件实现了解码、数据校验、识别、实时处理和数据显示等功能。从而简化了传统的微控制器控制的RFID读写器所需的庞大液晶驱动硬件电路和其引入的额外误差,具有重要的实用价值,并且功耗和成本很低。展开更多
基于模拟最小均方（analogue least mean square,ALMS）环路的反射功率对消器能够自适应地抑制载波干扰,故常应用于连续波雷达中。传统的反射功率对消器使用基于矢量调制器的独立馈通电路产生对消信号,对消电路复杂度高。提出一种改进...基于模拟最小均方（analogue least mean square,ALMS）环路的反射功率对消器能够自适应地抑制载波干扰,故常应用于连续波雷达中。传统的反射功率对消器使用基于矢量调制器的独立馈通电路产生对消信号,对消电路复杂度高。提出一种改进的反射功率对消器,对消器使用一个平衡式反射型正交调制器产生对消信号,避免了独立馈通电路的使用,从而简化了电路结构。平衡式的正交调制器具有较好的I/Q平衡性,确保ALMS控制环路在较宽的频带内保持稳定。最终制作了工作在UHF频段的对消器模块。测量结果显示,该模块对以915 MHz为中心频率、带宽60 MHz的线性调频干扰信号具有良好的自适应跟踪能力。在干扰功率＋10 dBm条件下,典型的等效输入噪声谱密度为-153 dBm/Hz@100 kHz。展开更多
文摘Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algorithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identification scheme for distribution networks.First,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.
文摘Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.
文摘针对目前各大型储物仓库货物数量多、质量大和人工分拣、搬运较为困难导致的一些仓库管理效率低下等弊端,文章设计了一款基于STM32的自动分拣式仓储物流机器人。该机器人采用射频识别(Radio Frequency Identification,RFID)技术自动识别货物信息;通过算法设计自动规划路径运行到相应的货架位置;采用超声波避障技术避开障碍完成智能寻路;利用物联网传感器技术进行称重、利用机械化结构技术进行减震、利用STM32单片机技术上下台阶和升降。该机器人在以上几个模块的协同配合下,能将货物安全、快速、准确地存放于货架上。与传统的人工分拣相比,该机器人使管理效率得到了显著提高,大大降低了人工成本和危险系数,在实现仓储物流的无人化管理方面具有广阔的发展空间与应用前景。
文摘提供了一种用于便携式射频识别读写器的低功耗液晶实时显示系统,工作频率在125kHz国际通用频段。基于降低功耗的考虑,采用当前先进的超低功耗的片上系统(SOC-System On Chip) ,将外围电路尽可能地放置于SOC芯片内,运用了集成在控制器内部的液晶驱动电路。运用软件实现了解码、数据校验、识别、实时处理和数据显示等功能。从而简化了传统的微控制器控制的RFID读写器所需的庞大液晶驱动硬件电路和其引入的额外误差,具有重要的实用价值,并且功耗和成本很低。
文摘基于模拟最小均方（analogue least mean square,ALMS）环路的反射功率对消器能够自适应地抑制载波干扰,故常应用于连续波雷达中。传统的反射功率对消器使用基于矢量调制器的独立馈通电路产生对消信号,对消电路复杂度高。提出一种改进的反射功率对消器,对消器使用一个平衡式反射型正交调制器产生对消信号,避免了独立馈通电路的使用,从而简化了电路结构。平衡式的正交调制器具有较好的I/Q平衡性,确保ALMS控制环路在较宽的频带内保持稳定。最终制作了工作在UHF频段的对消器模块。测量结果显示,该模块对以915 MHz为中心频率、带宽60 MHz的线性调频干扰信号具有良好的自适应跟踪能力。在干扰功率＋10 dBm条件下,典型的等效输入噪声谱密度为-153 dBm/Hz@100 kHz。
