The efficiency of inductive power links driven by Class-E amplifiers may deteriorate due to variation in the coupling coefficient when the relative position of the radio frequency (RF) coils changes.To solve this prob...The efficiency of inductive power links driven by Class-E amplifiers may deteriorate due to variation in the coupling coefficient when the relative position of the radio frequency (RF) coils changes.To solve this problem,a new design methodology of power links is presented in this paper.The aim of the new design is to use the feedback signal,which is a phase difference between the driving signal and the output current of the Class-E amplifier,to adjust the duty cycle and angular frequency of the driving signal to maintain the optimum state of the inductive power link,and to adjust the supply voltage to keep the output power constant when the coupling coefficient of the RF coils changes.The parameter adjustments with respect to the coupling coefficient and the feedback signal are derived from the design equation of the inductive power link.To validate the feedback control rules,a prototype of the inductive power link was constructed,and its performance validated with the coupling coefficient set at 0.2 and a duty cycle of 0.5.The experimental results showed that,by adjusting the duty cycle,the angular frequency,and the supply voltage,the power link can be kept in optimal operation with a constant output power when the coupling coefficient changes from 0.2 to 0.1 to 0.25.展开更多
Recently,the inductive coupling link is the most robust method for powering implanted biomedical devices,such as micro-system stimulators,cochlear implants,and retinal implants.This research provides a novel theoretic...Recently,the inductive coupling link is the most robust method for powering implanted biomedical devices,such as micro-system stimulators,cochlear implants,and retinal implants.This research provides a novel theoretical and mathematical analysis to optimize the inductive coupling link efficiency driven by efficient proposed class-E power amplifiers using high and optimum input impedance.The design of the coupling link is based on two pairs of aligned,single-layer,planar spiral circular coils with a proposed geometric dimension,operating at a resonant frequency of 13.56 MHz.Both transmitter and receiver coils are small in size.Implanted device resistance varies from 200Ωto 500Ωwith 50Ωof stepes.When the conventional load resistance of power amplifiers is 50Ω,the efficiency is 45%;when the optimum resonant load is 41.89Ωwith a coupling coefficient of 0.087,the efficiency increases to 49%.The efficiency optimization is reached by calculating the matching network for the external LC tank of the transmitter coil.The proposed design may be suitable for active implantable devices.展开更多
Inductive knowledge graph embedding(KGE)aims to embed unseen entities in emerging knowledge graphs(KGs).The major recent studies of inductive KGE embed unseen entities by aggregating information from their neighboring...Inductive knowledge graph embedding(KGE)aims to embed unseen entities in emerging knowledge graphs(KGs).The major recent studies of inductive KGE embed unseen entities by aggregating information from their neighboring entities and relations with graph neural networks(GNNs).However,these methods rely on the existing neighbors of unseen entities and suffer from two common problems:data sparsity and feature smoothing.Firstly,the data sparsity problem means unseen entities usually emerge with few triplets containing insufficient information.Secondly,the effectiveness of the features extracted from original KGs will degrade when repeatedly propagating these features to represent unseen entities in emerging KGs,which is termed feature smoothing problem.To tackle the two problems,we propose a novel model entitled Meta-Learning Based Memory Graph Convolutional Network(MMGCN)consisting of three different components:1)the two-layer information transforming module(TITM)developed to effectively transform information from original KGs to emerging KGs;2)the hyper-relation feature initializing module(HFIM)proposed to extract type-level features shared between KGs and obtain a coarse-grained representation for each entity with these features;and 3)the meta-learning training module(MTM)designed to simulate the few-shot emerging KGs and train the model in a meta-learning framework.The extensive experiments conducted on the few-shot link prediction task for emerging KGs demonstrate the superiority of our proposed model MMGCN compared with state-of-the-art methods.展开更多
基金Project (No.60271031) supported by the National Natural Science Foundation of China
文摘The efficiency of inductive power links driven by Class-E amplifiers may deteriorate due to variation in the coupling coefficient when the relative position of the radio frequency (RF) coils changes.To solve this problem,a new design methodology of power links is presented in this paper.The aim of the new design is to use the feedback signal,which is a phase difference between the driving signal and the output current of the Class-E amplifier,to adjust the duty cycle and angular frequency of the driving signal to maintain the optimum state of the inductive power link,and to adjust the supply voltage to keep the output power constant when the coupling coefficient of the RF coils changes.The parameter adjustments with respect to the coupling coefficient and the feedback signal are derived from the design equation of the inductive power link.To validate the feedback control rules,a prototype of the inductive power link was constructed,and its performance validated with the coupling coefficient set at 0.2 and a duty cycle of 0.5.The experimental results showed that,by adjusting the duty cycle,the angular frequency,and the supply voltage,the power link can be kept in optimal operation with a constant output power when the coupling coefficient changes from 0.2 to 0.1 to 0.25.
文摘Recently,the inductive coupling link is the most robust method for powering implanted biomedical devices,such as micro-system stimulators,cochlear implants,and retinal implants.This research provides a novel theoretical and mathematical analysis to optimize the inductive coupling link efficiency driven by efficient proposed class-E power amplifiers using high and optimum input impedance.The design of the coupling link is based on two pairs of aligned,single-layer,planar spiral circular coils with a proposed geometric dimension,operating at a resonant frequency of 13.56 MHz.Both transmitter and receiver coils are small in size.Implanted device resistance varies from 200Ωto 500Ωwith 50Ωof stepes.When the conventional load resistance of power amplifiers is 50Ω,the efficiency is 45%;when the optimum resonant load is 41.89Ωwith a coupling coefficient of 0.087,the efficiency increases to 49%.The efficiency optimization is reached by calculating the matching network for the external LC tank of the transmitter coil.The proposed design may be suitable for active implantable devices.
基金supported by the National Natural Science Foundation of China under Grant No.62272332the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No.22KJA520006.
文摘Inductive knowledge graph embedding(KGE)aims to embed unseen entities in emerging knowledge graphs(KGs).The major recent studies of inductive KGE embed unseen entities by aggregating information from their neighboring entities and relations with graph neural networks(GNNs).However,these methods rely on the existing neighbors of unseen entities and suffer from two common problems:data sparsity and feature smoothing.Firstly,the data sparsity problem means unseen entities usually emerge with few triplets containing insufficient information.Secondly,the effectiveness of the features extracted from original KGs will degrade when repeatedly propagating these features to represent unseen entities in emerging KGs,which is termed feature smoothing problem.To tackle the two problems,we propose a novel model entitled Meta-Learning Based Memory Graph Convolutional Network(MMGCN)consisting of three different components:1)the two-layer information transforming module(TITM)developed to effectively transform information from original KGs to emerging KGs;2)the hyper-relation feature initializing module(HFIM)proposed to extract type-level features shared between KGs and obtain a coarse-grained representation for each entity with these features;and 3)the meta-learning training module(MTM)designed to simulate the few-shot emerging KGs and train the model in a meta-learning framework.The extensive experiments conducted on the few-shot link prediction task for emerging KGs demonstrate the superiority of our proposed model MMGCN compared with state-of-the-art methods.
