Environmental issues associated with the aviation industry are getting more attention as air traffic increases.Stringent standards are imposed for fuel consumption and pollution emissions for next-generation aircraft....Environmental issues associated with the aviation industry are getting more attention as air traffic increases.Stringent standards are imposed for fuel consumption and pollution emissions for next-generation aircraft.Superconducting electrical propulsion aircraft(SEPA)have been seen as an efficient way to achieve this goal.High-temperature superconducting(HTS)devices are extensively used in the power system to supply enormous energy.Power is distributed to the different loads via a DC distribution network.However,it will generate an inrush current over ten times higher than the rated current in short-circuit state,which is very harmful to the system.Therefore,it is essential to adopt an appropriate protection scheme.This paper discusses one protection scheme that combines DC vacuum circuit breakers(DC VCB)and resistive superconducting current limiters(RSFCL)for superconducting aircraft applications.Considering problems of cost and loss,the auxiliary capacitor is pre-charged by system voltage,and mechanical elements extinguish the arc.Furthermore,combined with RSFCL,the interrupting environment is fully improved.RSFCL limits fault current,and then the VCB breaks this limited current based on creating an artificial current zero(ACZ).The prospective rated power is 8MW,rated voltage and current are 4 kV and 1 kA,respectively.In this paper,we discuss and simulate switching devices that protect SEPA.The interrupting performance of the circuit breaker is analysed in the DC short-circuit fault that occurs on the transmission line.Finally,the residual energy consumption of different situations is calculated.A comparison is made between using RSFCL with metal oxide varistor(MOV)and just using MOV.The scheme with RSFCL shows a significant advantage in energy consumption.展开更多
Based on brain-inspired computing frameworks,neuromorphic systems implement large-scale neural networks in hardware.Although rapid advances have been made in the development of artificial neurons and synapses in recen...Based on brain-inspired computing frameworks,neuromorphic systems implement large-scale neural networks in hardware.Although rapid advances have been made in the development of artificial neurons and synapses in recent years,further research is beyond these individual components and focuses on neuronal circuit motifs with specialized excitatory-inhibitory(E-I)connectivity patterns.In this study,we demonstrate a core processor that can be used to construct commonly used neuronal circuits.The neuron,featuring an ultracompact physical configuration,integrates a volatile threshold switch with a gate-modulated two-dimensional(2D)MoS_(2) field-effect channel to process complex E-I spatiotemporal spiking signals.Consequently,basic neuronal circuits are constructed for biorealistic neuromorphic computing.For practical applications,an algorithm-hardware co-design is implemented in a gatecontrolled spiking neural network with substantial performance improvement in human speech separation.展开更多
Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs...Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target label.While quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor attacks.However,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding methods.Given the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains uncertain.In this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the data.The victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the trigger.Furthermore,our proposed attack cannot be easily resisted by existing backdoor detection methods.展开更多
基金supported by the 2022 Open funding of the State Key Laboratory of Electrical Insulation and Power Equipment(EIPE22211)the National Natural Science Foundation of China,“Research Fund for International Young Scientist(RFIS-1)”,Project:52150410419the 2021 Jiangsu“Shuang-Chuang Doctor(Mass Innovation and Entrepreneurship)Talent Program”,Fund:JSSCBS20211187.
文摘Environmental issues associated with the aviation industry are getting more attention as air traffic increases.Stringent standards are imposed for fuel consumption and pollution emissions for next-generation aircraft.Superconducting electrical propulsion aircraft(SEPA)have been seen as an efficient way to achieve this goal.High-temperature superconducting(HTS)devices are extensively used in the power system to supply enormous energy.Power is distributed to the different loads via a DC distribution network.However,it will generate an inrush current over ten times higher than the rated current in short-circuit state,which is very harmful to the system.Therefore,it is essential to adopt an appropriate protection scheme.This paper discusses one protection scheme that combines DC vacuum circuit breakers(DC VCB)and resistive superconducting current limiters(RSFCL)for superconducting aircraft applications.Considering problems of cost and loss,the auxiliary capacitor is pre-charged by system voltage,and mechanical elements extinguish the arc.Furthermore,combined with RSFCL,the interrupting environment is fully improved.RSFCL limits fault current,and then the VCB breaks this limited current based on creating an artificial current zero(ACZ).The prospective rated power is 8MW,rated voltage and current are 4 kV and 1 kA,respectively.In this paper,we discuss and simulate switching devices that protect SEPA.The interrupting performance of the circuit breaker is analysed in the DC short-circuit fault that occurs on the transmission line.Finally,the residual energy consumption of different situations is calculated.A comparison is made between using RSFCL with metal oxide varistor(MOV)and just using MOV.The scheme with RSFCL shows a significant advantage in energy consumption.
基金National Natural Science Foundation of China,Grant/Award Numbers:92264106,U22A2076,62090034,DT23F0401,DT23F04008,DT23F04009Young Scientists Fund of the National Natural Science Foundation of China,Grant/Award Number:62204219。
文摘Based on brain-inspired computing frameworks,neuromorphic systems implement large-scale neural networks in hardware.Although rapid advances have been made in the development of artificial neurons and synapses in recent years,further research is beyond these individual components and focuses on neuronal circuit motifs with specialized excitatory-inhibitory(E-I)connectivity patterns.In this study,we demonstrate a core processor that can be used to construct commonly used neuronal circuits.The neuron,featuring an ultracompact physical configuration,integrates a volatile threshold switch with a gate-modulated two-dimensional(2D)MoS_(2) field-effect channel to process complex E-I spatiotemporal spiking signals.Consequently,basic neuronal circuits are constructed for biorealistic neuromorphic computing.For practical applications,an algorithm-hardware co-design is implemented in a gatecontrolled spiking neural network with substantial performance improvement in human speech separation.
基金supported by the National Natural Science Foundation of China(Grant No.62076042)the National Key Research and Development Plan of China,Key Project of Cyberspace Security Governance(Grant No.2022YFB3103103)the Key Research and Development Project of Sichuan Province(Grant Nos.2022YFS0571,2021YFSY0012,2021YFG0332,and 2020YFG0307)。
文摘Backdoor attacks are emerging security threats to deep neural networks.In these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor triggers.The backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target label.While quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor attacks.However,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding methods.Given the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains uncertain.In this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the data.The victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the trigger.Furthermore,our proposed attack cannot be easily resisted by existing backdoor detection methods.
