Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based cont...Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based control method is usually set manually rather than adjusted adaptively according to real time traffic conditions,thus declining the car-following performance.To solve this problem,a car-following strategy of ICV using EDO adjusted by reinforcement learning is proposed.Different from the conventional method,the gain of proposed strategy can be adjusted by reinforcement learning to improve its estimation accuracy.Since the“equivalent disturbance”can be compensated by EDO to a great extent,the disturbance rejection ability of the carfollowing method will be improved significantly.Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.展开更多
Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabili...Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabilities such as a global positioning system, camera, and network access, offer opportunities to estab- lish distributed monitoring networks that can perform a wide range of measurements for a landscape. This study examined the potential of mobile phone-based community monitoring of fall webworm (Hyphantria cunea Drury). We built a prototype of a participatory fall webworm monitoring System based on mobile devices that stream- lined data collection, transmission, and visualization. We also assessed the accuracy and reliability of the data collected by the local community. The system performance was evaluated at the Ziya commune of Tianjin municipality in northern China, where fall webworm infestation has occurred. The local community provided data with accuracy comparable to expert measurements (Willmott's index of agreement 〉0.85). Measurements by the local community effectively complemented remote sensing images in both temporal and spatial resolution.展开更多
Spiking neural networks(SNNs)have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption.The leaky integration and firing(LIF)...Spiking neural networks(SNNs)have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption.The leaky integration and firing(LIF)model and spike-timing-dependent plasticity(STDP)are the fundamental components of SNNs.Here,a neural device is first demonstrated by zeolitic imidazolate frameworks(ZIFs)as an essential part of the synaptic transistor to simulate SNNs.Significantly,three kinds of typical functions between neurons,the memory function achieved through the hippocampus,synaptic weight regulation and membrane potential triggered by ion migration,are effectively described through short-term memory/long-term memory(STM/LTM),long-term depression/long-term potentiation(LTD/LTP)and LIF,respectively.Furthermore,the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP.In addition,the postsynaptic currents of the channel directly connect to the very large scale integration(VLSI)implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential.The leaky integrator block,firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses.Finally,we recode the steadystate visual evoked potentials(SSVEPs)belonging to the electroencephalogram(EEG)with filter characteristics of LIF.SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%.This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.展开更多
The performance of tin-based perovskite solar cells has been substantially hampered by voltage loss caused by energy level mismatch,charge recombination,energetic disorder,and other issues.Here,a fused-ring electron a...The performance of tin-based perovskite solar cells has been substantially hampered by voltage loss caused by energy level mismatch,charge recombination,energetic disorder,and other issues.Here,a fused-ring electron acceptor based on indacenodithiophene(IDIC)was for the first time introduced as a transition layer between a tin-based perovskite layer and a C 60 electron transport layer,leading to better matched energy levels in the device.In addition,coordination interactions between IDIC and perovskite improved the latter's crystallinity.The introduction of IDIC raised the power conversion efficiency from 8.98%to 11.5%and improved the device's stability.The decomposition mechanism of tin-based perovskite was also revealed by detecting the optical properties of perovskite microdomains through innovative integration of confocal laser scanning microscopy and photoluminescence spectroscopy.展开更多
基金State Key Laboratory of Automotive Safety and Energy,Grant/Award Number:KFY2208National Natural Science Foundation of China,Grant/Award Numbers:U2013601,U20A20225+1 种基金Key Research and Development Plan of Anhui Province,Grant/Award Number:202004a05020058the Natural Science Foundation of Hefei,China(Grant No.2021032)。
文摘Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based control method is usually set manually rather than adjusted adaptively according to real time traffic conditions,thus declining the car-following performance.To solve this problem,a car-following strategy of ICV using EDO adjusted by reinforcement learning is proposed.Different from the conventional method,the gain of proposed strategy can be adjusted by reinforcement learning to improve its estimation accuracy.Since the“equivalent disturbance”can be compensated by EDO to a great extent,the disturbance rejection ability of the carfollowing method will be improved significantly.Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.
基金supported by National Science and Technology Major Projects of China(21-Y30B05-9001-13/15)
文摘Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabilities such as a global positioning system, camera, and network access, offer opportunities to estab- lish distributed monitoring networks that can perform a wide range of measurements for a landscape. This study examined the potential of mobile phone-based community monitoring of fall webworm (Hyphantria cunea Drury). We built a prototype of a participatory fall webworm monitoring System based on mobile devices that stream- lined data collection, transmission, and visualization. We also assessed the accuracy and reliability of the data collected by the local community. The system performance was evaluated at the Ziya commune of Tianjin municipality in northern China, where fall webworm infestation has occurred. The local community provided data with accuracy comparable to expert measurements (Willmott's index of agreement 〉0.85). Measurements by the local community effectively complemented remote sensing images in both temporal and spatial resolution.
基金This research was funded in part by the National Natural Science Foundation of China(62204210),the Natural Science Foundation of Jiangsu Province(BK20220284)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(22KJB510013)+3 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program(19KJB510059)the Suzhou Science and Technology Development Planning Project:Key Industrial Technology Innovation(SYG201924),the University Research Development Fund(RDF-17-01-13)the Key Program Special Fund in XJTLU(KSF-T-03,KSF-A-07)This work was partially supported by the XJTLU AI University Research Centre and Jiangsu(Provincial)Data Science and Cognitive Computational Engineering Research Centre at XJTLU,the Collaborative Innovation Center of Suzhou Nano Science&Technology,the 111 Project and Joint International Research Laboratory of Carbon-Based Functional Materials and Devices.For the purpose of open access,the authors have applied a Creative Commons Attribution(CC BY)license to any Author Accepted Manuscript version arising from this submission.
文摘Spiking neural networks(SNNs)have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption.The leaky integration and firing(LIF)model and spike-timing-dependent plasticity(STDP)are the fundamental components of SNNs.Here,a neural device is first demonstrated by zeolitic imidazolate frameworks(ZIFs)as an essential part of the synaptic transistor to simulate SNNs.Significantly,three kinds of typical functions between neurons,the memory function achieved through the hippocampus,synaptic weight regulation and membrane potential triggered by ion migration,are effectively described through short-term memory/long-term memory(STM/LTM),long-term depression/long-term potentiation(LTD/LTP)and LIF,respectively.Furthermore,the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP.In addition,the postsynaptic currents of the channel directly connect to the very large scale integration(VLSI)implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential.The leaky integrator block,firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses.Finally,we recode the steadystate visual evoked potentials(SSVEPs)belonging to the electroencephalogram(EEG)with filter characteristics of LIF.SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%.This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.
基金The authors gratefully acknowledge the financial support from the Beijing National Laboratory for Molecular Sciences and the National Natural Science Foundation of China(61935016 and 21771008)X.Z.thanks National Key Research and Development Program of China(2020YFB1506400).
文摘The performance of tin-based perovskite solar cells has been substantially hampered by voltage loss caused by energy level mismatch,charge recombination,energetic disorder,and other issues.Here,a fused-ring electron acceptor based on indacenodithiophene(IDIC)was for the first time introduced as a transition layer between a tin-based perovskite layer and a C 60 electron transport layer,leading to better matched energy levels in the device.In addition,coordination interactions between IDIC and perovskite improved the latter's crystallinity.The introduction of IDIC raised the power conversion efficiency from 8.98%to 11.5%and improved the device's stability.The decomposition mechanism of tin-based perovskite was also revealed by detecting the optical properties of perovskite microdomains through innovative integration of confocal laser scanning microscopy and photoluminescence spectroscopy.