Power transmission lines are a critical component of the entire power system,and ice accretion incidents caused by various types of power systems can result in immeasurable harm.Currently,network models used for ice d...Power transmission lines are a critical component of the entire power system,and ice accretion incidents caused by various types of power systems can result in immeasurable harm.Currently,network models used for ice detection on power transmission lines require a substantial amount of sample data to support their training,and their drawback is that detection accuracy is significantly affected by the inaccurate annotation among training dataset.Therefore,we propose a transformer-based detection model,structured into two stages to collectively address the impact of inaccurate datasets on model training.In the first stage,a spatial similarity enhancement(SSE)module is designed to leverage spatial information to enhance the construction of the detection framework,thereby improving the accuracy of the detector.In the second stage,a target similarity enhancement(TSE)module is introduced to enhance object-related features,reducing the impact of inaccurate data on model training,thereby expanding global correlation.Additionally,by incorporating a multi-head adaptive attention window(MAAW),spatial information is combined with category information to achieve information interaction.Simultaneously,a quasi-wavelet structure,compatible with deep learning,is employed to highlight subtle features at different scales.Experimental results indicate that the proposed model in this paper outperforms existing mainstream detection models,demonstrating superior performance and stability.展开更多
In the process of building a new power system dominated by new energy sources,power storage is a key supporting technology that ensures the safe and stable operation of the power grid,enables the flexible regulation o...In the process of building a new power system dominated by new energy sources,power storage is a key supporting technology that ensures the safe and stable operation of the power grid,enables the flexible regulation of the system,and raises the level of new energy consumption.It is also key to achieving carbon peak and neutrality as well as energy transformation.展开更多
Microelectrode arrays(MEAs)cultured with in vitro neural networks are gaining prominence in bio-integrated system research,owing to their inherent plasticity and emergent learning behaviors.Here,recent advances in mot...Microelectrode arrays(MEAs)cultured with in vitro neural networks are gaining prominence in bio-integrated system research,owing to their inherent plasticity and emergent learning behaviors.Here,recent advances in motion control tasks utilizing MEAs-based bio-integrated systems are presented,with a focus on encoding-decoding techniques.The bio-integrated system comprises MEAs integrated with neural networks,a bidirectional communication system,and an actuator.Classical decoding algorithms,such as firing-rate mapping and central firing-rate methods,along with cutting-edge artificial intelligence(AI)approaches,have been examined.These AI methods enhance the accuracy and adaptability of real-time,closed-loop motion control.A comparative analysis indicates that simpler,lower-complexity algorithms suit basic rapid-decision tasks,whereas deeper models exhibit greater potential in more complex temporal signal processing and dynamically changing environments.The review also systematically analyzes the prospects and challenges of bio-integrated systems for motion control.Future prospects suggest that MEAs cultured with in vitro neural networks may leverage their flexibility and low energy consumption to address diverse motion control scenarios,driving cross-disciplinary research at the intersection of neuroscience and artificial intelligence.展开更多
Multiplexing techniques have always been one of the important components of optical communication research.These techniques can transmit multiple signals in a shared information channel and can greatly increase the ma...Multiplexing techniques have always been one of the important components of optical communication research.These techniques can transmit multiple signals in a shared information channel and can greatly increase the maximum capacity of an information channel.The Dirac-vortex cavity is a type of photonic crystal surface emission system,and its characteristics of miniaturization and high stability make it very suitable for on-chip optical system.In this paper,we realized dual-channel emission of the Dirac-vortex cavity,which is achieved by modulating the size and phase of hexagonal holes in the hexagon lattice.The characteristics of dual-channel emission are investigated by numerical simulation,and the dual-channel emission rules are summarized.The double Diracvortex cavity model is not only explored for its multiplexing capability but also as an alternative scheme for the application of Dirac-vortex cavity in multiplex communication systems.展开更多
文摘Power transmission lines are a critical component of the entire power system,and ice accretion incidents caused by various types of power systems can result in immeasurable harm.Currently,network models used for ice detection on power transmission lines require a substantial amount of sample data to support their training,and their drawback is that detection accuracy is significantly affected by the inaccurate annotation among training dataset.Therefore,we propose a transformer-based detection model,structured into two stages to collectively address the impact of inaccurate datasets on model training.In the first stage,a spatial similarity enhancement(SSE)module is designed to leverage spatial information to enhance the construction of the detection framework,thereby improving the accuracy of the detector.In the second stage,a target similarity enhancement(TSE)module is introduced to enhance object-related features,reducing the impact of inaccurate data on model training,thereby expanding global correlation.Additionally,by incorporating a multi-head adaptive attention window(MAAW),spatial information is combined with category information to achieve information interaction.Simultaneously,a quasi-wavelet structure,compatible with deep learning,is employed to highlight subtle features at different scales.Experimental results indicate that the proposed model in this paper outperforms existing mainstream detection models,demonstrating superior performance and stability.
文摘In the process of building a new power system dominated by new energy sources,power storage is a key supporting technology that ensures the safe and stable operation of the power grid,enables the flexible regulation of the system,and raises the level of new energy consumption.It is also key to achieving carbon peak and neutrality as well as energy transformation.
基金sponsored by the National Key R&D Program of China(2022YFC2402500,2022YFB3205602)the National Natural Science Foundation of China(No.62121003,T2293730,T2293731,62333020,62171434 and 62471291)+3 种基金the Major Program of Scientific and Technical Innovation 2030(2021ZD02016030)the Joint Foundation Program of the Chinese Academy of Sciences(No.8091A170201)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(No.PTYQ2024BJ0009)the National Natural Science Foundation of Beijing(F252069).
文摘Microelectrode arrays(MEAs)cultured with in vitro neural networks are gaining prominence in bio-integrated system research,owing to their inherent plasticity and emergent learning behaviors.Here,recent advances in motion control tasks utilizing MEAs-based bio-integrated systems are presented,with a focus on encoding-decoding techniques.The bio-integrated system comprises MEAs integrated with neural networks,a bidirectional communication system,and an actuator.Classical decoding algorithms,such as firing-rate mapping and central firing-rate methods,along with cutting-edge artificial intelligence(AI)approaches,have been examined.These AI methods enhance the accuracy and adaptability of real-time,closed-loop motion control.A comparative analysis indicates that simpler,lower-complexity algorithms suit basic rapid-decision tasks,whereas deeper models exhibit greater potential in more complex temporal signal processing and dynamically changing environments.The review also systematically analyzes the prospects and challenges of bio-integrated systems for motion control.Future prospects suggest that MEAs cultured with in vitro neural networks may leverage their flexibility and low energy consumption to address diverse motion control scenarios,driving cross-disciplinary research at the intersection of neuroscience and artificial intelligence.
基金National Natural Science Foundation of China(12404423,62375007,62405012)Natural Science Foundation of Beijing Municipality(1232024)Applied Basic Research Fund of School of Physics and Optoelectronic Engineering,Beijing University of Technology(ABRFSPOE03).
文摘Multiplexing techniques have always been one of the important components of optical communication research.These techniques can transmit multiple signals in a shared information channel and can greatly increase the maximum capacity of an information channel.The Dirac-vortex cavity is a type of photonic crystal surface emission system,and its characteristics of miniaturization and high stability make it very suitable for on-chip optical system.In this paper,we realized dual-channel emission of the Dirac-vortex cavity,which is achieved by modulating the size and phase of hexagonal holes in the hexagon lattice.The characteristics of dual-channel emission are investigated by numerical simulation,and the dual-channel emission rules are summarized.The double Diracvortex cavity model is not only explored for its multiplexing capability but also as an alternative scheme for the application of Dirac-vortex cavity in multiplex communication systems.
