The pulse phase and doppler frequency estimation of X-ray pulsars in dynamic situations and its application in navigation is a problem that has not been fully investigated. In this paper, solutions are proposed to sol...The pulse phase and doppler frequency estimation of X-ray pulsars in dynamic situations and its application in navigation is a problem that has not been fully investigated. In this paper, solutions are proposed to solve this problem under conditions of spacecraft and binary motion. A high-precision doppler frequency (velocity) measurement model as well as a phase (range) measurement model is established. The averaged maximum-likelihood estimator is developed for the dynamic pulse phase estimation. The pulse phase tracking technique is used in the doppler frequency determination. The tracking filter is redesigned and compared with the existing algorithms. The comparison verifies the advantage of the filter algorithm presented in this pa- per. Unlike traditional views, it is found that in dynamic situations, shorter observation interval lengths will result in higher-accuracy phase and frequency estimates as the tracking filter outputs. A photon-level integrated numerical simulation is performed. Simulation results testify to the validity of the proposed phase and doppler frequency estimation scheme, and show that incorporation of velocity measurements as well as the range ones into the navigation estimator will improve the navigation steady-state performance.展开更多
In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and a...In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.展开更多
Almost all living organisms exhibit autonomic oscillatory activities,which are primarily generated by the rhythmic activities of their neural systems.Several nonlinear oscillator models have been proposed to elucidate...Almost all living organisms exhibit autonomic oscillatory activities,which are primarily generated by the rhythmic activities of their neural systems.Several nonlinear oscillator models have been proposed to elucidate these neural behaviors and subsequently applied to the domain of robot control.However,the oscillation patterns generated by these models are often unpredictable and need to be obtained through parameter search.This study introduces a mathematical model that can be used to analyze multiple neurons connected through fast inhibitory synapses.The characteristic of this oscillator is that its stationary point is stable,but the location of the stationary point changes with the system state.Only through reasonable topology and threshold parameter selection can the oscillation be sustained.This study analyzed the conditions for stable oscillation in two-neuron networks and three-neuron networks,and obtained the basic rules of the phase relationship of the oscillator network established by this model.In addition,this study also introduces synchronization mechanisms into the model to enable it to be synchronized with the sensing pulse.Finally,this study used these theories to establish a robot single leg joint angle generation system.The experimental results showed that the simulated robot could achieve synchronization with human motion,and had better control effects compared to traditional oscillators.展开更多
High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology.Here,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deplo...High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology.Here,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deployed in the southern north-south seismic zone(20°N-32°N,95°E-110°E)in 2011-2013 using 355 portable broadband seismic stations.CREDIT-X1local,the first release of the ChinArray Reference Earthquake Dataset for Innovative Techniques(CREDIT),includes comprehensive information for the 105,455 local events that occurred in the southern north-south seismic zone during array observation,incorporating them into a single HDF5 file.Original 100-Hz sampled three-component waveforms are organized by event for stations within epicenter distances of 1,000 km,and records of≥200 s are included for each waveform.Two types of phase labels are provided.The first includes manually picked labels for 5,999 events with magnitudes≥2.0,providing 66,507 Pg,42,310 Sg,12,823 Pn,and 546 Sn phases.The second contains automatically labeled phases for 105,442 events with magnitudes of−1.6 to 7.6.These phases were picked using a recurrent neural network phase picker and screened using the corresponding travel time curves,resulting in 1,179,808 Pg,884,281 Sg,176,089 Pn,and 22,986 Sn phases.Additionally,first-motion polarities are included for 31,273 Pg phases.The event and station locations are provided,so that deep learning networks for both conventional phase picking and phase association can be trained and validated.The CREDIT-X1local dataset is the first million-scale dataset constructed from a dense seismic array,which is designed to support various multi-station deep-learning methods,high-precision focal mechanism inversion,and seismic tomography studies.Additionally,owing to the high seismicity in the southern north-south seismic zone in China,this dataset has great potential for future scientific discoveries.展开更多
Existing linkage-optimization methods are designed for mechanical presses; few can be directly used for servo presses, so development of the servo press is limited. Based on the complementarity of linkage opti- mizati...Existing linkage-optimization methods are designed for mechanical presses; few can be directly used for servo presses, so development of the servo press is limited. Based on the complementarity of linkage opti- mization and motion planning, a phase-division-based linkage-optimization model for a drawing servo press is established. Considering the motion-planning principles of a drawing servo press, and taking account of work rating and efficiency, the constraints of the optimization model are constructed. Linkage is optimized in two modes： use of either constant eccentric speed or constant slide speed in the work segments. The performances of optimized link- ages are compared with those of a mature linkage SL4- 2000A, which is optimized by a traditional method. The results show that the work rating of a drawing servo press equipped with linkages optimized by this new method improved and the root-mean-square torque of the servo motors is reduced by more than 10%. This research pro- vides a promising method for designing energy-saving drawing servo presses with high work ratings.展开更多
Visual navigation is imperative for successful asteroid exploration missions.In this study,an integrated visual navigation system was proposed based on angles-only measurements to robustly and accurately determine the...Visual navigation is imperative for successful asteroid exploration missions.In this study,an integrated visual navigation system was proposed based on angles-only measurements to robustly and accurately determine the pose of the lander during the final landing phase.The system used the lander's global pose information provided by an orbiter,which was deployed in space in advance,and its relative motion information in adjacent images to jointly estimate its optimal state.First,the landmarks on the asteroid surface and markers on the lander were identified from the images acquired by the orbiter.Subsequently,an angles-only measurement model concerning the landmarks and markers was constructed to estimate the orbiter's position and lander's pose.Subsequently,a method based on the epipolar constraint was proposed to estimate the lander's inter-frame motion.Then,the absolute pose and relative motion of the lander were fused using an extended Kalman filter.Additionally,the observability criterion and covariance of the state error were provided.Finally,synthetic image sequences were generated to validate the proposed navigation system,and numerical results demonstrated its advance in terms of robustness and accuracy.展开更多
文摘The pulse phase and doppler frequency estimation of X-ray pulsars in dynamic situations and its application in navigation is a problem that has not been fully investigated. In this paper, solutions are proposed to solve this problem under conditions of spacecraft and binary motion. A high-precision doppler frequency (velocity) measurement model as well as a phase (range) measurement model is established. The averaged maximum-likelihood estimator is developed for the dynamic pulse phase estimation. The pulse phase tracking technique is used in the doppler frequency determination. The tracking filter is redesigned and compared with the existing algorithms. The comparison verifies the advantage of the filter algorithm presented in this pa- per. Unlike traditional views, it is found that in dynamic situations, shorter observation interval lengths will result in higher-accuracy phase and frequency estimates as the tracking filter outputs. A photon-level integrated numerical simulation is performed. Simulation results testify to the validity of the proposed phase and doppler frequency estimation scheme, and show that incorporation of velocity measurements as well as the range ones into the navigation estimator will improve the navigation steady-state performance.
基金the National Natural Science Foundation of China(Nos.41804047 and 42111540260)Fundamental Research Funds of the Institute of Geophysics,China Earthquake Administration(NO.DQJB19A0114)the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(No.IGGCAS-201904).
文摘In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.
基金supported in part by the National Nature Science Foudation under Grant 62333023in part by the Key Research and Development Program of Zhejiang Province under Grant 2021C03050in part by the Scientific Research Project of Agriculture and Social Development of Hangzhou under Grant 20212013B11.
文摘Almost all living organisms exhibit autonomic oscillatory activities,which are primarily generated by the rhythmic activities of their neural systems.Several nonlinear oscillator models have been proposed to elucidate these neural behaviors and subsequently applied to the domain of robot control.However,the oscillation patterns generated by these models are often unpredictable and need to be obtained through parameter search.This study introduces a mathematical model that can be used to analyze multiple neurons connected through fast inhibitory synapses.The characteristic of this oscillator is that its stationary point is stable,but the location of the stationary point changes with the system state.Only through reasonable topology and threshold parameter selection can the oscillation be sustained.This study analyzed the conditions for stable oscillation in two-neuron networks and three-neuron networks,and obtained the basic rules of the phase relationship of the oscillator network established by this model.In addition,this study also introduces synchronization mechanisms into the model to enable it to be synchronized with the sensing pulse.Finally,this study used these theories to establish a robot single leg joint angle generation system.The experimental results showed that the simulated robot could achieve synchronization with human motion,and had better control effects compared to traditional oscillators.
基金funded by the National Key R&D Program of China (No. 2021YFC3000702)the Special Fund of the Institute of Geophysics, China Earthquake Administration (No. DQJB20B15)+2 种基金the National Natural Science Foundation of China youth Grant (No. 41804059)the Joint Funds of the National Natural Science Foundation of China (No. U223920029)the Science for Earthquake Resilience of China Earthquake Administration (No. XH211103)
文摘High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology.Here,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deployed in the southern north-south seismic zone(20°N-32°N,95°E-110°E)in 2011-2013 using 355 portable broadband seismic stations.CREDIT-X1local,the first release of the ChinArray Reference Earthquake Dataset for Innovative Techniques(CREDIT),includes comprehensive information for the 105,455 local events that occurred in the southern north-south seismic zone during array observation,incorporating them into a single HDF5 file.Original 100-Hz sampled three-component waveforms are organized by event for stations within epicenter distances of 1,000 km,and records of≥200 s are included for each waveform.Two types of phase labels are provided.The first includes manually picked labels for 5,999 events with magnitudes≥2.0,providing 66,507 Pg,42,310 Sg,12,823 Pn,and 546 Sn phases.The second contains automatically labeled phases for 105,442 events with magnitudes of−1.6 to 7.6.These phases were picked using a recurrent neural network phase picker and screened using the corresponding travel time curves,resulting in 1,179,808 Pg,884,281 Sg,176,089 Pn,and 22,986 Sn phases.Additionally,first-motion polarities are included for 31,273 Pg phases.The event and station locations are provided,so that deep learning networks for both conventional phase picking and phase association can be trained and validated.The CREDIT-X1local dataset is the first million-scale dataset constructed from a dense seismic array,which is designed to support various multi-station deep-learning methods,high-precision focal mechanism inversion,and seismic tomography studies.Additionally,owing to the high seismicity in the southern north-south seismic zone in China,this dataset has great potential for future scientific discoveries.
基金Supported by National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant No.2015ZX04003004)
文摘Existing linkage-optimization methods are designed for mechanical presses; few can be directly used for servo presses, so development of the servo press is limited. Based on the complementarity of linkage opti- mization and motion planning, a phase-division-based linkage-optimization model for a drawing servo press is established. Considering the motion-planning principles of a drawing servo press, and taking account of work rating and efficiency, the constraints of the optimization model are constructed. Linkage is optimized in two modes： use of either constant eccentric speed or constant slide speed in the work segments. The performances of optimized link- ages are compared with those of a mature linkage SL4- 2000A, which is optimized by a traditional method. The results show that the work rating of a drawing servo press equipped with linkages optimized by this new method improved and the root-mean-square torque of the servo motors is reduced by more than 10%. This research pro- vides a promising method for designing energy-saving drawing servo presses with high work ratings.
基金supported by the National Natural Science Foundation of China(Grant Nos.61673057 and 61803028)。
文摘Visual navigation is imperative for successful asteroid exploration missions.In this study,an integrated visual navigation system was proposed based on angles-only measurements to robustly and accurately determine the pose of the lander during the final landing phase.The system used the lander's global pose information provided by an orbiter,which was deployed in space in advance,and its relative motion information in adjacent images to jointly estimate its optimal state.First,the landmarks on the asteroid surface and markers on the lander were identified from the images acquired by the orbiter.Subsequently,an angles-only measurement model concerning the landmarks and markers was constructed to estimate the orbiter's position and lander's pose.Subsequently,a method based on the epipolar constraint was proposed to estimate the lander's inter-frame motion.Then,the absolute pose and relative motion of the lander were fused using an extended Kalman filter.Additionally,the observability criterion and covariance of the state error were provided.Finally,synthetic image sequences were generated to validate the proposed navigation system,and numerical results demonstrated its advance in terms of robustness and accuracy.
