查看更多>>摘要：Because robotic milling has become an important means for machining significant large parts,obtaining the structural frequency response function(FRF)of a milling robot is an important basis for machining process optimization.However,because of its articulated serial structure,a milling robot has an enormous number of operating postures,and its dynamics are affected by the motion state.To accurately obtain the FRF in the operating state of a milling robot,this paper proposes a method based on the structural modification concept.Unlike the traditional excitation method,the proposed method uses robot joint motion excitation instead of hammering excitation to realize automation.To address the problem of the lack of information brought by motion excitation,which leads to inaccurate FRF amplitudes,this paper derives the milling robot regularization theory based on the sensitivity of structural modification,establishes the modal regularization factor,and calibrates the FRF amplitude.Compared to the commonly used manual hammering experiments,the proposed method has high accuracy and reliability when the milling robot is in different postures.Because the measurement can be performed directly and automatically in the operation state,and the problem of inaccurate amplitudes is solved,the proposed method provides a basis for optimizing the machining posture of a milling robot and improving machining efficiency.
查看更多>>摘要：Aromatic hydrocarbons generally refer to compounds containing benzene rings.Many types of isomers can be formed by replac-ing hydrogen atoms on the benzene ring.In this paper,an aromatic-hydrocarbon-inspired modular robot(AHIMR)is proposed.The robot can be reassembled into different configurations suitable for various task requirements.A vision-based docking system is designed for the AHIMR.The system primarily consists of two stages:a remote guidance stage and a precise docking stage.During the remote guidance stage,an object module is identified using an illumination adaptive target recognition algorithm,and then the active module moves to the docking area through communication with ZigBee.In the precise docking stage,the active module calculates the relative pose with the object module using a perspective-n-point method and dynamically adjusts its posture to dock.In this process,a Kalman filter is used to reduce target occlusion and jitter interference.In addition,the docking system feasibility is verified via several simulation experiments.The module docking accuracy is controlled within 0.01 m,which meets the reconfiguration task requirements of the AHIMR.In the AHIMR submodule docking experiment,the active module accurately moves to the expected position with a docking success rate of 95％.
查看更多>>摘要：Tactile signals play a crucial role in enabling robots to successfully manipulate unfamiliar objects.For robots to grasp unknown objects securely and without causing damage,it is essential that they can analyze grasping stability in real time through tactile signals and respond promptly.This study introduces a novel method for analyzing the stability of robotic hand grasping,utilizing the Wilcoxon signed rank test.The efficacy of this method is demonstrated through its static and dynamic performance,and evaluated across a series of experiments.The findings of this research highlight the method's ability to accurately detect when an object begins to slip from the robot's grasp.Employing this method allows the gripper to maintain a secure hold on objects by applying the minimal necessary force.It also enables the gripper to dynamically adjust the force it applies in real time,thus preventing the object from slipping during the movement of the robotic arm.Moreover,the gripper demonstrates the ability to stably grasp objects of varied weights and with unknown characteristics,showcasing the versatility and effectiveness of the proposed method.
查看更多>>摘要：Metal surfaces play a crucial role in numerous applications,from self-cleaning and anti-icing to anti-fogging and oil-water separation.The regulation of their wettability is essential to enhance their performance in these areas.This paper proposes a multi-state regulation method for metal surface wettability,leveraging nanosecond laser ablation.By creating non-uniform microstructures on a metal surface,the contact area between the solid and liquid phases can be increased,resulting in the attainment of superhydrophilic properties(contact angle(CA),ranging from 4.6° t0 8.5°).Conversely,the construction of uniform microstructures leads to a decreased solid-liquid contact area,thereby rendering the metal surface hydrophilic(CA=12.2°-53°).Furthermore,through heat treatment on a surface with uniform microstructures,organic matter adsorption can be promoted while simultaneously reducing surface energy.This process results in the metal surface acquiring hydrophobic properties(CA=92.1°-133.5°),facilitated by the"air cushion effect."Building on the hydrophobic surface,stearic acid modification can further reduce surface energy,ultimately bestowing the metal surface with superhydrophobic properties(CA=150.1°-152.7°,and sliding angle=3.8°).Performance testing has validated the durability and self-cleaning effectiveness of the fabricated superhydrophobic surface while also highlighting the excellent anti-fog performance of the superhydrophilic surface.These findings strongly indicate the immense potential of these surfaces in various engineering applications.
查看更多>>摘要：Arrhythmias may lead to sudden cardiac death if not detected and treated in time.A supraventricular premature beat(SPB)and premature ventricular contraction(PVC)are important categories of arrhythmia disease.Recently,deep learning methods have been applied to the PVC/SPB heartbeats detection.However,most researchers have focused on time-domain information of the electrocardiogram and there has been a lack of exploration of the interpretability of the model.In this study,we design an interpretable and accurate PVC/SPB recognition algorithm,called the interpretable multilevel wavelet decomposition deep network(IMWDDN).Wavelet decomposition is introduced into the deep network and the squeeze and excitation(SE)-Residual block is designed for extracting time-domain and frequency-domain features.Additionally,inspired by the idea of residual learning,we construct a novel loss function for the constant updating of the multilevel wavelet decomposition parameters.Finally,the IMWDDN is evaluated on the Third China Physiological Signal Challenge Dataset and the MIT-BIH Arrhythmia database.The comparison results show IMWDDN has better detection performance with 98.51％accuracy and a 93.75％F1-macro on average,and its areas of concern are similar to those of an expert diagnosis to a certain extent.Generally,the IMWDDN has good application value in the clinical screening of PVC/SPB heartbeats.
查看更多>>摘要：Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic systems.To investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,respectively.Taking the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the memristors.Dynamics distributions and bifurcation behaviours dependent on the control parameters are explored with numerical tools.Specifically,the memristor initial offset-boosting mechanism is theore-tically demonstrated,and memristor initial offset-boosting behaviours are numerically verified.The results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite attractors.In addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high randomness.Notably,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.
查看更多>>摘要：Conducting scientific drilling on subglacial lakes and obtaining samples of subglacial lake water holds great significance in unraveling the formation and evolution of Antarctic subglacial lakes and early Earth's life forms.Despite various approaches to access and directly sample subglacial water and sediments,clean access and exploration of subglacial lakes remain challenging.To address this concern,Jilin University has developed the RECoverable Autonomous Sonde(RECAS)prototype.This tech-nology enables sampling and in-situ detection of subglacial lake water while being isolated from the surface,thus minimizing the risk of pollution.Laboratory tests,including downward and upward drilling,long-running,remote-control,and cold-environ-ment assessments,were conducted to validate the sonde's principle and functionality.During the 38th Chinese National Antarctic Research Expedition,CHINARE(2021-2022 season),the RECAS prototype underwent testing on the flank region of Dålk glacier,10 km from Zhongshan Station in Antarctica.Three boreholes with depths of 200.3,183.2,and 133.5 m were successfully drilled,with the refrozen meltwater sealing the boreholes during the process.Approximately 600 mL of melted water samples were collected from each hole.Throughout the drilling tests,all systems of the RECAS prototype performed within the expected ranges.
查看更多>>摘要：We analyze the topside ionosphere power line radiation(PLR)at 60 Hz over the US using electric field data collected by CSES satellite between January 2019 and December 2022.The study aimed to further investigate the month-to-month variation characteristic of PLR occurrence rate observed by satellite and its several influencing factors,including solar radiation,lightning activity,and try to clarify the influence of electricity consumption.The results show that the solar radiation(solar zenith angle and F10.7)plays a major role in the variation of the PLR occurrence rate,and that there is no direct connection with the number of lightning.For the relationship between PLR occurrence rate and electricity consumption,the low occurrence rate associated with decreased weekend electricity consumption was not observed in the US.However,there is a significant difference in PLR occurrence rate between the East and West Coasts of the US at the same latitude during the same time period,suggesting that the significant difference in PLR occurrence rate is caused by the significant difference in electricity consumption between the two coasts.After excluding the effect of solar radiation on PLR occurrence rate,we concluded that only a significant difference in regional electricity consumption could lead to a corresponding change in PLR occurrence rate detected by the Low-Earth-Orbit satellite.Finally,we also found there is seasonal variation in the diurnal differences of the PLR occurrence rate caused by seasonal variation of the lower ionosphere.
查看更多>>摘要：We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model un-certainties and external disturbances of hypersonic winged-cone vehicles.The longitudinal nonlinear model is first established and transformed into the control-oriented error equations,and the control scheme is organized by a steady-compensation combination.To overcome and eliminate the impact of model uncertainties and external disturbances,an adaptive radial basis function neural network(RBFNN)is designed by a q-gradient approach.Taking the height-velocity error system with estimated uncertainties into account,the adaptive learning-based optimal tracking control(ALOTC)scheme is proposed by combining the critic-only adaptive dynamic programming(ADP)framework and parameter optimization of system settling time.Furthermore,a novel weight update law is proposed to satisfy the online iteration requirements,and the algorithm convergence and closed-loop stability are discussed by the Lyapunov theory.Finally,four simulation cases are provided to prove the effectiveness,accuracy,and robustness of the proposed scheme for the hypersonic longitudinal control system.
查看更多>>摘要：Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,＜169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall＞57.9 mm and rainfall intensity＞24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.
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