Non-obstacle design is critical to tailor physically handicapped workers in manufacturing system.Simultaneous consideration of variability in physically disabled users,machines and environment of the manufacturing sys...Non-obstacle design is critical to tailor physically handicapped workers in manufacturing system.Simultaneous consideration of variability in physically disabled users,machines and environment of the manufacturing system is extremely complex and generally requires modeling of physically handicapped interaction with the system.Most current modeling either concentrates on the task results or functional disability.The integration of physical constraints with task constraints is far more complex because of functional disability and its extended influence on adjacent body parts.A framework is proposed to integrate the two constraints and thus model the specific behavior of the physical handicapped in virtual environment generated by product specifications.Within the framework a simplified model of physical disabled body is constructed,and body motion is generated based on 3 levels of constraints(effecter constraints,kinematics constraints and physical constraints).The kinematics and dynamic calculations are made and optimized based on the weighting manipulated by the kinematics constraints and dynamic constraints.With object transferring task as example,the model is validated in Jack 6.0.Modelled task motion elements except for squatting and overreaching well matched with captured motion elements.The proposed modeling method can model the complex behavior of the physically handicapped by integrating both task and physical disability constraints.展开更多
The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input mult...The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input multi-output,nonlinearity,and strong coupling presents significant challenges.The substantial internal force generated during the adjustment process can potentially damage the LAC and degrade the assembly quality.Hence,a workspace-based hybrid force position control scheme was developed to achieve high quality assembly with high-precision and lower internal force.Firstly,an offline workspace analysis with inherent geometric characteristics to form time-varying posture error constraint.Then,the posture error is integrated into the online position axis control to ensure tracking the ideal posture,while the force control axis compensates for posture deviation by minimizing internal force,thereby achieving high precision and low internal force.Finally,the effectiveness was demonstrated through experiments.The root mean square errors of orientation and position are 104 rad and 0.1 mm,respectively.A reduction in internal force can range from 10.96%to 57.4%compared to the traditional method.Key points'max position error is decreased from 0.32 mm to 0.18 mm,satisfying the 0.5 mm tolerance.Therefore,the proposed method will help promote the development of high-performance manufacturing.展开更多
Objective:To observe the effect of acupuncture combined with the Thirteen-posture Tai Chi exercise prescription on the rehabilitation of cervical radiculopathy(CR).Methods:A total of 159 patients diagnosed with CR wer...Objective:To observe the effect of acupuncture combined with the Thirteen-posture Tai Chi exercise prescription on the rehabilitation of cervical radiculopathy(CR).Methods:A total of 159 patients diagnosed with CR were enrolled in a prospective study.They were randomly divided into an acupuncture group,an exercise group,and a combined group using the random number table method,with 53 cases in each group.All three groups received routine Western rehabilitation training.In addition,the acupuncture group was treated with“Si Tian Xue”[four points with“Tian”in their names,including Tianyou(TE16),Tianchuang(SI16),Tianrong(SI17),and Tianding(LI17)]acupuncture.The exercise group practiced according to the Thirteenposture Tai Chi exercise prescription.The combined group received“Si Tian Xue”acupuncture combined with the Thirteen-posture Tai Chi exercise prescription.All interventions lasted for 12 weeks in three groups.The neck disability index(NDI)and visual analog scale(VAS)scores were compared among the three groups before treatment and after 6 and 12 weeks of treatment.Before treatment and after 12-week treatment,the range of motion(ROM)of cervical in left rotation,right rotation,extension,and flexion,as well as the mean power frequency(MPF)of surface electromyography(sEMG)signals of the erector spinae and trapezius,the average blood flow velocity of the vertebral and basilar arteries,and the short-form 36-item health survey(SF-36)score was compared among the three groups.Results:After 6 and 12 weeks of treatment,the NDI and VAS scores of the three groups were significantly lower than those before treatment(P<0.05),and the NDI and VAS scores of the combined group were significantly lower than those of the acupuncture group and the exercise group at the same time points(P<0.05).After treatment,the cervical ROM in left rotation,right rotation,extension,and flexion in the three groups was significantly higher than that before treatment(P<0.05),and the combined group was significantly higher than the acupuncture group and the exercise group(P<0.05).After treatment,the MPF of the erector spinae and trapezius and the average blood flow velocity of the vertebral and basilar arteries in the three groups were significantly higher than those before treatment(P<0.05),and the combined group was significantly higher than the acupuncture group and the exercise group(P<0.05).After treatment,the SF-36 score of the three groups was significantly higher than that before treatment(P<0.05),and it was significantly higher in the combined group than in the acupuncture group and the exercise group(P<0.05).Conclusion:Compared to“Si Tian Xue”acupuncture or the Thirteen-posture Tai Chi exercise prescription alone,the combination of the two can more effectively improve cervical function and microcirculation,relieve pain,and improve the quality of life in patients with CR.展开更多
Technological development of motion and posture analyses is rapidly progressing,especially in rehabilitation settings and sport biomechanics.Consequently,clear discrimination among different measurement systems is req...Technological development of motion and posture analyses is rapidly progressing,especially in rehabilitation settings and sport biomechanics.Consequently,clear discrimination among different measurement systems is required to diversify their use as needed.This review aims to resume the currently used motion and posture analysis systems,clarify and suggest the appropriate approaches suitable for specific cases or contexts.The currently gold standard systems of motion analysis,widely used in clinical settings,present several limitations related to marker placement or long procedure time.Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies,especially outside laboratories.Similarly,new posture analysis techniques are emerging,often driven by the need for fast and non-invasive methods to obtain high-precision results.These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies.The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient.Herein,these devices and their uses are described,providing researchers,clinicians,orthopedics,physical therapists,and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis,therapy,and prevention.展开更多
This paper presents a novel control approach for achieving robust posture control in legged locomotion,specifically for SLIP-like bipedal running and quadrupedal bounding with trunk stabilization.The approach is based...This paper presents a novel control approach for achieving robust posture control in legged locomotion,specifically for SLIP-like bipedal running and quadrupedal bounding with trunk stabilization.The approach is based on the virtual pendulum concept observed in human and animal locomotion experiments,which redirects ground reaction forces to a virtual support point called the Virtual Pivot Point(VPP)during the stance phase.Using the hybrid averaging theorem,we prove the upright posture stability of bipedal running with a fixed VPP position and propose a VPP angle feedback controller for online VPP adjustment to improve performance and convergence speed.Additionally,we present the first application of the VPP concept to quadrupedal posture control and design a VPP position feedback control law to enhance robustness in quadrupedal bounding.We evaluate the effectiveness of the proposed VPP-based controllers through various simulations,demonstrating their effectiveness in posture control of both bipedal running and quadrupedal bounding.The performance of the VPP-based control approach is further validated through experimental validation on a quadruped robot,SCIT Dog,for stable bounding motion generation at different forward speeds.展开更多
This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessmen...This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessment Form has been assessed as severe articulation disorder.The patient has significantly improved his speech function and quality of life after systematic head control training,respiratory function training,articulation motor training,and articulation training.In the course of treatment,emphasis was placed on head postural control training and respiratory function training,and emphasis was placed on the strength and coordination training of articulatory organs,and the results were remarkable.After the patient was discharged from the hospital,the follow-up of basic daily life communication was not limited.展开更多
Considering passenger trains'key role in remote regions,this study employed machine vision technology to monitor five posture parameters of the second car of a conventional passenger train,aiming to investigate th...Considering passenger trains'key role in remote regions,this study employed machine vision technology to monitor five posture parameters of the second car of a conventional passenger train,aiming to investigate the influence of windbreaks and crosswinds along railways on the operating postures of conventional passenger trains.The study found that when passing through the anti-wind tunnel with holes,the amplitudes of posture parameters were smaller than those of other windbreaks,demonstrating the superior performance of this windbreak in maintaining posture stability compared to others.In tunnel sections,larger amplitudes of these parameters were observed for the tail car than the head car,while the opposite occurred in non-tunnel sections.Notably,during tunnel transit,their amplitudes did not increase monotonically with speed but peaked at a specific speed that most adversely affected the operating posture.These conclusions have a great significance for improving operating safety under crosswinds.展开更多
This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophagea...This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophageal reflux disease(GERD)monitoring.Unlike conventional approaches limited to four basic postures,CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions,providing enhanced resolution for personalized health assessment.The architecture introduces a unique integration of three complementary components:1D Convolutional Neural Networks(1D-CNN)for efficient local spatial feature extraction,Gated Recurrent Units(GRU)to capture short-termtemporal dependencieswith reduced computational complexity,and Bidirectional Long Short-Term Memory(Bi-LSTM)networks for modeling long-term temporal context in both forward and backward directions.This complementary integration allows the model to better represent dynamic and contextual information inherent in the sensor data,surpassing the performance of simpler or previously published hybrid models.Experiments were conducted on a benchmark dataset consisting of 18 volunteers(age range:19–24 years,mean 20.56±1.1 years;height 164.78±8.18 cm;weight 55.39±8.30 kg;BMI 20.24±2.04),monitored via a single abdominal accelerometer.A subjectindependent evaluation protocol with multiple random splits was employed to ensure robustness and generalizability.The proposed model achieves an average Accuracy of 87.60% and F1-score of 83.38%,both reported with standard deviations over multiple runs,outperforming several baseline and state-of-the-art methods.By releasing the dataset publicly and detailing themodel design,this work aims to facilitate reproducibility and advance research in sleep posture classification for clinical applications.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,...Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.展开更多
Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformat...Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformation models, neglecting the biomechanical differences between rigid structures and soft tissues, which compromises registration accuracy, especially during significant bone displacements. Method To address this issue, we introduce RE-Reg, a rigid-elastic CT-CBCT image registration framework that jointly learns rigid bone motion and soft tissue deformation. RE-Reg incorporates a rigid alignment(RA) module to estimate global bone motion and an elastic deformation(ED) module to model soft tissue deformation, preserving bony structures through bone shape preservation(BSP) loss. Result Our comprehensive evaluation on publicly available datasets demonstrates that RE-Reg significantly outperforms existing methods in terms of registration accuracy and rigid bone structure preservation, achieving a 1.3% improvement in Dice similarity coefficient(DSC) and a 23% reduction in rigid bone deformation(%Δvol) compared with the best baseline. Conclusion This framework not only enhances anatomical fidelity but also ensures biomechanical plausibility and provides a valuable tool for image-guided orthopedic surgery. This code is available athttps://github.com/Zq-Huang/RE-Reg.展开更多
In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A fi...In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.展开更多
Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame de...Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.展开更多
Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to ...Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.展开更多
[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical sim...[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical simulations and dynamic analysis methods,this study systematically investigates the coupled dynamic response characteristics during the cage-carrier vessel separation process to reveal its dynamic evolution patterns and key influence mechanisms.[Method]Based on potential flow theory,a fully coupled dynamic analysis model of crane vessel-net cage-semi-submersible barge was established for a marine ranch project in Guangdong.The complete lifting process was dynamically simulated using SESAM software.Five typical operating sea states were configured to investigate the influence of wave parameters on the system's motion response under combined wave-current-wind actions.[Result]The results demonstrate that wave period dominates the system stability.Under short-period conditions,the system maintains stable motion with relatively small horizontal relative displacements,while long-period conditions excite low-frequency resonance,leading to significant slow-drift motions.Vertical response analysis reveals that long-period waves cause severe relative displacement fluctuations between the cage and semi-submersible vessel,with actual displacement amplitudes doubling the preset safety target of 2.045 m.Quantitative analysis further indicates that when significant wave height increases from 1.0 m to 1.5 m,the actual displacement amplitude increases by approximately 20%relative to the target displacement of 2.045 m,demonstrating that its influence is significantly weaker than the displacement variations induced by wave period changes.The complete dynamic simulation successfully captures the continuous dynamic response characteristics during the lifting process.[Conclusion]This research clarifies the influence mechanisms of wave parameters on the cage lifting process,identifying wave period as the crucial factor for operational safety.An operation window assessment method incorporating multi-body coupling effects is established,proposing a safety criterion with peak period not exceeding six seconds as the core requirement.The findings provide theoretical foundation for safe installation of marine ranch net cages and offer valuable references for similar offshore lifting operations.展开更多
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.展开更多
The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion p...The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion parameters,directly influencing the regional seismic hazard risk level.This study methodically conducted on-site studies and observations of building collapses and damages resulting from seismic amplification effects,using the Wenchuan M_(S)8.0 earthquake as a case study.Comprehensive experimental and numerical simulation studies were carried out.A large-scale shaking table test was performed,and numerical models for 14 different loess sites types were established.Various types of seismic waves were incorporated into these models for systematic numerical simulation calculations.The research reveals the mechanisms by which loess deposit thickness and stratigraphic structure in the Yellow River Basin affect seismic ground motion amplification.The results indicate that as the epicentral distance increases,the peak ground motion shows a marked attenuation trend,with the horizontal component attenuating substantially faster than the vertical component.As the overlying loess layer thickness increases from 50 to 100 m,the seismic intensity may escalate by 3−4 degrees,and the peak acceleration may amplify by 1.5−2.2 times.With the augmentation of loess deposit thickness and the proliferation of soil layers,both the peak acceleration response spectrum and the characteristic period demonstrate an upward tendency,exhibiting slight fluctuations contingent upon the seismic wave type.展开更多
The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liqu...The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liquefaction.This study,which thoroughly investigated four sites at Palu,was conducted by performing a comprehensive ground motion parameter analysis.The ground motion characteristics were presented and justified,particularly for the most impacted direction.Ground motion predictions were analysed to define the spectral accelerations,and matching spectral accelerations were conducted to produce ground motions for each site.Non-linear seismic ground response analysis based on the hyperbolic model of pressure pressure-dependent was performed to investigate cyclic soil behaviour.The results revealed that ground motion is crucial in significant soil damage,and the earthquake energy could trigger deep liquefaction.As the most significant ground motion,the vertical ground motion is essential in determining deep liquefaction.The discussion on the impact of liquefaction based on the results of the numerical analysis is presented.Significant ground motion with a longer duration could have a substantial impact on deep liquefaction in the study area.These findings depict how the 2018 Indonesia Earthquake(M_(w)7.5)triggered a mega-liquefaction in Palu City.The results could enhance the understanding of the importance of seismic hazard assessment.It is recommended that site investigation and soil improvement should be planned to counteract liquefaction damage before construction.This study also suggests conducting seismic hazard assessments for city development to minimise the potential disaster impact in the study area.展开更多
Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the clou...Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.展开更多
The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to r...The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.展开更多
基金supported by National Natural Science Foundation of China(Grant No.60975058)
文摘Non-obstacle design is critical to tailor physically handicapped workers in manufacturing system.Simultaneous consideration of variability in physically disabled users,machines and environment of the manufacturing system is extremely complex and generally requires modeling of physically handicapped interaction with the system.Most current modeling either concentrates on the task results or functional disability.The integration of physical constraints with task constraints is far more complex because of functional disability and its extended influence on adjacent body parts.A framework is proposed to integrate the two constraints and thus model the specific behavior of the physical handicapped in virtual environment generated by product specifications.Within the framework a simplified model of physical disabled body is constructed,and body motion is generated based on 3 levels of constraints(effecter constraints,kinematics constraints and physical constraints).The kinematics and dynamic calculations are made and optimized based on the weighting manipulated by the kinematics constraints and dynamic constraints.With object transferring task as example,the model is validated in Jack 6.0.Modelled task motion elements except for squatting and overreaching well matched with captured motion elements.The proposed modeling method can model the complex behavior of the physically handicapped by integrating both task and physical disability constraints.
基金co-supported by the National Natural Science Foundation of China(No.52125504)the Liaoning Revitalization Talents Program(No.XLYC2202017)Dalian Support Policy Project for Innovation of Technological Talents(No.2023RG001)。
文摘The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input multi-output,nonlinearity,and strong coupling presents significant challenges.The substantial internal force generated during the adjustment process can potentially damage the LAC and degrade the assembly quality.Hence,a workspace-based hybrid force position control scheme was developed to achieve high quality assembly with high-precision and lower internal force.Firstly,an offline workspace analysis with inherent geometric characteristics to form time-varying posture error constraint.Then,the posture error is integrated into the online position axis control to ensure tracking the ideal posture,while the force control axis compensates for posture deviation by minimizing internal force,thereby achieving high precision and low internal force.Finally,the effectiveness was demonstrated through experiments.The root mean square errors of orientation and position are 104 rad and 0.1 mm,respectively.A reduction in internal force can range from 10.96%to 57.4%compared to the traditional method.Key points'max position error is decreased from 0.32 mm to 0.18 mm,satisfying the 0.5 mm tolerance.Therefore,the proposed method will help promote the development of high-performance manufacturing.
文摘Objective:To observe the effect of acupuncture combined with the Thirteen-posture Tai Chi exercise prescription on the rehabilitation of cervical radiculopathy(CR).Methods:A total of 159 patients diagnosed with CR were enrolled in a prospective study.They were randomly divided into an acupuncture group,an exercise group,and a combined group using the random number table method,with 53 cases in each group.All three groups received routine Western rehabilitation training.In addition,the acupuncture group was treated with“Si Tian Xue”[four points with“Tian”in their names,including Tianyou(TE16),Tianchuang(SI16),Tianrong(SI17),and Tianding(LI17)]acupuncture.The exercise group practiced according to the Thirteenposture Tai Chi exercise prescription.The combined group received“Si Tian Xue”acupuncture combined with the Thirteen-posture Tai Chi exercise prescription.All interventions lasted for 12 weeks in three groups.The neck disability index(NDI)and visual analog scale(VAS)scores were compared among the three groups before treatment and after 6 and 12 weeks of treatment.Before treatment and after 12-week treatment,the range of motion(ROM)of cervical in left rotation,right rotation,extension,and flexion,as well as the mean power frequency(MPF)of surface electromyography(sEMG)signals of the erector spinae and trapezius,the average blood flow velocity of the vertebral and basilar arteries,and the short-form 36-item health survey(SF-36)score was compared among the three groups.Results:After 6 and 12 weeks of treatment,the NDI and VAS scores of the three groups were significantly lower than those before treatment(P<0.05),and the NDI and VAS scores of the combined group were significantly lower than those of the acupuncture group and the exercise group at the same time points(P<0.05).After treatment,the cervical ROM in left rotation,right rotation,extension,and flexion in the three groups was significantly higher than that before treatment(P<0.05),and the combined group was significantly higher than the acupuncture group and the exercise group(P<0.05).After treatment,the MPF of the erector spinae and trapezius and the average blood flow velocity of the vertebral and basilar arteries in the three groups were significantly higher than those before treatment(P<0.05),and the combined group was significantly higher than the acupuncture group and the exercise group(P<0.05).After treatment,the SF-36 score of the three groups was significantly higher than that before treatment(P<0.05),and it was significantly higher in the combined group than in the acupuncture group and the exercise group(P<0.05).Conclusion:Compared to“Si Tian Xue”acupuncture or the Thirteen-posture Tai Chi exercise prescription alone,the combination of the two can more effectively improve cervical function and microcirculation,relieve pain,and improve the quality of life in patients with CR.
基金Supported by University Research Project GrantNo. PIACERI Found–NATURE-OA-2020-2022。
文摘Technological development of motion and posture analyses is rapidly progressing,especially in rehabilitation settings and sport biomechanics.Consequently,clear discrimination among different measurement systems is required to diversify their use as needed.This review aims to resume the currently used motion and posture analysis systems,clarify and suggest the appropriate approaches suitable for specific cases or contexts.The currently gold standard systems of motion analysis,widely used in clinical settings,present several limitations related to marker placement or long procedure time.Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies,especially outside laboratories.Similarly,new posture analysis techniques are emerging,often driven by the need for fast and non-invasive methods to obtain high-precision results.These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies.The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient.Herein,these devices and their uses are described,providing researchers,clinicians,orthopedics,physical therapists,and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis,therapy,and prevention.
基金This work was supported by the Touyan Innovation Program of Heilongjiang Province.
文摘This paper presents a novel control approach for achieving robust posture control in legged locomotion,specifically for SLIP-like bipedal running and quadrupedal bounding with trunk stabilization.The approach is based on the virtual pendulum concept observed in human and animal locomotion experiments,which redirects ground reaction forces to a virtual support point called the Virtual Pivot Point(VPP)during the stance phase.Using the hybrid averaging theorem,we prove the upright posture stability of bipedal running with a fixed VPP position and propose a VPP angle feedback controller for online VPP adjustment to improve performance and convergence speed.Additionally,we present the first application of the VPP concept to quadrupedal posture control and design a VPP position feedback control law to enhance robustness in quadrupedal bounding.We evaluate the effectiveness of the proposed VPP-based controllers through various simulations,demonstrating their effectiveness in posture control of both bipedal running and quadrupedal bounding.The performance of the VPP-based control approach is further validated through experimental validation on a quadruped robot,SCIT Dog,for stable bounding motion generation at different forward speeds.
基金Teaching and Research Project of Anhui Urban Management Vocational College(Project No.:2024kfkc001)。
文摘This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessment Form has been assessed as severe articulation disorder.The patient has significantly improved his speech function and quality of life after systematic head control training,respiratory function training,articulation motor training,and articulation training.In the course of treatment,emphasis was placed on head postural control training and respiratory function training,and emphasis was placed on the strength and coordination training of articulatory organs,and the results were remarkable.After the patient was discharged from the hospital,the follow-up of basic daily life communication was not limited.
基金Projects(52302447,52388102)supported by the National Natural Science Foundation of China。
文摘Considering passenger trains'key role in remote regions,this study employed machine vision technology to monitor five posture parameters of the second car of a conventional passenger train,aiming to investigate the influence of windbreaks and crosswinds along railways on the operating postures of conventional passenger trains.The study found that when passing through the anti-wind tunnel with holes,the amplitudes of posture parameters were smaller than those of other windbreaks,demonstrating the superior performance of this windbreak in maintaining posture stability compared to others.In tunnel sections,larger amplitudes of these parameters were observed for the tail car than the head car,while the opposite occurred in non-tunnel sections.Notably,during tunnel transit,their amplitudes did not increase monotonically with speed but peaked at a specific speed that most adversely affected the operating posture.These conclusions have a great significance for improving operating safety under crosswinds.
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number:NCUD.02-2024.11.
文摘This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophageal reflux disease(GERD)monitoring.Unlike conventional approaches limited to four basic postures,CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions,providing enhanced resolution for personalized health assessment.The architecture introduces a unique integration of three complementary components:1D Convolutional Neural Networks(1D-CNN)for efficient local spatial feature extraction,Gated Recurrent Units(GRU)to capture short-termtemporal dependencieswith reduced computational complexity,and Bidirectional Long Short-Term Memory(Bi-LSTM)networks for modeling long-term temporal context in both forward and backward directions.This complementary integration allows the model to better represent dynamic and contextual information inherent in the sensor data,surpassing the performance of simpler or previously published hybrid models.Experiments were conducted on a benchmark dataset consisting of 18 volunteers(age range:19–24 years,mean 20.56±1.1 years;height 164.78±8.18 cm;weight 55.39±8.30 kg;BMI 20.24±2.04),monitored via a single abdominal accelerometer.A subjectindependent evaluation protocol with multiple random splits was employed to ensure robustness and generalizability.The proposed model achieves an average Accuracy of 87.60% and F1-score of 83.38%,both reported with standard deviations over multiple runs,outperforming several baseline and state-of-the-art methods.By releasing the dataset publicly and detailing themodel design,this work aims to facilitate reproducibility and advance research in sleep posture classification for clinical applications.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by the Basic Science Research Program(2023R1A2C3004336,RS-202300243807)&Regional Leading Research Center(RS-202400405278)through the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)。
文摘Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.
基金Supported by the National Natural Science Foundation of China(Grant Nos.62025104,62331005,and U22A2052)the Beijing Natural Science Foundation(Grant No.L242100).
文摘Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformation models, neglecting the biomechanical differences between rigid structures and soft tissues, which compromises registration accuracy, especially during significant bone displacements. Method To address this issue, we introduce RE-Reg, a rigid-elastic CT-CBCT image registration framework that jointly learns rigid bone motion and soft tissue deformation. RE-Reg incorporates a rigid alignment(RA) module to estimate global bone motion and an elastic deformation(ED) module to model soft tissue deformation, preserving bony structures through bone shape preservation(BSP) loss. Result Our comprehensive evaluation on publicly available datasets demonstrates that RE-Reg significantly outperforms existing methods in terms of registration accuracy and rigid bone structure preservation, achieving a 1.3% improvement in Dice similarity coefficient(DSC) and a 23% reduction in rigid bone deformation(%Δvol) compared with the best baseline. Conclusion This framework not only enhances anatomical fidelity but also ensures biomechanical plausibility and provides a valuable tool for image-guided orthopedic surgery. This code is available athttps://github.com/Zq-Huang/RE-Reg.
文摘In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.
基金supported by the Major Program(JD)of Hubei Province under Grant No.2023BAA009the National Natural Science Foundation of China(Grant No.22105162)+1 种基金the Natural Science Foundation of Hubei Province(Grant No.2023AFB623)the Original Exploration Seed Fund of Hubei University。
文摘Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.
文摘[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical simulations and dynamic analysis methods,this study systematically investigates the coupled dynamic response characteristics during the cage-carrier vessel separation process to reveal its dynamic evolution patterns and key influence mechanisms.[Method]Based on potential flow theory,a fully coupled dynamic analysis model of crane vessel-net cage-semi-submersible barge was established for a marine ranch project in Guangdong.The complete lifting process was dynamically simulated using SESAM software.Five typical operating sea states were configured to investigate the influence of wave parameters on the system's motion response under combined wave-current-wind actions.[Result]The results demonstrate that wave period dominates the system stability.Under short-period conditions,the system maintains stable motion with relatively small horizontal relative displacements,while long-period conditions excite low-frequency resonance,leading to significant slow-drift motions.Vertical response analysis reveals that long-period waves cause severe relative displacement fluctuations between the cage and semi-submersible vessel,with actual displacement amplitudes doubling the preset safety target of 2.045 m.Quantitative analysis further indicates that when significant wave height increases from 1.0 m to 1.5 m,the actual displacement amplitude increases by approximately 20%relative to the target displacement of 2.045 m,demonstrating that its influence is significantly weaker than the displacement variations induced by wave period changes.The complete dynamic simulation successfully captures the continuous dynamic response characteristics during the lifting process.[Conclusion]This research clarifies the influence mechanisms of wave parameters on the cage lifting process,identifying wave period as the crucial factor for operational safety.An operation window assessment method incorporating multi-body coupling effects is established,proposing a safety criterion with peak period not exceeding six seconds as the core requirement.The findings provide theoretical foundation for safe installation of marine ranch net cages and offer valuable references for similar offshore lifting operations.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
基金supported by the Earthquake Science and Technology Spark Plan Project(No.XH23041C)The Natural Science Foundation of Gansu Province(No.22JR11RA090)Gansu Lanzhou Geophysics National Observation and Research Station(No.2021Y14).
文摘The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion parameters,directly influencing the regional seismic hazard risk level.This study methodically conducted on-site studies and observations of building collapses and damages resulting from seismic amplification effects,using the Wenchuan M_(S)8.0 earthquake as a case study.Comprehensive experimental and numerical simulation studies were carried out.A large-scale shaking table test was performed,and numerical models for 14 different loess sites types were established.Various types of seismic waves were incorporated into these models for systematic numerical simulation calculations.The research reveals the mechanisms by which loess deposit thickness and stratigraphic structure in the Yellow River Basin affect seismic ground motion amplification.The results indicate that as the epicentral distance increases,the peak ground motion shows a marked attenuation trend,with the horizontal component attenuating substantially faster than the vertical component.As the overlying loess layer thickness increases from 50 to 100 m,the seismic intensity may escalate by 3−4 degrees,and the peak acceleration may amplify by 1.5−2.2 times.With the augmentation of loess deposit thickness and the proliferation of soil layers,both the peak acceleration response spectrum and the characteristic period demonstrate an upward tendency,exhibiting slight fluctuations contingent upon the seismic wave type.
基金The World Class Professor(WCP)Program of the Directorate of Resources,Directorate General of Higher Education,Ministry of Education and Culture in 2023 supports this studythe JAPAN-ASEAN Science and Technology Innovation Platform(JASTIP-WP4)+3 种基金the University of Bengkulu's International Collaboration Research Fund(2183/UN30.15/LT/2019)for partial fundingthe C2F Fund for Postdoctoral Fellowship from Chulalongkorn Universitythe Thailand Science Research and Innovation Fund Chulalongkorn University(DISF68210001)the National Research Council of Thailand(N42A670572)。
文摘The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liquefaction.This study,which thoroughly investigated four sites at Palu,was conducted by performing a comprehensive ground motion parameter analysis.The ground motion characteristics were presented and justified,particularly for the most impacted direction.Ground motion predictions were analysed to define the spectral accelerations,and matching spectral accelerations were conducted to produce ground motions for each site.Non-linear seismic ground response analysis based on the hyperbolic model of pressure pressure-dependent was performed to investigate cyclic soil behaviour.The results revealed that ground motion is crucial in significant soil damage,and the earthquake energy could trigger deep liquefaction.As the most significant ground motion,the vertical ground motion is essential in determining deep liquefaction.The discussion on the impact of liquefaction based on the results of the numerical analysis is presented.Significant ground motion with a longer duration could have a substantial impact on deep liquefaction in the study area.These findings depict how the 2018 Indonesia Earthquake(M_(w)7.5)triggered a mega-liquefaction in Palu City.The results could enhance the understanding of the importance of seismic hazard assessment.It is recommended that site investigation and soil improvement should be planned to counteract liquefaction damage before construction.This study also suggests conducting seismic hazard assessments for city development to minimise the potential disaster impact in the study area.
基金Supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R896).
文摘Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.
基金The Central Government Guides Local Foundation for Science and Technology Development(Grant No.YDZJSX2024B004).
文摘The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.
