Investigation on the Radial Micro-motion about Piston of Axial Piston Pump

The limit working parameters and service life of axial piston pump are determined by the carrying ability and lubrication characteristic of its key friction pairs. Therefore, the design and optimization of the key friction pairs are always a key and difficult problem in the research on axial piston pump. In the traditional research on piston/cylinder pair, the assembly relationship of piston and cylinder bore is simplified into ideal cylindrical pair, which can not be used to analyze the influences of radial micro-motion of piston on the distribution characteristics of oil-film thickness and pressure in details. In this paper, based on the lubrication theory of the oil film, a numerical simulation model is built, taking the influences of roughness, elastic deformation of piston and pressure-viscosity effect into consideration. With the simulation model, the dynamic characteristics of the radial micro-motion and pressure distribution are analyzed, and the relationships between radial micro-motion and carrying ability, lubrication condition, and abrasion are discussed. Furthermore, a model pump for pressure distribution measurement of oil film between piston and cylinder bore is designed. The comparison of simulation and experimental results of pressure distribution shows that the simulation model has high accuracy. The experiment and simulation results demonstrate that the pressure distribution has peak values that are much higher than the boundary pressure in the piston chamber due to the radial micro-motion, and the abrasion of piston takes place mainly on the hand close to piston ball. In addition, improvement of manufacturing roundness and straightness of piston and cylinder bore is helpful to improve the carrying ability of piston/cylinder pair. The proposed research provides references for designing piston/cylinder pair, and helps to prolong the service life of axial piston pump.

Micro-motion dynamics analysis of ballistic targets based on infrared detection

The dynamic characteristics related to micro-motions, such as mechanical vibration or rotation, play an essential role in classifying and recognizing ballistic targets in the midcourse, and recent researches explore ways of extracting the micro-motion features from radar signals of ballistic targets. In this paper, we focus on how to investigate the micro-motion dynamic characteristics of the ballistic targets from the signals based on infrared (IR) detection, which is mainly achieved by analyzing the periodic fluctuation characteristics of the target IR irradiance intensity signatures. Simulation experiments demonstrate that the periodic characteristics of IR signatures can be used to distinguish different micro motion types and estimate related parameters. Consequently, this is possible to determine the micro-motion dynamics of ballistic targets based on IR detection.

基于Leap Motion手势交互技术的博物馆数字展示应用研究

随着科技的不断发展,博物馆数字展示已成为呈现文物与历史不可或缺的重要手段之一。在数字化潮流的推动下,Leap Motion技术凭借其卓越的手势交互能力引起了广泛的关注与兴趣。本研究旨在探索如何充分利用Leap Motion手势交互技术,设计并实现一种全新的博物馆数字展示交互系统,以提升观众的参与度和沉浸式体验。本研究采用实验研究方法,开发并实施了一套基于LeapMotion技术的手势交互系统。通过实验设计、设备选择与安装、手势识别与映射、展示内容的创建与集成、以及用户测试与优化等步骤完成研究。其结果表明,该系统能够准确捕捉观众的手部动作,并将其映射到超大屏幕的交互中,显著提升了展示效果和观众的互动体验。手势识别的误差率控制在5%以内,交互响应速度优秀,达到了预期目标。本研究验证了LeapMotion手势交互技术在博物馆数字展示中的有效性和可行性,提供了一种创新的展示方式,为公众呈现出更为丰富、引人入胜的文化体验,为未来博物馆展示的数字化发展方向提供有益的参考与借鉴,以促进整个行业的创新发展。

基于Leap Motion手势识别的三维交互系统

随着虚拟交互技术的发展,人们迈入了“体验式经济时代”,消费者越来越关注个性体验,因此,基于Leap Motion手势识别设备,设计了一种三维虚拟室内交互系统。该系统以Unity3D作为开发工具,Leap Motion作为硬件平台,结合C#语言进行脚本的编译,利用3ds Max平台对室内进行场景搭建,通过Unity3D工具将组件整合,设计了七种手势,使用Leap Motion硬件设备对场景中物体进行各种不同的操作。经试验表明,该系统实现了用户与场景中物体的交互能力,可以应用在室内装修和设计等方面,增强人们的体验感与趣味性。

3D dynamic motion of a dielectric micro-sphere within optical tweezers

Known as laser trapping,optical tweezers,with nanometer accuracy and pico-newton precision,plays a pivotal role in single bio-molecule measurements and controllable motions of micro-machines.In order to advance the flourishing applications for those achievements,it is necessary to make clear the three-dimensional dynamic process of micro-particles stepping into an optical field.In this paper,we utilize the ray optics method to calculate the optical force and optical torque of a micro-sphere in optical tweezers.With the influence of viscosity force and torque taken into account,we numerically solve and analyze the dynamic process of a dielectric micro-sphere in optical tweezers on the basis of Newton mechanical equations under various conditions of initial positions and velocity vectors of the particle.The particle trajectory over time can demonstrate whether the particle can be successfully trapped into the optical tweezers center and reveal the subtle details of this trapping process.Even in a simple pair of optical tweezers,the dielectric micro-sphere exhibits abundant phases of mechanical motions including acceleration,deceleration,and turning.These studies will be of great help to understand the particle-laser trap interaction in various situations and promote exciting possibilities for exploring novel ways to control the mechanical dynamics of microscale particles.

An approach of motion compensation and ISAR imaging for micro-motion targets

Inverse synthetic aperture radar(ISAR)imaging of the target with the non-rigid body is very important in the field of radar signal processing.In this paper,a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain,and the micro-Doppler(m-D)signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition(CEMD)algorithm,which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center.Then,a better focused ISAR image of the target with the non-rigid body can be obtained consequently.Results of the simulated and raw data demonstrate the effectiveness of the algorithm.

High-Precision Wideband Phase-Derived Velocity Measurement for Micro-Motion Extraction

A phase-derived velocity measurement method is proposed in a wideband coherent system,based on a precise echo model considering the inner pulse Doppler effect caused by fast moving targets.The Cramer-Rao low band of velocity measurement precision is deduced,demonstrating the high precision of the proposed method.Simulations and out-field experiments further validate the effectiveness of the proposed method in high-precision measurement and micro-motion extraction for targets with weak reflection intensity.Compared with the long-time integration approaches for velocity measurement,the phase-derived method is easy to implement and meets the requirement for high data rate,which makes it suitable for micro-motion feature extraction in wideband systems.

Micro-Scale Motion Precision Simulation Method for a New-Type 6-DOF Micro-Manipulation Robot

A new 6-DOF micro-manipulation robot based on 3-PPTTRS parallel mechanisms in combination with flexure hinges is proposed. The design principle of the mechanism is introduced, and the kinematics analysis method based on differentiation is used to get the (inverse) kinematics equations. Then a micro-scale motion precision simulation method is proposed according to finite element analysis (FEA), and the prediction of robot’s motion precision in design phase is realized. The simulation result indicates that the 6-DOF micro-manipulation robot can meet the design specification.

Pulses in ground motions identified through surface partial matching and their impact on seismic rocking consequence

In seismology and earthquake engineering,it is fundamental to identify and characterize the pulse-like features in pulse-type ground motions.To capture the pulses that dominate structural responses,this study establishes congruence and shift relationships between response spectrum surfaces.A similarity search between spectrum surfaces,supplemented with a similarity search in time series,has been applied to characterize the pulse-like features in pulse-type ground motions.The identified pulses are tested in predicting the rocking consequences of slender rectangular blocks under the original ground motions.Generally,the prediction is promising for the majority of the ground motions where the dominant pulse is correctly identified.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

基于Leap motion 的大学物理实验虚拟课堂设计

通过利用手势控制器(Leap Motion)技术,设计并实施一套创新的大学物理虚拟实验课程.通过在Unity3D软件中整合Leap Motion,创建一个大学物理实验测量杨氏模量实验场景,主要是利用C#语言进行开发,通过设计出多个模块,包括主界面设计和基本实验组件来完成物理实验的设计.在Leap Motion官网上下载关于Unity3D的SDK资源包并且导入Unity3D中,用其建立手部模型,实现Leap Motion控制器与Unity3D的手部交互实验系统的设计.人机交互技术的引入不仅可以丰富大学物理实验的教学手段,而且还能提高学生的学科理解和实际操作能力,同时培养其创新思维和科技素养.

Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

Single-pixel imaging of a moving object with multi-motion

Motion blur restoration is essential for the imaging of moving objects,especially for single-pixel imaging(SPI),which requires multiple measurements.To reconstruct the image of a moving object with multiple motion modes,we propose a novel motion blur restoration method of SPI using geometric moment patterns.We design a novel localization method that uses normalized differential first-order moments and central moment patterns to determine the object's translational position and rotation angle information.Then,we perform motion compensation by using shifting Hadamard patterns.Our method effectively improves the detection accuracy of multiple motion modes and enhances the quality of the reconstructed image.We perform simulations and experiments,and the results validate the effectiveness of the proposed method.

Multiparameter Numerical Investigation of Two Types of Moving Interactions Between the Deep-Sea Mining Vehicle Track Plate and Seabed Soil:Digging and Rotating Motions

To ensure the safe performance of deep-sea mining vehicles(DSMVs),it is necessary to study the mechanical characteristics of the interaction between the seabed soil and the track plate.The rotation and digging motions of the track plate are important links in the contact between the driving mechanism of the DSMV and seabed soil.In this study,a numerical simulation is conducted using the coupled Eulerian–Lagrangian(CEL)large deformation numerical method to investigate the interaction between the track plate of the DSMV and the seabed soil under two working conditions:rotating condition and digging condition.First,a soil numerical model is established based on the elastoplastic mechanical characterization using the basic physical and mechanical properties of the seabed soil obtained by in situ sampling.Subsequently,the soil disturbance mechanism and the dynamic mechanical response of the track plate under rotating and digging conditions are obtained through the analysis of the sensitivity of the motion parameters,the grouser structure,the layered soil features and the soil heterogeneity.The results indicate that the above parameters remarkably influence the interaction between the DSMV and the seabed soil.Therefore,it is important to consider the rotating and digging motion of the DSMV in practical engineering to develop a detailed optimization design of the track plate.

Shugan Jieyu capsule effects on peripheral blood micro-124, micro- 132, and brain-derived neurotrophic factor in patients with mild to moderate depression

BACKGROUND To assess the effectiveness of Shugan Jieyu capsules on peripheral blood miR-124,miR-132,and brain-derived neurotrophic factor(BDNF)levels in patients with mild to moderate depression following coronary artery intervention[percuta-neous coronary intervention(PCI)]for coronary heart disease.Patients with mild-to-moderate depression of the liver-qi stagnation type after PCI for coronary heart disease at the 305th Hospital of the People’s Liberation Army were enrolled from June 2022 to November 2023 and randomly assigned to two groups:Experimental(treated with Shugan Jieyu capsules)and control(tr-eated with escitalopram oxalate tablets).This study compared the antidepressant effects of these treatments using 17-item Hamilton Rating Scale for Depression(HAMD-17)scores,metabolic equivalents,low-density lipoprotein cholesterol,BDNF,high-sensitivity C-reactive protein levels,miR-124 and miR-132 levels,distribution of immune-related lymphocyte subsets,and traditional Chinese me-dicine syndrome scores before and after 6 weeks of treatment.RESULTS No significant difference was observed in any index between the two groups before treatment(P>0.05).After treatment,the total efficacy rates were 93.33%and 90.00%in the experimental and control groups,respectively.Experimental group had significantly lower scores for the main and secondary syndromes compared to the control group(P<0.05).No significant difference was observed in the metabolic equivalents between the two groups be-fore and after treatment(P>0.05).The levels of low-density lipoprotein cholesterol,high-sensitivity C-reactive pro-tein,and miR-132 were significantly lower,whereas those of miR-124,BDNF,CD3+T lymphocytes,CD3+CD4+T helper lymphocytes,and CD3+CD4+/CD3+CD8+cells were significantly higher in the experimental group com-pared to the control group(P<0.05).The incidence of adverse reactions during experimental group was signi-ficantly lower than that in control group(P<0.05).CONCLUSION Shugan Jieyu capsules have good efficacy in patients with mild-to-moderate depression after PCI,and its me-chanism may contribute to the regulation of miR-124,miR-132,BDNF levels,and lymphoid immune cells.

基于三维运动捕捉技术构建蒙医震脑术三维虚拟仿真平台的视觉呈现

背景:基于三维运动捕捉技术可以构建精准的、客观量化的医学虚拟仿真模型,有利于临床学习者精准、深入理解和掌握各种传统疗术。目前中医虚拟仿真模型已有报道,但蒙医传统疗术的虚拟仿真模型尚未见报道。目的:构建一种基于三维运动捕捉技术的交互式三维可视化的蒙医震脑术虚拟仿真模型。方法:使用Motion Analysis三维光学运动捕捉系统和足底测力台采集蒙医专家的运动捕捉数据,在Motion Builder软件中构建震脑术三维动作模型,使用Maya软件构建角色模型并与动作模型相匹配,运动Unity3D软件构建蒙医震脑术虚拟仿真系统,系统整合蒙医震脑术操作3D动画、运动学和动力学参数信息。结果与结论:通过三维运动捕捉技术和计算机仿真重现蒙医震脑术操作,能够显示施术者和受试者的运动姿态,记录关节运动的关键空间位置参数与变化情况,得出运动过程中的运动学、动力学参数。运用交互式三维虚拟仿真技术,实现蒙医震脑术的三维虚拟仿真的视觉呈现,为蒙医震脑术手法的标准化、数字化、可视化研究奠定基础。

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.