NONLINEAR MOTION MECHANICS MODEL OF CURVED BLOOD VESSEL

A geometric model of curved blood vessels is established based on some reasonable hypotheses; the nonlinear motion mechanics model of the curved blood vessel is established according to basic mechanics laws. This model includes much more physiological factors. It couples the interaction of blood flow with mechanical factors such as the displacement, deformation, strain and stress etc. of the curved blood vessel. It is of great importance for investigating the circulation rules of the cardiovascular system and the nonlinear pulse wave propagation in curved blood vessels.

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。

About the Nature of the Quantum System—An Examination of the Random Discontinuous Motion Interpretation of the Quantum Mechanics

What is the quantum system? Consider the wave-function of the electron—what we call “single particle wave-function”—and assume that it contains N wave-packets. If we pass all the wave-packets through an electric field, all are deflected, as if each one of them contains an electron. However, if we bring any two wave-packets to travel close to one another, they don’t repel one another, as if at least one of them contains no charge. In trying to solve the measurement problem of the quantum mechanics (QM), different interpretations were proposed, each one coming with a particular ontology. However, only one interpretation paid explicit attention to the contradiction mentioned above. This interpretation was proposed by S. Gao who named it “random discontinuous motion” (RDM), because it assumes the existence of a particle that jumps from place to place at random. The particle carries all the physical properties of the respective type of particle, mass, charge, magnetic momentum, etc. It jumps under the control of an “instantaneous condition” about which Gao did not give details so far. Along with presenting problems of the QM that this interpretation solves, this text reveals difficulties vis-à-vis entanglements and the special relativity.

FLUID MECHANICS OF MICROVASCULAR VASOMOTION AND THE EFFECTS OF BLOOD VISCOELASTICITY

This paper deals with blood flow caused by microvascular vasomotion with the focus on the effects of blood viscoelasticity on the pressure rise and wall resistance. It is shown that microvascular vasomotion plays a role of the 'second heart' of the body which is of importance in conveying blood, and that the effects of blood viscoelasticity greatly depend on the Weissenberg number and mean flow rate.

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.

Fractal Approximation of Motion and Its Implications in Quantum Mechanics

Inconsistencies of some standard quantum mechanical models (Madelung’s, de Broglie’s models) are eliminated as- suming the micro particle movements on continuous, but non-differentiable curves (fractal curves). This hypothesis, named by us the fractal approximation of motion, will allow an unitary approach of the phenomena in quantum me-chanics (separation of the physical motion of objects in wave and particle components depending on the scale of resolution, correlated motions of the wave and particle, i.e. wave-particle duality, the mechanisms of duality, by means of both phase wave-particle coherence and wave-particle incoherence, the particle as a clock, particle incorporation into the wave and the implications of such a process). Moreover, correspondences with standard gravitational models (Einstein’s model, string theory) can be also distinguished.

Bidirectional rotating direct-current triboelectric nanogenerator with self-adaptive mechanical switching for harvesting reciprocating motion

Amid the growing interest in triboelectric nanogenerators(TENGs)as novel energy-harvesting devices,several studies have focused on direct current(DC)TENGs to generate a stable DC output for operating electronic devices.However,owing to the working mechanisms of conventional DC TENGs,generating a stable DC output from reciprocating motion remains a challenge.Accordingly,we propose a bidirectional rotating DC TENG(BiR-TENG),which can generate DC outputs,regardless of the direction of rotation,from reciprocating motions.The distinct design of the BiR-TENG enables the mechanical rectification of the alternating current output into a rotational-direction-dependent DC output.Furthermore,it allows the conversion of the rotational-direction-dependent DC output into a unidirectional DC output by adapting the configurations depending on the rotational direction.Owing to these tailored design strategies and subsequent optimizations,the BiR-TENG could generate an effective unidirectional DC output.Applications of the BiR-TENG for the reciprocating motions of swinging doors and waves were demonstrated by harnessing this output.This study demonstrates the potential of the BiR-TENG design strategy as an effective and versatile solution for energy harvesting from reciprocating motions,highlighting the suitability of DC outputs as an energy source for electronic devices.

Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour recognition.These applications have dramatically increased the diversity of IoT systems.Specifically,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension.Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial information.However,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos.In this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing systems.In particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and UCF101.The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

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.

Effect of a Non-Zero Velocity of the Motion Frame on the Kinetic Energy of a Free Particle Examined with the Aid of the Relativistic Mechanics

The paper examines the change of the relativistic kinetic energy of a free particle due to the velocity change of the motion frame in a special case when this reduction leads to the kinetic energy equal to zero. The difference of velocities gives a functional dependent solely on the velocity frame and original velocity of the particle. An analysis applied to the functional gives simple formulae for the extremal values of the mentioned velocity parameters. In the next step, solutions of the equation presented with the functional provide us with the velocities necessary for the vanishing property of the kinetic energy. A characteristic point is that a condition of the velocity of the motion frame smaller than the velocity of light is obtained directly in the applied formalism. This property holds with no reference done to the well-known postulate of the dominant value of the light velocity entering the relativity theory.

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.

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.

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.

Relationship between self-propelled velocity and Brownian motion for spherical and ellipsoid particles

The Brownian motion of spherical and ellipsoidal self-propelled particles was simulated without considering the effect of inertia and using the Langevin equation and the diffusion coefficient of ellipsoidal particles derived by Perrin.The P´eclet number(Pe)was introduced to measure the relative strengths of self-propelled and Brownian motions.We found that the motion state of spherical and ellipsoid self-propelled particles changed significantly under the influence of Brownian motion.For spherical particles,there were three primary states of motion:1)when Pe<30,the particles were still significantly affected by Brownian motion;2)when Pe>30,the self-propelled velocities of the particles were increasing;and 3)when Pe>100,the particles were completely controlled by the self-propelled velocities and the Brownian motion was suppressed.In the simulation of the ellipsoidal self-propelled particles,we found that the larger the aspect ratio of the particles,the more susceptible they were to the influence of Brownian motion.In addition,the value interval of Pe depended on the aspect ratio.Finally,we found that the directional motion ability of the ellipsoidal self-propelled particles was much weaker than that of the spherical self-propelled particles.