集成Vissim-Python和Qt的信控交叉口DRL配时仿真系统设计

针对目前Vissim-Python联合仿真系统在信控交叉口DRL配时研究中算法移植性不高且难以实际应用的问题,设计一款交叉口配时仿真系统。在该系统中,为满足实际应用的需要,提出同时考虑检测器数据和倒计时的DRL配时模型,根据此模型需求,通过Python设计必要的Vissim组件接口,并封装为Gym强化学习仿真环境。为解决算法移植性不高的问题,基于深度学习框架PyTorch对配时算法的接口规范化,并使用PyQt5设计可视化操作界面,可灵活调整算法参数。为提高系统运行效率,使用四种技巧加速仿真进程。最后以珠海市柠溪路与兴业路交叉口为例进行仿真测试,结果表明,该系统运行良好,既可对实际交叉口的配时效果进行评估,也可作为相关算法研究者的测试平台。

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。

基于VISSIM仿真的城市信号交叉口优化设计

信号交叉口是限制车流消散的主要瓶颈,是导致城市道路交通拥堵的主要节点,对其优化设计逐渐成为研究热点。针对南京市中央路-湖南路交叉口存在的转弯半径过小、交通流量饱和、路口间距过短等问题,提出渠化车道、增加禁止掉头标志、增加相邻路口右转信号控制、增加相邻路口相位的优化方案,运用韦伯斯特模型计算各相位的信号配时方案,使用VISSIM仿真软件对设计方案实施前后的效果进行对比。结果表明:实施优化方案后,平均排队长度由12.96 m减少为11.3 m,减少12.81%;车辆平均延误由44.36 s减少到39.48 s,减少11%;平均停车次数由1.15次减少到0.96次,减少16.52%。对南京市中央路-湖南路交叉口提出的优化方案是合理的、有效的,研究结果对方案落地实施具有指导作用。

基于VISSIM的高速公路虚拟应急车道建模研究

为了解决高速公路建设规模和数量的提升导致的交通安全问题,对虚拟应急车道的工作原理进行研究,建立基于虚拟应急车道的高速公路行车规则模型,在事故紧急救援不同时间段构建车道数不同的虚拟应急车道控制策略模型。仿真实验结果表明,该模型可以缩短高速公路事故路段上车辆的平均行程时间,减少平均排队长度和平均延误,提高了事故救援效率,为高速公路救援体系建设提供思路和方向。

基于VISSIM仿真的信号交叉口优化研究

随着中国国民经济的不断发展,城镇化进程日益加快,产生了一系列的城市交通问题,也使得城市交通体系的压力逐年加大,交通拥堵成为大中小城市的难题。交叉口作为影响城市道路交通问题的重要节点,是城市交通中问题最多的部分。文章以上海市博山东路与枣庄路交叉口为例,在经过实地调查的基础上,对交叉口的交通状况进行分析,对车道功能划分和信号配时进行优化,使用VISSIM仿真软件对其进行交通仿真,找出存在的交通问题,提出优化设计方案并仿真对比验证,从而达到提高交叉口通行效率,保障城市交通安全的目的。研究结果表明,优化后的交叉口车辆平均延误减少了69.71%,交叉口的服务水平得到明显的提高。

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VISSIM交叉口仿真的信号配时优化研究

随着交通压力的增加,为了缓解交通压力,提高交叉口通行能力,改善交叉口交通状况,以昆明市白龙路-南院路交叉口为研究对象,采用Webster信号配时法优化此目标交叉口信号配时以降低车辆延误时间、排队长度及对环境的污染等。通过VISSIM仿真分析交叉口,对比优化前后的平均排队长度、延误时间、一氧化碳排放量、氮氧化合物排放量、停车时间等数据。结果表明:在实现Webster算法后,车辆平均排队长度减少25%、延误时间减少23%、一氧化碳排放减少6.9%、氮氧化合物排放下降6.9%、停车时间下降24.7%,优化交叉口信号配时在一定程度上缓解了交叉口通行压力,明显改善了城市交通状况。

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.

基于Vissim仿真系统的公共停车楼交通影响评估

为避免大型公共停车楼建成后,产生吸引的交通量对周边道路交通产生严重影响。该文以重庆礼嘉公共停车楼为例,开展交通影响评估工作。首先,采用传统的“四阶段”法,对停车楼建成后早晚高峰时段周边路段交通量变化进行预测分析;其次,借助Vissim仿真软件进行模拟评估,对公共停车楼建成前后城市路网进行交通仿真应用,得到客观评估结论,从而为规划管理部门提供决策参考。

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.

Quantitative analysis of the morphing wing mechanism of raptors:IMMU-based motion capture system and its application on gestures of a Falco peregrinus

This paper presented a novel tinny motion capture system for measuring bird posture based on inertial and magnetic measurement units that are made up of micromachined gyroscopes, accelerometers, and magnetometers. Multiple quaternion-based extended Kalman filters were implemented to estimate the absolute orientations to achieve high accuracy.Under the guidance of ornithology experts, the extending/contracting motions and flapping cycles were recorded using the developed motion capture system, and the orientation of each bone was also analyzed. The captured flapping gesture of the Falco peregrinus is crucial to the motion database of raptors as well as the bionic design.

A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves

Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.