Tuan Anh Nguyen (121944) · Figshare · 2023
<div><p>In this article, the author introduces a new fuzzy control solution to direct active anti-roll bars (hydraulic stabilizer bars) in order to enhance the vehicle’s roll stabilization efficiency. The original fuzzy algorithm designed in this work can satisfy all the roll stability, comfort, and response speed requirements, while previous algorithms could only meet one of these criteria. In addition, a fully dynamic model is established to simulate the vehicle’s roll oscillations instead of only using a simple half-dynamic model combined with the single-track dynamic model. The calculation and simulation processes take place in the Simulink environment. Two cases of steering are used as input to the simulation problem; the car’s speed is gradually increased through three levels. According to results of research, the roll angle and roll index of the car increase as the speed and steering angle increase. The interaction between the road and wheel decreases sharply as the roll angle increases, which can lead to a rollover. In the first case, the rollover occurs only when the car travels at <i>v</i><sub><i>3</i></sub> without the stabilizer bar and has a maximum roll angle of 9.81°. In the second case, this occurs for the (None) situation when traveling at speed <i>v</i><sub><i>1</i></sub> with a maximum roll angle of 9.52° and for the (Passive) situation when traveling at speed <i>v</i><sub><i>2</i></sub> with a peak value of 11.93°. Meanwhile, the vehicle’s stability is still well guaranteed when utilizing active anti-roll bars controlled by an original fuzzy algorithm.</p></div>
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Hammoud, Ali · Cifra LLC Journals · 2024
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Dil Muhammad Akbar Hussain · VBN Forskningsportal (Aalborg Universitet) · 2006
The paper presents a simulation study to track a maneuvering target using a selective approach in choosing Interacting Multiple Models (IMM) algorithm to provide a wider coverage to track such targets. Initially, there are two motion models in the system to track a target. Probability of each model being correct is computed through a likelihood function for each model. The study presented a simple technique to introduce additional models into the system using deterministic acceleration which basically defines the dynamics of the system. Therefore, based on this value more motion models can be employed to increase the coverage. Finally, the combined estimate is obtained using posteriori probabilities from different filter models. The implemented approach provides an adaptive scheme for selecting various number of motion models. Motion model description is important as it defines the kind of tracking system one is building, for example if a system is built based on the assumption of a straight line motion model then it can not track a maneuvering target.
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<ns0:vcard>BEGIN:vCardVERSION:3.0FN:Andrew DuffyORG:Boston UniversityADR;TYPE=WORK,POSTAL,PARCEL:;;SCI 109A;590 Commonwealth Ave;Boston;MA;02215;USTEL;TYPE=Department:+617-353-2634TEL;TYPE=Department:+617-353-9089EMAIL;TYPE=INTERNET,Departmental:duffy@physics.bu.eduEMAIL;TYPE=INTERNET,Departmental:duffy@buphy.bu.eduEND:vCard</ns0:vcard> · The Physics Video Demonstration Database (Cornell University)
The Pendulum Motion Model shows the motion of a simple pendulum. You can change the mass of the pendulum bob, the length of the string, the acceleration due to gravity, and the initial position of the pendulum. You can then see the resulting motion of the pendulum, as well as see bar graphs of the energy and plots of the pendulum's angular position, angular speed, and angular acceleration as a function of time. The Pendulum Motion model was created using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the ejs_bu_pendulum.jar file will run the program if Java is installed.
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Sociedade Brasileira De Matemática, David R. Brillinger, Haiganoush K. Preisler et al. · 2008
Abstract. The concern is with the properties of stochastic differential equations (SDEs) describing the motion of particles in 3 dimensional space, on the sphere or in the plane. There is consideration of the case where the drift function comes from a potential function. There is study of SDEs whose parameters are periodic in time. These are useful for incorporating circadian rhythm in the behavior. The cases of a seal in a frozen lake in Alaska, an elephant seal migrating a great distance in the Pacific Ocean and of a group of “free-ranging ” elk in a reserve in Oregon are referred to. For the elk nonparametric estimates of the drift and variance terms of an SDE model are discussed and evaluated and the fit of the model assessed. One issue is how to include explanatories, beyond location and time, in the model. A number of questions motivated by the wildlife motion concerning diffusion processes of the type considered are posed at the end of the paper.
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Russell S. A. Brinkworth (369527), David C. O'Carroll (51416) · Figshare · 2015
<p>A) Schematic of a basic correlator elementary motion detector (EMD) used as the fundamental motion detection algorithm in this paper. B) Diagrammatic representation of the fully elaborated motion processing model used in this study. C) Legend describing the symbolic representations used in B). Each stage of the model represents the processing occurring on a pixel-wise basis within the insect visual system. Connections between near-by processing columns (nearest or next-nearest neighbors) in the 2D network occur between stages, mostly in the form of spatial high-pass filtering, with the only global stage a final spatial summation at the start of stage 5. Each stage is further divided into smaller processing steps involving operations such as 1st order low-pass filtering, centre-surround antagonism, non-linear gains or divisive feedback. Further detail is presented in the text.</p>
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<vcard>BEGIN:vCard\VERSION:3.0\FN:Andrew Duffy\ORG:Boston University\ADR;TYPE=WORK,POSTAL,PARCEL:;;SCI 109A;590 Commonwealth Ave;Boston;MA;02215;US\TEL;TYPE=Department:+617-353-2634\TEL;TYPE=Department:+617-353-9089\EMAIL;TYPE=INTERNET,Departmental:duffy@physics.bu.edu\EMAIL;TYPE=INTERNET,Departmental:duffy@buphy.bu.edu\END:vCard</vcard> · The Physics Video Demonstration Database (Cornell University)
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Ian Engstrom (15453603), Katelyn Anderson (15453606), Eleanna Bez (15453609) et al. · Figshare · 2023
<div><p>Rowing ergometers are popular tools for general fitness and competitive crew teams. The effect of the equipment set up on the rowing stroke has received limited attention. This study aimed to determine the effects of altering the foot-stretcher position on rowing kinematics across different stroke rates. Eleven college-level rowers took part in this study. A rowing ergometer was modified to allow the height and angle of the foot-stretcher to be adjusted. Seven foot-stretcher positions were tested, each at rates of 22, 26, and 32 strokes per minute. Sagittal plane kinematic waveforms were compared between conditions for all major joints using statistical parametric mapping, and temporal variables were assessed (<i>p</i> < 0.05). Stroke rate was found to affect kinematic patterns for all joints. The effect of the foot-stretcher position was limited to the ankle and hip. Similarly, the timing of events during the rowing stroke was affected by the stroke rate, but not foot position. These results indicate that while some limited changes to the stroke technique can be caused by altering the foot-stretcher position, the changes were largely compensated for by the rowers and are generally smaller than differences between stroke rates.</p></div>
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Dieter Jaeger, Ranu Jung · Encyclopedia of Computational Neuroscience · 2022
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Hideki Sakai, Yukiharu Satoh · TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C · 1999
This paper focuses on the lateral motion of the automobile considering roll dynamics at high speed. The equation of motion of the vehicle response to steering input considering coupling motion between roll and lateral direction is theoretically solved by means of replacing roll motion by tire equivalent lateral deformation. The result shows that the effect of the coupling motion reduces yawing damping ratio, keeps the yawing natural frequency, and delays yawing response to steering input. Moreover, good agreement is found between the theoretical and experimental frequency response to steering input.
Cited by 1Open access
Xiangyu Han, Xu Hu, Huawei Sun · 2024
Long-term human movement prediction has become a key research focus, driven by its numerous applications in domain such as robotics, virtual reality, video analysis. Traditional methodologies, primarily based on artificial modeling, often unable capture the complicated dynamics of human movement. Recent developments have explored recurrent neural networks and generative models for sequence prediction, but challenges persist in achieving accurate and realistic long-term predictions. This study introduces a deep generative motion infiller leveraging a diffusion model, capable of seamlessly generating motion sequences of arbitrary length from historical or sparsely available key-frames. This model adeptly addresses the complexities of filling in missing or occluded body movements by utilizing observed motion data and predicting new sequences. The proposed method incorporates a Transformer-based structure optimized for lightweight operation, which is employed within a sliding window framework to establish a recurrent generation system. Additionally, a novel method is proposed for style transfer, applicable during inference, enhancing the diversity and the resulting sense of motion. The proposed model also demonstrates effectiveness in generating short-term and long predictions, comprehensive evaluations indicate that the proposed approach yields more diverse and realistic motion predictions compared to existing methods, we evaluated the model in indicate of global and local diversity, which have the better result.
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Kohei Hakozaki, T. Murakami, Tsubasa Uchida et al. · 2024
No abstract available.
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Ganesh Ramamoorthy · 1992
The static perception of scene structure contributes in part to the perception of 3D motion, in the human visual system. In order to examine this process, a model is used to make explicit the fixed and changing properties of rigid body structure. The changing state of the model is continuously evaluated using features from a sequence of 2D images. This is also known as the model-based visual tracking problem. We seek to reproduce this ability to perceive and understand motion by building a machine vision system. This system uses multi-resolution concepts from visual information processing of biological systems. It then combines these concepts with computational schemes that express spatio-temporal transformations from a sequence of images, in the form of mathematical relations. Such a system tracks perceived motion using spatio-temporal sequences. Our work uses a motion-searching paradigm combined with a new optical flow estimation algorithm. A parallel object recognition scheme generates model pose hypotheses, until correspondence with the configuration of image features is verified. The models used are those of polyhedral objects. The spatio-temporal assumption is exploited in computing the optical flow, which when used with the object recognition scheme, allows for chosen features on the object to be tracked and used to obtain a depth estimate of the object, along the line of sight. The parallel implementation of the tracking scheme on a fine-grained data parallel machine, the Connection Machine, is an additional issue of this research, aiming at real-time realization of the tracking system.
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Niloofar Gheissari, Alireza Bab‐Hadiashar · Figshare · 2004
A new model selection criterion based on physical characteristics of underlying motion models is proposed. The proposed criterion is then incorporated in a robust motion segmentation scheme, which is based upon robust least K-th order statistical model fitting. The proposed model criterion has been compared with many other competing techniques and is shown to be more suitable for the motion segmentation task. The motion segmentation algorithm has been tested (and shown to be successful) on a number of synthetic and real image sequences.
Cited by 12Open access
Gheissari, Niloofar, Bab-Hadiashar, Alireza · Swinburne Research Bank (Swinburne University of Technology) · 2003
A new model selection criterion based on physical characteristics of underlying motion models is proposed. The proposed criterion is then incorporated in a robust motion segmentation scheme, which is based upon robust least K-th order statistical model fitting. The proposed model criterion has been compared with many other competing techniques and it’s shown to be more suitable for the motion segmentation task. The motion segmentation algorithm has been tested (and shown to be successful) on a number of synthetic and real image sequences.
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Z. Altamimi, Laurent Métivier, Paul Rebischung et al. · Geophysical Research Letters · 2023
Abstract A tectonic Plate Motion Model (PMM) is essential for geodetic applications, while contributing to the understanding of geodynamic processes affecting the Earth's surface. We introduce a PMM derived from the horizontal velocities of 518 sites extracted from the ITRF2020 solution. These sites were chosen away from plate boundaries, Glacial Isostatic Adjustment regions, and other deforming zones. Unlike the ITRF2014‐PMM, which showed no significant Origin Rate Bias (ORB), velocities used to determine the ITRF2020‐PMM exhibit a statistically significant ORB (0.74 ± 0.09 mm/yr along the Z‐component). Users are advised to add the estimated ORB to the horizontal velocities predicted by the ITRF2020‐PMM rotation poles for full consistency with the ITRF2020. However, the predicted vertical velocities resulting from the addition of the ORB should be discarded. The overall precision with which the ITRF2020 velocity field is represented by the rigid ITRF2020‐PMM is at the level of 0.25 mm/yr WRMS.
Cited by 42Open access
<ns0:vcard>BEGIN:vCardVERSION:3.0FN:Andrew DuffyORG:Boston UniversityADR;TYPE=WORK,POSTAL,PARCEL:;;SCI 109A;590 Commonwealth Ave;Boston;MA;02215;USTEL;TYPE=Department:+617-353-2634TEL;TYPE=Department:+617-353-9089EMAIL;TYPE=INTERNET,Departmental:duffy@physics.bu.eduEMAIL;TYPE=INTERNET,Departmental:duffy@buphy.bu.eduEND:vCard</ns0:vcard> · The Physics Video Demonstration Database (Cornell University)
The Graph Matching Motion Model shows the graph of position as a function of time, and the motion diagram, for a particular object that experiences one-dimensional motion with constant acceleration. Using the sliders, try to match the motion of the red object to that of the blue object - this should also get the two position versus time graphs to match. Then, predict what the velocity and acceleration graphs look like, by sliding the end points of the red lines up or down to give correct straight-line graphs. The Graph Matching Motion model was created using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the ejs_bu_vector_graph_matching.jar file will run the program if Java is installed.
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Joao Carvalho · TIB Data Manager · 2024
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Guy Tevet, Sigal Raab, Brian A. Gordon et al. · arXiv (Cornell University) · 2022
Natural and expressive human motion generation is the holy grail of computer animation. It is a challenging task, due to the diversity of possible motion, human perceptual sensitivity to it, and the difficulty of accurately describing it. Therefore, current generative solutions are either low-quality or limited in expressiveness. Diffusion models, which have already shown remarkable generative capabilities in other domains, are promising candidates for human motion due to their many-to-many nature, but they tend to be resource hungry and hard to control. In this paper, we introduce Motion Diffusion Model (MDM), a carefully adapted classifier-free diffusion-based generative model for the human motion domain. MDM is transformer-based, combining insights from motion generation literature. A notable design-choice is the prediction of the sample, rather than the noise, in each diffusion step. This facilitates the use of established geometric losses on the locations and velocities of the motion, such as the foot contact loss. As we demonstrate, MDM is a generic approach, enabling different modes of conditioning, and different generation tasks. We show that our model is trained with lightweight resources and yet achieves state-of-the-art results on leading benchmarks for text-to-motion and action-to-motion. https://guytevet.github.io/mdm-page/ .
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Hartharn-Evans, Samuel, Carr, Magda, Stastna, Marek · Newcastle University · 2023
Code to implement Float Motion Model described in <i>"Interactions between Internal Solitary Waves and Sea Ice"</i> Takes an input of horizontal (1-D) velocity through time (x, t) and particle properties and outputs the path of the particle. Code is provided to use data in the DigiFlow Image (timeseries based on PIV data) data format, DJL solutions (from the DJLES package) and SPINS numerical model data, but can easily be adapted for other data sources.
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