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To face the climate crisis, sustainable energy harvesting systems are critical. Triboelectric nanogenerators (TENGs) are in the spotlight for their inexpensive and non‐toxic manufacturing, allied with their excellence in harvesting low frequency vibrations. Ocean waves are a prime example of energy source in this regime, and their successful harvesting with TENGs has been widely demonstrated. However, the influence of wave characteristics on the power output of these devices has so far been overlooked. Here, the existence of a moving element that acts upon the nanogenerators is explored both with multiphysics simulations and an extensive experimental characterization under different operating conditions. It is shown how different sea states modulate the moving body velocity, and how the generator can be optimized to harvest waves with specific characteristics. In parallel, it is demonstrated that the power output is proportional to the contacting triboelectric layers velocity. Optimizing the dynamics of the moving body provides an efficient path to intentionally tune the contacting triboelectric layers velocity, and therefore improve the output of TENGs and their suitability to harvest specific sea states. [ABSTRACT FROM AUTHOR]

Industrial machines regularly work at their limits causing excessive long-term vibrations that deteriorate their movement, stability, and precision. In this sense, reference profiles are able to reduce the detrimental vibration effects by manipulating the machine motion dynamics using predefined movement trajectories. Most of the approaches for lessening damages due to long-term machine vibrations are based on polynomial functions with high computational complexity and high resources demand. Hence, in this work, an innovative acceleration outline based on a Gaussian function is proposed for machine motion trajectories. The introduced strategy simplifies the position-profile estimation that works as reference for restraining the machine movements through the parameters that define its motion dynamics; thus, a smooth and continuous jerk contour is produced, which reduces vibrations and improves the machine stability. Exhaustive computer-based and real-time experimental comparisons of the introduced scheme produces a significantly lower maximum jerk value than any of the others. The assessment of the presented approach was performed utilizing the software Matlab (R2020a) on a PC with an Intel Core i7-6500U microprocessor at 2.5 GHz, with 16 GB in RAM and a 64-bit operating system.

More certain therapeutic and significantly facilitated solution of everyday endodontic problems are provided by the introduction of NiTi rotary files in endodontic practice and their proper application. However, their widespread use has caused more and more frequent fractures and has become a significant frustrating factor that diminishes their numerous benefits. Torsional stress and cyclic fatigue are the main reasons for the occurrence of a fracture, and the frequency of NiTi file fractures is exactly proportional to the degree of bending of the canal, knowledge of the features of the file, ie. the skill and expertise of the therapist. The most dominant factor for the occurrence of fractures is the dentist who, in addition to manual dexterity, must have a good knowledge of the anatomy of various canal systems and file design in order to make the best choice for each individual case. Properly formed access cavity and knowledge of the working part of NiTi files enable maximum realization of endodontic skills, increased efficiency of intervention and reduced possibility of error during canal instrumentation. The special treatment of NiTi alloy and new solutions related to the design of the working part have significantly increased file flexibility and resistance to cyclic fatigue. Specific design (reduced number of threads, change of conicity, interruption of blade continuity), reduced engagement of file, ie. change of dynamics of movement in the canal (full rotation, reciprocal, eccentric, transaxial), as well as reduced number of processing files, have also significantly influenced the reduction of fracture risks. The aim of this paper was to analyze the most common reasons for the fracture of NiTi files and emphasize measures and factors that can increase their safe use and reduce complications during canal instrumentation.

Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.

In the article the results of simulation and experimental studies of the movement of a four-wheeled mobile platform, taking into account wheel slip have been presented. The simulation results have been based on the dynamics of the four-wheel mobile platform. The dynamic model of the system motion takes into account the relationship between the active and passive forces accompanying the platform motion, especially during wheel slip. The formulated initial problem describing the motion of the system has been solved by the Runge-Kutta method of the fourth order. The proposed computational model including the platform dynamics model has been verified in experimental studies using the LEO Rover robot. The motion parameters obtained on the basis of the adopted computational model in the form of trajectories, velocities and accelerations have been compared with the results of experimental tests, and the results of this comparison have been included in the paper. The proposed computational model can be useful in various situations, e.g., real-time control, where models with a high degree of complexity are useless due to the computation time. The simulation results obtained on the basis of the proposed model are sufficiently compatible with the results of experimental tests of motion parameters obtained for the selected type of mobile robot. [ABSTRACT FROM AUTHOR]

The existing computational models used to estimate motion sickness are incapable of describing the fact that the predictability of motion patterns affects motion sickness. Therefore, the present study proposes a computational model to describe the effect of the predictability of dynamics or the pattern of motion stimuli on motion sickness. In the proposed model, a submodel – in which a recursive Gaussian process regression is used to represent human features of online learning and future prediction of motion dynamics – is combined with a conventional model of motion sickness based on an observer theory. A simulation experiment was conducted in which the proposed model predicted motion sickness caused by a 900 s horizontal movement. The movement was composed of a 9 m repetitive back-and-forth movement pattern with a pause. Regarding the motion condition, the direction and timing of the motion were varied as follows: (a) Predictable motion (M_P): the direction of the motion and duration of the pause were set to 8 s; (b) Motion with unpredicted direction (M_dU): the pause duration was fixed as in (M_P), but the motion direction was randomly determined; (c) Motion with unpredicted timing (M_tU): the motion direction was fixed as in (M_P), but the pause duration was randomly selected from 4 to 12 s. The results obtained using the proposed model demonstrated that the predicted motion sickness incidence for (M_P) was smaller than those for (M_dU) and (M_tU) and no considerable difference was found between M_dU and M_tU. This tendency agrees with the sickness patterns observed in a previous experimental study in which the human participants were subject to motion conditions similar to those used in our simulations. Moreover, no significant differences were found in the predicted motion sickness incidences at different conditions when the conventional model was used.

The existing computational models used to estimate motion sickness are incapable of describing the fact that the predictability of motion patterns affects motion sickness. Therefore, the present study proposes a computational model to describe the effect of the predictability of dynamics or the pattern of motion stimuli on motion sickness. In the proposed model, a submodel – in which a recursive Gaussian process regression is used to represent human features of online learning and future prediction of motion dynamics – is combined with a conventional model of motion sickness based on an observer theory. A simulation experiment was conducted in which the proposed model predicted motion sickness caused by a 900 s horizontal movement. The movement was composed of a 9 m repetitive back-and-forth movement pattern with a pause. Regarding the motion condition, the direction and timing of the motion were varied as follows: (a) Predictable motion (M_P): the direction of the motion and duration of the pause were set to 8 s; (b) Motion with unpredicted direction (M_dU): the pause duration was fixed as in (M_P), but the motion direction was randomly determined; (c) Motion with unpredicted timing (M_tU): the motion direction was fixed as in (M_P), but the pause duration was randomly selected from 4 to 12 s. The results obtained using the proposed model demonstrated that the predicted motion sickness incidence for (M_P) was smaller than those for (M_dU) and (M_tU) and no considerable difference was found between M_dU and M_tU. This tendency agrees with the sickness patterns observed in a previous experimental study in which the human participants were subject to motion conditions similar to those used in our simulations. Moreover, no significant differences were found in the predicted motion sickness incidences at different conditions when the conventional model was used. [ABSTRACT FROM AUTHOR]

Multiple object tracking sets the foundation for many intelligent video applications. The authors present a novel tracking solution using the ability of recurrent neural networks to effectively model complex temporal dynamics between objects irrespective of appearances, pose, occlusions, and illumination. For online tracking, a real-time and accurate association of objects with active tracks poses the major algorithmic challenge. Additionally, re-entry of objects should also be correctly resolved. They follow tracking-by-detection methodology using hierarchical long short term memory (LSTM) network structure for modelling the motion dynamics between objects by learning the fusion of appearance and motion cues. Existing works capture object's perspective for tracking within the detected bounding boxes. They also incorporate object instance segments for track modelling by applying the maskRCNN detector. They present a novel motion coding scheme that anchors the LSTM structure to effectively model the motion and relative position between objects in a single representation scheme. The proposed motion representation and deep features representing objects appearances are fused in an embedded space learned by the hierarchical LSTM structure for predicting the object to track association. The authors present experimental validation of the proposed approach on multiple object tracking challenge datasets and demonstrate that their solution naturally deals with major tracking challenges under all uncertainties.

This study synthesises modelling techniques and dynamic state estimation techniques for the simultaneous estimation of the muscle states, muscle forces, and joint motion states of a dynamic human arm model. The estimator considers both muscle dynamics and motion dynamics. The arm model has two joints and six muscles and contains dynamics both of the muscles and of the motion. We develop an optimally tuned extended Kalman filter using noisy measurements of joint angles with standard deviation 2.87°, of joint velocities with standard deviation 6.9°/s, and of muscle activations with standard deviation 10% of their peak values, and then simultaneously estimate joint angles, joint velocities, muscle forces, joint moments, and muscle states. The standard deviations of estimation errors (SDEE) are no more than 0.07° for joint angles, 1°/s for joint velocities, 0.6 mm for muscle–tendon lengths, and 0.1 Nm for joint torques. The results are compared with a previously developed static optimisation method, and verify the effectiveness of the proposed estimator in providing lower SDEE for both muscle and motion dynamics of the human arm model compared to the static optimisation method. [ABSTRACT FROM AUTHOR]

An improved model for the motion dynamics of a metallic particle located in the non‐uniform electric field area in the gas insulated system is proposed. To represent the special area, a diverging conducting electrode is used in this study. Based on the fluid mechanics theory, the forces imposed on metallic particle are first developed. The motion process is then divided into two modes, i.e. the particle rolls on the electrode surface and moves in the gap. The collision and reflection processes between the particle and the electrode are crucial to the motion, and they are fully considered in the proposed model. The trajectory of the particle under different voltages is obtained. The evolution of metallic particle is divided into four stages, and it is featured by four quantities, i.e. the critical jumping point, the minimum horizontal position, the falling point, and the motion time. The characteristics for each stage are analysed, and the sensitivity analysis of the quantities are performed. The impacts of the applied voltage, the particle radius, the initial position etc. are discussed. The results provide guidance for particle trap design. [ABSTRACT FROM AUTHOR]

Multiple object tracking sets the foundation for many intelligent video applications. The authors present a novel tracking solution using the ability of recurrent neural networks to effectively model complex temporal dynamics between objects irrespective of appearances, pose, occlusions, and illumination. For online tracking, a real-time and accurate association of objects with active tracks poses the major algorithmic challenge. Additionally, re-entry of objects should also be correctly resolved. They follow tracking-by-detection methodology using hierarchical long short term memory (LSTM) network structure for modelling the motion dynamics between objects by learning the fusion of appearance and motion cues. Existing works capture object's perspective for tracking within the detected bounding boxes. They also incorporate object instance segments for track modelling by applying the maskRCNN detector. They present a novel motion coding scheme that anchors the LSTM structure to effectively model the motion and relative position between objects in a single representation scheme. The proposed motion representation and deep features representing objects appearances are fused in an embedded space learned by the hierarchical LSTM structure for predicting the object to track association. The authors present experimental validation of the proposed approach on multiple object tracking challenge datasets and demonstrate that their solution naturally deals with major tracking challenges under all uncertainties. [ABSTRACT FROM AUTHOR]

Decoupling the end-effector motion from that of the mobile base of mobile manipulators traversing uneven terrains is important. This is especially so in mining, where material spillage from excavators and front-end loaders reduces productivity and slows down operations because of increased clean-up and maintenance times. Thus, this article proposes a strategy that relies on H∞ feedback control combined with feedforward action to improve the rejection of terrain disturbances that affect the orientation of the end-effector. The dynamic model of the mobile manipulator considers a floating base with nonpermanent contacts at each wheel, hydraulic actuators with nonlinear dynamics and a nonrigid arm. The arm flexibility is modeled as a passive spring-damper joint to account for inherent cantilever effects of real excavators and loaders. The analysis considers three different H∞ controller structures (single-input single-output (SISO) with feedforward, single-input multiple-output without feedforward, and multiple-input multiple-output without feedforward) to determine the benefits or disadvantages of employing the pitch rate of the mobile base as a feedforward control action or as an input handled by the H∞ controller and using only the end-effector actuator or also the other actuated joints of the arm. The root-mean-square error (RMSE) was reduced between 73.8%–86.0% when driving an industrial semiautonomous skid-steer loader over a ramp using the SISO H∞ controller with feedforward action. The tilt angle error was kept on average less than 0.9 ± 0.1°. The same controller yields a reduction of the RMSE between 23.5%–38.4% and a tilt angle error smaller than 3.39 ± 0.07° on average when traversing over a bump. Hence, the strategy proposed to reject ground disturbances should contribute to reducing material spillage of existing autonomous machines that navigate with little operator intervention along mining galleries, but that cannot avoid disturbing material lying on the ground or the characteristic unevenness of mining terrains. [ABSTRACT FROM AUTHOR]

Prion diseases are neurodegenerative disorders caused by the misfolding of the cellular prion protein (PrPC). Gerstmann–Sträussler–Scheinker syndrome is an inherited prion disease with one early-onset allele (HRdup) containing an eight-amino-acid insertion; this LGGLGGYV insert is positioned after valine 129 (human PrPC sequence) in a hydrophobic tract in the natively disordered region. Here we have characterized the structure and explored the molecular motions and dynamics of HRdup PrP and a control allele. High-resolution NMR data suggest that the core of HRdup has a canonical PrPC structure, yet a nascent β-structure is observed in the flexible elongated hydrophobic region of HRdup. In addition, using mouse PrPC sequence, we observed that a methionine/valine polymorphism at codon 128 (equivalent of methionine/valine 129 in human sequence) and oligomerization caused by high protein concentration affects conformational exchange dynamics at residue G130. We hypothesize that with the β-structure at the N-terminus, the hydrophobic region of HRdup can adopt a fully extended configuration and fold back to form an extended β-sheet with the existing β-sheet. We propose that these structures are early chemical events in disease pathogenesis. Unlabelled Image • Nascent β-structure is characterized in the central disordered region of HRdup. • Exchange dynamics at the first β-strand of PrP is related to M/V 128 polymorphisms and oligomerization. • Mechanistic model proposed for initialization of PrP misfolding. [ABSTRACT FROM AUTHOR]

The development of high‐voltage direct current circuit breaker (HVDCCB) has progressed rapidly. One of the major reasons for the failure of a circuit breaker is the metallic particle contamination. An enhanced mathematical model is proposed to simulate the motion dynamics of a spherical copper particle in the break of sulphur hexafluoride (SF6)[AQ ID=Q2] HVDCCB. By linking COMSOL and MATLAB, the interaction between particle motion and electric field distribution and plastic random movement in the collision process is considered. The effects of voltage magnitude and particle size on the particle motion are discussed. By means of statistical methods, the obtained findings show that, as voltage magnitude decreases or particle size increases, the particle–anode collision frequency decreases, and the particle–anode collision region increases in the x‐direction but decreases in the y‐direction. The accuracy of the proposed model is verified by experimental observations via high‐speed video camera. [ABSTRACT FROM AUTHOR]

• We propose a novel non-autoregressive Kinematic and Dynamic coupled transFormer (KD-Former) network for 3D human motion prediction. • Our KD-Former leverages the complementary characteristics of motion kinematics and dynamics for performance improvements. • We formulate a simplified reduced-order dynamic algorithm, largely enhancing computation efficiency and prediction error. Extensive experiments on Human 3.6M and CMU MoCap datasets demonstrate the superior performance of our method. Recent studies have made remarkable progress on 3D human motion prediction by describing motion with kinematic knowledge. However, kinematics only considers the 3D positions or rotations of human skeletons, failing to reveal the physical characteristics of human motion. Motion dynamics reflects the forces between joints, explicitly encoding the skeleton topology, whereas rarely exploited in motion prediction. In this paper, we propose the K inematic and D ynamic coupled trans Former (KD-Former), which incorporates dynamics with kinematics, to learn powerful features for high-fidelity motion prediction. Specifically, We first formulate a reduced-order dynamic model of human body to calculate the forces of all joints. Then we construct a non-autoregressive encoder-decoder framework based on the transformer structure. The encoder involves a kinematic encoder and a dynamic encoder, which are respectively responsible for extracting the kinematic and dynamic features for given history sequences via a spatial transformer and a temporal transformer. Future query sequences are decoded in parallel in the decoder by leveraging the encoded kinematic and dynamic information of history sequences. Experiments on Human3.6M and CMU MoCap benchmarks verify the effectiveness and superiority of our method. Code will be available at: https://github.com/wslh852/KD-Former.git. [ABSTRACT FROM AUTHOR]

Pulse reducers provide intermittent motion or continuous movement with predetermined non-uniformity and high reduction ratio. The aim of the research is to study the dynamics of new compact construction of pulse reducer. Reducer's mathematical model consisting of three systems of differential equations is developed. Systems of equations are related to each other through temporal parameters as integration limits on corresponding periods of links movement. Feature of mathematical model developing of this design reducer as a transmission with variable structure during the cycle is in coincidence of periods of holders joint motion for one freewheeling mechanism (FWM) and in independent motion of holders for another FWM Movement dynamics research of pulse reducer links are conducted for successive periods of FWM’s jamming, for joint motion of holders and their independent movement. Dynamic characteristics and law of motion of reducer’s links are defined. Numerical analysis of construction and inertial characteristics and their impact on reducer’s dynamic characteristics became possible on the basis of developed mathematical model.

Polish market of small boats has been developed very dynamically in recent years. Market competition forces the shipyards to build new more efficient hull forms and to cut the cost of production as well. This is why modern computer simulation programs are used more often by naval architects. Another trend is to design more universal ships that may be used by larger number of diversified customers. This paper presents project proposal of multipurpose boat hull form. The boat was design to fulfil the requirements imposed by public services like water police, fire brigades, and border guards. It is supposed to be operated on unexplored floodplains and other type shallow waters. The analysis of boat’s motion was based on computer simulations. The resistance curve was evaluated with two methods: comparison study of model test results of similar ships and CFD methods. The results obtained from Ansys Fluent and FINE/Marine systems were compared in this paper. It was shown that taking into consideration dynamic trim and sinkage has a significant impact on free surface capture and resistance values.

A particular problem in aerospace control and applications is the active target defence differential game (ATDDG), where an attacker missile tries to capture a target aircraft which is significantly slower than the attacker. A third agent is a defender missile which is fired by the Target's wingman. The defender's mission is to intercept the attacker in time to enable a successful target evasion and its survival. The problem is naturally posed as a zero‐sum DG. The target and the defender form a team; their objective is to determine a cooperative strategy to maximise the terminal separation between the target aircraft and the point where the Attacker missile is intercepted by the Defender missile. The Attacker is the opposition and its task is to devise its own strategy to minimise the same terminal distance. The solution of the ATDDG provides the instantaneous optimal heading angles for each agent playing the game in order to achieve their objectives. Most importantly, the agents' strategies are state feedback laws. The assumption of simple motion dynamics of the agents yields their optimal headings which is adequate in a beyond visual range engagement, as it is currently envisioned to be the pertinent operational scenario. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods. [ABSTRACT FROM AUTHOR]

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).

In this paper, the motion dynamics of a large tanker is modeled by the generalized ellipsoidal function-based fuzzy neural network (GEBF-FNN). The reference model of tanker motion dynamics in the form of nonlinear difference equations is established to generate training data samples for the GEBF-FNN algorithm which begins with no hidden neuron. In the sequel, fuzzy rules associated with the GEBF-FNN-based model can be online self-constructed by generation criteria and parameter estimation, and can dynamically capture essential motion dynamics of the large tanker with high prediction accuracy. Simulation studies and comprehensive comparisons are conducted on typical zig-zag maneuvers with moderate and extreme steering, and demonstrate that the GEBF-FNN-based model of tanker motion dynamics achieves superior performance in terms of both approximation and prediction. [ABSTRACT FROM PUBLISHER]

Pulse reducers provide intermittent motion or continuous movement with predetermined non-uniformity and high reduction ratio. The aim of the research is to study the dynamics of new compact construction of pulse reducer. Reducer's mathematical model consisting of three systems of differential equations is developed. Systems of equations are related to each other through temporal parameters as integration limits on corresponding periods of links movement. Feature of mathematical model developing of this design reducer as a transmission with variable structure during the cycle is in coincidence of periods of holders joint motion for one freewheeling mechanism (FWM) and in independent motion of holders for another FWM Movement dynamics research of pulse reducer links are conducted for successive periods of FWM's jamming, for joint motion of holders and their independent movement. Dynamic characteristics and law of motion of reducer's links are defined. Numerical analysis of construction and inertial characteristics and their impact on reducer's dynamic characteristics became possible on the basis of developed mathematical model. [ABSTRACT FROM AUTHOR]

Radar backscattering from human objects is subject to micro-Doppler modulations because of their flexible body articulations and complicated movement patterns, which can help identify the interested targets and provide valuable information about their motion dynamics. In this paper, a novel theoretical method to extract target micro-Doppler trajectories from continuous-wave radar echo is proposed with a united application of a modified high-order ambiguity function and an adaptive denoising technology. Through this method, multiple components corresponding to different target scattering parts and their micro-Doppler trajectories can be accurately extracted and estimated even in a time-varying low signal-to-noise ratio environment. Finally, a series of simulations is conducted to illustrate the validity and performance of the proposed techniques. [ABSTRACT FROM PUBLISHER]