Speed Profile Research Articles

230 Machine Learning for Automated and Real-time 2D-3D Registration of the Spine Using Only a Single Radiograph

INTRODUCTION: Authors present a 2-dimensional to 3-dimensional (2D-3D) registration algorithm that identifies the position of a C-arm relative to a 3D volume. METHODS: This work utilizes digitally reconstructed radiographs (DRR) which are synthetic radiographic images generated by simulating the x-ray projections as they would pass through a CT volume. To evaluate our algorithm, we used cone-beam CT data from 127 patients obtained from a de-identified registry of cervical, thoracic, and lumbar scans. We systematically evaluated and tuned a self-supervised algorithm, quantified the limitations and convergence rate of our model by simulating C-arm registrations with 80 randomly simulated DRRs for each CT volume. The endpoints for this study were the time to convergence, accuracy of convergence for each of the C-arm's degrees of freedom (DoF), and overall registration accuracy based on a voxel-by-voxel measurement. RESULTS: 10,160 unique fluoroscopic images were simulated from 127 CT scans. Our algorithm successfully converged to the correct solution 81% of the time with an average of 1.97 seconds of computation. The fluoroscopic images for which the algorithm converged to the solution achieved 99.9% registration accuracy despite only utilizing single-precision computation for speed. Our algorithm was found to be optimized for convergence when the search space was limited to a ± 45° offset in the RAO/LAO, cranial/caudal and receiver rotation angles with the radiographic isocenter contained within 8000cm3 of the volumetric center of the CT volume. CONCLUSIONS: The machine learning algorithm we present has the potential to aid surgeons with many aspects of spine surgery through an automated 2D-3D registration process. Future work will focus on algorithmic optimizations adn deep-learning to improve the convergence rate and speed profile.

Geometric stochastic ray propagation using the special Euclidean group.

This paper describes a stochastic model of ray trajectory propagation through a medium-such as the ocean-which has an uncertain sound speed profile. We frame ray propagation as a geometric fractal Brownian motion process on the special Euclidean group of dimension two, SE(2). The framing includes diffusion parameters to describe how the stochastic rays deviate from the expected rays, and these diffusion parameters are a function of the uncertainty in the sound speed profile. We demonstrate this framing for the classical Munk profile and a double-ducted profile in the Beaufort.

Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm

Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm

Intersection movements delay modelling based on crowd-sensed global positioning system trajectory data

Developing accurate large-scale transportation models, used to guide policy adoption and evaluate infrastructure alternatives or changes in sociodemographic conditions, is data and resource intensive. This research proposes a novel method for modeling intersection movement delay using crowd-sensed Global Positioning System (GPS) data. This is achieved by providing a general definition of turning movements and extracting travel times thought GPS trajectory data analysis. Additionally, a straightforward method is proposed to integrate the observed delays per movement type into volume-delay functions. The spatial definition provided for turning movements captured distinct speed profiles per turn type. The significant differences in mean speeds for different turn types highlights the importance of integrating turn penalty functions based on real observations and underscore the importance of crowd-sensed GPS data. A simple technique is also proposed to integrate the proposed method into the volume-delay functions used in large scale transport models.

Combined longitudinal and lateral dynamics regulation of autonomous vehicles via induced ℒ2$$ {\mathcal{L}}_2 $$‐gain linear parameter varying control strategies based on parameter‐dependent Lyapunov functions

AbstractThis article considers a path tracking control problem for autonomous vehicles constrained to maintain a safe distance from the next vehicle in the lane. The control design problem is solved by considering a mathematical model of the vehicle that jointly describes the lateral and longitudinal dynamics. Specifically, the optimal controller is designed aimed at minimizing the orientation and position tracking errors with respect to a given reference path. Moreover, the controller is able to ensure that the vehicle can both follow a speed profile and automatically adjusts the vehicle speed to maintain a proper distance from the next vehicle. The lateral dynamic is regulated by a 2‐DOF feedforward/feedback controller, where the feedforward law is implemented by exploiting an inverse kinematic model of the vehicle. On the other side, the feedback action, along with the action of the controller in charge to regulate the longitudinal dynamics, is the optimal gain‐scheduling state‐feedback controller. In particular, a parameter‐dependent quadratic Lyapunov function approach is used to reduce the conservativeness of the solution and a suitable convex LMI optimization problem is formulated for the synthesis. The vehicle is described by a seven‐degrees‐of‐freedom (7DOF) full‐car model, with the tire‐generated forces described by the classical Pacejka's Magic Formula. In order to assess the performance of the proposed control strategy, simulations have been undertaken in Matlab/Simulink by considering an autonomous vehicle driving into an urban scenario.

Aerodynamic properties of boundary layers along the roof of a large banana screenhouse under near-neutral atmospheric stability

Cultivation under screens and in screenhouses protects the crops from insects and extreme climate conditions and allows water saving. Previous studies considered the aerodynamic properties of boundary layers along two types of screens: a small shading thermal screen covering a hedgerow citrus orchard and a large 50-mesh, insect-proof screen covering a pepper plantation. To extend the previous results, a third type of screen was analysed in this study, a large flat-roof 17-mesh white woven shading screen covering a banana plantation. Vertical profiles of wind speed, air temperature, relative humidity, and friction velocity were measured by a lifting tower, and wind profiles were analysed under near-neutral atmospheric stability. Results show that normalised friction velocity, normalised roughness length, normalised zero plane displacement height and aerodynamic resistance are closer to the previous results of the large insect-proof screenhouse than those of the small shade net.

The Effects of Speed and Delays on Test-Time Performance of End-to-End Self-Driving.

This study investigates the effects of speed variations and computational delays on the performance of end-to-end autonomous driving systems (ADS). Utilizing 1:10 scale mini-cars with limited computational resources, we demonstrate that different driving speeds significantly alter the task of the driving model, challenging the generalization capabilities of systems trained at a singular speed profile. Our findings reveal that models trained to drive at high speeds struggle with slower speeds and vice versa. Consequently, testing an ADS at an inappropriate speed can lead to misjudgments about its competence. Additionally, we explore the impact of computational delays, common in real-world deployments, on driving performance. We present a novel approach to counteract the effects of delays by adjusting the target labels in the training data, demonstrating improved resilience in models to handle computational delays effectively. This method, crucially, addresses the effects of delays rather than their causes and complements traditional delay minimization strategies. These insights are valuable for developing robust autonomous driving systems capable of adapting to varying speeds and delays in real-world scenarios.

Train headway estimation for maglev with blocking time theory

As the commercialization of maglev trains continues to accelerate, effective improvement of maglev train operation has become a topic for researchers. The train headway of the maglev is the preparation basis for the train timetable and is also an essential factor affecting the line capacity utilization. This paper proposed an approach to estimate the train headway by considering the characteristics of partitioned control of maglev operations. First, we build the tracking model for maglev with the theory of blocking time, in which the train speed profile is the key input source. Then, a customized method is proposed to estimate the minimum headway of maglev trains. According to the experiment, we can effectively obtain the minimum train headway by the approach, and the result of improving the maglev line capacity utilization is verified.

Relegated Thrust Ripples for Linear Induction Motors Based-Four Voltage Vectors Finite-Set Predictive Control and Model Reference Adaptive System

—These days, there are extensive signifies to the evolution of newfound control procedures like model predictive control. The finite-state predictive thrust control (FSPTC) for LIMs is still not deftly addressed by scholars and limited research was acted to exploit the FSPTC for LIM drive systems. Therefore, the current study proposes a novel finite-state predictive thrust control (NFSPTC) for linear induction motors (LIMs) with reduced numbers of voltage vectors (VVs) in the prediction phase. The NFSPTC utilizes the conventional direct thrust control (DTC) concept to reduce the required VVs from eight to only four which consist of two active vectors and two zero vectors. Decreasing the number of predicted VVs leads to a significant reduction in computational time. Moreover, the weighting factor is removed to reduce the effort in selecting the best weighting factor. Since the LIM drive system can be succeeded by developing a sensorless speed estimation, the linear speed is estimated depending on the model reference adaptive system (MRAS) before using actual sensors; thus, low cost can be attained. The suggested control approach is validated by simulation proofs based on a 3-kW arc machine. The gained findings clarified the effectiveness of the proposed four VVs control over the eight VVs procedure in dipping the thrust ripple. Besides, using the four VVs control under variable speed and fixed thrust load, the execution time was decreased by 50%. Furthermore, the proposed control procedures ensued in maintaining the primary flux linkage constant alongside succeeding a glowing-tracking speed profile throughout the tackled speed and thrust load variations.”

Inferred wind speed and direction during the descent and landing of Perseverance on Mars

Perseverance successfully parachuted into Jezero crater close to its western rim on a Martian spring afternoon. In situ observations of the wind conditions during the parachute and powered descent are unavailable. These observations are important for characterising the Planetary Boundary Layer (PBL) and to better constrain atmospheric conditions in Jezero crater.We infer the winds during Perseverance's parachute descent from an altitude of 12 km down to 2 km by fitting a trajectory model to the reconstructed trajectory data published by the M2020 Entry, Descent and Landing team. Below 2 km altitude we infer the winds by analysing a High Resolution Imaging Science Experiment (HiRISE) image of the landing site that was obtained about one sol after the landing.Our inferred wind speed and direction profile indicate high speed winds at an altitude of 7 km blowing from the east and at 3 km blowing from the southwest. Below an altitude of 2 km, the winds are inferred to be easterlies. The heat shield impact on the surface was observed by Perseverance's Lander Vision System Camera (LCAM). Afterwards, ejecta clouds were observed moving towards the north of the impact site. These observations, together with the orientation of the parachute canopy on the surface, suggest near-surface winds at the time were southerlies.We have inferred the wind speed and direction during Perseverance's descent that are consistent with its horizontal motion as revealed by trajectory data and HiRISE image. Comparisons with atmospheric models suggest the high speed winds could be related to topographic forcing. This study provides constraints for atmospheric modelling and advances the characterisation of winds in Jezero crater, focusing on the vertical structure of the complex wind field present in the crater.

Analysis of the Anisotropy of Sound Propagation Velocity in Thin Wooden Plates Using Lamb Waves.

The objective of the study was to analyze the influence of coating treatments on sound propagation speeds in thin boards, along the longitudinal and radial directions of resonance wood. The samples studied were thin boards made of spruce and maple wood with dimensions of 240 mm × 80 mm × 4 mm (length × width × thickness) subjected to different coating treatments (oil-based varnish and alcohol varnish) as well as unvarnished samples, exposed to radiation UV, and specimens treated in the saline fog. The test method consisted of evaluating the propagation speeds of Lamb waves applied to thin plates, according to a semicircular test model, so that the results highlighted both the acoustic response in the longitudinal and radial directions as well as the variation in the anisotropy of the samples with the change in the sound propagation direction relative to wood fibers. Based on the statistical analysis, sound propagation speed profiles were obtained in each of the 38 directions analyzed for all wood samples. The results highlighted that the oil-based varnish led to a decrease in the speed of propagation in the radial direction, compared to the alcoholic varnish, whose major effect was in the longitudinal direction, on the spruce wood. On maple wood, increasing the number of varnish layers, regardless of the type of varnish, led to a decrease in the anisotropy ratio between the longitudinal and radial directions.

Energy-saving speed profile planning for a connected and automated electric bus considering motor characteristic

Speed profiles play an important impact on the operational energy consumption of electric buses (EBs). In this study, an eco-driving method is proposed for a connected and automated EB serving on the bus rapid transit (BRT) route. Considering the effect of speed and road load conditions on motor efficiency in estimating energy consumption, a nonlinear programming model is proposed to optimize the energy-saving speed profile. Then the sparrow search algorithm is used for the solution to ensure that the computing time is less than the predetermined time gap. Finally, a real BRT route is taken as an example to carry out comparative and sensitivity analyses. Results indicate that: (i) the average motor efficiency can be improved by 2.11% after taking into account the effect of speed and road load conditions on motor efficiency in estimating energy consumption; (ii) the number of driving stages with uniform speed on each segment is recommended as 2 for balancing the computation time and solution quality; (iii) planning the energy-saving speed profile while regulating the signal timing at intersections improves the operational efficiency and exploit the energy-saving potential of the CAEB; (iv) as bus operation punctuality constraints become tighter, there is an increase in the operational efficiency, but a decline in the energy-saving potential of the CAEB.

The present and future contribution of ships to the underwater soundscape

Since the industrial revolution the ocean has become noisier. The global increase in shipping is one of the main contributors to this. In some regions, shipping contributed to an increase in ambient noise of several decibels, especially at low frequencies (10 to 100 Hz). Such an increase can have a substantial negative impact on fish, invertebrates, marine mammals and birds interfering with key life functions (e.g. foraging, mating, resting, etc.). Consequently, engineers are investigating ways to reduce the noise emitted by vessels when designing new ships. At the same time, since the industrial revolution (starting around 1760) greenhouse gas emissions have increased the atmospheric carbon dioxide fraction x(CO2) by more than 100 μmol mol-1. The ocean uptake of approximately one third of the emitted CO2 decreased the average global surface ocean pH from 8.21 to 8.10. This decrease is modifying sound propagation, especially sound absorption at the frequencies affected by shipping noise lower than 10 kHz, making the future ocean potentially noisier. There are also other climate change effects that may influence sound propagation. Sea surface warming might alter the depth of the deep sound speed channel, ice melting could locally decrease salinity and more frequent storms and higher wind speed alter the depth of the thermocline. In particular, modification of the sound speed profile can lead to the appearance of new ducts making specific depths noisier. In addition, ice melting and the increase in seawater temperature will open new shipping routes at the poles increasing anthropogenic noise in these regions. This review aims to discuss parameters that might change in the coming decades, focusing on the contribution of shipping, climate change and economic and technical developments to the future underwater soundscape in the ocean. Examples are given, contrasting the open ocean and the shallow seas. Apart from the changes in sound propagation, this review will also discuss the effects of water quality on ship-radiated noise with a focus on propeller cavitation noise.

A New Nonlinear Virtual Inertia Approach to Mitigate Destructive Effects of Cyber Attacks on Active Power and Rotor Speed Profiles of Wind Turbine DFIG Sustainable Energy Production

A New Nonlinear Virtual Inertia Approach to Mitigate Destructive Effects of Cyber Attacks on Active Power and Rotor Speed Profiles of Wind Turbine DFIG Sustainable Energy Production

Decoding the dynamic perception of risk and speed using naturalistic stimuli: A multivariate, whole-brain analysis.

Time-resolved decoding of speed and risk perception in car driving is important for understanding the perceptual processes related to driving safety. In this study, we used an fMRI-compatible trackball with naturalistic stimuli to record dynamic ratings of perceived risk and speed and investigated the degree to which different brain regions were able to decode these. We presented participants with first-person perspective videos of cars racing on the same course. These videos varied in terms of subjectively perceived speed and risk profiles, as determined during a behavioral pilot. During the fMRI experiment, participants used the trackball to dynamically rate subjective risk in a first and speed in a second session and assessed overall risk and speed after watching each video. A standard multivariate correlation analysis based on these ratings revealed sparse decodability in visual areas only for the risk ratings. In contrast, the dynamic rating-based correlation analysis uncovered frontal, visual, and temporal region activation for subjective risk and dorsal visual stream and temporal region activation for subjectively perceived speed. Interestingly, further analyses showed that the brain regions for decoding risk changed over time, whereas those for decoding speed remained constant. Overall, our results demonstrate the advantages of time-resolved decoding to help our understanding of the dynamic networks associated with decoding risk and speed perception in realistic driving scenarios.

Inversion of partially spanning array data for normal mode parameters using seabed impedance constraint

Previous work with fully or nearly spanning vertical line arrays has shown that normal mode shapes can be identified as the eigenvectors of the covariance matrix averaged over lengthy source tracks that served to de-correlate the modes. Recent work with partially spanning arrays has used compressed sensing or super-resolution methods like MUSIC to estimate mode wavenumbers. These methods assume the sound speed profile is available, but not the seabed properties. We will present another alternative for inverting for normal modes under these conditions, including a partially spanning vertical line array, in which sets of candidate modes are restricted to modes that satisfy the same impedance boundary condition at the seabed. Each distinct impedance condition defines a physically consistent set of modes. Thus, our inversion is sparse in impedance conditions. We demonstrate how the normal modes resulting from this process are used to form matched field processing replicas that recover source tracks in range and depth.

A study of range dependent acoustical field based on the FVCOM model: A Case of typhoon NANMADOL

This study simulated the acoustical field in the western North Pacific (WNP) during typhoon NANMADOL, based on the FVCOM model and Range dependent Acoustic Model RAM. We implements a new approach, which helps to accurately characterize the energy distribution of acoustical field obtained by considering the range dependent ocean dynamical environment elements. The results show that, the sound speed profile in the upper-layer ocean of the sea water decreases to a certain extent on the day after the typhoon’s arrival, while the maximum decrease in the sound speed after the typhoon’s transit occurs 1∼2 days. The maximum decrease in the seawater sound speed can be up to 2-3 m/s 2 days after the typhoon’s transit. The passage of the typhoon mainly affects the far-field acoustical energy distribution when the sound source is located near the upper-layer ocean.

Fleet Speed Profile Optimization for Autonomous and Connected Vehicles

Fleet Speed Profile Optimization for Autonomous and Connected Vehicles

Optimal Cooperative Eco-Driving of Multitrain With TLET Comprehensive System

Aiming to minimize the total electricity usage in realistic operation of urban rail transit (URT) trains, this paper establishes the coupled model for the train-line-electric network-timetable (TLET) comprehensive system, and proposes a multi-train cooperative eco-driving method to achieve the system energy optimal. The dynamic power flow within the DC railway system with multiple trains is analyzed by modeling the traction power supply system (TPSS), and an improved power flow calculation (PFC) method is proposed to calculate the dynamic power flow distribution. To achieve the total substation energy minimization, a space-time-speed (STS) three-dimensional network is presented to transform continuous train motion process into discrete states on space-speed plane, and a multiple individuals dynamic programming (MIDP) algorithm is throughly developed to cooperatively optimize the multi-train trajectories along the time horizon, with which the optimal speed profiles and timetables of each train can be obtained simultaneously. Moreover, a computationally efficient heuristic algorithm together with a train-to-train (T2T) communication policy are developed as a comparative study. Numerical experiment with field data from Guangzhou Metro Line 8 is implemented, to illustrate the energy-saving performance of the proposed methods.

Passive acoustic localization using dictionary learning

Ray-trace simulations are used to demonstrate the use of Dictionary Learning to improve passive localization of a source monitored with a vertical linear acoustic array. The learned dictionary, generated using historical sound velocity profile (SVP) data from a region of interest, is an over complete, sparse representation of the SVP training set. By minimizing an objective function that measures the differences between multipath reception intervals detected at a receiver for propagation through candidate and baseline SVP profiles, we show that an optimal SVP match to the “unknown” baseline can be reconstructed by an efficient dictionary search. Ray traces back-propagated through the reconstructed SVP according to beamformed receive angles computed with the baseline SVP are then found to well estimate the source position as the centroid of a cluster of multipath signal intersections. The accuracy for small unit-sparsity dictionaries of size up to 50 was evaluated on a randomly sampled, 30 SVP testing set obtained from an area close to that of the training set, demonstrating mean location errors less than 5% in both distance and depth. Five representative profiles spanning the range of observed sound speed variations were used to assess localization performance at source depths from 50 to 450 m and at source ranges from 2000 to 4500 m. Compared to using the average sound speed profile to estimate source position, using the learned dictionary produced a general performance increase in position accuracy.