Speed Estimation Research Articles

Using heart rate and acceleration biologgers to estimate winter activity costs in free-swimming largemouth bass

Winter is a critical period for largemouth bass (Micropterus nigricans) with winter severity and duration limiting their population growth at northern latitudes. Unfortunately, we have an incomplete understanding of their winter behaviour and energy use in the wild. More winter-focused research is needed to better understand their annual energy budget, improve bioenergetics models, and establish baselines to assess the impacts of climate warming; however, winter research is challenging due to ice cover. Implantable tags show promise for winter-focused research as they can be deployed prior to ice formation. Here, using swim tunnel respirometry, we calibrated heart rate and acceleration biologgers to enable estimations of metabolic rate (ṀO2) and swimming speed in free-swimming largemouth bass across a range of winter-relevant temperatures. In addition, we assessed their aerobic and swim performance. Calculated group thermal sensitivities of most performance metrics indicated the passive physicochemical effects of temperature, suggesting little compensation in the cold; however, resting metabolic rate and critical swimming speed showed partial compensation. We found strong relationships between acceleration and swimming speed, as well as between ṀO2 and heart rate, acceleration, or swimming speed. Jackknife validations indicated that these modeled relationships accurately estimate swimming speed and ṀO2 from biologger recordings. However, there were relatively few reliable heart rate recordings to model the ṀO2 relationship. Recordings of heart rate were high-quality during holding but dropped during experimentation, potentially due to interference from aerobic muscles during swimming. The models informed by acceleration or swimming speed appear to be best suited for field applications.

Rapid automatised naming is related to reading and arithmetic for different reasons in Chinese: Evidence from Hong Kong third graders

BackgroundRapid automatised naming (RAN) has been found to predict children's reading and arithmetic abilities. However, the underlying mechanisms for its involvement in the two abilities are not clear. This study examines how RAN shared variances with domain‐general and domain‐specific abilities in predicting reading and arithmetic in Chinese children.MethodsOne hundred and sixty‐four children (mean age = 8 years 0 months, SD = 4 months) were administered with RAN tasks, word reading and arithmetic tasks and measures of working memory, processing speed, morphological awareness, phonological awareness and number line estimation.ResultsRAN mainly shared variance with morphological awareness in predicting word reading, while it shared variance with processing speed and number line estimation in predicting arithmetic calculation.ConclusionsThe findings indicated that RAN was related to reading and arithmetic for different reasons. The RAN–reading relationship partly reflected the semantic facilitation of the orthography–phonology links for both RAN stimuli and Chinese characters, while the RAN–arithmetic relationship partly reflected the shared process of retrieving semantic information from long‐term memory embedded in the two tasks.

On the speed of propagation in Turing patterns for reaction–diffusion systems

This study investigates transient wave dynamics in Turing pattern formation, focusing on waves emerging from localised disturbances. While the traditional focus of diffusion-driven instability has primarily centred on stationary solutions, considerable attention has also been directed towards understanding spatio-temporal behaviours, particularly the propagation of patterning from localised disturbances. We analyse these waves of patterning using both the well-established marginal stability criterion and weakly nonlinear analysis with envelope equations. Both methods provide estimates for the wave speed but the latter method, in addition, approximates the wave profile and amplitude. We then compare these two approaches analytically near a bifurcation point and reveal that the marginal stability criterion yields exactly the same estimate for the wave speed as the weakly nonlinear analysis. Furthermore, we evaluate these estimates against numerical results for Schnakenberg and CDIMA (chlorine dioxide–iodine–malonic acid) kinetics. In particular, our study emphasises the importance of the characteristic speed of pattern propagation, determined by diffusion dynamics and a complex relation with the reaction kinetics in Turing systems. This speed serves as a vital parameter for comparison with experimental observations, akin to observed pattern length scales. Furthermore, more generally, our findings provide systematic methodologies for analysing transient wave properties in Turing systems, generating insight into the dynamic evolution of pattern formation.

Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network

Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network

Vehicle Classification and Counting for Traffic Analysis based on Single-stage YOLOv8 Model

Intelligent traffic systems are emerging and becoming part of smart city infrastructure that requires computer vision applications to provide traffic information like vehicle classification and counting in real-time. Vehicle counting helps detect heavy traffic on roads, and vehicle classification helps enforce further processes like speed estimation to enforce speed limit laws based on the vehicle class. Deep learning computer vision-based systems provide automatic feature extractions that are robust to changes in lighting, shadows, and occlusions. This paper proposes a software solution for a real-time traffic monitoring system based on a cutting-edge single-stage deep learning model through the state-of-the-art YOLOv8 algorithm. YOLOv8 is the most recent model of the YOLO family, which provides object detection and classification through its CNN architecture. The proposed work detects vehicles and counts them based on their class. The four common vehicle classes are sedan cars, buses, trucks, and motorcycles, and a counter for each class is displayed on the system’s output screen in real-time and recorded in a log file. The results of the proposed system running on the Nvidia GTX 1070 GPU show an average accuracy of 96.58% with an average error of 3.42% for vehicle detection and an average accuracy of 97.54% with a 2.46% average error for vehicle counting. For vehicle classification, the results for the four vehicle classes (car, bus, truck, and motorcycle) show an accuracy of (94.7%, 94.7%, 96.2%, 99.7%), precision (95%, 100%, 81.4%, 100%), recall (97.9%, 36.3%, 100%, 66.6%), and the f1-score (96.3%, 53.2%, 89.7%, 79.9%), respectively. Index Terms— Computer Vision, Deep Learning, Vehicle Detection, Traffic Analysis, YOLO.

Current Sensor Fault-Tolerant Control Strategy for Speed-Sensorless Control of Induction Motors Based on Sequential Probability Ratio Test

In the speed-sensorless vector control of induction motors (IMs), the speed estimation accuracy suffers from the deteriorated current measurement caused by the current sensor faults, such as open circuit in one phase, DC bias, and odd harmonics. In this paper, a novel speed estimation strategy based on the current sensor fault-tolerant control is proposed to improve the speed estimation accuracy under the current sensor faults. First, to detect the current sensor faults in real time, the sequential probability ratio test is introduced to the system by using the innovations of the extended Kalman filter (EKF). Second, to ensure speed estimation accuracy, a double-cascading second-order generalized integrator (DSOGI) is employed to reconstruct the faulty current information when a fault is identified. Finally, the reconstructed current information is fed back to the sequential probability extended Kalman filter (SPEKF), which estimates the rotor speed of the IM, and high-accuracy speed estimation under the condition of current sensor faults is achieved. The effectiveness of the proposed strategy is validated by a series of experiments, which were conducted on a 3 kW induction motor drive platform.

Velocity of viscous fingers in miscible displacement: Intermediate concentration

We investigate one-phase flow in porous medium corresponding to a miscible displacement process in which the viscosity of the injected fluid is smaller than the viscosity in the reservoir fluid, which frequently leads to the formation of a mixing zone characterized by thin fingers. The mixing zone grows in time due to the difference in speed between its leading and trailing edges. The transverse flow equilibrium (TFE) model provides estimates of these speeds. We propose an enhancement for the TFE estimates, and provide its theoretical justification. It is based on the assumption that an intermediate concentration exists near the tip of the finger, which allows to reduce the integration interval in the speed estimate. Numerical simulations were conducted that corroborate the new estimates within the computational fluid dynamics model. The refined estimates offer greater accuracy than those provided by the original TFE model.

Determination of Motorcycle Equivalent Unit and Road Capacity: A Case Study of Selected Road Sections of Kathmandu Valley

The research focused on road congestion in Kathmandu Valley, Nepal, caused by the prevalence of small motorized vehicles, especially motorcycles. The objective of the study was to quantify the impact of different vehicle classes on congestion by calculating Motorcycle Equivalent Units (MEU) and determining roadway capacity using speed and effective space parameters. The field data collected over two locations in Kathmandu Valley were analyzed using Speed Estimation from Video Data (SEV) software that revealed a strong correlation between vehicle speed and effective space, represented by a quadratic non-linear model. MEU values were determined for different vehicle classes, and fundamental speed-flow-density relationships were used to calculate roadway capacity. Vehicles like motorcycles, standard cars, big cars, utility, minibusses, buses, light commercial vehicles, two/three-axle trucks, and multi-axle trucks had MEU values of 1, 3.32, 4.91, 4.65, 9.22, 12.22, 6.72, 13.57 and 18.04 respectively. The study indicated peak capacities during the evening hours on specific road sections. For instance, the Gatthaghar-Kaushaltar and Balkumari-Gwarko sections reached their maximum capacities at 12,335 and 20,589 (motorcycles per hour per direction), respectively. The study emphasized the significant influence of speed on effective space and roadway capacity, suggesting further exploration of additional factors like driver characteristics, gender, age, income, road geometry, and level of service using MEU, along with the potential for developing a motorcycle simulation model in Kathmandu valley.

Minimal effect of scanning parameters on ultrasound shear wave elastography variability in tendons.

Ultrasound shear wave elastography has potential use in assessing tendon tissue; however, reducing measurement variability remains challenging. The primary purpose of this study was to identify the amount of variability accounted for by ultrasound parameter (frequency, harmonics and CrossXBeam) settings on shear wave speed at two testing sites. Shear wave elastography images of the Achilles tendon were obtained from individuals with healthy tendons (n = 28) at two testing sites with standardised image acquisition/postprocessing protocols. Images were acquired at a range of frequencies (7-15 MHz) with CrossXBeam (a filtering technique) and harmonics settings toggled on and off. Variance decomposition analysis was performed to identify the amount of variability in shear wave speed accounted for by scan acquisition settings and testing sites. Shear wave speed variance was primarily attributed to participants (56.87% of variance; residual error: 35%). All scanning parameters, testing site and interaction terms each accounted for less than 2.5% of the variance. A statistically significant, negative relationship was observed between shear wave speed and image quality (p = 0.001) suggesting poor image quality yields higher shear wave speed estimates. The findings of this study suggest that natural variation in Achilles tendon mechanics between individuals without tendon pathology accounts for most of the shear wave speed variability. Optimising image quality, which may be observed in higher frequencies, should be considered to improve shear wave speed estimation. Clinically, this study highlights the need to take multiple images, maintain consistent ultrasound settings when tracking patient progress over time and use caution when comparing raw values from tendon scans performed in different clinics with shear wave elastography. Level III.

Baseline vulnerable road user injury risk in multiple U.S. dense urban driving environments

Objective Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data. Methods From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists). Data consisted of anonymized video and sensor data (Global Positioning System and accelerometer) from vehicles equipped with Nexar dash cameras. Each event was qualitatively evaluated to assess the collision geometries and other extrinsic collision factors (e.g., time of day, roadway location, presence of occlusions). Previously published injury risk models were utilized to provide estimates of Maximum Abbreviated Injury Scale MAIS2+ and MAIS3+ injury potential. Collision severity distributions and aggregated injury risk estimates were compared. Results Vehicle speeds at the time of impact ranged from 0 to 39 mph, and VRU speeds ranged from 0 to 27 mph. In general, vehicles were traveling at lower speeds at the time of impact when turning in comparison to straight travel. Nearly all events (95%) were associated with MAIS3+ injury risk estimates below 10%. Collisions involving a potential occlusion or the vehicle responding to surprising behavior by the VRU were associated with higher estimates of injury risk than those without occlusion or with the vehicle initiating the conflict. Based on accumulated risk for each event, it can be estimated that approximately 55 persons could be moderately injured (MAIS2+) and approximately 6 persons could be seriously injured (MAIS3+). Conclusion These data indicate that responding to surprising VRU behavior, having visibility be potentially occluded, and vehicle travel behavior were associated with differences in collision speed and injury risk estimation. The specific comparisons made in this study are not intended to be comprehensive but serve as a starting point for considering baseline driving risk associated with VRU collisions in dense urban areas.

Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor Based on RPLESO

In view of the problem of the low-speed jitter of household lawn mowers driven by a permanent magnet synchronous motor (PMSM) at low speeds and high torque, and the complicated parameters of traditional non-linear active disturbance rejection controllers, a partially optimized linear active disturbance rejection control (LADRC) driving PMSM strategy is proposed. First, the linear extended state observer (LESO), which bears a significant burden in terms of speed and load estimation in active disturbance rejection control, is optimized by reducing its order to improve the anti-disturbance performance of the active disturbance rejection controller within a limited bandwidth. Secondly, the reduced-order parallel linear extended state observer (RPLESO) is obtained by optimizing the parallel structure of the order-reduced LESO, which improves the control precision and robustness of the system. Through a simulation and experimental verification, the optimized LADRC control of the PMSM system is shown to improve the parameter adjustability, speed estimation precision and system robustness.

Dynamics for a Ratio-Dependent Prey–Predator Model with Different Free Boundaries

In this paper, we study the dynamics of the ratio-dependent type prey–predator model with different free boundaries. The two free boundaries, determined by prey and predator, respectively, implying that they may intersect with each other as time evolves, are used to describe the spreading of prey and predator. Our primary focus lies in analyzing the long-term behaviors of both predator and prey. We establish sufficient conditions for the spreading and vanishing of prey and predator. Furthermore, in cases where spread occurs, we offer estimates for the asymptotic spreading speeds of prey and predator, denoted as u and v, respectively, as well as the asymptotic speeds of the free boundaries, denoted by h and g. Our findings reveal that when the predator’s speed is lower than that of the prey, it leads to a reduction in the prey’s asymptotic speed.

Assessing the behavior of historic masonry buildings to tornadic events: A comparative study of code-based analysis and point cloud based simulation

During the Midwest Tornado outbreak in December 2021, the historic downtown of Mayfield, Kentucky was heavily impacted. Employing digital documentation, numerical analysis, and on-site wind speed estimations, this study evaluates the behavior of the impacted historic masonry buildings. It explores the structural damages and stress distributions observed in historic masonry buildings impacted by tornadoes, comparing them to predictions made by the ASCE 7–22 standard for tornado loads. Through this investigation, it becomes evident that the stress distribution and damages produced by the on-site tornado wind speeds were significantly different than those derived from the ASCE 7–22 standard. The current standard does not account for most historic structures, typically classified as Risk Category.II. Based on the results of this research, even when these structures are treated as Risk Category III, the stresses estimated by the standard are much lower than the ones experienced on-site. This disparity raises critical questions for preservationists evaluating similar masonry structures in tornado-prone regions. Historic preservation emphasizes minimal intervention, so understanding and addressing the specific vulnerabilities of historic masonry structures to tornadoes is crucial. The analysis in this study identified uplift forces on the roof and high stresses on windward walls as primary causes of damage in these structures, which aligned with the observed collapse mechanisms. Preservationists can use these insights to develop targeted retrofitting strategies that address these vulnerabilities while minimizing impact on the historic fabric. Recognizing these nuances and their impact on structural behavior is crucial for safeguarding the resilience and significance of historic civil structures. Thus, this study lays the groundwork for developing evidence-based, preservation-sensitive guidelines for mitigating tornado damage to irreplaceable historic masonry structures.

Ensemble Random Forest-based Gradient Optimization based Energy Efficient Video Processing System for Smart Traffic Surveillance System

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods.

Estimation of spreading speeds and travelling waves for the lattice pioneer-climax competition system

ABSTRACT This paper concerns the invasion dynamics of the lattice pioneer-climax competition model with parameter regions in which the system is non-monotone. We estimate the spreading speeds and establish appropriate conditions under which the spreading speeds are linearly selected. Moreover, the existence of travelling waves is determined by constructing suitable upper and lower solutions. It shows that the spreading speed coincides with the minimum wave speed of travelling waves if the diffusion rate of the invasive species is larger or equal to that of the native species. Our results are new to estimate the spreading speed of non-monotone lattice pioneer-climax systems, and the techniques developed in this work can be used to study the invasion dynamics of the pioneer-climax system with interaction delays, which could extend the results in the literature. The analysis replies on the construction of auxiliary systems, upper and lower solutions, and the monotone dynamical system approach.

Performance parameters estimation of high speed Silicon/Germanium/InGaAsP avalanche photodiodes wide bandwidth capability in ultra high speed optical communication system

Abstract This paper has clarified the performance parameters estimation of high speed silicon/germanium/InGaAsP avalanche photodiodes wide bandwidth capability in ultrahigh speed optical communication system. The basic structure configuration of avalanche photodiode is clarified. The basic depletion region configuration schematic view is demonstrated. As well as the maximum electric field is indicated for impact ionization through avalanche photodiode in the presence of load. Various avalanche photo-detectors multiplication factor is clarified versus reverse bias voltage at room temperature. Different avalanche photo-detectors dark current is measured against avalanche photo-detectors volume at room temperature. Various avalanche photo-detectors effective doping concentration is demonstrated against ambient temperature variations. Si/Ge/InGaAsP avalanche photo-detectors excess noise factor is demonstrated versus the ionization ratio at room temperature (T = 300 K) and different ambient temperature (T = 350 K and T = 400 K). Various avalanche photo-detectors excess noise factor is measured clearly versus the multiplication factor at room temperature. Si/Ge/InGaAsP avalanche photo-detectors excess noise factor is demonstrated versus reverse bias voltage at room temperature. Si/Ge/InGaAsP avalanche photo-detectors noise equivalent power is clarified versus multiplication factor at room temperature. Various avalanche photo-detectors noise equivalent power is studied versus temperature variations. Si/Ge/InGaAsP avalanche photo-detectors sensitivity is measured in relation to the temperature variations. Different avalanche photo-detectors sensitivity is demonstrated in relation to reverse bias voltage variations at room temperature.

Integrated methodology for gas content assessment and prediction in shallow muddy lake sediments: acoustic mapping and correlation analysis

This paper provides a step-by-step description of integrated methodology for quantification and prediction of gas (methane, CH4) content dynamics in shallow aquatic sediments under changing spatial and temporal conditions. Presence of gas bubbles even in small concentrations significantly affects sediment compressibility, which in turn decreases sound speed in sediment. Our integrated methodology consists of two basic steps. In the first step, free gas content is evaluated by acoustic applications based on the sound speed inferred from the reflection coefficient from gassy bottom. The experimental bottom reflections are registered and compared to the simulated ones, using a geoacoustic inversion technique. The best match between the model and the experiment provides sediment sound speed estimate, which is converted into free gas content using a basic relation. In the second step, a multivariate linear regression is fitted for gas content and closed form expression of gas content dependence on the following predictors, which change spatially and temporally over the aquatic ecosystem, is obtained: 1) water depth, 2) short-leaving CH4 production rate peaks fueled by punctuated organic matter deposition; and 3) CH4 bubble dissolution rates.•Gas content and sound speed in the sediment are estimated via the geoacoustic inversion technique by matching the experimentally recorded and simulated bottom reflections•Only single source and receiver are required for the acoustic methodology•A multivariate linear regression is fitted for gas content to indicate its dependence on various predictors that change spatially and temporally over the lake

Revisiting the estimation of extreme wind speed considering directionality

In the estimation of extreme wind speed, the effect of wind direction is usually ignored. Nevertheless, as a vital parameter of the wind, it should be reasonably taken into consideration. Nowadays, the influence of wind direction can be considered through directional wind speed or directly regarding it as a variable. Based on wind speed data of climate stations, the wind directions are divided into uniform and non-uniform sectors. Copula function is then applied to establish the joint distribution of directional wind speed. The effect of the partition of directional sectors is discussed subsequently. Meanwhile, a joint distribution model of wind speed and wind direction based on copula function is also set up. According to the joint distribution, extreme wind speeds under specified wind directions are estimated based on conditional probability. Besides, extreme wind speeds considering wind directionality based on the aforementioned two methods are compared. Results show that the classification of wind direction will affect the estimation of extreme wind speed. In addition, the estimation of extreme wind speed under specified wind direction based on the joint distribution of wind speed and wind direction does not reflect the actual wind characteristics. Through the comparison of different methods considering directionality, the result based on the joint distribution of directional wind speed seems more reasonable.

Pipelined Neural Network Assisted Mobility Speed Estimation Over Doubly-Selective Fading Channels

The speed estimation has been widely used for tracking mobile device locations, providing essential information in location/mobility-aware communications, enhancing received signal quality/robustness, and reducing energy consumption and latency. Deep learning can be used to improve the performance constrained by signal/system model. This work focuses on the issues on machine learning (ML) based speed estimation using primary synchronous signal (PSS), which is embedded in the 5G standards, over general time-variant multipath channels. Aiming to reduce the complexity involved in the ML algorithms for the speed estimation in mobile networks, we propose a pipelined ML algorithm to decompose the original ML model into several smaller ones. The advantages of the proposed convolutional neural network (CNN) based approach have been verified by simulations.

Development of a novel method for the correction of the nacelle wind speed in stall-controlled wind turbines

Accurate estimation of the oncoming wind is key to ensure an accurate control of any wind turbine. The wind speed is commonly measured with an anemometer located on the nacelle; hence, the measurement is influenced by the rotor and the nacelle itself and needs to be corrected so as not to incur inaccurate energy yield assessments. This study introduces an innovative method for correcting the nacelle wind speed in stall-controlled wind turbines. The development of the method has benefitted from the unique possibility of exploiting two datasets containing 10-minute averaged wind data from two identical EUNICE EW16 wind turbines and a meteorological mast located at the same site. The innovative method is systematically compared with the Nacelle Transfer Function outlined in the IEC 61400-12-2, serving as a benchmark for evaluation. The high accuracy and simplicity of the proposed method make it particularly suitable for the optimization of wind turbine performance in industrial applications. Moreover, an accurate estimation of the incoming wind speed can enable innovative control techniques, such as those based on Tip-Speed-Ratio (TSR) tracking. This is addressed in the study through simulations by comparing a TSR-Tracking strategy with the most common k-ω2 strategy. The study demonstrated that the TSR-Tracking strategy could be adopted in stall-controlled wind turbines if an accurate estimation of the free-stream wind speed is available.