Speed Interval Research Articles

Uncertainty prediction of wind speed based on improved multi-strategy hybrid models

<p>Accurate interval prediction of wind speed plays a vital role in ensuring the efficiency and stability of wind power generation. Due to insufficient traditional wind speed interval prediction methods for mining nonlinear features, in this paper, a novel interval prediction method was proposed by combining improved wavelet threshold and deep learning (BiTCN-BiGRU) with the nutcracker optimization algorithm (NOA). First, NOA was used to optimize the wavelet transform (WT) and BiTCN-BiGRU. Second, we applied NOA-WT to smooth the wind speed data. Then, to capture nonlinear features of time series, phase space reconstruction (PSR) was utilized to identify chaotic characteristics of the processed data. Finally, the NOA-BiTCN-BiGRU model was built to perform wind speed interval prediction. Under the same hyperparameters and network structure settings, a comparison with other deep learning methods showed that the prediction interval coverage probability (PICP) and prediction interval mean width (PIMW) of NOA-WT-BiTCN-BiGRU model achieves the best balance, with good prediction accuracy and generalization performance. This research can provide reference and guidance for nonlinear time-series interval prediction in the real world.</p>

Impact of speed on injury severity in single-vehicle run-off-road crashes: Insights from partially temporal constrained modeling approach

Single-vehicle run-off-road crashes accounts for approximately 35% of all the traffic fatalities in the U.S during the period of 2019–2021. This paper explores the association between driving speed and injury severity outcomes of single-vehicle run-off-road crashes. The single-vehicle run-off-road crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods of pre-, during-, and post-COVID-19 pandemic. The partially constrained temporal and temporal unconstrained random parameters logit models are developed considering three injury severity outcomes: no injury, minor injury and serious injury/fatality. Multiple variables in terms of driver, vehicle, roadway, environmental, crash, and temporal attributes are observed to significantly affect the injury severity. Moreover, temporal instability and transferability issues are validated through likelihood ratio test and out-of-sample prediction. In the partially constrained models, numerous variables such as indicators of new vehicle, male driver, and restraint-protected driving consistently yield identical parameter values across all periods, whereas various variables clearly illustrate the distinct differences across the three periods and three speed intervals. The marginal effects in the unconstrained models also display the obvious differences across three periods and three speed intervals. Moreover, the findings corroborate the increased risk outcomes linked to larger speed differences and the COVID-19 pandemic period. These results provide better understanding of the risk mechanisms underlying run-off-road crashes and furnish valuable direction for the formulation of effective safety interventions.

Locomotor performance parameters as predictors of high-performing male soccer teams. A multiple-season study on professional soccer

This study aims to explore the interplay between locomotor demands and goal differentials to better understand their combined influence on overall success. Spanning three competitive seasons within the male Turkish Super League, this study analyzed all participating teams across 124 matches. Locomotor demands, including total distance (m) covered (TD), distances covered (m) at different speed thresholds (0.21–2.0 m/s; 2.01–4.0 m/s; 4.01–5.5 m/s; and 5.5–7.7 m/s), and the number of accelerations in range of 5.5–7.0 m/s (n), were quantified using an optical tracking system. Subsequently, regression models were employed to predict the total points earned by all teams over the three seasons. The logistic regression model, tailored to predict team categorization as high-points earners (HPE) or low-points earners (LPE) based on locomotor variables, exhibited a mean accuracy of 74%. Notably, total distance covered, running speed intervals between 4.4 and 5.5 m/s, and the number of accelerations in range of 5.5–7.0 m/s emerged as significant predictors of team success. Our findings highlight the pivotal role of running speed (4.01–5.5 m/s), number of accelerations, and total distance in predicting success for high-performing teams. Coaches can leverage these insights to refine training programs, thereby optimizing team performance, and fostering success in competitive environments.

Increasing the capabilities of a test ballistic missile to separate test objects

The subject of this study is the trajectory characteristics of the long-range test ballistic missile (TBM). The purpose of the study is to increase the capabilities of TBM in separating test object (TO). At the same time, as a generalized quantitative measure of this increasing the post-boost vehicle (PBV) fuel reserve consumed for separation of TO is taken. The design-ballistic task of rationalizing the distribution of the available fuel of the TBM DS between the following main characteristic section of its flight has been set and numerically and analytically solved: final sustainer stage underperformance compensation; turns with subsequent angular stabilization, retreats and lead away; TO disconnection (separation section). As a result, it is shown that without reducing the quality of TBM launch tasks, it is permissible to redistribute the consumed fuel of the PBV between these section relative to the distribution for a standard ballistic missile (SBM), leading to a significant increase in its reserve consumed in the section of disconnection of the TO (when flying along the ballistic vertical). At the same time, the purpose of the study is achieved – the capabilities of the TBM in the separation of the TO are increased, which, while direct planning of launches is evaluating, can be expressed in an increase in the number and/or total mass of the TO and/or an increase in speed or time intervals in the order of sequential disconnections of the TO. The given numerical examples (using a converted three-stage SBM as a TBM) also show a significant dependence of the amount of fuel increment of the PBV consumed at the TO disconnections of the trajectory conditions of test launches (the corresponding initial data (ID) for calculations are borrowed from the author’s previously published work): length of the route; kinematic parameters of launch at the moment of independent PBS flight beginning determined by the launch task. During the study, methods of flight theory and design ballistics of LR missiles were used. As a conclusion, it can be noted that the considered task and methods for its solution can be useful (of course, taking into account the necessary specialized improvements) in works of the executive ballistics level when planning and evaluating the result of TBM launches.

On Velocity Stopbands in Electrostatic Solitary Wave Propagation in Magnetospheric Plasma in the Presence of an Ion Beam—Application in the Solar Wind

Satellite observations in the Earth’s magnetosphere suggest a coexistence of different types of ion populations, such as protons and heavier helium ions (alpha particles) transported by the solar wind. We have undertaken an investigation, from first principles, of the conditions for the occurrence of electrostatic solitary waves in such a multi-ion plasma environment. Nonlinear analysis has been employed to explore the effect of an ion beam on the occurrence of stopbands in a multicomponent plasma consisting of Boltzmann electrons and two ion species, modeled as cold and warm (adiabatic) ions. A “stopband” is an interval of pulse speed (mach (Mach number) values in which solitary waves cannot propagate. Such stopbands are known to exist in relation to fast ion-acoustic solitary waves (exclusively). Our parametric analysis reveals that the stopband widens over the range of cold ion charge density (values) upon increasing speed of a hot ion beam moving along the direction of wave propagation. However, the stopband becomes narrower in the case of a counterpropagating beam. Similarly, the streaming of a cold ion beam fluid along (or antiparallel) to the wave direction leads to the narrowing (or widening, respectively) of the stopband. The fast soliton existence (velocity) domain is characterized by two critical points (a crossover and a turning point), which are affected by the beam. Our study could be relevant to understanding energy dissipation processes in the fast solar wind associated with relative drifts between the major (protons) and minor (alpha particles) ions which results in the heating of both species.

An Estimation of Baricitinib by AQbD-driven UV Spectrophotometry Development and Validation Process

Background: Baricitinib (BCTB) is a novel Janus Kinase (JAK) 1 and 2 inhibitor used in the therapy of rheumatoid arthritis, approved by the “Food and Drug Administration” in 2018. It has significant dose-dependent effectiveness and severe side effects. Thus, it is crucial to figure out its concentration in developed dosage forms. The literature search revealed that there has only been one UV spectroscopy technique documented up to this point. Methanol was chosen as the detection medium in this approach, which is not comparable with plasma or serum. As a result, the preliminary research suggested developing a UV spectroscopic approach that can estimate BCTB concentration and compare it to its concentration in the plasma or serum. Thus, in the proposed method, 7.4 pH phosphate buffer was selected as a mobile phase. Aim: Using the Analytical Quality by Design (AQbD) methodology, a simple, robust spectrophotometric method for the detection of BCTB in API form and Niosomes drug delivery system is designed and assessed. Methods: In the AQbD approach, a face-centered CCD design of Design Expert 13 software was used to evaluate two critical method variables: scanning speed and sampling interval. The design space suitability was confirmed by standard error and overlay plots. The 2-D contour and 3-D response surface plots were used to forecast the relationship between the response variable and predictor variables. Results: Baricitinib displays an absorption maximum at 249.40 nm in saline phosphate buffer pH 7.4. The distinguished linearity of the method was obtained over a concentration of 5–30 µg/ml with a correlation coefficient (R2 ) value of 0.998. BCTB % assay was found to be near 99 %. Intraday and Interday precision were found to have % RSDs of 0.067–0.488 and 0.146–0.942, respectively. Conclusion: The established spectrophotometric technique was observed to be precise as per ICH revised guidelines ICH Q2 (R1) and Q14 for analytical method validation. Our findings are instructional for the future design and development of safe and reliable therapeutic dosage forms of BCTB for rheumatoid arthritis.

Bivariate cubic normal distribution for non-Gaussian problems

Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. Traditional parameter-based bivariate distribution models are typically developed for specific types of random variables, which limits their flexibility and applicability. In this study, a flexible bivariate distribution model is proposed, in which the joint cumulative distribution function (JCDF) is derived by expressing the probability as the summation of three basic probabilities corresponding to simple functions. These probabilities are computed using a univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN distribution accurately fits the JCDFs of both theoretical distributions and practical datasets, offering significant improvement over existing models. Furthermore, the proposed BCN distribution is utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.

Achievement of ductile-regime removal in fabricating Gaussian curved microstructure processed by micro ball-end milling on soft-brittle KDP surface

Laser-induced damage points (known as defects) would seriously reduce the service life of large-aperture KDP optics in high-power laser devices. The ball-end milling procedure is recognized as an efficient method for creating a Gaussian mitigation pit (GMP) to restore the optical transmission performance of functional KDP crystals by removing defects. Nevertheless, achieving smooth and flawless Gaussian curved microstructures is a massive challenge for soft-brittle KDP crystals. Herein, a judging criterion of the ductile-regime machining for the GMP is developed by the models of uncut chip thickness (UCT) and critical milling depth. Simultaneously, the obtained judging criterion can be validated by the microstructure fabrication experiments. Besides, considering the spindle vibration, plowing effect, and machined surface texture, the influence of spindle speed (n), feed rate (f), and tool mark interval (d) on the surface formation mechanism of the GMP is analyzed, respectively. It can be discovered that the n of up to 60,000 r/min can lead to severe velocity fluctuation of the motion system, increasing the UCT and causing brittle fractures on the KDP surface. A low f can result in an undesirable plowing phenomenon, and a large number of crystal materials are accumulated in the up-cut process. Once the f reaches 72 mm/min, the tool path would fluctuate significantly, resulting in poor GMP surface texture. When the d exceeds 15 μm, the surface quality of the GMP can no longer meet the engineering requirements of the Ra ≤ 50 nm. Moreover, the optimized processing parameters of the microstructure fabrication are 47,800 r/min in the n, 30 mm/min in the f, and 5 μm in the d. This study can provide crucial guidance for obtaining the ultra-smooth and defect-free GMP processed in the ductile regime, which would resultantly possess significant theoretical importance and practical value in enhancing the optical properties of flawed KDP crystals.

Effect of Protein Supplementation Combined With Resistance Training in Gait Speed in Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

We aimed to evaluate the effectiveness of the combination of protein supplementation and resistance training (RT), compared with RT alone or combined with a placebo, in improving gait speed. We searched PubMed, Web of Science, Cochrane Library, and SPORTDiscus databases, and 18 randomized controlled trials with 1,147 older participants were included for meta-analysis. Data were pooled as the effect sizes (Hedges' g) with 95% confidence interval (CI) of the gait speed (in meters per second). The random-effect meta-analysis, subgroup analyses, meta-regression, and sensitivity analysis were conducted. The combination of protein supplementation and RT significantly improved gait speed (Hedges' g: 0.52m/s, 95% confidence interval [0.17, 0.86], p = .005; I2 = 86.5%) compared with the RT alone. The subgroup analyses revealed that the significant improvement in gait speed postprotein intervention plus RT was observed only in participants who consumed protein after RT (Hedges' g: 0.90m/s, 95% confidence interval [0.46, 1.33], p = .001; I2 = 79.6%). The pooled result did not significantly change after excluding any single study at one time or excluding smaller studies with large effect sizes. Protein supplementation combined with RT could significantly improve the gait speed of older adults compared with RT alone. This positive effect is more pronounced in people who consume protein after RT.

Enhanced particle mixing performance of liquid-solid reactor under non-periodic chaotic stirring

This paper introduces the Chebyshev non-periodic chaotic stirring speed based on chaos theory. Additionally, it constructs the DEM-VOF coupled computational model to investigate non-periodic mixing within the reactor system. Numerical calculations are employed to compare and analyze Constant Stirring Speed (CSS) with Chebyshev Non-Periodic Chaotic Stirring Speed and its reverse (CRS, CRS-R). The results indicate that the number of particles deposited at the bottom of end diffusion decreases by 54.3 %. Moreover, it samples particle concentration per unit volume, resulting in a 70.3 % decrease in the Relative Concentration Standard Deviation (RCSD) of particles. Furthermore, it quantifies the mixing effect based on the distribution of distances between particles, leading to a 20.3 % increase in the Unit Block Mixing Index (UBMI). In conclusion, this study improves particle distribution characteristics by utilizing appropriate non-periodic variable speed intervals. Additionally, it provides technical support for production scenarios involving the mixing and dispersion of multiphase non-homogeneous systems.

Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control

Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan.

Adapting the percentage intensity method to assess accelerations and decelerations in football: moving beyond absolute and arbitrary thresholds.

We adapted the percentage intensity approach to monitor accelerations and decelerations allowing players' individualisation. Forty-two players were monitored during four microcycles via global navigation satellite system devices. Raw velocity and time data were collected to calculate acceleration and deceleration magnitudes according to specific starting speed intervals, and the efforts intensities were established as very low (<25% of the maximal effort), low (25-50%), moderate (50-75%) and high (>75%). Linear regressions and Pearson correlation (r) analysed the relationship between maximal efforts and starting speeds; additionally, mean paired differences compared efforts magnitudes between subsequent starting speed intervals. Most very low intensity accelerations (86%) and decelerations (79%) started from <5 km.h-1. Correlation between maximal efforts and starting speeds were r = -0.97 (p < .001) for acceleration, and r = -0.94 (p < .01) for deceleration. Maximal acceleration decreased as starting speed increases (very large effect sizes), but deceleration is less starting speed dependent (unclear to large effect sizes). This adaptation allows practitioners to individualise accelerations and decelerations classification during real-life scenarios, leading to a more precise training prescription. The very low intensity interval could be excluded to consider only relevant efforts. Maximal acceleration should be collected for each starting speed interval because accelerations are starting speed dependents.

Analysis of Resistance Influencing Factors of a Bench System Based on a Self-Developed Four-Wheel Drive Motor Vehicle Chassis Dynamometer

In order to accurately simulate the actual road driving resistance of four-wheel drive motor vehicles based on the chassis dynamometer and efficiently test the vehicle performance, it is necessary to analyze the influencing factors of the additional loss resistance and the loading resistance of the chassis dynamometer bench system. In this paper, the effects of the drum speed, the sampling speed interval, and basic inertia on the test results of the additional loss resistance are tested and analyzed based on the self-developed chassis dynamometer of a four-wheel drive motor vehicle. The static and dynamic components of the additional loss resistance are defined by linear regression through ordinary least squares, and the additional loss resistance of the four-axis eight-drum chassis dynamometer and mainstream chassis dynamometer system for four-wheel drive motor vehicles are compared. In addition, the effects of the dynamometer type and the control strategy on the loading resistance are discussed, and the transient condition, steady-state condition, and overall operating condition deviation coefficient of loading force are defined, according to which the advantages and disadvantages of the control strategy of the chassis dynamometer system for four-wheel drive motor vehicles are evaluated. The analysis of the influencing factors and laws of the resistance of the four-wheel drive motor vehicle chassis dynamometer bench system can provide a reference basis for accurately simulating the resistance of vehicle road driving based on the bench testing.

Nonlinear dynamics of a dual-rotor system with active elastic support/dry friction dampers based on complex nonlinear modes

In this study, the nonlinear dynamics of a dual-rotor system with active elastic support/dry friction dampers (ESDFDs) are investigated based on complex nonlinear modes (CNMs). The finite element method (FEM) combined with a full-3D friction model is introduced to construct the governing equation for the system. Additionally, the Craig–Bampton technique is applied to downscale the finite element model of the system. Based on the reduced order model (ROM), the nonlinear modal damping ratio of the target mode is employed to measure the dry friction damping performance of active ESDFD. The effects of the active ESDFD position, normal force, and tangential contact stiffness on the nonlinear modal damping ratio and modal frequency are analysed. Moreover, the softening characteristics of the active ESDFD are revealed, and the critical speed intervals of the active ESDFD/dual-rotor system are determined. Furthermore, by using the harmonic balance–alternating frequency/time domain (HB–AFT) method, the steady-state response of the system under unbalanced excitation is calculated. The accuracy and effectiveness of nonlinear modal analysis are validated based on the relationships between nonlinear modes and steady-state unbalanced responses. Conversely, the vibration mitigation effects of active ESDFD are determined by the unbalanced response amplitude. Additionally, the controllable region and optimal normal force for effective vibration control in the target mode are defined. Depending on the controllable region, a control strategy for turning on/off the optimal normal force is developed. The findings demonstrate that the developed control strategy enables the active ESDFD to significantly reduce the response amplitude of the dual-rotor system across various excitation levels, showing substantial potential for engineering applications.

Impact of Impeller Speed Adjustment Interval on Hemolysis Performance of an Intravascular Micro-Axial Blood Pump.

In recent years, intravascular micro-axial blood pumps have been increasingly used in the treatment of patients with cardiogenic shock. The flow rate of such blood pumps requires adjustment based on the patient's physiological condition. Compared to a stable flow state with fixed rotation speed, adjusting the speed of blood pump impeller to alter flow rate may lead to additional hemolysis. This study aimed at elucidating the relationship between adjusting interval of a blood pump's impeller speed and the hemolysis index. By comparing simulation results with P-Q characteristic curves of the blood pump measured by experiments, the accuracy of the blood pump flow field simulation model was confirmed. In this study, a drainage tube was employed as the device analogous to an intravascular micro-axial blood pump for achieving similar shear stress levels and residence times. The hemolysis finite element prediction method based on a power-law model was validated through hemolysis testing of porcine blood flow through the drainage tube. The validated models were subsequently utilized to investigate the impact of impeller speed adjusting intervals on hemolysis in the blood pump. Compared to steady flow, the results demonstrate that the hemolysis index increased to 6.3% when changing the blood pump flow rate from 2 L/min to 2.5 L/min by adjusting the impeller speed within 0.072 s. An adjustment time of impeller speed longer than 0.072 s can avoid extra hemolysis when adjusting the intravascular micro-axial blood pump flow rate from 2 L/min to 2.5 L/min.

Joint Modeling of Wind Speed and Power via a Nonparametric Approach

Power output from wind turbines is influenced by wind speed, but the traditional theoretical power curve approach introduces uncertainty into wind energy forecasting models. This is because it assumes a consistent power output for a given wind speed. To address this issue, a new nonparametric method has been proposed. It uses K-means clustering to estimate wind speed intervals, applies kernel density estimation (KDE) to establish the probability density function (PDF) for each interval and employs Monte Carlo simulation to predict power output based on the PDF. The method was tested using data from the MERRA-2 database, covering five wind farms in Brazil. The results showed that the new method outperformed the conventional estimation technique, improving estimates by an average of 47 to 49%. This study contributes by (i) proposing a new nonparametric method for modeling the relationship between wind speed and power; (ii) emphasizing the superiority of probabilistic modeling in capturing the natural variability in wind generation; (iii) demonstrating the benefits of temporally segregating data; (iv) highlighting how different wind farms within the same region can have distinct generation profiles due to environmental and technical factors; and (v) underscoring the significance and reliability of the data provided by the MERRA-2 database.

Parametric influence and optimization of the dehumidification performance of a variable-frequency dehumidifier

This paper optimizes a variable-frequency dehumidification system (VFDS) based on the principle of vapor compression refrigeration (VCR), wherein the compressor frequency and fan speed can be changed. The effect of the compressor frequency and fan speed on the operating parameters of the VFDS, including the evaporating temperature (T e), condensing temperature (T c), superheating (ΔT sh), and subcooling (ΔT sc), is studied experimentally under three operating conditions. The effect of variable-frequency on the dehumidification performance is analyzed at the mechanistic level through two dehumidification performance indicators, namely, the dehumidification capacity (DC) and dehumidification energy efficiency (DEE). ΔT sh is primarily controlled by the capillary throttling effect, which always starts to cross the “0” point when the fan speed is 600 rpm under different conditions. An increase in the compressor frequency leads to the opposite trend for DC and DEE, but their corresponding optimal fan speeds are relatively close. At 27 °C/60%, the optimal fan speed intervals with DC and DEE as single optimization objectives are both [600 rpm, 700 rpm]. However, at 30 °C/80%, the overall difference between the optimal fan speed intervals with DC and DEE as optimization objectives is 50–100 rpm. Importantly, the risk of frost at 18.3 °C/60% can lead to a significant extension of the optimal fan speed interval, which is [500 rpm, 900 rpm], making it more difficult to control. The concept of the dehumidification operating temperature difference (ΔT) is proposed to comprehensively evaluate the dehumidification capacity and as a control index for variable frequencies. The relationships among the optimal ΔT and the ambient dew point temperature (Td ) and compressor frequency are established through response surface methodology (RSM) with a maximum deviation of 5.97%. This work can provide an optimized variable-frequency solution for the VFDS and some guidance for the control logic development of the VFDS.

Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions

The widening gap between real-world vehicle energy consumption and modeled predictions can be attributed to discrepancies between actual ambient temperatures and assumptions made in laboratory tests. This study collected a detailed, extensive dataset comprising 25,640,666 records of real-world vehicle operating (speed, acceleration, etc.) and fuel consumption data alongside 124,938 hourly meteorological profiles (temperature, relative humidity, etc.). High-resolution fuel consumption rates (FCRs) based on ambient temperature were developed, and adjustment factors were introduced based on ambient temperature and vehicle specific power (VSP) binning. Fuel consumption factors (FCFs) were compared across different temperatures by incorporating VSP distributions and the adjusted FCRs, revealing larger FCFs at extreme temperatures compared to moderate ones. Fuel consumption inventories, both with and without temperature adjustments, were evaluated. The results indicated a 6% underestimation of annual fuel consumption in Beijing when disregarding temperature adjustments. The variation was observed across months (in July and August, underestimations can reach 11%) and across VSP bins (larger impact in low VSP bins). The relationship between FCR and ambient temperature is similar to a quadratic curve, with the lowest consumption occurring at 10 °C–20 °C. The FCF adjustment factor does not vary across speed intervals in cold weather and remains stable at approximately 1.15 at −10 °C, but it drops from 1.25 to 1 as speed increases from 5 to 100 km/h in hot weather. This study underscores the importance of considering ambient temperature in vehicle energy consumption modeling and the necessity of temperature-adjusted approaches for accurate fuel consumption estimations.

Abnormal Pavement Condition Detection with Vehicle Posture Data Considering Speed Variations.

Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors.

An improved interval prediction method for recurrence period wind speed

Based on the improved interval operation theory, an improved expression of the return period wind speed interval prediction is constructed by using an approximate first-order Taylor series expansion. According to the measured wind speed data in Beijing, Jinan, Nanjing, Wuxi, Shanghai and Shenzhen, the improved method and the traditional method are respectively used to predict the interval of the return period wind speed. Furthermore, the interval results predicted by the improved method and the traditional method are compared and analyzed under the same confidence level. Results show that the improved method has good applicability for different parameter estimation methods under the condition of certain extreme value distribution model, and the interval prediction results of the return period wind speed are basically stable. Compared with the interval results predicted by the traditional method, the interval predicted by the improved method is more likely to be close to or contain the exact solution of the return period wind speed, which has higher prediction accuracy. In addition, the calculation process of the improved method is relatively simple and can realize the simplified calculation of interval prediction.