Capacity Model Research Articles

RJ-TinyViT: an efficient vision transformer for red jujube defect classification.

Compared to the surface defect detection of industrial products produced according to specified processes, the detection of surface defects in naturally grown red jujubes poses unique and significant challenges for researchers. The high diversity of surface defects, subtle distinctions from the background, low contrast, varying scales, and the presence of high levels of noise in images are among the factors that greatly amplify the complexity of defect detection tasks. Existing methods show some deficiencies in addressing these issues, mainly due to insufficient feature extraction capabilities and overly complex network structures, leading to limitations in model efficiency and practical application performance. To tackle the challenges associated with red jujube surface defect detection, this study proposes an optimized Tiny Vision Transformer (TinyViT) network structure, named RJ-TinyViT. This method refines the TinyViT-5m network structure to reduce network burden and introduces an improved Multi-Kernel Block (MK Block) and an improved Mobile Inverted Bottleneck Convolution Block (MBConv Block) to enhance feature extraction capabilities. Additionally, we have integrated the Coordinate Attention (CA) module to enhance the model's capacity for recognizing and focusing on features of surface defects on red jujubes. Experimental results show that RJ-TinyViT achieved a classification accuracy of 93.38%, marking an improvement of 1.84% over the original TinyViT network. At the same time, its Floating-point Operations (FLOPs) and Parameters (Params) were reduced to 58.97% and 39.84% of the original TinyViT network, respectively. These results not only demonstrate that RJ-TinyViT achieves model lightweighting while maintaining high accuracy but also highlight its value in practical industrial applications.

Influence of Compressive Strength and Steel-Tube Thickness on Axial Compression Performance of Ultra-High-Performance Concrete-Filled Stainless-Steel Tube Columns Containing Coarse Aggregates

With the increasing use of concrete-filled steel tubular (CFST) structures, exposed steel tubes are highly susceptible to corrosion, posing potential safety hazards. This study innovatively proposes the use of stainless-steel tubes instead of traditional carbon-steel ones and introduces coarse aggregates into ultra-high-performance concrete (UHPC), forming a coarse aggregate-incorporated ultra-high-performance concrete-filled stainless-steel tube (CA-UFSST). The inclusion of coarse aggregates not only compensates for the shortcomings of UHPC but also enhances the overall mechanical performance of the composite structure. Twenty sets of specimens were designed to analyze the influence of four parameters, including the coarse aggregate content, compressive strength, stainless-steel-tube thickness, and stainless-steel type on the axial compression performance of UHPC. The experimental results indicate that the failure mode of UHPC is related to the confinement ratio. As the confinement ratio increases, the failure mode transitions from shear failure to bulging failure. The addition of coarse aggregates enhances the stiffness of the specimens. Furthermore, this paper discusses the applicability of six current codes in predicting the bearing capacity of CA-UFSST and finds that the European code exhibits the best prediction performance. However, as the confinement ratio increases, the prediction accuracy becomes notably insufficient. Therefore, it is necessary to establish a more accurate calculation model for the axial compression bearing capacity.

Spatial-temporal heterogeneity and controlling factors of evapotranspiration in Nujiang River Basin based on Variable Infiltration Capacity (VIC) model

Spatial-temporal heterogeneity and controlling factors of evapotranspiration in Nujiang River Basin based on Variable Infiltration Capacity (VIC) model

The Distributed Xin’anjiang Model Incorporating the Analytic Solution of the Storage Capacity Under Unsteady-State Conditions

Developing a functional linkage between hydrological variables and easily accessible terrain and soil information is a novel concept for distributed hydrological models. This approach aims to address limitations imposed by data scarcity and high computational demands. The model hypothesizes that the relationship between the evaporation flux and the absolute value of the matric potential follows a power exponential pattern. Analytic solutions for the groundwater depth, the evaporation capacity, and the storage capacity are derived with respect to the topographic index, considering the relationship between the groundwater depth and the topographic index and the influence of setting off. Subsequently, a distributed Xin’anjiang Model using the analytic solution of the storage capacity under unsteady-state conditions is constructed. This new model is employed to simulate soil moisture and discharge in the Tarrawarra Watershed. The simulation results for soil moisture and discharge are compared with those from the Storage Capacity Model and the DHSVM. Additionally, the computational speeds of all three models are compared. The findings indicate that the simulation accuracy of the new model for soil moisture and discharge surpasses that of the Storage Capacity Model and the DHSVM. Meanwhile, the computational speed of the new model is significantly faster than the DHSVM and slightly slower than the Storage Capacity Model. It offers a balance between computational efficiency, predictive accuracy, and physical mechanism representation. The data requirements of the new model are minimal and easy to procure, and it requires less computational effort. Moreover, it accurately captures the spatial and temporal dynamics of soil moisture and the discharge process of the watershed.

Deformation and Fracture Mechanisms of Thick Hard Roofs in Upward Mining Coalfaces: A Mechanical Model and Its Validation

The safety and efficiency of underground coal mining are threatened by thick hard roofs characterized by large overhang areas, problematic spontaneous caving, and high dynamic load upon their breakage. In this study, a mechanical model of the bearing capacity of thick hard roofs in upward mining coalfaces associated with mining activities is built based on bending theories for beams with single generalized displacement and the elastic foundation beam theory. Using this method, we analyze the deformation and fracture mechanisms of a thick hard roof during upward mining. We further derive the mechanical equations of rotational angle, bending moment, shear force, and deflection of the free overhang and coal-bearing zone in the thick hard roof and an equation for calculating the limiting span. The mechanical behaviors of the thick hard roof bearing state are analyzed under different parameters. The results show that the foundation coefficient, roof thickness, and angle of upward mining have little influence on the roof bending moment but are positively correlated to the limiting span. Roof load and overhang length have a significant influence on the roof bending moment. They are negatively and positively correlated with the limiting span, respectively. Finally, a case study is performed on the Ш601 upward mining coalface in the Zhuzhuang Coal Mine. The distribution characteristics of the bending moment of the thick hard roof at different extraction stages are analyzed. At each stage, the limiting spans of the thick hard roof upon breaking were calculated as 13.18, 18.82, and 22.50 m, respectively, being close to the on-site measured periodic weighting lengths of 13.33, 19.33 m, and 22.67 m. This close fit proves the feasibility and accuracy of the developed mechanical model. The present study offers theoretical guidance for estimating the weighting length of thick hard roofs in coalfaces and for engineering technology control in similar scenarios.

Evidence for widespread thermal acclimation of canopy photosynthesis.

Plants acclimate to temperature by adjusting their photosynthetic capacity over weeks to months. However, most evidence for photosynthetic acclimation derives from leaf-scale experiments. Here we address the scarcity of evidence for canopy-scale photosynthetic acclimation by examining the correlation between maximum photosynthetic rates (Amax,2,000) and growth temperature ( ) across a range of concurrent temperatures and canopy foliage quantity, using data from >200 eddy covariance sites. We detect widespread thermal acclimation of canopy-scale photosynthesis, demonstrated by enhanced Amax,2,000 under higher , across flux sites with adequate water availability. A 14-day period is identified as the most relevant timescale for acclimation across all sites, with a range of 12-25 days for different plant functional types. The mean apparent thermal acclimation rate across all ecosystems is 0.41 (-0.38-1.04 for 5th-95th percentile range) µmol m-2 s-1 °C-1, with croplands showing the largest acclimation rates and grasslands the lowest. Incorporating an optimality-based prediction of leaf photosynthetic capacities into a biochemical photosynthesis model is shown to improve the representation of thermal acclimation. Our results underscore the critical need for enhanced understanding and modelling of canopy-scale photosynthetic capacity to accurately predict plant responses to warmer growing seasons.

An Artificial Intelligence Approach for Test-Free Identification of Sarcopenia.

The diagnosis of sarcopenia relies extensively on human and equipment resources and requires individuals to personally visit medical institutions. The objective of this study was to develop a test-free, self-assessable approach to identify sarcopenia by utilizing artificial intelligence techniques and representative real-world data. This multicentre study enrolled 11 661 middle-aged and older adults from a national survey initialized in 2011. Follow-up data from the baseline cohort collected in 2013 (n = 9403) and 2015 (n = 10 356) were used for validation. Sarcopenia was retrospectively diagnosed using the Asian Working Group for Sarcopenia 2019 framework. Baseline age, sex, height, weight and 20 functional capacity (FC)-related binary indices (activities of daily living = 6, instrumental activities of daily living = 5 and other FC indices = 9) were considered as predictors. Multiple machine learning (ML) models were trained and cross-validated using 70% of the baseline data to predict sarcopenia. The remaining 30% of the baseline data, along with two follow-up datasets (n = 9403 and n = 10 356, respectively), were used to assess model performance. The study included 5634 men and 6027 women (median age = 57.0 years). Sarcopenia was identified in 1288 (11.0%) individuals. Among the 20 FC indices, the running/jogging 1 km item showed the highest predictive value for sarcopenia (AUC [95%CI] = 0.633 [0.620-0.647]). From the various ML models assessed, a 24-variable gradient boosting classifier (GBC) model was selected. This GBC model demonstrated favourable performance in predicting sarcopenia in the holdout data (AUC [95%CI] = 0.831 [0.808-0.853], accuracy = 0.889, recall = 0.441, precision = 0.475, F1 score = 0.458, Kappa = 0.396 and Matthews correlation coefficient = 0.396). Further model validation on the temporal scale using two longitudinal datasets also demonstrated good performance (AUC [95%CI]: 0.833 [0.818-0.848] and 0.852 [0.840-0.865], respectively). The model's built-in feature importance ranking and the SHapley Additive exPlanations method revealed that lifting 5 kg and running/jogging 1 km were relatively important variables among the 20 FC items contributing to the model's predictive capacity, respectively. The calibration curve of the model indicated good agreement between predictions and actual observations (Hosmer and Lemeshow p = 0.501, 0.451 and 0.374 for the three test sets, respectively), and decision curve analysis supported its clinical usefulness. The model was implemented as an online web application and exported as a deployable binary file, allowing for flexible, individualized risk assessment. We developed an artificial intelligence model that can assist in the identification of sarcopenia, particularly in settings lacking the necessary resources for a comprehensive diagnosis. These findings offer potential for improving decision-making and facilitating the development of novel management strategies of sarcopenia.

Study of the Stress Distribution and a Calculation Model for the Local Bearing Capacity of Concrete Under Headed Bars

In order to obtain the calculation model for the local bearing capacity of concrete Fl under two headed bars, six pull-out concrete specimens were prepared. The effect of the net distance between two headed bars c on Fl was mainly investigated. The test results show that the local bearing capacity of concrete would first decrease and then increase with the increase in c, and the boundary point of the two stages was c = 40 mm. It is determined that the stress transformation from the local compression state to the axial compression state in the stress distribution model is characterized by the variation rate of the vertical stress under individual headed bars, which would infinitely approach a constant value. The constant value under individual headed bars is used as the limit value. The height of the vertical stress under two headed bars is modified, and then the height of the tensile region of the specimen with different c values is determined. Combined with the experimental phenomena and the results, two stages of calculation models are established, respectively: the integral calculation model and the individual calculation model. The integral calculation model focuses on the interaction of the compression region under two headed bars. The individual calculation model mainly focuses on the interaction of the tensile region under two headed bars. The calculation equations considering the influence of the height of the tensile region are established. Two groups of similar test data regarding the local bearing capacity were collected and verified as the integral calculation model and the individual calculation model. The average value of the ratio between the test value and the calculated value is 1.057 and 1.061, the standard deviation is 0.153 and 0.091, and the coefficient of variation is 0.055 and 0.086. It is proved that the calculation model proposed in this paper has a certain accuracy. It can provide a reference for calculating the local bearing capacity of concrete under multiple headed bars.

Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images.

Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data. Moreover, existing object detection algorithms have a large number of parameters, posing a challenge for deployment on drones with limited hardware resources. We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. Firstly, we propose that the Reparameterized Multi-scale Inverted Blocks (RepNIBMS) module is implemented to replace the C2f module of the Yolov8n backbone extraction network to enhance the information extraction capability of small objects. Secondly, a cross-level multi-scale feature fusion structure, wave feature pyramid network (WFPN), is designed to enhance the model's capacity to integrate spatial and semantic information. Meanwhile, a small-object detection head is incorporated to augment the model's ability to identify small objects. Finally, a tri-focal loss function is proposed to address the issue of imbalanced samples in aerial images in a straightforward and effective manner. In the VisDrone2019 test set, when the input size is uniformly 640 × 640 pixels, the parameters of ESOD-YOLO are 4.46 M, and the average mean accuracy of detection reaches 29.3%, which is 3.6% higher than the baseline method YOLOv8n. Compared with other detection methods, it also achieves higher detection accuracy with lower parameters.

Load-bearing characteristics of surface conductor in deepwater gas hydrate formations

The surface conductor is the first structural casing in deepwater natural gas hydrate (NGH) development, bearing the top load while suspending the casings of various layers. NGH decomposition leads to formation settlement, changing the mechanical properties of the formation and reducing the bearing capacity of the surface conductor, threatening the safety and stability of the wellhead. Understanding the bearing characteristics of the surface conductor in the hydrate formation can guide the safe drilling operation in the field. By introducing the negative skin friction theory of pile foundations and based on conventional bearing capacity models, a method for calculating the bearing capacity of surface conductors in NGH formations was developed. Using an NGH drilling simulation apparatus, the accuracy of the bearing capacity theoretical model was verified, empirical coefficients under different conditions were obtained, and the influence of soil parameters, hydrate saturation, and decomposition temperature on the bearing capacity of surface conductors was quantified. The results indicated that compared to clay, sandy soils have higher porosity and significantly weakened strength after the decomposition of NGH; when the hydrate saturation in the formation is 20%, the reduction in bearing capacity of the surface conductor in sand exceeds 30%, and in clay soils, it decreases by 25% after complete decomposition of NGH; as the hydrate saturation increases, the reduction in the bearing capacity of surface conductors after decomposition becomes more significant. Verified through Experimentation, the error of the hydrate-bearing strata-bearing capacity model is around 10%. For short-term test production operations of NGH in water, the design depth for surface conductors is around 100 meters. These research results can provide a scientific theoretical basis for the design of conductor depth below the mud， and reduce operational risks.

Experimental and numerical study on behavior of UHPC-filled circular stainless steel tube stub columns under eccentric compression

Ultra-high performance concrete (UHPC) filled stainless steel tube (UFSST) is a newly proposed composite structure specifically designed to withstand harsh natural environments. To study the behavior of the UFSST column, 3 axial compression and 9 eccentric compression UFSST stub columns were tested respectively. And a comprehensive parametric analysis was conducted by employing a refine finite element model validated by the test data. The results show that the thickness (t) of stainless steel tubes is a key parameter affecting the eccentric compression performance of UFSST stub column. The increase in t can significantly improve the performance of UFSST stub columns, including the ultimate bearing capacity (Nu), ultimate bending moment (Mu), and ductility (DI). Finally, a modified computational model for the bearing capacity of UFST and UFSST stub columns under eccentric compression was proposed and showed a higher prediction accuracy. For UFST and UFSST stub columns, the prediction model yielded mean values of 0.99 and 1.00 for the ratio of predicted to experimental results, with standard deviations of 0.06 and 0.08, respectively.

The axial compression mechanical performance of CFRP-confined fully replaced nonnatural coal gangue aggregate columns after freeze[sbnd]thaw cycles

With the continuous mining of coal worldwide, the accumulation of coal gangue, a byproduct of coal production, has increasingly posed severe environmental challenges. To mitigate the environmental pollution caused by coal gangue, this paper proposes the use of processed coal gangue as a replacement for coarse aggregates in concrete. Additionally, carbon fiber-reinforced polymers (CFRP) are employed to confine these square concrete columns, with the aim of expanding the application of nonnatural coal gangue concrete in freezethaw environments. Taking the number of CFRP layers, number of freezethaw cycles, and concrete strength grade as variables, a total of 54 samples were designed. Experimental and theoretical analyses were conducted to investigate the mechanical behavior of CFRP-confined fully replaced nonnatural coal gangue aggregate (FNCGA) columns. The specific conclusions are as follows: (1) Under a freezethaw environment, the stressstrain behavior of the CFRP-confined FNCGA columns undergoes three stages: an elastic ascending stage, a plastic descending stage, and a plastic ascending stage. (2) When the number of freezethaw cycles increases from 100 to 300, the Poisson's ratio of the elastic stage of the sample exhibiting an upward trend. (3) For the three-layer CFRP confined sample, the volumetric strain is positive (in compression), and the bearing capacity of the sample exceeds that of the unconfined concrete by approximately 10%. (4) Compared with existing models and experimental results, the bearing capacity and stressstrain model proposed in this study have greater accuracy, enabling better simulation of the mechanical performance of CFRP-confined FNCGA columns.

Equivalent thickness method of CFRP in chloride ion diffusion equation for CFRP–strengthened RC members and its load capacity assessment method

Carbon fiber reinforced polymer (CFRP)–strengthened reinforced concrete (RC) structures exhibit resistance to chloride ion penetration. However, accurately quantifying this resistance and predicting the load capacity of CFRP–strengthened RC beams subjected to chloride ion erosion requires further investigation. This paper introduces two equivalent thickness methods for non-fully bonded CFRP within the chloride ion diffusion equation: first method assumes CFRP having a fixed equivalent thickness, while the second method allows for CFRP having a linearly decreasing equivalent thickness depending on the depth within the concrete. A model is established between the reduction factor of load capacity in CFRP–strengthened RC beams and the concentration of diffusing chloride ions. Based on experimental results from our research group regarding chloride ion erosion in CFRP–strengthened RC members, both the equivalent thickness method and the load capacity model for CFRP were evaluated. The findings indicate that the chloride ion concentration predicted by the second equivalent thickness method outperforms the first, with an error of 13.1%. Furthermore, this method accurately predicts the chloride ion concentration after 60 days of exposure, exhibiting an error of 7.0%. Additionally, employing the proposed load capacity model, derived from the second equivalent thickness method, the calculated load capacity of CFRP–strengthened RC members subjected to chloride ion erosion shows only a 5.2% deviation from experimental results. This study concludes that the equivalent thickness of non-fully bonded CFRP decreases linearly with depth in concrete and establishes a direct correlation between load capacity and chloride ion concentration, thereby offering more targeted characterization methods.

HTXRD based lattice thermal expansion and specific heat capacity modelling of C15-Fe2Zr Laves phase

Crystallographic phase pure C15-Fe2Zr was obtained through arc melting technique. X-ray Diffraction (XRD), scanning electron microscope (FESEM) along with energy-dispersive X-ray analysis (EDS) and differential scanning calorimetry (DSC)were employed to study the single phase in Fe-33.3at.% Zr alloy. Rietveld refinement of the high-temperature XRD patterns (from 298K-873 K) revealed the average linear and volumetric coefficients of thermal expansion of the alloys as αai= 0.3643 × 10−5 K−1 and αVi = 1.0916 × 10−5 K−1, respectively. The experimental heat capacity and enthalpy increment of the C15-Fe2Zr phase from 310K to 890K was modelled employing Quasi-harmonic Debye Grüneisen theoretical modelling by identifying the vibrational, electronic, anharmonic and magnetic contributions. Curie temperature(~625K) was determined from the magnetic contribution of the heat capacity.

LS-VIT: Vision Transformer for action recognition based on long and short-term temporal difference.

Over the past few years, a growing number of researchers have dedicated their efforts to focusing on temporal modeling. The advent of transformer-based methods has notably advanced the field of 2D image-based vision tasks. However, with respect to 3D video tasks such as action recognition, applying temporal transformations directly to video data significantly increases both computational and memory demands. This surge in resource consumption is due to the multiplication of data patches and the added complexity of self-aware computations. Accordingly, building efficient and precise 3D self-attentive models for video content represents as a major challenge for transformers. In our research, we introduce an Long and Short-term Temporal Difference Vision Transformer (LS-VIT). This method incorporates short-term motion details into images by weighting the difference across several consecutive frames, thereby equipping the original image with the ability to model short-term motions. Concurrently, we integrate a module designed to understand long-term motion details. This module enhances the model's capacity for long-term motion modeling by directly integrating temporal differences from various segments via motion excitation. Our thorough analysis confirms that the LS-VIT achieves high recognition accuracy across multiple benchmarks (e.g., UCF101, HMDB51, Kinetics-400). These research results indicate that LS-VIT has the potential for further optimization, which can improve real-time performance and action prediction capabilities.

Extrusion Parameters Optimization and Mechanical Properties of Bio-Polyamide 11-Based Biocomposites Reinforced with Short Basalt Fibers.

The increasing demand for sustainable materials in high-value applications, particularly in the automotive industry, has prompted the development of biocomposites based on renewable or recyclable matrices and natural fibers as reinforcements. In this context, this paper aimed to produce composites with improved mechanical and thermal properties (tensile, flexural, and heat deflection temperature) through an optimized process pathway using a biobased polyamide reinforced with short basalt fibers. This study emphasizes the critical impact of fiber length, matrix adhesion, and the variation in matrix properties with increasing fiber content. These factors influence the properties of short-fiber composites produced via primary processing using extrusion and shaped through injection molding. The aim of this work was to optimize extrusion conditions using a 1D simulation software to minimize excessive fiber fragmentation during the extrusion process. The predictive model's capacity to forecast fiber degradation and the extent of additional fiber breakage during extrusion was evaluated. Furthermore, the impact of injection molding on these conditions was investigated. Moreover, a comprehensive thermomechanical characterization of the composites, comprising 10%, 20%, and 30% fiber content, was carried out, focusing on the correlation with morphology and processing using SEM and micro-CT analyses. In particular, how the extrusion process parameters adopted can influence fiber breakage and how injection molding can influence the fiber orientation were investigated, highlighting their influence in determining the final mechanical properties of short fiber composites. By optimizing the process parameters, an increment with respect to bio-PA11 in the tensile strength of 38%, stiffness of 140%, and HDT of 77% compared to the matrix were obtained.

A Review of Expert Systems Integration in Signal Plan Optimisation

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

Quantitation of Rainfall Retention Capacity for Small Reservoirs Considering Spatial Soil Moisture

To realize the estimation of rainfall retention capacity for small reservoirs considering spatial soil moisture, a rainfall retention capacity model and its parameter schemes have been developed in this study. An iterative trial solution method considering potential rainfall and soil moisture for the model constructed was proposed for efficient computation. The rainfall retention capacity of 32 pilot small reservoirs located in ungauged basins of Hunan province was calculated starting from 21 August 2023. In addition, a continuous calculation was carried out from 1 August to 30 September 2023 using the proposed method for Heping reservoir. The results show that the Pearson’s correlation coefficients between rainfall retention capacity and available reservoir capacity and soil moisture, are 0.36 and −0.64, respectively. Using Heping reservoir as an example, this study effectively characterized the dynamic change in its rainfall retention capacity, which ranged from 123.6 mm to 68 mm in August 2023. The analysis indicates the rainfall retention capacity of the pilot small reservoirs calculated is reasonably related to the soil moisture, supporting risk visualization for small reservoirs within rain-affected regions. Furthermore, the impact of the antecedent precipitation on rainfall retention capacity can also be dynamically quantified in real time, which provides reference for the continuous management of small reservoirs.

A Study of Mixed Non-Motorized Traffic Flow Characteristics and Capacity Based on Multi-Source Video Data.

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of "last-mile" transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060-3297 bikes/(h·m) and a pure bicycle capacity of 1538-2460 bikes/(h·m). The density-flow model calculates a pure EV capacity of 2349-2897 bikes/(h·m) and a pure bicycle capacity of 1753-2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion.

Parameter Identification Method of a Double-Layer Supercapacitor by Using a Real Voltage Source

This article presents a new method for obtaining the electrical parameters of a supercapacitor (SC) modeled as a constant resistor in series with a capacitance that linearly varies with its internal voltage. This model provides sufficiently accurate results when the SC is subjected to rapid, short-term charging and discharging. In other methods described in the literature, the parameters are obtained by charging or discharging the SC with a constant current source of high value. In this study, the electrical parameters are calculated by charging or discharging the cell with a real constant voltage source (RVS) or by discharging the SC through a known and constant resistance. The calculation procedure requires the measurement of the cell voltage as a function of time. Two alternative estimation methods have been employed: the three-point method (3PM) and the least squares method (LSM). A series of experimental tests were conducted on cells from various manufacturers, with capacitances ranging from 150 F to 600 F. The laboratory measurements were then compared with the results obtained from theoretical models incorporating the parameters obtained for the variable capacity model. The results demonstrated that this straightforward procedure is capable of accurately characterizing the main branch of any SC.