Search Results (626)

The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage. Full article

Controlling the backfat thickness of sows within an appropriate range during different production stages helps to increase the number of pigs weaned per sow per year and ultimately enhances the economic benefit to the pig farm. To obtain the backfat thickness of sows automatically, a backfat thickness estimation method based on machine vision is proposed. First, the backfat thickness values and 3D images of the buttocks of 154 Landrace–Yorkshire crossbred sows were obtained using a veterinary ultrasound backfat meter and Azure Kinect DK camera. After preprocessing the 3D images utilizing Python 3.9.16 software, 10 external morphological parameters reflecting the area, width, height, and contour radius of the sow’s buttocks were extracted. The relationships between backfat thickness and external morphological parameters were analyzed in a randomly selected group of 100 sows. A significant positive correlation was observed between backfat thickness and buttock morphological parameters, with the Pearson coefficient for the fitted ellipse area achieving values up to 0.90. A backfat thickness estimation model was developed based on selected buttock feature parameters. The model’s generalization performance was evaluated using 54 additional sows that were not involved in the model development. The coefficient of determination (R²) between the estimated and actual backfat thicknesses was 0.8923, with a mean absolute error (MAE) of 1.23 mm and a mean absolute percentage error (MAPE) of 5.73%. These metrics indicate that the model can meet production requirements, and the proposed technique offers improved estimation accuracy compared to existing methods. Ultimately, a backfat thickness automatic estimation system was developed using LabVIEW 2023 Q1 (64-bit) software. This research helps to address the cumbersome process of measuring sow backfat thickness and promotes the automation of sow farms. Full article

During the execution of autonomous tasks within sheltered space environments, unmanned vehicles demand highly precise and seamless continuous positioning capabilities. While the existing visual–inertial-based positioning methods can provide accurate poses over short distances, they are prone to error accumulation. Conversely, radio-based positioning techniques could offer absolute position information, yet they encountered difficulties in sheltered space scenarios. Usually, three or more base stations were required for localization. To address these issues, a binocular vision/inertia/ultra-wideband (UWB) combined positioning method based on factor graph optimization was proposed. This approach incorporated UWB ranging and positioning information into the visual–inertia system. Based on a sliding window, the joint nonlinear optimization of multi-source data, including IMU measurements, visual features, as well as UWB ranging and positioning information, was accomplished. Relying on visual inertial odometry, this methodology enabled autonomous positioning without the prerequisite for prior scene knowledge. When UWB base stations were available in the environment, their distance measurements or positioning information could be employed to institute global pose constraints in combination with visual–inertial odometry data. Through the joint optimization of UWB distance or positioning measurements and visual–inertial odometry data, the proposed method precisely ascertained the vehicle’s position and effectively mitigated accumulated errors. The experimental results indicated that the positioning error of the proposed method was reduced by 51.4% compared to the traditional method, thereby fulfilling the requirements for the precise autonomous navigation of unmanned vehicles in sheltered space. Full article

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors. Full article

Regarding the decision to opt for vehicles with electric propulsion systems to achieve a sustainable future, much research has focused on the electrification of passenger cars, since this class of vehicles is the largest contributor of greenhouse gas emissions in the transportation sector. The purpose of this paper is to assess the energy performance of an electric vehicle used as a taxi in Loja, Ecuador, an intermediate Andean city, using a model-driven approach. Data acquisition was performed through the OBDII port of the KIA SOUL EV for 24 days and the variable mass of the vehicle was recorded as a function of the number of passengers; the effects of road gradient were also considered. The energy performance of the vehicle was simulated by developing an analytical model in MATLAB/Simulink. An average measured battery performance of 8.49 ± 1.4 km/kWh per day was obtained, where the actual energy regenerated was 31.2 ± 1.5%. To validate the proposed model, the results of the daily energy performance estimated with the simulation were compared with those measured in real driving conditions. The results demonstrated a Pearson correlation coefficient of 0.93, indicating a strong positive linear dependence between the variables. In addition, a coefficient of determination of 0.86 and a mean absolute percentage error of 3.35% were obtained, suggesting that the model has a satisfactory predictive capacity for energy performance. Full article

Currently, many digital elevation models (DEMs) are derived from airborne LiDAR data acquisition flights. The vertical accuracy of both products has typically been evaluated using methods based on randomly sampled control points. However, due to the superficial nature of the DEM, logic suggests that it is more appropriate to use a superficial object as an evaluation and control element, that is, a “control surface” or “control patch”. Our approach proposes a method for obtaining each patch from a georeferenced point cloud (PC) measured with a terrestrial laser scanner (TLS). In order to reduce the dilution of precision due to very acute angles of incidence that occur between the terrain and the scanner′s rays when it is stationed on a conventional tripod, a system has been created that allows the scanner to be placed face down at a height of up to 7 m. Stationing the scanner at that height also has the advantage of reducing shadow areas in the presence of possible obstacles. In our experiment, the final result is an 18 m × 18 m PC patch which, after resampling, can be transformed into a high-density (10,000 points/m²) and high-quality (absolute positional uncertainty < 0.05 m) DEM patch, that is, with a regular mesh format. This DEM patch can be used as the ground truth to assess the surface accuracy of DEMs (DEM format) or airborne LiDAR data acquisition flights (PC format). Full article

Coal pillar dams are affected by mining disturbance, which threatens the efficient operation of the underground reservoir. To study the deformation behaviors and failure mechanism of coal pillars under mining disturbance, an acoustic emission (AE) system and a deformation field system were applied to conduct uniaxial compression tests at various displacement rates. The AE characteristics and deformation field evolution of coal were investigated, and the microfailure mechanism was identified. The result shows that the deformation field evolutions are the same under various displacement rates. The increment of accumulated absolute energy near the peak stress rises with the displacement rates. The increase rate of the mean vertical displacement is positively correlated with the displacement rate. The coefficient of variation (C_V) of the deformation field can be applied to identify the deformation behaviors of coal and shows the fluctuate–slow increase–rapid increase trend. The distribution ranges of AF (count/duration) and RA (rise time/amplitude) are mainly 0–750 kHz and 0–700 μs/dB. The microfailure mechanism is mainly tensile failure and is accompanied by some shear failure. The percentage of shear failure increases with the increase in the displacement rate. The result provides a reference for the design and stability evaluation of the underground reservoir. Full article

Background and Objectives: Patients with chronic kidney disease (CKD) are at increased risk of severe COVID-19 outcomes due to their compromised immune systems and chronic inflammatory state. This study aimed to evaluate and compare the inflammatory status of COVID-19 patients with CKD, stratified by creatinine clearance (CrCl) levels: CrCl < 30 mL/min, CrCl 30–60 mL/min, and CrCl > 60 mL/min. Multiple inflammatory scores combining laboratory parameters were assessed, including novel scores and established indices. Methods: In this retrospective cohort study, 223 patients admitted with confirmed COVID-19 were included and divided into three groups based on CrCl levels: CrCl < 30 (n = 41), CrCl 30–60 (n = 78), and CrCl > 60 (n = 104). Laboratory parameters including C-reactive protein (CRP), interleukin-6 (IL-6), neutrophil-to-lymphocyte ratio (NLR), ferritin, platelet count, absolute neutrophil count (ANC), absolute lymphocyte count (ALC), and serum albumin were collected. Multiple inflammatory scores were calculated, including inflammation scores (IS1–IS4), the systemic inflammatory index (SII), the C-reactive protein-to-albumin ratio (CAR), the lymphocyte-to-C-reactive protein ratio (LCR), and the prognostic nutritional index (PNI). Statistical analyses were performed to compare inflammatory scores among groups and assess correlations with clinical outcomes. Results: The CrCl < 30 group exhibited significantly higher levels of inflammatory markers and inflammatory scores compared with the other groups (p < 0.001). Among the additional scores, CAR and SII were significantly elevated in patients with lower CrCl levels, while LCR and PNI were decreased. CAR showed a strong positive correlation with COVID-19 severity (r = 0.65, p < 0.001), and PNI was inversely correlated with mortality (r = −0.58, p < 0.001). Multivariate regression analysis indicated that lower CrCl levels, higher IS3 and CAR, and lower PNI were independent predictors of severe COVID-19 outcomes. Conclusions: CKD patients with lower CrCl levels have an amplified inflammatory response during COVID-19 infection, as evidenced by elevated inflammatory scores. The additional inflammatory scores, particularly CAR and PNI, may serve as valuable tools for risk stratification and management of COVID-19 in CKD patients. Early identification of patients with high CAR and low PNI could improve clinical outcomes through timely therapeutic interventions. Full article

The Internet of Things (IoT) has emerged as a popular topic in both industrial and academic research. IoT devices are often equipped with rapid response capabilities to ensure seamless communication and interoperability, showing significant potential for IoT-based maritime traffic monitoring and navigation safety tasks. However, this also presents major challenges for maritime surveillance systems. The diversity of IoT devices and variability in collected data are substantial. Visual image ship detection is crucial for maritime tasks, yet it must contend with environmental challenges such as haze and waves that can obscure ship details. To address these challenges, we propose an adaptive query selection transformer (AQSFormer) that utilizes two-dimensional rotational position encoding for absolute positioning and integrates relative positions into the self-attention mechanism to overcome insensitivity to the position. Additionally, the introduced deformable attention module focuses on ship edges, enhancing the feature space resolution. The adaptive query selection module ensures a high recall rate and a high end-to-end processing efficiency. Our method improves the mean average precision to 0.779 and achieves a processing speed of 31.3 frames per second, significantly enhancing both the real-time capabilities and accuracy, proving its effectiveness in ship detection. Full article

In this paper, fractional calculus is used to develop a generalized fractional dynamic model of an electrohydraulic system composed of a servo valve and a hydraulic cylinder, where a fractional position controller $P{I}^{\gamma }{D}^{\mu }$ is proposed for minimizing the performance index according to the integral of the time-weighted absolute error (ITAE). First, the general mathematical equations of the cylinder and servo valve are used to obtain the transfer functions in fractional order by applying Caputo’s definition and a Laplace transform. Then, through a block diagram of the closed-loop system without a controller, the fractional model is validated by comparing its performance concerning the integer-order electrohydraulic system model reported in the literature. Subsequently, a fractional PID controller is designed to control the cylinder position. This controller is included in the closed-loop system to determine the fractional exponents of the transfer functions of the servo valve, cylinder, and control, as well as to tune the controller gains, by using the ITAE objective function, with a comparison of the following: (1) the electrohydraulic system model in integer order and the controller in fractional order; (2) the electrohydraulic system model in fractional order and the controller in integer order; and (3) both the system model and the controller in fractional order. For each of the above alternatives, numerical simulations were carried out using MATLAB^®/Simulink^® R2023b and adding white noise as a perturbation. The results show that strategy (3), where electrohydraulic system and controller model are given in fractional order, develops the best performance because it generates the minimum value of ITAE. Full article

Image matching-based visual simultaneous localization and mapping (vSLAM) extracts low-level pixel features to reconstruct camera trajectories and maps through the epipolar geometry method. However, it fails to achieve correct trajectories and mapping when there are low-quality feature correspondences in several challenging environments. Although the RANSAC-based framework can enable better results, it is computationally inefficient and unstable in the presence of a large number of outliers. A Faster R-CNN learning-based semantic filter is proposed to explore the semantic information of inliers to remove low-quality correspondences, helping vSLAM localize accurately in our previous work. However, the semantic filter learning method generalizes low precision for low-level and dense texture-rich scenes, leading the semantic filter-based vSLAM to be unstable and have poor geometry estimation. In this paper, a GGCM-E-based semantic filter using YOLOv8 is proposed to address these problems. Firstly, the semantic patches of images are collected from the KITTI dataset, the TUM dataset provided by the Technical University of Munich, and real outdoor scenes. Secondly, the semantic patches are classified by our proposed GGCM-E descriptors to obtain the YOLOv8 neural network training dataset. Finally, several semantic filters for filtering low-level and dense texture-rich scenes are generated and combined into the ORB-SLAM3 system. Extensive experiments show that the semantic filter can detect and classify semantic levels of different scenes effectively, filtering low-level semantic scenes to improve the quality of correspondences, thus achieving accurate and robust trajectory reconstruction and mapping. For the challenging autonomous driving benchmark and real environments, the vSLAM system with respect to the GGCM-E-based semantic filter demonstrates its superiority regarding reducing the 3D position error, such that the absolute trajectory error is reduced by up to approximately 17.44%, showing its promise and good generalization. Full article

In this study, the design of experiments (DOE) method is used to find the optimum values of the tribological system in a 40–120 °C range with 0.1–1 wt% zirconia nanoadditives in a base oil. Significant factors were identified. The studied parameters include friction absolute integral, static friction, the wear scar diameter and the wear volume of the specimens. The measurements were carried out on a tribometer. The results were pre-estimated using statistical software; then, validation measurements were made using the estimated optimum point. The results show that the FAI value differed by 0.008, the COF value by 0.017, the WSD value by 4 μm and the WV value by 110,000 μm³. At 1 wt%, zirconia can have a positive effect at high temperatures. As temperatures increase, wear parameters decrease and friction values remain stable. Full article

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management. Full article

(1) Background: Imageless computer navigation systems have the potential to improve the accuracy of acetabular cup position in total hip arthroplasty (THA). Popular imageless navigation methods include locating the patient in a three-dimensional space (registration method) while using a baseline to angle the acetabular cup (reference plane). This study aims to compare the accuracy of different methods for determining postoperative acetabular cup positioning in THA via the direct anterior approach. (2) Methods: Fifty-one participants were recruited. Optical and inertial sensor imageless navigation systems were used simultaneously with three combinations of registration methods and reference planes: the anterior pelvic plane (APP), the anterior superior iliac spine (ASIS) and the table tilt (TT) method. Postoperative acetabular cup position, inclination, and anteversion were assessed using CT scans. (3) Results: For inclination, the mean absolute error (MAE) was lower using the TT method (2.4° ± 1.7°) compared to the ASIS (2.8° ± 1.7°, p = 0.17) and APP method (3.7° ± 2.1°, p < 0.001). For anteversion, the MAE was significantly lower for the TT method (2.4° ± 1.8°) in contrast to the ASIS (3.9° ± 3.2°, p = 0.005) and APP method (9.1° ± 6.2°, p < 0.001). (4) Conclusion: A functional reference plane is superior to an anatomic reference plane to accurately measure intraoperative acetabular cup inclination and anteversion in THA using inertial imageless navigation systems. Full article

This paper provides an in-depth analysis of a novel methodology to enhance the commissioning processes of robotic production lines in the automotive sector, with a particular emphasis on the implementation of offline programming (OLP) methods. The proposed innovative methodology, verified within the automotive industry, introduces a systematic, iterative process for calibrating and aligning the local user coordinate system (UCS) with high-precision external measurements, ensuring minimal discrepancy between simulated and actual robot paths. A significant contribution of this study is an original adjustment of the numerical algorithm applying a closed-form solution to the absolute orientation problem where unit quaternions are used to establish a UCS and evaluate positioning errors. The experimental validation study draws from 485 measurement datasets gathered across more than 300 robot stations, with each dataset comprising at least six measured point pairs, using readings from both internal robot positioning systems and a Leica AT403 laser tracker, aligned with nominal tooling values. This approach addresses discrepancies between simulated and actual environments, and our findings show an 83.51% success rate for direct implementation of simulated robot path programs. This result underscores the effectiveness of the proposed method and demonstrates the accuracy of the developed numerical algorithm, providing a reliable measure of real OLP implementation effectiveness in the automotive sector. This method further streamlines multi-robot station setup through centralized UCS alignment, significantly reducing commissioning time and enhancing efficiency in both the assembly and commissioning stages of robotized production lines. The proposed methodology facilitates precise alignment in the commissioning stage and highlights the need for synchronized simulation updates, robust offline programming practices, and regular kinematic error verification to further enhance OLP accuracy. Full article