Search Results (2,205)

Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. The proposed methodology establishes a predictive baseline model of the system’s healthy behavior under normal operating conditions, enabling real-time detection of deviations between expected and actual performance. Faults such as string disconnections, module short-circuits, and shading effects have been identified using two key indicators: current error (Ec) and voltage error (Ev). By focusing on power losses as a fault indicator, this method provides high-accuracy fault detection without requiring extensive labeled data, a significant advantage for large-scale PV systems where data acquisition can be challenging. Additionally, a key contribution of this work is the identification and correction of faulty sensors, specifically pyranometer misalignment, which leads to inaccurate irradiation measurements and disrupts fault diagnosis. The approach ensures reliable input data for the predictive models, where RF achieved an R² of 0.99657 for current prediction and 0.99459 for power prediction, while KNN reached an R² of 0.99674 for voltage estimation, improving both the accuracy of fault detection and the system’s overall performance. The outlined approach was experimentally validated using real-world data from a 500 kWp grid-connected PV system in Ain El Melh, Algeria. The results demonstrate that this innovative method offers an efficient, scalable solution for real-time fault detection, enhancing the reliability of large PV systems while reducing maintenance costs. Full article

Optical window freeform surfaces have emerged as a critical research focus in advanced optical engineering owing to their extensive surface degrees of freedom. These surfaces enable the simultaneous correction of on-axis and off-axis aberrations while satisfying stringent requirements for high-performance, lightweight, and compact optical systems. In the initial metrological characterization of these surfaces, coordinate measuring machines (CMMs) are conventionally employed for target point cloud acquisition. However, the achievable measurement accuracy (>2 μm) inherently constrained by CMM precision imposes fundamental limitations for subsequent optical inspections requiring sub-micron to nanometer-level resolution. Meanwhile, although optical measurement methods can result in higher measurement accuracy, they also lead to an increase in costs and testing difficulties. To overcome these limitations, we propose an accelerated point cloud registration methodology based on point-to-triangulation distance estimation. In simulation, using optimal coordinate transformation enabled good capabilities for exceptional surface characterization: peak-to-valley (PV) surface error of 10⁻⁶ nm, residual error of 5 nm, and registration accuracy of log₁₀ (mm/°). Further, in the experiment, the PV surface error was reduced from 27.3 μm to 6.9 μm, equivalent to a reduction of 3.95 times. These results confirm that the point-to-triangulation distance approximation maintains sufficient fidelity to the nominal point-to-surface distance, thereby empirically validating the efficacy of our proposed methodology. Notably, compared with conventional 3D alignment methods, our novel 2D estimation registration approach with point-to-triangulation surface normal vectors demonstrates significant advantages in computational complexity, which achieved a 78% reduction from O(n³) to O(n) while maintaining sub-millisecond alignment times. We believe that the method has potential for use as a low-cost optical precision measurement in manufacturing technology. Full article

Landsat 8 Level 2 Collection 2 (L2C2) surface reflectance (SR) products are widely used in various scientific applications by the remote sensing community, where their accuracy is vital for reliable analysis. However, discrepancies have been observed at shorter wavelength bands, which can affect certain applications. This study investigates the root cause of these differences by analyzing the assumptions made in the Land Surface Reflectance Code (LaSRC), the atmospheric correction algorithm of Landsat 8, as currently implemented at United States Geological Survey Earth Resources Observation and Science (USGS EROS), and proposes a correction method. To quantify these discrepancies, ground truth SR measurements from the Radiometric Calibration Network (RadCalNet) and Arable Mark 2 sensors were compared with the Landsat 8 SR. Additionally, the surface pressure measurements from RadCalNet and the National Centers for Environmental Information (NCEI) were evaluated against the LaSRC-calculated surface pressure values. The findings reveal that the discrepancies arose from using a single scene center surface pressure for the entire Landsat 8 scene pixels. The pressure-related discrepancies were most pronounced in the coastal aerosol and blue bands, with greater deviations observed in regions where the elevation of the study area differed substantially from the scene center, such as Railroad Valley Playa (RVUS) and Baotao Sand (BSCN). To address this issue, an exponential correction model was developed, reducing the mean error in the coastal aerosol band for RVUS from 0.0226 to 0.0029 (about two units of reflectance), which can be substantial for dark vegetative and water targets. In the blue band, there is a smaller improvement in the mean error, from 0.0095 to −0.0032 (about half a unit of reflectance). For the green band, the reduction in error was much less due to the significantly lesser impact of aerosol on this band. Overall, this study underscores the need for a more precise estimation of surface pressure in LaSRC to enhance the reliability of Landsat 8 SR products in remote sensing applications. Full article

A common and simple estimate of variability is the sample range, which is the difference between the maximum and minimum values in the sample. While other measures of variability are preferred in most instances, process owners and operators regularly use range (R) control charts to monitor process variability. The center line and limits of the R charts use constants that are based on the first two moments (mean and variance) of the distribution of the range of normal random variables. Historically, the computation of moments requires the use of tabulated constants approximated using numerical integration. We provide exact results for the moments for sample sizes 2 through 5. For sample sizes from 6 to 1000, we used the differential correction method to find Chebyshev minimax rational-function approximations of the moments. The rational function we recommend for the mean (R-chart constant d₂) has a polynomial of order two in the numerator and six in the denominator and achieves a maximum error of 4.4 × 10⁻⁶. The function for the standard deviation (R-chart constant d₃) has a polynomial of order two in the numerator and seven in the denominator and achieves a maximum error of 1.5 × 10⁻⁵. The exact and approximate expressions eliminate the need for table lookup in the control chart design phase. Full article

This study investigates the ionospheric response over China during the geomagnetic storm that occurred on 1–2 December 2023. The data used include GPS measurements from the Crustal Movement Observation Network of China, BDS-GEO satellite data from IGS MEGX stations, [O]/[N₂] ratio information obtained by the TIMED/GUVI, and electron density (Ne) observations from Swarm satellites. The Prophet time series forecasting model is employed to detect ionospheric anomalies. VTEC variations reveal significant daytime increases in GNSS stations such as GAMG, URUM, and CMUM after the onset of the geomagnetic storm on 1 December, indicating a dayside positive ionospheric response primarily driven by prompt penetration electric fields (PPEF). In contrast, the stations JFNG and CKSV show negative responses, reflecting regional differences. The [O]/[N₂] ratio increased significantly in the southern region between 25°N and 40°N, suggesting that atmospheric gravity waves (AGWs) induced thermospheric compositional changes, which played a crucial role in the ionospheric disturbances. Ne observations from Swarm A and C satellites further confirmed that the intense ionospheric perturbations were consistent with changes in VTEC and [O]/[N₂], indicating the medium-scale traveling ionospheric disturbance was driven by atmospheric gravity waves. Precise point positioning (PPP) analysis reveals that ionospheric variations during the geomagnetic storm significantly impact GNSS positioning precision, with various effects across different stations. Station GAMG experienced disturbances in the U direction (vertical positioning error) at the onset of the storm but quickly stabilized; station JFNG showed significant fluctuations in the U direction around 13:00 UT; and station CKSV experienced similar fluctuations during the same period; station CMUM suffered minor disturbances in the U direction; while station URUM maintained relatively stable positioning throughout the storm, corresponding to steady VTEC variations. These findings demonstrate the substantial impact of ionospheric disturbances on GNSS positioning accuracy in southern and central China during the geomagnetic storm. This study reveals the complex and dynamic processes of ionospheric disturbances over China during the 1–2 December 2023 storm, highlighting the importance of ionospheric monitoring and high-precision positioning corrections during geomagnetic storms. The results provide scientific implications for improving GNSS positioning stability in mid- and low-latitude regions. Full article

We demonstrate the reconstruction of battery electrochemical impedance spectroscopy (EIS) curves from time-domain pulse testing and the distribution of relaxation times (DRT) analysis. In the proposed approach, the DRT directly utilizes measured current data instead of simulated current patterns, thereby enhancing robustness against current variations and data anomalies. The method is demonstrated with a simulation, a single cylindrical battery cell experiment, and an experimental EIS of a completely assembled module of 448 cells. For the 3.7 kWh battery module, we applied a transient current pulse and analyzed the dynamic voltage responses. The EIS curves were reconstructed with DRT and compared to experiments across different states of charge (SoC). The experimental EIS data were corrected by a multistep calibration workflow in a frequency range from 50 mHz to 1 kHz, achieving error corrections of up to 80% at 1 kHz. The reconstructed impedances from the pulse test data are in good agreement with the EIS experiments in a broad frequency range, delivering relevant electrochemical information including the ohmic resistance and dynamic time constants of a battery module and its corresponding submodules. With the proposed workflow, rapid pulse tests can be used for extracting electrochemical information faster than standard EIS, with a 67% reduction in measurement time. This time-domain pulsing approach provides an alternative to EIS characterization, making it particularly valuable for battery monitoring, the classification of battery packs upon their return to the manufacturer, second-life applications, and recycling. Full article

Determining the sea surface height using Global Navigation Satellite System (GNSS) buoys is an important method for satellite altimetry calibration. The buoys observe the absolute height of the sea surface using GNSS positioning technology, which is then used to correct the systematic deviation of the altimeter of the orbiting satellite. Due to the challenging observational conditions, such as significant multipath errors in GNSS code observation and complex variations in buoy position and attitude, gross errors in GNSS buoy positioning reduce the accuracy and stability of the calculated sea surface heights. To accurately detect and remove these gross errors from GNSS coordinate time series, the complementary ensemble empirical mode decomposition (CEEMD) method and the interquartile range (IQR) method were adopted to enhance the accuracy and stability of GNSS sea surface altimetry. Firstly, the raw GNSS sequential coordinate series are decomposed into main terms, such as trend contents and periodic contents, and high-frequency noise terms using the CEEMD method. Subsequently, the high-frequency noise terms of the GNSS coordinate series are regarded as the residual sequences, which are used to detect gross errors using the IQR method. This approach, which integrates the CEEMD and IQR methods, was named CEEMD-IQR and enhances the ability of the traditional IQR method to detect subtle gross errors in GNSS coordinate time series. The results indicated that the CEEMD-IQR method effectively detected gross errors in offshore GNSS coordinate time series using GNSS buoys, presenting a significant enhancement in the gross error detection rate of at least 35.3% and providing a “clean” time series for sea level measurements. The resulting GNSS sea surface altimetry accuracy was found to be better than 1.51 cm. Full article

Traditional remote sensing techniques face notable limitations in accurately estimating forest canopy height. Optical data often suffer from vegetation occlusion, while radar systems, though capable of penetrating foliage, show reduced accuracy in complex terrains. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne LiDAR mission, offers high-resolution measurements that address these challenges. However, the complexity of waveform processing and the influence of geolocation uncertainty demand rigorous assessment. This study employs GEDI Version 2.0 data, which demonstrates substantial improvement in geolocation accuracy compared to Version 1.0, and integrates airborne laser scanning (ALS) data from the Changbai Mountain forest region to simulate GEDI waveforms. A Monte Carlo-based approach was used to quantify and correct geolocation offsets, resulting in a reduction in the average relative error (defined as the mean of the absolute differences between estimated and reference canopy heights divided by the reference values) in canopy height estimates from 11.92% to 8.55%. Compared to traditional correction strategies, this method demonstrates stronger robustness in heterogeneous forest conditions. The findings emphasize the effectiveness of simulation-based optimization in enhancing the geolocation accuracy and canopy height retrieval reliability of GEDI data, especially in complex terrain environments. This contributes to more precise global forest structure assessments and provides a methodological foundation for future improvements in spaceborne LiDAR applications. Full article

The Façade Opening Rate (FOR) reflects a building’s capacity to withstand seismic loads, serving as a crucial foundation for seismic risk assessment and management. However, FOR data are often outdated or nonexistent in rural areas, which are particularly vulnerable to earthquake damage. This paper proposes a practical framework for estimating FORs from real-scene 3D models derived from UAV photogrammetry. The framework begins by extracting individual buildings from 3D models using annotated roof outlines. The known edges of the roof outline are then utilized to sample and generate orthogonally projected front-view images for each building façade, enabling undistorted area measurements. Next, a modified convolutional neural network is employed to automatically extract opening areas (windows and doors) from the front-view façade images. To enhance the accuracy of opening area extraction, a vanishing point correction method is applied to open-source street-view samples, aligning their style with the front-view images and leveraging street-view-labeled samples. Finally, the FOR is estimated for each building by extracting the façade wall area through simple spatial analysis. Results on two test datasets show that the proposed method achieves high accuracy in FOR estimation. Regarding the mean relative error (MRE), a critical evaluation metric which measures the relative difference between the estimated FOR and its ground truth, the proposed method outperforms the closest baseline by 5%. Moreover, on the façade images we generated, the MRE of our method was improve by 1% and 2% compared to state-of-the-art segmentation methods. These results demonstrate the effectiveness of our framework in accurately estimating FORs and highlight its potential for improving seismic risk assessment in rural areas. Full article

This study presents a high-precision warehouse material level monitoring method that integrates millimeter-wave radar with 3D surface reconstruction to address the limitations of LiDAR, which is highly susceptible to dust and haze interference in complex storage environments. The proposed method employs Chirp-Z Transform (CZT) super-resolution processing to enhance spectral resolution and measurement accuracy. To improve grain surface identification, an anomalous signal correction method based on angle–range feature fusion is introduced, mitigating errors caused by weak reflections and multipath effects. The point cloud data acquired by the radar undergo denoising, smoothing, and enhancement using statistical filtering, Moving Least Squares (MLS) smoothing, and bicubic spline interpolation to ensure data continuity and accuracy. A Poisson Surface Reconstruction algorithm is then applied to generate a continuous 3D model of the grain heap. The vector triple product method is used to estimate grain volume. Experimental results show a reconstruction volume error within 3%, demonstrating the method’s accuracy, robustness, and adaptability. The reconstructed surface accurately represents grain heap geometry, making this approach well suited for real-time warehouse monitoring and providing reliable support for material balance and intelligent storage management. Full article

This study examines the short- and long-term effects of various economic, environmental, and policy factors on renewable energy production (REP) in Saudi Arabia from 1990 to 2024, using the Autoregressive Distributed Lag (ARDL) approach and Vector Error Correction Model (VECM) techniques. The analysis focuses on fossil fuel consumption (FFC), renewable energy investment (REI), carbon emissions (CEs), energy prices (EPs), government policies (GPs), technological advancements (TAs), socioeconomic factors (SEFs), and economic growth (EG) as determinants of REP, measured as electricity generated from solar power sources in kilowatt-hours (kWh). Short-term findings reveal a positive momentum effect, where prior REP levels significantly influence current production, driven by factors such as learning by doing, economies of scale, and consistent policy support. However, FFC negatively impacts REP, highlighting resource competition and market dynamics favoring fossil fuels. Positive short-term influences include REI, CEs, EPs, GPs, TAs, SEFs, and EG, which collectively enhance renewable energy adoption through investments, technological innovation, policy incentives, and economic development. Long-term analysis underscores a strong negative relationship between FFC and REP, with a 7503-unit decline in REP associated with increased fossil fuel dependency. Conversely, REP benefits from REI, CEs, EPs, GPs, TAs, and EG, with significant contributions from technological advancements (3769-unit increase) and economic growth (9191-unit increase). However, SEFs exhibit a slight negative impact, suggesting that rapid urbanization and population growth may outpace renewable infrastructure development. Overall, the study highlights the complex interplay of factors shaping renewable energy production, emphasizing the importance of sustained investments, supportive policies, and technological innovation, while addressing challenges posed by fossil fuel reliance and socioeconomic pressures. These insights provide valuable implications for policymakers and stakeholders aiming to accelerate the transition to renewable energy in Saudi Arabia. Full article

Most existing mobile LIDAR measurement systems use a GNSS/INS combination method for attitude positioning. This method requires a constant GNSS signal to correct the IMU’s positioning and attitude. In the absence of GNSS signals, the IMU’s positioning accuracy rapidly deteriorates from the centimeter to sub-meter, or even meter levels. To address the positioning limitations of mobile measurement systems without GNSS signals, this paper presents a dual-mode mobile lidar measurement system that combines the GNSS/INS and INS/wheel speed sensor positioning methods, with a time-synchronization controller that automatically switches between the two modes to cope with the loss of GNSS signals. The system uses a high-precision quartz crystal oscillator to simulate the GNSS time data and converts them to an NEMA standard time signal and a PPS signal to synchronize each sensor. The experimental results of the system show that the trajectory error of the dual-mode mobile measurement system reaches 3 m in the X-direction within 600 S compared with that of the ordinary mobile measurement system with GNSS/INS integration mode, and the dual-mode mobile measurement system controls the trajectory error within 1 m, reduces the error in the Y-direction from 2 m to less than 1 m, and reduces the error in the Z-direction from 3 m to less than 2 m. The dual-mode mobile LiDAR measurement system is not only suitable for outdoor road measurement, but also can effectively correct the positioning and attitude errors in the environment without GNSS signals, such as underground and tunnel, showing significant advantages in a variety of measurement scenarios. Full article

By using clear vapor–liquid interface line images of the liquid inside the capillary, the measurement coordinate points of the vapor–liquid interface line were measured. A new method for measuring liquid contact angle has been proposed, which was used to calculate the actual coordinate points and fit the actual vapor–liquid interface line of the liquid. Finally, an angle measurement tool is used to measure the angle of the actual vapor–liquid interface line and obtain the actual contact angle of the liquid. Effectively reducing the influence of refraction on the contact angle by correcting the errors caused by the refractive index of different materials, it can be used for the precise measurement of the static contact angle of liquids. By measuring the static contact angle of the upper and lower liquid surfaces of the liquid column, it was found that the presence of refraction caused a difference of [1.84°, 5.61°] between the actual and measured values of the static contact angle. Full article

Accurate estimation of the Rate of Penetration (ROP) is essential for optimizing drilling operations, particularly in deep wells where traditional methods based on nominal depth measurements often fall short. These conventional approaches typically overlook dynamic influences such as drill string elongation caused by tension, hydraulic pressure, and thermal effects, leading to significant errors in ROP estimation. This study introduces a comprehensive method that integrates well depth correction and dynamic modeling of drill string elongation to enhance ROP accuracy. Furthermore, model-based filtering techniques are incorporated into a dynamic ROP estimation framework to improve real-time performance. Simulation results indicate that the combination of elongation corrections and filtering significantly reduces estimation errors and enhances reliability under varying drilling conditions. The proposed method provides a more accurate and robust ROP estimation framework, contributing to improved real-time monitoring and operational decision-making in drilling processes. Full article

The periodic rotation of the Ocean Color and Temperature Scanner (OCTS) introduces jitter errors in the HaiYang-3A (HY-3A) satellite, leading to internal geometric distortion in optical imagery and significant registration errors in multispectral images. These issues severely influence the application value of the optical data. To achieve near real-time compensation, a novel jitter error estimation and correction method based on multi-source attitude data fusion is proposed in this paper. By fusing the measurement data from star sensors and gyroscopes, satellite attitude parameters containing jitter errors are precisely resolved. The jitter component of the attitude parameter is extracted using the fitting method with the optimal sliding window. Then, the jitter error model is established using the least square solution and spectral characteristics. Subsequently, using the imaging geometric model and stable resampling, the optical remote sensing image with jitter distortion is corrected. Experimental results reveal a jitter frequency of 0.187 Hz, matching the OCTS rotation period, with yaw, roll, and pitch amplitudes quantified as 0.905”, 0.468”, and 1.668”, respectively. The registration accuracy of the multispectral images from the Coastal Zone Imager improved from 0.568 to 0.350 pixels. The time complexity is low with the single-layer linear traversal structure. The proposed method can achieve on-orbit near real-time processing and provide accurate attitude parameters for on-orbit geometric processing of optical satellite image data. Full article