Search Results (2,626)

Despite the great success in the global exploration and development of reef reservoirs, research on bioclastic shoals under reefs is still in its infancy. Bioclastic shoal reservoirs are very thin, with multiple vertical levels and fast lateral changes, which makes accurate prediction of the reservoir’s location much tougher. To further implement the reservoir distribution, under the guidance of sequence stratigraphy, the prediction of thin shoals under the control of an isochronous stratigraphic framework was established. Using the combination of spectrum shaping and F-X domain noise suppression techniques and utilizing the signal-to-noise ratio spectrum set as the reference, logging curve as supervision, and well seismic calibration and isochronal amplitude slicing as quality control, the seismic frequency band was extended, and the seismic data resolution and signal-to-noise ratio were improved. After frequency extension, the global optimal seismic automatic interpretation technique was used to construct an isochronal stratigraphic framework model. Through waveform facies-controlled inversion and waveform facies-controlled simulation techniques, the elastic properties of the shoal reservoir were obtained, from which the planar distribution of the reservoir was accurately predicted. The above methods were applied to the prediction of the bioclastic shoal reservoir in the lower submember of the Changxing formation in the Yuanba gas field (China). The plane distribution of bioclastic shoal in the first and second levels was identified, which provides a guideline for the prediction of thin shoal reservoirs. Full article

This paper presents a Compact Integrated Water–Land Survey System (CIWS), which combines a remote sensing camera and a LiDAR module, and proposes an innovative underwater topography retrieval technique based on this system. This technique utilizes high-precision water depth points obtained from LiDAR measurements as control points, and integrating them with the grayscale values from aerial photogrammetry images to construct a bathymetry retrieval model. This model can achieve large-scale bathymetric retrieval in shallow waters. Calibration of the UAV-mounted LiDAR system was conducted using laboratory and Dongjiang Bay marine calibration fields, with the results showing a laser depth measurement accuracy of up to 10 cm. Experimental tests near Miaowan Island demonstrated the generation of high-precision 3D seabed topographic maps for the South China Sea area using LiDAR depth data and remote sensing images. The study validates the feasibility and accuracy of this integrated scanning method for producing detailed 3D seabed topography models. Full article

Nonlinearity in sensor measurements reduces the sensor’s accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors. Full article

Many transportation agencies have deployed pan–tilt–zoom (PTZ) closed-circuit television (CCTV) cameras to monitor roadway conditions and coordinate traffic incident management (TIM), particularly in urbanized areas. Pre-programmed “presets” provide the ability to rapidly position a camera on regions of highways. However, camera views occasionally develop systematic deviations from their original presets due to a variety of factors, such as camera change-outs, routine maintenance, drive belt slippage, bracket movements, and even minor vehicle crashes into the camera support structures. Scheduled manual calibration is one way to systematically eliminate these positioning problems, but it is more desirable to develop automated techniques to detect and alert agencies of potential drift. This is particularly useful for agencies with large camera networks, often numbering in the 1000’s. This paper proposes a methodology using the mean Structured Similarity Index Measure (SSIM) to compare images for a current observation to a stored original image with identical PTZ coordinates. Analyzing images using the mean SSIM generates a single value, which is then aggregated every week to generate potential drift alerts. This methodology was applied to 2200 images from 49 cameras over a 12-month period, which generated less than 30 alerts that required manual validation to determine the confirmed drift detection rate. Approximately 57% of those alerts were confirmed to be camera drift. This paper concludes with the limitations of the methodology and future research opportunities to possibly increase alert accuracy in an active deployment. Full article

Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique’s response and the target analyte’s standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument’s response is dependent. The inverse equation is the opposite: the instrument’s response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument’s response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced. Full article

The detection and quantification of additives in tobacco products are critical for ensuring consumer safety and compliance with regulatory standards. Traditional analytical techniques, like gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), and others, although effective, suffer from drawbacks, including complex sample preparation, high costs, lengthy analysis times, and the requirement for skilled operators. This study addresses these challenges by evaluating the efficacy of mid-infrared (MIR) spectroscopy and near-IR (NIR) spectroscopy, coupled with multivariate analysis, as potential solutions for the detection and quantification of additives in tobacco products. So, a representative set of tobacco products was selected and spiked with the targeted additives, namely caffeine, menthol, glycerol, and cocoa. Multivariate analysis of MIR and NIR spectra consisted of principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) to classify samples based on targeted additives. Based on the unsupervised techniques (PCA and HCA), a distinction could be made between spiked and non-spiked samples for all four targeted additives based on both MIR and NIR spectral data. During supervised analysis, SIMCA achieved 87–100% classification accuracy for the different additives and for both spectroscopic techniques. PLS-DA models showed classification rates of 80% to 100%, also demonstrating robust performance. Regression studies, using PLS, showed that it is possible to effectively estimate the concentration levels of the targeted molecules. The results also highlight the necessity of optimizing data pretreatment for accurate quantification of the target additives. Overall, NIR spectroscopy combined with SIMCA provided the most accurate and robust classification models for all target molecules, indicating that it is the most effective single technique for this type of analysis. MIR, on the other hand, showed the overall best performance for quantitative estimation. Full article

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups. Full article

A low-cost laser-induced breakdown spectroscopy (LIBS) instrument equipped with a charge-coupled device (CCD) was tested in the atmospheric environment for the quantification of K, Ca, Mg, and Mn in some organo–mineral fertilizers, mineral P fertilizers, and rock fertilizers of various compositions and origins, using flame atomic absorption spectrometry (FAAS) as the reference technique. The correlation analysis performed between each CCD pixel and the corresponding element concentration measured by FAAS allowed to choose the most appropriate K, Ca, Mg and Mn emission lines for LIBS analysis. The normalization process applied to LIBS spectra to correct physical matrix effects and small fluctuations was able to increase the linear correlation of the calibration curves between LIBS data and FAAS data by an average of 0.15 points of the R-value for all elements of interest. The R values of calibration curves were 0.97, 0.96, 0.86 and 0.84, for K, Ca, Mg and Mn, respectively. The limits of detection (LOD) were 66 mg/kg (K), 35 mg/kg (Ca), 5.4 mg/kg (Mg) and 0.8 mg/kg (Mn) when using LIBS in the quantification model. The cross-validation (leave-one-out) analysis yielded an absolute average error of 12% (K), 21% (Ca), 8% (Mg) and 13% (Mn) when LIBS data were correlated to FAAS ones. These results showed that the calibration models used were close to the optimization limit and satisfactory for K, Ca, Mg, and Mn quantification in the fertilizers and rocks examined. Full article

This study examines the measurement of film thickness, curvature, and defects on the surface or inside of an optical element using a highly accurate and efficient method. This is essential to ensure their quality and performance. Existing methods are unable to simultaneously extract the three types of information: thickness, curvature, and defects. Spectral-domain optical coherence tomography (SD-OCT), a non-invasive imaging technique with imaging depths down to the millimeter scale, provides the possibility of detecting the optical element components’ parameters. In this paper, we propose an error correction model for compensating delay differences in A-scan, field curvature, and aberration to improve the accuracy of system fitting measurements using SD-OCT. During data processing, we use the histogram-equalized gray stretching (IAH-GS) method to deal with strong reflections in the thin film layers inside the optics using individual A-scan averages. In addition, we propose a window threshold cutoff algorithm to accurately identify defects and boundaries in OCT images. Finally, the system is capable of rapidly detecting the thickness and curvature of film layers in optical elements with a maximum measurement depth of 4.508 mm, a diameter of 15 × 15 mm, a resolution of 5.69 microns, and a sampling rate of 70 kHz. Measurements were performed on different standard optical elements to verify the accuracy and reliability of the proposed method. To the best of our knowledge, this is the first time that thickness, curvature, and defects of an optical film have been measured simultaneously, with a thickness measurement accuracy of 1.924 µm, and with a difference between the calibrated and nominal curvature measurements consistently within 1%. We believe that this research will greatly advance the use of OCT technology in the testing of optical thin films, thereby improving productivity and product quality. Full article

The present work is a relevant advance in the validation of the incremental step loading technique (ASTM F1624 standard) when applied to Small Punch tests (SPT) for the threshold load determination of medium- and high-strength steels in aggressive environments, as a novel alternative to conventional time-consuming tests under constant load. It completes previous works by the authors on this topic, extending a methodology to estimate the threshold stress from SPT tests in aggressive environments, covering the whole range of hardness marked by ASTM F1624 as the main goal. This is achieved by calibrating a model of the material’s hardness by the use of a coefficient in function of it. For this purpose, four medium- and high-strength steels of 33, 35, 50 and 60 HRC (Hardness Rockwell C) are exposed to three different cathodic polarization hydrogen embrittlement environments of 1, 5 and 10 mA/cm² in 1N H₂SO₄ acid electrolyte connected to a platinum anode. Threshold stresses in these circumstances are obtained by uniaxial specimens following ASTM F1624 and compared to their homologous threshold loads obtained by Small Punch tests according to the authors’ original methodology proposal. Finally, the aforementioned model, consisting of a correlation based on composing an elastic and a plastic part, is calibrated for a hardness ranging 33–60 HRC, this being the main original contribution of this work; the elastic part is dependent just on the elastic-to-plastic transition SPT load, while the plastic part is ruled by a material hardness-dependent coefficient. This technique supposes an advance in engineering tools, due to its applicability in situations of material shortage, such as in-service components, welded joints, local areas, complex geometries, small thicknesses, etc., often present in aerospace, automotive or oil–gas, among others. Full article

Biological warfare agents are infectious microorganisms or toxins capable of harming or killing humans. Francisella tularensis is a potential bioterrorism agent that is highly infectious, even at very low doses. Biosensors for biological warfare agents are simple yet reliable point-of-care analytical tools. Developing highly sensitive, reliable, and cost-effective label-free DNA biosensors poses significant challenges, particularly when utilizing traditional techniques such as fluorescence, electrochemical methods, and others. These challenges arise primarily due to the need for labeling, enzymes, or complex modifications, which can complicate the design and implementation of biosensors. In this study, we fabricated Graphene Quantum dot (GQD)-functionalized biosensors for highly sensitive label-free DNA detection. GQDs were immobilized on the surface of screen-printed gold electrodes via mercaptoacetic acid with a thiol group. The single-stranded DNA (ssDNA) probe was also immobilized on GQDs through strong π−π interactions. The ssDNA probe can hybridize with the ssDNA target and form double-stranded DNA, leading to a decrease in the effect of GQD but a positive shift associated with the increase in DNA concentration. The specificity of the developed system was observed with different microorganism target DNAs and up to three-base mismatches in the target DNA, effectively distinguishing the target DNA. The response time for the target DNA molecule is approximately 1010 s (17 min). Experimental steps were monitored using UV/Vis spectroscopy, Atomic Force Microscopy (AFM), and electrochemical techniques to confirm the successful fabrication of the biosensor. The detection limit can reach 0.1 nM, which is two–five orders of magnitude lower than previously reported methods. The biosensor also exhibits a good linear range from 10⁵ to 0.01 nM and has good specificity. The biosensor’s detection limit (LOD) was evaluated as 0.1 nM from the standard calibration curve, with a correlation coefficient of R² = 0.9712, showing a good linear range and specificity. Here, we demonstrate a cost-effective, GQD-based SPGE/F. tularensis DNA test suitable for portable electrochemical devices. This application provides good perspectives for point-of-care portable electrochemical devices that integrate sample processing and detection into a single cartridge without requiring a PCR before detection. Based on these results, it can be concluded that this is the first enzyme-free electrochemical DNA biosensor developed for the rapid and sensitive detection of F. tularensis, leveraging the nanoenzyme and catalytic properties of GQDs. Full article

Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate staves, compensating for natural variations and characteristics like dirt and saw-induced aberrations. These variations pose significant challenges for automatic inspection systems. Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. To enhance performance and accuracy, we employ a data augmentation strategy using cropping techniques to address intra-class variability in individual staves. (3) Results: Our approach significantly improves accuracy and reliability in classifying wood with irregular textures and heterogeneity. The use of explainable AI and model calibration offers a deeper understanding of the model’s decision-making process, ensuring robustness and transparency, and setting confidence thresholds for outputs. (4) Conclusions: The proposed system enhances the performance of automatic wood inspection technologies, providing a robust solution for industries requiring precise wood quality assessment, particularly in wine barrel production. Full article

A self-calibration method utilizing spherical constraints is proposed for calibration of robot end-effectors. The method establishes a mathematical model to account for both the geometric errors of the robot and the deformation errors of the end-effector. A nonlinear least-squares parameter identification technique based on spherical constraints is employed to achieve autonomous calibration of the end-effector. Contrasted with methodologies relying on point plane or distance constraints, this novel technique delivers superior positioning accuracy, streamlined operational procedures and enhanced efficiency. Both simulation and experimental validation confirm that the self-calibration method using spherical constraints improves the positioning accuracy of the robot end-effector from 3 mm to 0.3 mm, showing the effectiveness of the method. Full article

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied. Full article

Accurate and efficient estrus detection in sows is crucial in modern agricultural practices to ensure optimal reproductive health and successful breeding outcomes. A non-contact method using computer vision to detect a change in a sow’s vulva size holds great promise for automating and enhancing this critical process. However, achieving precise and reliable results depends heavily on maintaining a consistent camera distance during image capture. Variations in camera distance can lead to erroneous estrus estimations, potentially resulting in missed breeding opportunities or false positives. To address this challenge, we propose a robust six-step methodology, accompanied by three stages of evaluation. First, we carefully annotated masks around the vulva to ensure an accurate pixel perimeter calculation of its shape. Next, we meticulously identified keypoints on the sow’s vulva, which enabled precise tracking and analysis of its features. We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. By identifying the keypoints, we performed precise calculations of the Euclidean distances: first, between each labium (horizontal distance), and second, between the clitoris and the perineum (vertical distance). Additionally, by segmenting the vulva’s size, we gained valuable insights into its shape, which helped with performing precise perimeter measurements. Equally important was our effort to calibrate the camera using monocular depth estimation. This calibration helped establish a functional relationship between the measurements on the image (such as the distances between the labia and from the clitoris to the perineum, and the vulva perimeter) and the depth distance to the camera, which enabled accurate adjustments and calibration for our analysis. Lastly, we present a classification method for distinguishing between estrus and non-estrus states in subjects based on the pixel width, pixel length, and perimeter measurements. The method calculated the Euclidean distances between a new data point and reference points from two datasets: “estrus data” and “not estrus data”. Using custom distance functions, we computed the distances for each measurement dimension and aggregated them to determine the overall similarity. The classification process involved identifying the three nearest neighbors of the datasets and employing a majority voting mechanism to assign a label. A new data point was classified as “estrus” if the majority of the nearest neighbors were labeled as estrus; otherwise, it was classified as “non-estrus”. This method provided a robust approach for automated classification, which aided in more accurate and efficient detection of the estrus states. To validate our approach, we propose three evaluation stages. In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. Then, we provided a quantitative analysis and performance comparison, including a comparison between our previous U-Net model and our new YOLOv8 segmentation model. This comparison focused on each model’s performance in terms of accuracy and speed, which highlighted the advantages of our new approach. Lastly, we evaluated the estrus–not-estrus classification model by defining the confusion matrix. By using this comprehensive approach, we significantly enhanced the accuracy of estrus detection in sows while effectively mitigating human errors and resource wastage. The automation and optimization of this critical process hold the potential to revolutionize estrus detection in agriculture, which will contribute to improved reproductive health management and elevate breeding outcomes to new heights. Through extensive evaluation and experimentation, our research aimed to demonstrate the transformative capabilities of computer vision techniques, paving the way for more advanced and efficient practices in the agricultural domain. Full article