Search Results (830)

The precise acquisition of water depth data in nearshore shallow waters bears considerable strategic significance for marine environmental monitoring, resource stewardship, navigational infrastructure development, and military security. Conventional bathymetric survey methodologies are constrained by their spatial and temporal limitations, thus failing to satisfy the requirements of large-scale, real-time surveillance. While satellite remote sensing technologies present a novel approach to water depth inversion in shallow waters, attaining high-precision inversion in nearshore areas characterized by elevated levels of suspended sediments and diminished transparency remains a formidable challenge. To tackle this issue, this study introduces an enhanced XGBoost model grounded in the Newton–Raphson optimizer (NRBO–XGBoost) and successfully applies it to water depth inversion investigations in the nearshore shallow waters of the Beibu Gulf. The research amalgamates Sentinel-2B multispectral imagery, nautical chart data, and in situ water depth measurements. By ingeniously integrating the Newton–Raphson optimizer with the XGBoost framework, the study realizes the automatic configuration of model training parameters, markedly elevating inversion accuracy. The findings reveal that the NRBO–XGBoost model attains a coefficient of determination (R²) of 0.85 when compared to nautical chart water depth data, alongside a scatter index (SI) of 21%, substantially surpassing conventional models. Additional validation analyses indicate that the model achieves a coefficient of determination (R²) of 0.86 with field-measured data, a mean absolute error (MAE) of 1.60 m, a root mean square error (RMSE) of 2.13 m, and a scatter index (SI) of 13%. Moreover, the model exhibits exceptional performance in extended applications within the waters of Zhanjiang Port (R² = 0.90), unequivocally affirming its dependability and practicality in intricate nearshore water environments. This study not only provides a fresh solution for remotely sensing water depth in complex nearshore water settings but also imparts valuable technical insights into the associated underwater surveys and marine resource exploitation. Full article

Background: The evolution of “omic” technologies, which measure all biological molecules of a specific type (e.g., genomics), has enabled rapid and cost-effective data acquisition, depending on the technique and sample size. This, however, generates new hurdles that need to be addressed and should be improved upon. This includes selecting the appropriate statistical test based on study design in a high-throughput manner. Methods: An automated statistical analysis pipeline for omic datasets that we coined STATom@ic (pronounced stat-o-matic) was developed in R programming language. Results: We developed an R package that enables statisticians, bioinformaticians, and scientists to perform assumption tests (e.g., normality and variance homogeneity) before selecting appropriate statistical tests. This analysis package can handle two-group and multiple-group comparisons. In addition, this R package can be used for many data formats including normalized counts (RNASeq) and spectral abundance (proteomics and metabolomics). STATom@ic has high precision but lower recall compared to DeSeq2. Conclusions: The STATom@ic R Package is a user-friendly stand-alone or add-on to current bioinformatic workflows that automatically performs appropriate statistical analysis based on the characteristics of the data. Full article

This study’s primary goal is to use computer vision and ultra-wideband (UWB) localisation techniques to automatically mark numerals in cow photos. In order to accomplish this, we created a UWB-based cow localisation system that involves installing tags on cow heads and placing several base stations throughout the farm. The system can determine the distance between each base station and the cow using wireless communication technology, which allows it to determine the cow’s current location coordinates. The study employed a neural network to train and optimise the ranging data gathered in the 1–20 m range in order to solve the issue of significant ranging errors in conventional UWB positioning systems. The experimental data indicates that the UWB positioning system’s unoptimized range error has an absolute mean of 0.18 m and a standard deviation of 0.047. However, when using a neural network-trained model, the ranging error is much decreased, with an absolute mean of 0.038 m and a standard deviation of 0.0079. The average root mean square error (RMSE) of the positioning coordinates is decreased to 0.043 m following the positioning computation utilising the optimised range data, greatly increasing the positioning accuracy. This study used the conventional camera shooting method for image acquisition. Following image acquisition, the system extracts the cow’s coordinate information from the image using a perspective transformation method. This allows for accurate cow identification and number labelling when compared to the location coordinates. According to the trial findings, this plan, which integrates computer vision and UWB positioning technologies, achieves high-precision cow labelling and placement in the optimised system and greatly raises the degree of automation and precise management in the farming process. This technology has many potential applications, particularly in the administration and surveillance of big dairy farms, and it offers a strong technical basis for precision farming. Full article

Addressing the challenges and significant risks associated with diagnosing faults in wind turbine yaw systems, along with the typically low diagnostic accuracy, this study introduces a Long Short-Term Memory (LSTM) neural network augmented by a self-attention mechanism (SAM) as a novel fault diagnosis technique for wind turbine yaw systems. The method integrates the automatic weighting capability of the self-attention mechanism on input features with the advantage of LSTM in processing time series data, thereby effectively capturing key information and long-term dependencies in the operating data of the yawing system. This combination enhances the accuracy of fault feature extraction to more accurately identify various types of fault modes within the yawing system. Six types of feature parameters are extracted from the raw data collected by the SCADA (Supervisory Control And Data Acquisition) system of the wind turbine and are utilized as inputs for the diagnostic model. These parameters are then fed into the self-attention–LSTM neural network model to diagnose the health status of the yaw system, including yaw bearing damage, yaw gearbox failure, yaw motor failure, and sensor failure. The experimental results demonstrate that the accuracy of LSTM fault diagnosis, when enhanced with the self-attention mechanism, can reach 98.67% with an appropriate amount of training samples, verifying its significant advantages in terms of accuracy and stability of fault diagnosis. The proposed fault diagnosis method exhibits a better model fitting effect, strong generalization ability, and high accuracy compared to other methods, providing robust support for the reliable operation and maintenance of wind turbines. Full article

Recently, the impact of climate change has led to an increase in the scale and frequency of extreme rainfall and flash floods. Due to this, the occurrence of floods and various river disasters has increased, necessitating the acquisition of technologies to prevent river disasters. Owing to the nature of rivers, areas with poor accessibility exist, and obtaining information over a wide area can be time-consuming. Artificial intelligence technology, which has the potential to overcome these limits, has not been broadly adopted for river detection. Therefore, the current study conducted a performance analysis of artificial intelligence for automatic river path setting via the YOLOv8 model, which is widely applied in various fields. Through the augmentation feature in the Roboflow platform, many river images were employed to train and analyze the river spatial information of each applied image. The overall results revealed that the models with augmentation performed better than the basic models without augmentation. In particular, the flip and crop and shear model showed the highest performance with a score of 0.058. When applied to rivers, the Wosucheon stream showed the highest average confidence across all models, with a value of 0.842. Additionally, the max confidence for each river was extracted, and it was found that models including crop exhibited higher reliability. The results show that the augmentation models better generalize new data and can improve performance in real-world environments. Additionally, the RivDet artificial intelligence model for automatic river path configuration developed in the current study is expected to solve various problems, such as automatic flow rate estimation for river disaster prevention, setting early flood warnings, and calculating the range of flood inundation damage. Full article

To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level. Furthermore, the pre-trained networks achieved higher accuracies on defect classification compared to the self-built network, with ResNet-50 displaying higher accuracy. The inspection system consistently obtained fast and accurate surface classifications because it imposed OK classification on models trained with images from both illumination modes. The obtained surface information was then successfully sent to a server to be forwarded to a graphical user interface for visualization. The developed system showed considerable robustness, demonstrating its potential as an efficient tool for industrial quality control. Full article

Industrial buildings are a key element in the industrial fabric, and their maintenance is essential to ensure their proper functioning and avoid disruptions and costly economic losses. Continuous maintenance based on an accurate diagnosis makes it possible to meet the challenges of aging infrastructures, which demands a reliable data-based assessment for maintenance management implementing corrective and preventive actions, according to the damage criticality. This paper researches an innovative digitalized process for the inspection and diagnosis of industrial buildings, which leads to categorizing and prioritizing maintenance actions in an objective and cost-effective way from the inspection data. The process integrates some technical developments carried out in this work, aimed to automate the workflow: the drone-based inspection, the building condition assessment from the definition of a standardized construction pathology library, and a visual analysis of pathology evolution based on photogrammetry. The use of drones for digitalized inspection involves some challenges related to the positioning of the drone for damage localization, which has been herein overcome by developing a geo-annotation system for image acquisition. This system has also enabled the capture of geo-located images intended to generate 3D photogrammetric models for quantifying the pathological process evolution. Moreover, the assessment procedure outlined through multi-criteria decision-making methodology MIVES establishes a single criterion to automatically weight the relative importance of the damage defined in the library. As a result, this procedure yields the so-called Intervention Urgency Index (IUI), which allows prioritizing the maintenance actions associated with the damage while also considering economic criteria. In such a way, the overall process aims to increase reliability and consistency in the results of inspection and diagnosis needed for the effective maintenance management of industrial buildings. Full article

Background: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have a one-to-one correspondence with the histopathological grading of tumors. Recently, artificial intelligence (AI)-based algorithms and textural analysis, a subdivision of radiomics, have shown potential in bridging this gap. Objectives: We aimed to develop a machine-learning algorithm that predicts the ISUP grade of manually contoured prostate nodules on T2-weighted images and classifies them into clinically significant and indolent ones. Materials and Methods: We included 55 patients with 76 lesions. All patients were examined on the same 1.5 Tesla mpMRI scanner. Each nodule was manually segmented using the open-source 3D Slicer platform, and textural features were extracted using the PyRadiomics (version 3.0.1) library. The software was based on machine-learning classifiers. The accuracy was calculated based on precision, recall, and F1 scores. Results: The median age of the study group was 64 years (IQR 61–68), and the mean PSA value was 11.14 ng/mL. A total of 85.52% of the nodules were graded PI-RADS 4 or higher. Overall, the algorithm classified indolent and clinically significant PCas with an accuracy of 87.2%. Further, when trained to differentiate each ISUP group, the accuracy was 80.3%. Conclusions: We developed an AI-based decision-support system that accurately differentiates between the two PCa prognostic groups using only T2 MRI acquisitions by employing radiomics with a robust machine-learning architecture. Full article

The early and precise identification of a brain tumour is imperative for enhancing a patient’s life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation. Full article

In modern agriculture, plant protection is the key to ensuring crop health and improving yields. Intelligent pesticide prescription spraying (IPPS) technologies monitor, diagnose, and make scientific decisions about pests, diseases, and weeds; formulate personalized and precision control plans; and prevent and control pests through the use of intelligent equipment. This study discusses key IPSS technologies from four perspectives: target information acquisition, information processing, pesticide prescription spraying, and implementation and control. In the target information acquisition section, target identification technologies based on images, remote sensing, acoustic waves, and electronic nose are introduced. In the information processing section, information processing methods such as information pre-processing, feature extraction, pest and disease identification, bioinformatics analysis, and time series data are addressed. In the pesticide prescription spraying section, the impact of pesticide selection, dose calculation, spraying time, and method on the resulting effect and the formulation of prescription pesticide spraying in a certain area are explored. In the implement and control section, vehicle automatic control technology, precision spraying technology, and droplet characteristic control technology and their applications are studied. In addition, this study discusses the future development prospectives of IPPS technologies, including multifunctional target information acquisition systems, decision-support systems based on generative AI, and the development of precision intelligent sprayers. The advancement of these technologies will enhance agricultural productivity in a more efficient, environmentally sustainable manner. Full article

The dynamics of territorial planning, the management of its tourism products, and the monitoring of demand flows and their impact on the territorial structure (social, economic and environmental) require tools that support the acquisition of reliable quantitative data, as far as possible in real time, that are easy to manage and allow immediate analysis. In the case of structures and equipment anchored in the nature tourism segment, in particular hiking trails, in addition to determining the demand indices in a network of hiking trails and understanding their territorial and temporal dynamics, the data collected through automatic counters is a crucial tool to support territorial management and evaluate the patterns and flows of tourist demand. Based on these assumptions, this research seeks to analyse demand data observed on eleven hiking trails in the Historic Villages of Portugal, collected through automatic monitoring systems (counters). In four years, between 2020 and 2023, the trails analysed generated a demand of almost 190,000 passages, which translates into an annual average of 47,500 passages in the tourism product “Historic Villages of Portugal” (more than 4800 passages for each trail), mostly in the spring and autumn months, mainly on weekends. Full article

Real-time condition monitoring of machinery is increasingly being adopted to minimize costs and enhance operational efficiency. By leveraging large-scale data acquisition and intelligent algorithms, failures can be detected and predicted, thereby reducing machine downtime. In this paper, we present a novel hybrid edge–cloud system for detecting rotational bearing failures using accelerometer data. We evaluate both supervised and unsupervised neural network approaches, highlighting their respective strengths and limitations. Supervised models demonstrate high accuracy but require labeled datasets representative of the failures of interesting data that are challenging to acquire due to the rarity of anomalies. Conversely, unsupervised models rely on data from normal operational conditions, which is more readily available. However, these models classify all deviations from normalcy as anomalies, including those unrelated to failure, leading to costly false positives. To address these challenges, we propose a distributed system that integrates supervised and unsupervised learning. A compact unsupervised model is deployed on edge devices near the machines to compress sensor data, which are then transmitted to a centralized cloud-based system. Over time, these data are automatically labeled and used to train a supervised model, improving the accuracy of failure predictions. Our approach enables efficient, scalable failure detection across a fleet of machines while balancing the trade-offs between supervised and unsupervised learning. Full article

Congenital heart defects (CHDs) are the most common congenital defect, occurring in approximately 1 in 100 live births and being a leading cause of perinatal morbidity and mortality. Of note, approximately 25% of these defects are classified as critical, requiring immediate postnatal care by pediatric cardiology and neonatal cardiac surgery teams. Consequently, early and accurate diagnosis of CHD is key to proper prenatal and postnatal monitoring in a tertiary care setting. In this scenario, fetal echocardiography is considered the gold standard imaging ultrasound method for the diagnosis of CHD. However, the availability of this examination in clinical practice remains limited due to the need for a qualified specialist in pediatric cardiology. Moreover, in light of the relatively low prevalence of CHD among at-risk populations (approximately 10%), ultrasound cardiac screening for potential cardiac anomalies during routine second-trimester obstetric ultrasound scans represents a pivotal aspect of diagnosing CHD. In order to maximize the accuracy of CHD diagnoses, the views of the ventricular outflow tract and the superior mediastinum were added to the four-chamber view of the fetal heart for routine ultrasound screening according to international guidelines. In this context, four-dimensional spatio-temporal image correlation software (STIC) was developed in the early 2000s. Some of the advantages of STIC in fetal cardiac evaluation include the enrichment of anatomical details of fetal cardiac images in the absence of the pregnant woman and the ability to send volumes for analysis by an expert in fetal cardiology by an internet link. Sequentially, new technologies have been developed, such as fetal intelligent navigation echocardiography (FINE), also known as “5D heart”, in which the nine fetal cardiac views recommended during a fetal echocardiogram are automatically generated from the acquisition of a cardiac volume. Furthermore, artificial intelligence (AI) has recently emerged as a promising technological innovation, offering the potential to warn of possible cardiac anomalies and thus increase the ability of non-cardiology specialists to diagnose CHD. In the early 2010s, the advent of 3D reconstruction software combined with high-definition printers enabled the virtual and 3D physical reconstruction of the fetal heart. The 3D physical models may improve parental counseling of fetal CHD, maternal–fetal interaction in cases of blind pregnant women, and interactive discussions among multidisciplinary health teams. In addition, the 3D physical and virtual models can be an useful tool for teaching cardiovascular anatomy and to optimize surgical planning, enabling simulation rooms for surgical procedures. Therefore, in this review, the authors discuss advanced image technologies that may optimize prenatal diagnoses of CHDs. Full article

The developments in sensing, actuation, and algorithms, both in terms of Artificial Intelligence (AI) and data treatment, have open up a wide range of possibilities for improving the quality of the production systems in diverse industrial fields. The present paper describes the automatizing process performed in a production line for high-quality bearings. The actuation considered new sensing elements at the machine level and the treatment of the information, fusing the different sources in order to detect quality defects in the grinding process (waviness, burns) and monitoring the state of the tool. At a supervision level, an AI model has been developed for monitoring the complete line and compensating deviations in the dimension of the final assembly. The project also contemplated the hardware architecture for improving the data acquisition and communication among the machines and databases, the data treatment units, and the human interfaces. The resulting system gives feedback to the operator when deviations or potential errors are detected so that the quality issues are recognized and can be amended in advance, thereby reducing the quality cost. Full article

Pod counting of rapeseed is a critical step in breeding, cultivation, and agricultural machinery research. Currently, this process relies entirely on manual labor, which is both labor-intensive and inefficient. This study aims to develop a semi-automatic counting instrument based on transmission image processing and proposes a new algorithm for processing transmission images of pods to achieve non-destructive, accurate, and rapid determination of the seed count per pod. Initially, the U-NET network was used to segment and remove the stem and beak from the pod image; subsequently, adaptive contrast enhancement was applied to adjust the contrast of the G-channel image of the pod to an appropriate range, effectively eliminating the influence of different varieties and maturity levels on the translucency of the pod skin. After enhancing the contrast, the Sauvola algorithm was employed for threshold segmentation to remove the pod skin, followed by thinning and dilation of the binary image to extract and remove the central ridge lines, detecting the number and area of connected domains. Finally, the seed count was determined based on the ratio of each connected domain’s area to the mean area of all connected domains. A transmission imaging device that mimics the human eye’s method of counting seeds was designed, incorporating an LED transmission light source, photoelectric switch-triggered imaging slot, an industrial camera, and an integrated packaging frame. Human–machine interaction software based on PyQt5 was developed, integrating functions such as communication between upper and lower machines, image acquisition, storage, and processing. Operators simply need to place the pod in an upright position into the imaging device, where its transmission image will be automatically captured and processed. The results are displayed on a touchscreen and stored in Excel spreadsheets. The experimental results show that the instrument is accurate, user-friendly, and significantly reduces labor intensity. For various varieties of rapeseed pods, the seed counting accuracy reached 97.2% with a throughput of 372 pods/h, both of which are significantly better than manual counting and have considerable potential for practical applications. Full article