Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

For the purpose of applying information technology to the creation of ink style painting, the algorithm of ink painting rendering based on the deep learning framework and convolutional neural network model is designed and improved. Firstly, the ink style rendering program is written in Python. Secondly, VGG under Caffe architecture and Illustration 2Vec models are transplanted to TensorFlow architecture, and the image is rendered in ink style based on deep learning framework and convolutional neural network model. Finally, based on Node.js, the server-side program for image ink style rendering is built. Among them, Express is adopted as the Web-side framework, and the front-end page effect is completed. The results show that the ink rendering logic program is applicable, and the expected purpose is achieved.

Recently, various artificial intelligence (AI)-based techniques have been developed to estimate tropical cyclone (TC) intensity by analyzing patterns in satellite imagery. Most of these models rely solely on currently available satellite data. However, traditional methods used in operational TC forecasting, such as the advanced Dvorak technique (ADT), as well as forecasters’ practices in TC intensity estimation, consider not only the current information but also historical data (e.g., previous TC intensities and their trends). Additionally, forecasters compare TC intensity from ADT and analysis track data, and if a bias is identified, they take this into account when estimating the current TC intensity. This study investigates how much the performance of current AI-based TC intensity estimation can be improved by incorporating historical information and bias estimates. Three experiments were conducted: (1) control (CTRL), which uses only current satellite images; (2) Experiment 1 (Exp 1), which includes current data along with satellite imagery and intensity from 6 hours earlier; and (3) Experiment 2 (Exp 2), which further incorporates bias estimates from the past 18 hours into a self-diagnosis algorithm. The results showed that Exp 1 reduced intensity estimation errors by 64.7% compared to CTRL, while Exp 2 further decreased errors by 32.3% compared to Exp 1. This suggests that utilizing historical TC information and a self-diagnosis approach in operational systems is crucial for improving the accuracy of AI-based TC intensity estimation.

This study examines the potential limit in predicting tropical cyclone (TC) intensity under idealized conditions. Using the phase-space reconstruction method for TC intensity time series obtained from the CM1 idealized simulations, it is found that CM1 axisymmetric dynamics contain low-dimensional chaos at the maximum intensity equilibrium. Examination of several attractor invariants including the largest Lyapunov exponent, the Sugihara–May correlation, and the correlation dimension captures a consistent range of the chaotic attractor dimension between 4 and 5 for TC intensity at the maximum intensity equilibrium. In addition, the intensity error doubling time estimated from the largest Lyapunov exponent is roughly 1–3 h, which accords with the decay time obtained from the Sugihara–May correlation. Furthermore, the findings in this study reveal a relatively short TC intensity predictability limit for CM1, which is ∼3–9 h based on the maximum tangential wind but noticeably longer for the minimum central pressure (∼12–18 h) after reaching the mature stage. So long as the traditional metrics for TC intensity such as the maximum surface wind or the minimum central pressure is used for intensity forecast, our results support that TC intensity forecast errors will not be reduced indefinitely in any model, even in the absence of all model and observational errors. As such, the future improvement of TC intensity forecast should be based on different metrics beyond the absolute intensity errors that are currently used in real-time intensity verification. Significance Statement Using the phase-space reconstruction method for tropical cyclone (TC) intensity time series obtained from idealized axisymmetric simulations, we show that TC axisymmetric dynamics in CM1 possesses low-dimensional chaos at the maximum intensity equilibrium. This low-dimensional dynamics explains the long tradition of representing TC intensity by a few measures as in the current practice. The chaotic property of CM1 axisymmetric dynamics also suggests a relatively short predictability range for TC intensity at the maximum intensity equilibrium. The potential existence of low-dimensional chaos for TC intensity in CM1 idealized simulations as found in this study supports the use of different intensity verification metrics beyond the traditional absolute intensity errors currently used in operational model evaluation.

Several key issues in the simple time-dependent theories of tropical cyclone (TC) intensification developed in recent years remain, including the lack of a closure for the pressure dependence of saturation enthalpy at sea surface temperature (SST) under the eyewall and the definition of environmental conditions, such as the boundary layer enthalpy in TC environment and the TC outflow-layer temperature. In this study, some refinements to the most recent time-dependent theory of TC intensification have been accomplished to resolve those issues. The first is the construction of a functional relationship between the surface pressure under the eyewall and the TC intensity, which is derived using the cyclostrophic wind balance and calibrated using full-physics axisymmetric model simulations. The second is the definition of TC environment that explicitly includes the air–sea temperature difference. The third is the TC outflow-layer temperature parameterized as a linear function of SST based on global reanalysis data. With these refinements, the updated time-dependent theory becomes self-contained and can give both the intensity-dependent TC intensification rate (IR) and the maximum potential intensity (MPI) under given environmental thermodynamic conditions. It is shown that the pressure dependence of saturation enthalpy at SST can lead to an increase in the TC MPI and IR by about half of that induced by dissipative heating due to surface friction. Results also show that both MPI and IR increase with increasing SST, surface enthalpy exchange coefficient, environmental air–sea temperature difference, and decreasing environmental boundary layer relative humidity, but the maximum IR is insensitive to surface drag coefficient. Significance Statement A new advancement in the recent decade is the development of simple time-dependent theories of tropical cyclone (TC) intensification, which can provide quantitative understanding of TC intensity change. However, several key issues in these simple time-dependent theories remain, including the lack of a closure for the pressure dependence of saturation enthalpy at sea surface temperature under the eyewall and the definition of environmental conditions. These are resolved in this study with several refinements, which make the most recent time-dependent theory of TC intensification self-contained and practical.

Rotating machinery plays an important role in industrial production. Most failures of rotating machinery are caused by the failure of its key components, among which bearings are the most frequently malfunctioning component. This paper takes bearings as the research object, and designs a bearing fault diagnosis algorithm by extracting the harmonic dynamic characteristics of bearing vibration signals and combining them with a deep learning convolutional neural network model. This algorithm is based on Fourier transform analysis of the frequency spectrum distribution of the vibration signal of the faulty bearing. The harmonic dynamic features of the vibration signal are extracted using signal fitting algorithm. Then, the feature data is made into a dataset and input into a convolutional neural network for learning and testing. Finally, it is verified on the CWRU dataset. The results aims to achieve better bearing diagnosis results, providing new ideas and methods for the development of bearing fault diagnosis technology, and providing more reliable guarantees for the stable operation of rotating machinery in industrial production

Teachers use observational cues in the classroom to identify attentiveness of students and guide the pace of their lecture. However, effectiveness of this technique decreases with increasing class size. This paper presented an approach for automating these observational cues from the students' facial expressions and identifying their attentiveness via a neural network machine learning model. Results of the deep learning Convolutional Neural Network model were then compared with the range of confidence values obtained from a cloud-based emotion recognition service to identify the correlations with human observer. Real time videos of students were collected during classes and sample dataset were created for attentive and inattentive students in order to train the machine learning model. This system can be highly useful for inexperienced teachers for early identification of inattentive students in classroom and thereby taking necessary actions to enhance student learning.

Background and Objectives: Commonly being the first step in trauma routine imaging, up to 67% fractures are missed on plain radiographs of the thoracolumbar (TL) spine. The aim of this study was to develop a deep learning model that detects traumatic fractures on sagittal radiographs of the TL spine. Identifying vertebral fractures in simple radiographic projections would have a significant clinical and financial impact, especially for low- and middle-income countries where computed tomography (CT) and magnetic resonance imaging (MRI) are not readily available and could help select patients that need second level imaging, thus improving the cost-effectiveness. Materials and Methods: Imaging studies (radiographs, CT, and/or MRI) of 151 patients were used. An expert group of three spinal surgeons reviewed all available images to confirm presence and type of fractures. In total, 630 single vertebra images were extracted from the sagittal radiographs of the 151 patients—302 exhibiting a vertebral body fracture, and 328 exhibiting no fracture. Following augmentation, these single vertebra images were used to train, validate, and comparatively test two deep learning convolutional neural network models, namely ResNet18 and VGG16. A heatmap analysis was then conducted to better understand the predictions of each model. Results: ResNet18 demonstrated a better performance, achieving higher sensitivity (91%), specificity (89%), and accuracy (88%) compared to VGG16 (90%, 83%, 86%). In 81% of the cases, the “warm zone” in the heatmaps correlated with the findings, suggestive of fracture within the vertebral body seen in the imaging studies. Vertebras T12 to L2 were the most frequently involved, accounting for 48% of the fractures. A4, A3, and A1 were the most frequent fracture types according to the AO Spine Classification. Conclusions: ResNet18 could accurately identify the traumatic vertebral fractures on the TL sagittal radiographs. In most cases, the model based its prediction on the same areas that human expert classifiers used to determine the presence of a fracture.

This research describes “EspiNet”, a Deep Learning Convolutional Neural Network model, in conjunction with a Markov Decision Process (MDP) tracker for detection and tracking of occluded motorcycles in urban environments. The model is trained and evaluated, using a new public dataset with up to 10,000 annotated images, created for this research, and captured in real urban traffic scenes. Images were captured using a moving camera mounted in a drone, where more than 60% of the motorcycles are affected by occlusions. The network design involves many tests, where a promising result of 88.84% in average precision (AP) is achieved, despite the considerable number of occluded vehicles, the movement of the camera and the low angle used for capture. The model predictions are used as input to an MDP tracker, reaching results up to 85.2% in Multiple Object Tracking Accuracy (MOTA). The proposed network architecture outperforms state of the art YOLO (You Look Only Once) v3.0 and Faster R-CNN (VGG16 based) detection models, producing also better tracking results in comparison with the use of the other two models as detector base for the MDP tracker.

Approximately 530 million children have untreated cavities, which could affect future permanent teeth if not treated well. Researchers have enhanced X-ray detection of tooth decay to improve the detection of cavities. However, dentophobia and lack of insurance and dentist availability are barriers that constrain thousands from receiving proper dental care. This study used a deep learning convolutional neural network model to address this problem to detect cavities. Three hundred twenty-two photos taken by a camera of child patients were used. The dataset was classified into two classes: cavity and no cavity. The MobileNetV2 architecture was used for feature extraction and cavity detection. The model was then trained and tested so that it classified photos into two categories. The model achieved an accuracy of 93.5%.

In recent years, hand gesture recognition has received attention for its wide range of applications and its ability to efficiently interact with machines. Hand signs have become part of basic music theory in educational settings, one of which is through the use of Kodaly hand signs. Kodaly Hand Signs are used as a concept in interactive angklung performances to determine the notes to be played. Thus, the automation of hand gesture recognition in playing music is one of the innovations in the field of artificial intelligence and machine learning. This paper discusses a deep learning convolutional neural network model that can classify 8 Kodaly hand signs in real-time. The hand gesture recognition process begins with image segmentation, displays the gray intensity value, resizes the image, then proceeds to the preprocessing stage by converting the image into a vector form, then the image data is trained to produce a model. It is models such as these that will be useful in education in providing a human-computer interface for musical expression. The experimental results show that the proposed CNN model achieves an average accuracy rate of 98.29%, 99.95% precision, 99.97% recall, and 99.96% F1 score.

Real-time monitoring of forward osmosis membrane fouling in wastewater reuse process performed with a deep learning model

Lung cancer is one of the major illnesses that contribute to millions of fatalities worldwide. Numerous deaths could be saved through the early identification and categorization of lung cancers. However, with traditional approaches, classification accuracy cannot be produced. To detect and classify lung diseases, a deep learning convolutional neural network model has been developed. LDDC, the customized local trilateral filter, is used for pre-processing the lung images from computing tomography for non-local trilateral filters. The region of interest for lung cancer was successfully restricted throughout the segmentation of the disease using hybrid fuzzy morphological procedures. To extract the deep seismic features, the Laplacian pyramid decomposition method was utilized for the segmented image. This paper covers an overall analysis of non-local trilateral filter Processing, hybrid fuzzy morphological techniques and analysis of patient and disease characteristics of LIDR- IDRI and FDA data of Group A (no co-AGA), P-value, Multi-mut Patient, Group B (with a co-AGA).

Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting.

In this study, the relationship between tropical cyclone (TC) intensity and rain rate over the ocean is investigated using a full-physics numerical model (WRF) and a physics-based TC rainfall model (TCR). TC intensity is found to be nearly linearly correlated with the average rain rate in the inner core [∼0.97 (mm h−1 m−2)/(m s−1)], while the correlation is weak at outer radii. This difference is induced because TC intensity is significantly correlated with both the vertical velocity and specific humidity in the inner core but is not significantly correlated with the vertical velocity in the outer radii. Further investigation shows that the intensity–rain-rate relationship at the outer radii is influenced by the TC evolution stage. The rain rate for the outer radii is positively correlated with TC intensity for nondecaying TCs, while this correlation is reduced for decaying TCs due to systematic downdrafts in the outer radii. In the context of climate change, the sensitivity of the TC rain rate to sea surface temperature (SST) is found to be +9% per 1 K increase of SST, roughly the product of the sensitivity of TC intensity to SST (+3%) and the Clausius-Clapeyron scaling (+7%). Coupled with synthetic storms, evolution of the TC rain rate over the twenty-first century under the SSP5-8.5 scenario is projected by the TCR (calibrated with the WRF simulations). The annual increase rates of averaged TC rain rate are 0.17% and 0.20% for the inner core and outer radii, respectively, larger than the annual increase rate of TC intensity (0.046%) but comparable to that of cube of intensity (0.18%).

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness.