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An automated Machine Learning based approach for a reproducible and efficient evaluation of industrial Charpy V-notch specimens
Published 2025-09-01“…An objective and automated method for the quantification of macroscopic images of tested Charpy V-notch specimens, specifically focusing on their ductility/brittleness characteristics based on a realistic, homogeneous and industrial environment is proposed. …”
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Rail Surface Defect Diagnosis Based on Image–Vibration Multimodal Data Fusion
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Research on LSTM-PPO Obstacle Avoidance Algorithm and Training Environment for Unmanned Surface Vehicles
Published 2025-02-01“…In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. We integrate the motion characteristics of the unmanned boat and the influencing factors of the surface environment, based on the curiosity-driven set reward function, to improve its autonomous obstacle avoidance ability, combined with the LSTM network to identify and save obstacle information to improve the adaptability to the unknown environment; virtual simulation is performed in Unity. …”
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Race course characteristics are the most important predictors in 48 h ultramarathon running
Published 2025-03-01“…This study tried to determine the origin of the fastest 48-hour runners and the predictor factors associated with 48-hour ultra-marathon performance, such as age, gender, event country, country of origin and race course specific characteristics. A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). …”
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Using Deep Learning to Improve Short-term Climate Prediction of Summer Precipitation in Southwestern China
Published 2025-06-01“…In recent years， Southwestern China， including Yunnan， Guizhou， Sichuan， and Chongqing， has been frequently hit by flood disasters caused by climate change， resulting in severe casualties and enormous property losses.The occurrence of these disasters is closely related to abnormal precipitation.Although traditional statistical methods and atmospheric models have achieved certain effectiveness in precipitation forecasting， effective approaches for dealing with the complex spatiotemporal characteristics of precipitation data are still lacking.With the development of machine learning technology， the convolutional long short-term memory network （ConvLSTM）， which integrates convolutional neural networks （CNN） and long short-term memory networks （LSTM）， has shown outstanding performance in addressing spatiotemporal sequence problems， particularly in the field of precipitation forecasting.In order to more accurately predict the summer precipitation in the southwestern region of China for the next year （short-term climate prediction of precipitation）， this study constructed a dataset by integrating global sea surface temperature and precipitation data in Southwestern China.The ConvLSTM was used for training and named SST-ConvLSTM.This model not only captures the spatiotemporal characteristics in real precipitation data but also learns some information from global sea surface temperature data， thereby enhancing the accuracy of short-term climate prediction of precipitation.The results show that compared to ConvLSTM that does not consider sea surface temperature and a traditional atmospheric model， SST-ConvLSTM model has significant advantages in short-term climate prediction of summer precipitation in Southwestern China.（1） Numerically， the predictions of the SST-ConvLSTM model are closest to the real precipitation data， with similar trend changes.In contrast， both ConvLSTM and the traditional atmospheric model show certain deviations in their predictions.（2） Spatially， the SST-ConvLSTM model also performs well.Its predictions are consistent with the spatial distribution of real precipitation data and accurately reflect the spatial distribution of precipitation.（3） In model evaluation， three evaluation metrics were used to assess the performance of the SST-ConvLSTM model.The results show that the SST-ConvLSTM model performs well in all evaluation metrics and achieves the best scores.These findings provide important references and insights for future research on precipitation prediction in Southwestern China.…”
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A Few-Shot Steel Surface Defect Generation Method Based on Diffusion Models
Published 2025-05-01“…Few-shot steel surface defect generation remains challenging due to the limited availability of training samples and the complex visual characteristics of industrial defects. …”
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A Deep Learning Method for Inversing 3D Temperature Fields Using Sea Surface Data in Offshore China and the Northwest Pacific Ocean
Published 2024-12-01“…In the present study, based on sea surface data, a deep learning model is constructed using the U-net method to reconstruct the three-dimensional temperature structure of the Northwest Pacific and offshore China. …”
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Improving Atmospheric Correction Algorithms for Sea Surface Skin Temperature Retrievals from Moderate-Resolution Imaging Spectroradiometer Using Machine Learning Methods
Published 2024-12-01“…Satellite-retrieved sea-surface skin temperature (<i>SST<sub>skin</sub></i>) is essential for many Near-Real-Time studies. …”
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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities
Published 2025-01-01“…Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. …”
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A Multi-Feature Fusion Approach for Road Surface Recognition Leveraging Millimeter-Wave Radar
Published 2025-06-01“…This combination enables the efficient classification of diverse road surface types and conditions. Firstly, the discriminability of radar echo signals corresponding to different road surface types is verified via statistical analysis. …”
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Advanced time-series InSAR analysis to estimate surface deformation and utilization of hybrid deep learning for susceptibility mapping in the Jakarta metropolitan region
Published 2025-12-01“…A reliable method for surface deformation measurements needs to be developed to address this issue. …”
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Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models
Published 2025-04-01“…In the current research, ensemble machine learning (ML) models (i.e., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient Boosting (GB), HistGradientBoosting, Extreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM)) were applied in attempt to predict the adsorption efficiency of several heavy metals (i.e., Pb, Cd, Ni, Cu, and Zn) according to different factors including temperature, pH, and biochar characteristics. …”
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A Lightweight TA-YOLOv8 Method for the Spot Weld Surface Anomaly Detection of Body in White
Published 2025-03-01“…The deep learning architecture YOLO (You Only Look Once) has demonstrated its superior visual detection performance in various computer vision tasks and has been widely applied in the field of automatic surface defect detection. …”
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A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data
Published 2025-01-01“…In this work, we propose a general paddy rice mapping (GPRM) model by combining biological characteristics, deep learning, and multisource remote sensing data. …”
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