Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation by Xu Zhang, Gaoquan Gu

“…Experimental results using variable working condition datasets demonstrate that the proposed method consistently achieves diagnostic accuracies exceeding 95%, substantiating its feasibility and effectiveness.…”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern by Q. He, Y. J. Zheng, C.L. Zhang, H. Y. Wang

“…The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. …”
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Intelligent recognition method for personnel intrusion hazardous area in fully mechanized mining face by Qinghua MAO, Jiao ZHAI, Xin HU, Yinan SU, Xusheng XUE

“…To address the problems of low accuracy of video AI recognition of personnel intrusion hazardous areas in fully mechanized mining face caused by factors such as variable personnel scales, and dynamic changes of hazardous areas, an intelligent recognition method for personnel intrusion hazardous areas of fully mechanized mining face based on RSCA-YOLOv8s and automatic division of hazardous areas is proposed. …”
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MSFUnet: A Semantic Segmentation Network for Crop Leaf Growth Status Monitoring by Zhihan Cheng, He Yan

“…In addition, standard image augmentations (e.g., contrast/brightness adjustments) were applied to mitigate the impact of variable lighting conditions on leaf appearance in the input images, thereby improving model robustness. …”
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Improved method for a pedestrian detection model based on YOLO by Yanfei LI, Chengyi DONG

“…The proposed method had superior performance in dense agricultural contexts while improving detection capabilities for pedestrian distribution patterns under complex farmland conditions, including variable lighting and mechanical occlusions. The main innovations were: (1) integration of spatial pyramid dilated (SPD) operations with conventional convolution layers to construct SPD-Conv modules, which effectively mitigated feature information loss while enhancing small-target detection accuracy; (2) incorporation of selective kernel attention mechanisms to enable context-aware feature selection and adaptive feature extraction. …”
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Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk by Alejandro Peña, Lina M. Sepúlveda-Cano, Juan David Gonzalez-Ruiz, Nini Johana Marín-Rodríguez, Sergio Botero-Botero

“…The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. …”
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Research on Long-Distance Snow Depth Measurement Method Based on Improved YOLOv8 by Jia-Wen Wang, Yu Cao, Zong-Kai Guo, Cheng Xu

“…Second, the introduction of the variable kernel convolution (AKConv) module improves the adaptability of convolutional operations, boosting the model’s performance in snow depth detection. …”
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SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment by Chunyou Guo, Feng Tan

“…However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. …”
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Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU by Sio-Kei Im, Ka-Hou Chan

“…Predicting the motion of handwritten digits in video sequences is challenging due to complex spatiotemporal dependencies, variable writing styles, and the need to preserve fine-grained visual details—all of which are essential for real-time handwriting recognition and digital learning applications. …”
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Prediction of Grain Yield in Henan Province Based on Grey BP Neural Network Model by Bingjun Li, Yifan Zhang, Shuhua Zhang, Wenyan Li

“…BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. …”
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Multimodal anomaly detection in complex environments using video and audio fusion by Yuanyuan Wang, Yijie Zhao, Yanhua Huo, Yiping Lu

“…The algorithm combines the innovative methods of spatio-temporal feature extraction and noise suppression, and aims to improve the processing performance, especially in complex environments, by introducing an improved Variable Auto Encoder (VAE) structure. The model named Spatio-Temporal Anomaly Detection Network (STADNet) captures the spatio-temporal features of video images through multi-scale Three-Dimensional (3D) convolution module and spatio-temporal attention mechanism. …”
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Vehicle detection method based on multi-layer selective feature for UAV aerial images by Yinbao Ma, Yuyu Meng, Jiuyuan Huo

“…However, this task remains challenging due to variable high-altitude viewpoints, complex environmental interference, and limitations in algorithmic efficiency. …”
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Lightweight detection of cotton leaf diseases using StyleGAN2-ADA and decoupled focused self-attention by Henghui Mo, Linjing Wei

“…Current models face challenges like diverse disease traits, variable stages, small target detection, uneven lighting, and occlusions, resulting in low accuracy and adaptability. …”
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An intelligent prediction method for rock core integrity based on deep learning by Zhaoxia Hu, Hua Mei, Lei Yu

“…In IDA-RCF, a two-branch feature extraction network is firstly proposed, in which branch one is used to fully extract the complex and variable local detail fissure features by Deformable convolution, and branch two is used to capture the global context information of the rock core images by EfficientViT network based on the self-attention. …”
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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. …”
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Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm by Yubo Zhao, Mo Chen

“…In addition, wavelet transform is used to explore the temporal correlations between DO and meteorological and water quality variables, further enhancing the interpretability of the deep learning approach. …”
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Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8 by Jiang Liu, Jingxin Yu, Changfu Zhang, Huankang Cui, Jinpeng Zhao, Wengang Zheng, Fan Xu, Xiaoming Wei

“…Three key innovations address YOLOv8’s limitations: (1) an SE attention module boosts feature representation in cluttered environments, (2) GhostConv replaces standard convolution to reduce computational load by 19% while preserving feature discrimination, and (3) a scale-adaptive WIoU_v2 loss function optimizes gradient allocation for variable-quality data. …”
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FD²-YOLO: A Frequency-Domain Dual-Stream Network Based on YOLO for Crack Detection by Junwen Zhu, Jinbao Sheng, Qian Cai

“…However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is often ineffective in detecting slender or variable cracks in complex scenarios. We propose a novel network, FD²-YOLO, based on frequency-domain dual-stream YOLO, for accurate and efficient detection of cement cracks. …”
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Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network by Cunsheng Zhao, Bo Tong, Chao Zhou, Qingrong Fan

“…Finally, the relation module of the RN was used for the nonlinear distance determination of the fault feature vector set and to generate the relation score for the few-shot variable condition bearing fault diagnosis. In this paper, EEMD module is introduced into RN to construct multi-dimensional fault characteristics of the original fault signal. …”
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