Resilience driven EV coordination in multiple microgrids using distributed deep reinforcement learning by Yuxin Wu, Ting Cai, Xiaoli Li

“…The proposed method applies an architecture with multi-actor, single-learner to reduce training complexity, employing a convolutional neural network to capture spatial characteristics from the CPTN, and incorporating a long short-term memory to derive temporal sequence features across multiple time steps, thereby enhancing the exploration efficiency of the action space. …”
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UETT4K Anti-UAV: A Large Scale 4K Benchmark Dataset for Vision-Based Drone Detection in High-Resolution Imagery by Mughees Sarwar Awan, Syed Azhar Ali Zaidi, Junaid Mir

“…Vision-based approaches, especially those employing deep convolutional neural networks (DCNNs), show great promise in addressing the need for an accurate and cost-effective drone detection system. …”
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Row middle herbicide programs for plasticulture vegetables using targeted herbicide applications by Ana C. Buzanini, Arnold W. Schumann, Nathan S. Boyd

“…This technology has the potential to reduce herbicide use and lower input costs. A prototype smart-spray system was developed at the Gulf Coast Research and Education Center in Wimauma, FL, that uses YOLO-V3 convolutional neural networks to differentiate broadleaf, grass, and nutsedge weeds in row middles. …”
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A unified approach for weakly supervised crack detection via affine transformation and pseudo label refinement by Zhongmin Huangfu, Yibo Jiao, Fupeng Wei, Ge Shi, Hangcheng Dong

“…Deep neural networks perform well in this discipline, although their pixel-level labeling reliance increases labeling costs. Thus, weakly supervised learning methods have emerged. …”
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An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks by Zhifeng Ma, Zhanjun Hao, Zhenya Zhao

“…According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. …”
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VM-UNet++ research on crack image segmentation based on improved VM-UNet by Wenliang Tang, Ziyi Wu, Wei Wang, Youqin Pan, Weihua Gan

“…In recent years, with advancements in deep learning, particularly the widespread use of Convolutional Neural Networks (CNNs) and Transformers, significant breakthroughs have been made in the field of crack detection. …”
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Multi-source data fusion-based knowledge transfer for unmanned aerial vehicle flight data anomaly detection and recovery by Lei Yang, Shaobo Li, Liya Yu, Caichao Zhu, Congbao Wang

“…First, a data-driven framework based on one-dimensional convolutional neural network and bi-directional long short-term memory (1D CNN-BiLSTM) with parameter selection and residual smoothing (1DCB-PSRS) is proposed. …”
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Enhancing prostate cancer segmentation in bpMRI: Integrating zonal awareness into attention-guided U-Net by Chao Wei, Zheng Liu, Yibo Zhang, Lianhui Fan

“…First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. …”
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Smart Agriculture: Predicting Diseases in olive using Deep Learning Algorithms by Rahman F., Raghatate Kapesh Subhash

“…Utilizing a large number of olive leaf image data and corresponding environment factors, we developed and evaluated Convolutional Neural Networks (CNNs) and Long Short Time Memory networks (LSTMs). …”
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A Multimodal Multi-Stage Deep Learning Model for the Diagnosis of Alzheimer’s Disease Using EEG Measurements by Tuan Vo, Ali K. Ibrahim, Hanqi Zhuang

“…At the frame level, convolutional neural networks (CNNs) are employed to extract features from spectrograms, scalograms, and Hilbert spectra. …”
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Fast Identification and Detection Algorithm for Maneuverable Unmanned Aircraft Based on Multimodal Data Fusion by Tian Luan, Shixiong Zhou, Yicheng Zhang, Weijun Pan

“…This integrated solution provides cost-effective, high-precision drone surveillance for resource-constrained airports.…”
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Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals by Xingguo Zhang, Tengfei Li, Maoxun Sun, Lei Zhang, Cheng Zhang, Yue Zhang

“…This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. …”
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How to detect occluded crosswalks in overview images? Comparing three methods in a heavily occluded area by Yuanyuan Zhang, Joseph Luttrell, IV, Chaoyang Zhang

“…However, obtaining such data at a large scale is often challenging due to the high cost associated with traditional collection methods. …”
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Effective Identification of Variety and Origin of Chenpi Using Hyperspectral Imaging Assisted with Chemometric Models by Hangxiu Liu, Youyou Wang, Yiheng Wang, Jingyi Wang, Hanqing Hu, Xinyi Zhong, Qingjun Yuan, Jian Yang

“…To overcome the inefficiency and high cost of conventional detection methods, this study proposed a nondestructive approach that integrates hyperspectral imaging (HSI) with deep learning to classify Chenpi varieties and their geographical origins. …”
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YOLO-WTB: Improved YOLOv12n Model for Detecting Small Damage of Wind Turbine Blades From Aerial Imagery by Phat T. Nguyen, Duy C. Huynh, Loc D. Ho, Matthew W. Dunnigan

“…In addition, in the backbone part, we also propose to remove a Convolution module and an Area Attention Concatenate-Convolution-Fusion module and add an improved SoftPool Feature Spatial Pyramid Pooling - Fast module to increase the feature extraction ability while maintaining the complexity of the model. …”
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MXT-YOLOv7t: An Efficient Real-Time Object Detection for Autonomous Driving in Mixed Traffic Environments by Afdhal Afdhal, Khairun Saddami, Mirshal Arief, Sugiarto Sugiarto, Zahrul Fuadi, Nasaruddin Nasaruddin

“…The enhancements include refining the feature extraction network by integrating a lightweight attention mechanism into the ELAN blocks and replacing the activation function in each convolution layer with a sigmoid-weighted linear unit. …”
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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

“…The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. …”
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SAM-CTMapper: Utilizing segment anything model and scale-aware mixed CNN-Transformer facilitates coastal wetland hyperspectral image classification by Jiaqi Zou, Wei He, Haifeng Wang, Hongyan Zhang

“…Additionally, existing methods encounter difficulties in practical wetland classification tasks due to the high cost of hyperspectral wetland data labeling. This paper introduces SAM-CTMapper, a coastal wetland classification framework that incorporates a scale-aware mixed CNN-Transformer (CTMapper) to precisely identify wetland cover types using hyperspectral images, and the advanced segment anything model (SAM) to save labor costs in data labeling. …”
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MFFCI–YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Multiscale Features Fusion and Context Information by Sheng Xu, Lin Song, Junru Yin, Qiqiang Chen, Tianming Zhan, Wei Huang

“…First, we introduce the lightweight CSP bottleneck with attention module, which utilizes partial convolution calculation and SimAM attention mechanisms to decrease the number of parameters and computational complexity while enhancing feature extraction capabilities. …”
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FP-YOLOv8: Surface Defect Detection Algorithm for Brake Pipe Ends Based on Improved YOLOv8n by Ke Rao, Fengxia Zhao, Tianyu Shi

“…Lastly, an asymmetric small-target detection head, FADH, is proposed to utilize depth-separable convolution to accomplish classification and regression tasks, enabling more precise capture of detailed information across scales and improving the detection of small-target defects. …”
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