Urban Traffic Flow Forecasting Based on Graph Structure Learning by Guangyu Huo, Yong Zhang, Yimei Lv, Hao Ren, Baocai Yin

“…The transportation system is a complex dynamic giant system which integrates and intertwines the elements of people, vehicles, roads, and the environment. …”
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Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings by Jing Gu, Yijuan Pan, Jingjing Zhang

“…To solve the problem of insufficient feature extraction of small targets in bridge disease images under complex backgrounds and noise, we propose the YOLOv8 Dynamic Plus model. First, we enhanced attention on multi-scale disease features by implementing structural reparameterization with parallel small-kernel expansion convolution. …”
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Polarization-sensitive in-sensor computing in chiral organic integrated 2D p-n heterostructures for mixed-multimodal image processing by Je-Jun Lee, Seong-Jun Han, Changsoon Choi, Chaewon Seo, Seungkwon Hwang, Jihyun Kim, Jung Pyo Hong, Jisu Jang, Jihoon Kyhm, Jung Woo Kim, Byoung-Soo Yu, Jung Ah Lim, Gunuk Wang, Joohoon Kang, Yonghun Kim, Suk-kyun Ahn, Jongtae Ahn, Do Kyung Hwang

“…Our device exhibits a high dissymmetry factor (1.90), allowing effective separation of mixed circularly polarized images, along with a rapid photoresponse (4 μs) and wide linear dynamic range (up to 114.1 dB), suitable for analog multiply-and-accumulate operations in convolution-based in-sensor computing. …”
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Multitask semantic change detection guided by spatiotemporal semantic interaction by Yinqing Wang, Liangjun Zhao, Yueming Hu, Hui Dai, Yuanyang Zhang

“…To further enhance detection performance, a dynamic depthwise separable convolution is designed in the CTIM module, which can adaptively adjust convolution kernels to more precisely capture change features in different regions of the image. …”
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Retinal vascular segmentation network based on dual-scale morphological enhancement by Yunfeng Ni, Pei Wang, Wei Chen, Jie Qi

“…Subsequently, a feature aggregation module based on dynamic snake-shaped convolutions is introduced, which adapts convolution paths to achieve the fusion of features at different levels. …”
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An Optimized Dual-View Snake Unet Model for Tunnel Lining Crack Detection by Baoxian Li, Hao Xu, Xin Jin, Huaizhi Zhang, Shuo Jin, Qianyu Chen, Fengyuan Wu

“…The HSC module enhances the network’s capability of extracting tunnel lining cracks by synergistically combining features derived from standard convolutions and bidirectional dynamic snake convolutions, thereby capturing intricate geometric and contextual information. …”
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Online evaluation method for MMC submodule capacitor aging based on CapAgingNet by Xinlan Deng, Youhan Deng, Liang Qin, Weiwei Yao, Min He, Kaipei Liu

“…Subsequently, the CapAgingNet model is introduced, incorporating key technical modules to enhance performance: the Deep Stem module, which extracts larger receptive fields through multiple convolution layers and mitigates the impact of data sparsity in capacitor aging on feature extraction; the efficient channel attention (ECA) module, utilizing one-dimensional convolution for dynamic weighting to adjust the importance of each channel, thereby enhancing the ability of the model to process high-dimensional features in capacitor aging data; and the multiscale feature fusion (MSF) module, which integrates capacitor aging information across different scales by combining fine-grained and coarse-grained features, thus improving the capacity of the model to capture high-frequency variation characteristics. …”
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Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention by Aiguo Lu, Pandi Liu, Jie Yang, Zhe Li, Ke Wang

“…Firstly, a module called D_C3, which combines deformable convolution, was integrated into the backbone network to enhance the model’s learning ability and adaptability in specific scenes. …”
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Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD by Chao Chen, Zhuo Chen, Hao Li, Yawen Wang, Guangzhou Lei, Lingling Wu

“…A lightweight dynamic feature upsampling operator improves the feature map quality. …”
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YOLO-SSFA: A Lightweight Real-Time Infrared Detection Method for Small Targets by Yuchi Wang, Minghua Cao, Qing Yang, Yue Zhang, Zexuan Wang

“…SSFF improves multi-scale feature representation through adaptive fusion; LiteShiftHead boosts efficiency via sparse convolution and dynamic integration; and NSN enhances localization accuracy by focusing on key regions. …”
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YOLO-DroneMS: Multi-Scale Object Detection Network for Unmanned Aerial Vehicle (UAV) Images by Xueqiang Zhao, Yangbo Chen

“…And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. …”
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S³DR-Det: A Rotating Target Detection Model for High Aspect Ratio Shipwreck Targets in Side-Scan Sonar Images by Quanhong Ma, Shaohua Jin, Gang Bian, Yang Cui, Guoqing Liu, Yihan Wang

“…In this paper, to address the discrepancies in the above three aspects, we propose the Side-scan Sonar Dynamic Rotating Target Detector (S³DR-Det), which is a model with a dynamic rotational convolution (DRC) module designed to effectively gather rotating targets’ high-quality features during the model’s feature extraction phase, a feature decoupling module (FDM) designed to distinguish between the various features needed for regression and classification in the detection phase, and a dynamic label assignment strategy based on spatial matching prior information (S-A) specific to rotating targets in the training phase, which can more reasonably and accurately classify positive and negative samples. …”
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Enhancing the trustworthiness of chaos and synchronization of chaotic satellite model: a practice of discrete fractional-order approaches by Saima Rashid, Sher Zaman Hamidi, Saima Akram, Moataz Alosaimi, Yu-Ming Chu

“…Abstract Accurate development of satellite maneuvers necessitates a broad orbital dynamical system and efficient nonlinear control techniques. …”
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Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet by Jalit S. A., Warkad S. B., Rane P. R., Bonde S. V.

“…To address these issues, this proposal introduces the Topo-Behavioral Hybrid Learning Model (TBHLM) for dynamic pricing in smart grids. TBHLM has five key modules, ST-GNN-PNet: Uses temporal graph convolutions to forecast loads, congestion, and locational marginal prices (LMPs) with <3.5% MAPE and <3s latency. …”
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U-shape-based network for left ventricular segmentation in echocardiograms with contrastive pretraining by Zhengkun Qian, Tao Hu, Jianming Wang, Zizhong Yang

“…Extensive experimental results on the EchoNet-Dynamic dataset demonstrate that the proposed modifications deliver competitive performance at a lower computational cost.…”
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FAULT DIAGNOSIS OF GEARBOX UNDER VARIABLE WORKING CONDITION BASED ON WEIGHTED SUBDOMAIN ADAPTIVE ADVERSARIAL NETWORK by ZHANG Huiyun, ZUO Fangjun, YU Xi, YANG Ting

“…Subsequently, a self-calibrated convolutions network (SCNet) incorporating an efficient channel attention (ECA) mechanism acted as a feature extractor, dynamically adjusting the interactions and dependencies between multi-source heterogeneous signals to balance the scale differences between the source and target domain heterogeneous data. …”
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Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…First, to enhance feature extraction at various levels of abstraction in the input data, this paper proposes a novel segment-wise convolution module, C2f-GB. This module performs convolution in stages on the feature map, generating more feature maps with fewer parameters and computational resources, thereby improving the model’s feature extraction capability while reducing parameter count and computational cost. …”
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YOLO-GCOF: A Lightweight Low-Altitude Drone Detection Model by Wanjun Yu, Kongxin Mo

“…YOLO-GCOF incorporates the GSConv-Integrated Dynamic Group Convolution Shuffle Transformer (GI-DGCST) module as the feature extraction module, which captures fine-grained details and improves the detection of small-scale features. …”
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FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms by Xu Ma, Jialong Lai, Zhicheng Zhong, Feifei Cui

“…However, the Martian surface topography's complexity and the atmospheric environment's dynamic changes make accurate identification of water ice clouds difficult. …”
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STar-DETR: A Lightweight Real-Time Detection Transformer for Space Targets in Optical Sensor Systems by Yao Xiao, Yang Guo, Qinghao Pang, Xu Yang, Zhengxu Zhao, Xianlong Yin

“…Second, group shuffle convolution (GSConv) is incorporated into the efficient hybrid encoder, which reduces convolution parameters while facilitating information exchange between channels. …”
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