Electronics

28 pages, 1674 KB

Open AccessArticle

Cross-Domain Salt Body Segmentation via Dual-Branch Collaborative Modeling and Dual-Granularity Prototype Contrast

by Liechong Wang, Ruonan Yin, Guangyue Zhou, Kewen Li and Qingshan Wu

Electronics 2026, 15(11), 2233; https://doi.org/10.3390/electronics15112233 (registering DOI) - 22 May 2026

Accurate segmentation of seismic salt bodies is of great significance for oil and gas exploration. Although existing deep learning methods have achieved remarkable progress within a single survey, segmentation performance degrades significantly when deployed to target surveys that exhibit systematic differences in geological settings and salt morphology, owing to a distributional shift in feature space. To address the cross-domain generalization problem, this paper first designs a CNN-Transformer dual-branch fusion network, DBF-CTSaltNet, as the backbone, which enhances the collaborative modeling of local boundaries and global morphology through the synergy of a Morphology-Adaptive Residual Unit, a Structured Global Guidance Unit, and a Bidirectional Cross-aware Fusion Unit. Building upon this, an unsupervised domain adaptation framework, UDA-DBF-CTSaltNet, is proposed, which independently constructs class prototypes in the respective feature spaces of the two branches and simultaneously drives cross-domain alignment at both local-boundary and global-morphology semantic granularities via dual-granularity prototype contrast. Experiments demonstrate that DBF-CTSaltNet outperforms mainstream models on the TGS source domain, and UDA-DBF-CTSaltNet significantly improves cross-domain segmentation performance on both the F3 and SEAM target domains. Full article

(This article belongs to the Section Artificial Intelligence)

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16 pages, 3229 KB

Open AccessArticle

Design of a Rapid License Plate Localization Algorithm Utilizing Color Statistical Features

by Mingjin Li, Xianfeng Tang, Ying Xiong, Huajie Guo, Jingqian Wu, Chao Jiang, Rui Han, Hengjia Xiang, Zhe Wang, Zhongfu Zhang and Juan Gao

Electronics 2026, 15(11), 2232; https://doi.org/10.3390/electronics15112232 (registering DOI) - 22 May 2026

Abstract

Aiming at the problems of weak background adaptive ability, high dependence on edge features, high computational complexity of some traditional license plate location algorithms, high deployment cost and strong training dependence of location model based on deep learning, this paper proposes a fast license plate location algorithm based on statistical color features. The algorithm uses the HSV color space as the main processing channel, and quantifies the regional color distribution characteristics by constructing the hue histogram and calculating its standard deviation and other statistics, which significantly improves the discrimination and illumination adaptability of the license plate mask in complex background. Compared with the lightweight deep learning models such as “You Only Look Once Version 12 Nano”, this algorithm does not need GPU acceleration and model loading, eliminates the need for data training, significantly reduces the deployment cost and complexity, and can run efficiently on the general computing platform. The experimental results show that compared with the YOLOv12n model, the average processing time of this algorithm is shortened by 30.81% (when YOLOv12n is evaluated with GPU) or 48.42% (when YOLOv12n is evaluated with CPU) at the cost of sacrificing about 5.8% positioning accuracy. The positioning accuracy still reaches 93.7%, demonstrating high processing efficiency and excellent platform adaptability. The algorithm has the advantages of being lightweight, efficient and interpretable, and is especially suitable for intelligent parking lots, edge devices and other scenes sensitive to real time, cost and energy consumption. Full article

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32 pages, 3635 KB

Open AccessArticle

Graph Spatiotemporal World-Model-Driven Rolling MPC for Low-Carbon Economic Dispatch of Industrial-Park Integrated Electricity–Heat–Hydrogen Energy Systems

by Junling Liu, Xiaojun Wang, Leilei Wang and Yu Song

Electronics 2026, 15(11), 2231; https://doi.org/10.3390/electronics15112231 (registering DOI) - 22 May 2026

Abstract

Industrial-park integrated electricity–heat–hydrogen energy systems (IEHESs) face a challenging rolling dispatch problem because strong multi-energy coupling, intertemporal storage dynamics, and forecast uncertainty make it difficult to achieve economy, low-carbon operation, and hard-constraint feasibility simultaneously. To address this issue, this paper proposes a graph spatiotemporal world-model-driven rolling model predictive control (MPC) framework, termed GraphWorldModel_MPC, for low-carbon economic dispatch of industrial-park IEHESs. First, a unified graph-based representation is constructed to characterize the topology-aware coupling relationships among the electricity, heat, and hydrogen subsystems. Second, a graph spatiotemporal world model is developed to learn multi-step state transitions, while constraint-aligned physics-consistency terms are incorporated to align the predicted trajectories with multi-energy balance, storage-boundary evolution, and ramping semantics. In addition, the learned dynamics are embedded into a hard-constrained economic MPC framework, and a quantile-based safety-tightening mechanism is adopted to mitigate residual prediction uncertainty and enhance closed-loop feasibility. Case studies on an industrial-park IEHES show that the proposed method achieves an average 24-step normalized root mean square error (NRMSE) of 4.28% and reduces the monthly total operating cost by 6.07%, 3.83%, and 10.79% compared with conventional economic MPC (EMPC), distributionally robust adaptive MPC (DRAMPC), and GRU-MPC, respectively. It also reduces equivalent carbon emissions by 6.89%, 4.52%, and 9.50% relative to these benchmarks, while maintaining zero dispatch violations in the tested monthly horizon. Full article

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Electronics, Volume 15, Issue 11 (June-1 2026) – 3 articles

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