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OneForecast: A Universal Framework for Global and Regional Weather Forecasting

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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

Optimal Minimum Energy Path Planning of Mobile Robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

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Optimal Minimum Energy Path Planning of Mobile Robot Based o...

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jiang Du Zhou Da-Lin Li Gong-Fa Lu Bao-Li Yun Jun-Tong Liu Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Computing University of Portsmouth Portsmouth UK Hubei key laboratory of hydroelectric machinery design & maintenance China Three Gorges University Yichang China Key Laboratory of Mechanical Transmission and Manufacturing Engineering of Hubei Province Wuhan University of Science and Technology Wuhan China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan China Institute of Semiconductors Chinese Academy of Sciences Beijing China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing.

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GLOBAL SOLVABILITY IN A TWO-SPECIES CHEMOTAXIS SYSTEM WITH SIGNAL PRODUCTION

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arXiv 2021年

作者： Ren, Guoqiang Xiang, Tian School of Mathematics and Statistics Huazhong University of Science and Technology Hubei Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan430074 China Institute for Mathematical Sciences Renmin University of China Bejing100872 China

In this paper, we study the following prototypical two-species chemotaxis system with Lotka-Volterra competition and signal production: (Equation presented). We show that if (Equation presented). the associated Neumann initial-boundary value problem for (∗) admits a global generalized solutions in a bounded and smooth domain Ω ⊂ N (N ≥ 2) under merely integrable initial *** Codes 35A01, 35K57, 35Q92, 92C17 © 2021, CC BY.

关键词： Initial value problems

Convergence analysis of one-point large deviations rate functions of numerical discretizations for stochastic wave equations with small noise

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arXiv 2022年

作者： Jin, Diancong Hong, Jialin Sheng, Derui School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

In this work, we present the convergence analysis of one-point large deviations rate functions (LDRFs) of the spatial finite difference method (FDM) for stochastic wave equations with small noise, which is essentially about the asymptotical limit of minimization problems and not a trivial task for the nonlinear cases. In order to overcome the difficulty that objective functions for the original equation and the spatial FDM have different effective domains, we propose a new technical route for analyzing the pointwise convergence of the one-point LDRFs of the spatial FDM, based on the Γ -convergence of objective functions. Based on the new technical route, the intractable convergence analysis of one-point LDRFs boils down to the qualitative analysis of skeleton equations of the original equation and its numerical discretizations. Copyright © 2022, The Authors. All rights reserved.

关键词： Finite difference method

GLOBAL SOLVABILITY AND ASYMPTOTICAL BEHAVIOR IN A TWO-SPECIES CHEMOTAXIS MODEL WITH SIGNAL ABSORPTION

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arXiv 2021年

In this work, we study global existence, eventual smoothness and asymptotical behavior of positive solutions for the following two-species chemotaxis consumption model: ut = ∆u - χ1∇ · (u∇w), x ∈ Ω, t > 0, {{{{{{{ vt = ∆v - χ2∇ · (u∇w), x ∈ Ω, t > 0, wt = ∆w - (αu + βv)w, x ∈ Ω, t > 0, in a bounded smooth but not necessarily convex domain Ω ⊂ Rn(n = 2, 3, 4, 5) with nonnegative initial data u0, v0, w0 and homogeneous Neumann boundary data. Here, the parameters χ1, χ2 are positive and α, β are nonnegative. Under a smallness condition max{χ1, χ2}kw0kL∞ MSC Codes 35K59, 35K57 © 2021, CC BY.

关键词： Biochemistry

Fine-grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks

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arXiv 2023年

作者： Huang, Shaoyuan Zhang, Heng Wang, Xiaofei Chen, Min Li, Jianxin Leung, Victor C.M. Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin University Tianjin300350 China The School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China The School of Information Technology Deakin University BurwoodVIC3220 Australia The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

The 5G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5G UDNs. In this paper, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76m, with up to 80% of the unknown areas. © 2023, CC BY-NC-ND.

关键词： Forecasting

The Exponential Transformation Based Lattice Boltzmann Model for Convection-Diffusion Equation

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SSRN

SSRN 2022年

作者： Zhang, Ting Cui, Shuqi Hong, Ning Shi, Baochang College of Sciences Wuhan University of Science and Technology Wuhan430081 China School of General Education Wuchang University of Technology Wuhan430223 China Institute of Interdisciplinary Research for Mathematics and Applied Science School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

In this paper, an exponential transformation based lattice Boltzmann (LB) model for solving the n -dimensional ( n D) convection-diffusion equation(CDE) is developed. Firstly, a class of exponential transformation is proposed to convert the n D CDE into a diffusion equation. Then, the converted diffusion equation is solved by the LB model. So, compared to the available LB models for CDE, the present LB model can eliminate the difficulty in treating the convection term. With the direct Taylor expansion method, it is shown that the CDE can be exactly derived from the exponential transformation based LB model. Finally, a variety of numerical tests have been conducted to validate the present LB model. It can be found that the numerical results agree well with the analytical solutions. Moreover, we also find that the present LB model has a second-order convergence rate in space, and it is more effective and more stable than the previous LB model for the CDE. © 2022, The Authors. All rights reserved.

关键词： Boltzmann equation

See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification

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arXiv 2024年

作者： Han, Xiyu Zhong, Xian Huang, Wenxin Jia, Xuemei Liu, Wenxuan Yu, Xiaohan Kot, Alex Chichung Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572025 China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Computer Science Wuhan University Wuhan430072 China School of Computer Science Peking University Beijing100091 China School of Computing Macquarie University SydneyNSW2109 Australia Rapid-Rich Object Search Lab School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing. Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features but often struggle to capture complex semantic information. In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features. Specifically, we introduce Semantic Separation Enhancement (SSE) module, which uses dual learnable text tokens to separately capture confounding and clothing-related semantic information, effectively isolating ID-relevant features from distracting clothing semantics. Additionally, we develop a Semantic-Guided Interaction Module (SIM) that uses orthogonalized text features to guide visual representations, sharpening the model’s focus on distinctive ID characteristics. This integration enhances the model’s discriminative power and enriches the visual context with high-dimensional semantic insights. Extensive experiments on three CC-ReID datasets demonstrate that our method outperforms state-of-the-art techniques. The code will be released at github. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Hidden Follower Detection via Refined Gaze and Walking State Estimation

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Hidden Follower Detection via Refined Gaze and Walking State...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yaxi Chen Ruimin Hu Danni Xu Zheng Wang Linbo Luo Dengshi Li National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computing the National University of Singapore Singapore School of Cyber Engineering Xidian University Xi’an China School of Artificial Intelligence Jianghan University Wuhan China

Hidden following is following behavior with special intentions, and detecting hidden following behavior can prevent many criminal activities in advance. The previous method uses gaze and spacing behaviors to distinguish hidden followers from normal pedestrians. However, they express gaze behaviors in a coarse-grained way with binary values, making it difficult to accurately depict the gaze state of pedestrians. To this end, we propose the Refined Hidden Follower Detection (RHFD) model by choosing a suitable mapping function based on the principle that the closer the gaze direction is to someone, the more likely it is to gaze at someone, which converts the gaze direction into a continuous estimated gaze state representing the complex and variable gaze behavior of pedestrians. Simultaneously, we introduce variations in the magnitude and direction of pedestrian velocity to refine the representation of pedestrian walking states. Experimental results on the surveillance dataset show that RHFD outperforms state-of-the-art methods.

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