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MTTF: a multimodal transformer for temperature forecasting

International Journal of Computers and Applications 2024年第2期46卷 122-135页

作者： Cao, Yang Zhai, Junhai Zhang, Wei Zhou, Xuesong Zhang, Feng Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Hebei Province Baoding China

Accurate weather forecasting is crucial for various applications, including agriculture and environmental monitoring. However, existing deep learning based methods typically use only temperature observations as input, which do not consider spatial location (e.g. neighboring regions usually show similar temperature trends) and increase the difficulty in predicting anomalous fluctuations in temperature. To address the issue, a multimodal transformer model for temperature forecasting is proposed. This model not only uses observational data (time series data) as input but also incorporates spatially rich ERA5 reanalysis temperature data (spatio-temporal data). The proposed method has two distinctive features: (1) a sequence merging module to highlight dominating features and reduce the cost of attention calculations. (2) a cross-all-modal attention mechanism to capture dependencies between the current modality and all modalities. The method was evaluated on three temperature datasets from a plain and two challenging high-altitude mountainous regions. Results showed that the proposed method outperforms existing methods in terms of MAE and MSE, offering a promising new solution for temperature prediction. Our code and data are available at https://***/Adam618/MTTF. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Weather forecasting

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Regularity for minimizing sequences of some variational integrals

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Science China Mathematics 2023年第4期66卷 777-798页

作者： Hongya Gao Yanan Shan Wei Ren Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information ScienceHebei UniversityBaoding 071002China

This paper deals with regularity properties for minimizing sequences of some integral functionals related to the nonlinear elasticity *** some structural conditions,we derive that the minimizing sequence and the deriv... 详细信息

关键词： regularity minimizing sequence variational integral energy Ekeland variational principle

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Feature Norm-Aware and Hardness-Guided Complementary Entropy Balanced Loss for Long-Tailed Image Classification 25th

Feature Norm-Aware and Hardness-Guided Complementary Entrop...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Feng Wang, Jia-Xin Hua, Qiang Dong, Chun-Ru Zhu, Jie Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9789819642069

To handle model bias from long-tailed image datasets, most existing studies have proposed to strengthen the focus on tail classes. However, they usually overlook the intricacies of sample learning hardness and feature norms’ impact on accuracy, as well as the non-target class contribution. We designed a new loss using logit adjustment. It considers the distribution of the number of class samples, sample learning hardness, and feature norms. Moreover, it combines a complementary entropy regularization term designed for long-tailed data. Extensive experiments on long-tailed datasets confirm the proposed loss’s superiority in enhancing classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Discriminative Score Suppression for Weakly Supervised Video Anomaly Detection

Discriminative Score Suppression for Weakly Supervised Video...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Xu, Chen Li, Chunguo Xing, Hongjie College of Mathematics and Information Science Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding071002 China School of Cyber Security and Computer Hebei University Baoding071000 China

ISBN: (纸本)9798331510831

Weakly supervised video anomaly detection (WSVAD) often relies on Multiple Instance learning (MIL). However, selecting only the most discriminative segments for training limits the model's ability to comprehensively detect anomalous events, particularly hard anomalies. To overcome this limitation, we propose the Discriminative Score Suppression (DSS) module. This module suppresses the discriminative scores of the most prominent anomalies, shifting the model's attention to less obvious but important hard anomalies. This approach guides the model to learn the critical features of hard anomalies, enabling a more comprehensive detection of anomalous events. Additionally, the Anomaly Score Refinement (ASR) module constructs a dissimilarity-based classifier by storing normal patterns as prototypes, and integrates this with a neural network classifier. By combining the anomaly scores from both classifiers, more accurate detection of true hard anomalies is achieved. A score-sensitive inner-bag loss function not only adjusts penalties based on anomaly scores but also ensures that the model avoids erroneous selections. Our method accurately detects various anomalies, including challenging and multi-segment anomalies, while minimizing false positives for normal events. Extensive experiments show that the proposed framework outperforms state-of-the-art methods on the UCF-Crime and XD-Violence datasets. © 2025 IEEE.

关键词： Supervised learning

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Global Integrability for Solutions to Obstacle Problems

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Journal of Partial Differential Equations 2022年第4期35卷 320-330页

作者： SHAN Yanan GAO Hongya Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information ScienceHebei UniversityBaoding 071002China

DenoteκψØ(Ω)={υ∈w1,p(Ω):υ≥ψ,a,***υ-Ø∈w1,po(Ω)},where is any function in Q C R^(N),N≥2,with values in RU[±∞]and e is a measurable *** paper deals with global integrability for u E Kμ,e suc... 详细信息

关键词： Global integrability obstacle problem A-harmonic equation

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Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

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Tsinghua Science and Technology 2022年第5期27卷 855-868页

作者： Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province the College of Mathematics and Information ScienceHebei UniversityBaoding 071002China School of Applied Mathematics Beijing Normal University ZhuhaiZhuhai 519087China Department of Computer Teaching Hebei UniversityBaoding 071002China

The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation *** a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among *** these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering ***,the diversity and amount of input information are enhanced to generate highly useful representations for downstream ***,the topological graph is constructed to express the spatial relationship of *** a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively *** external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering *** results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service *** GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech ***,DBI indicates the dispersion of cluster distribution and compactness within the cluster.

关键词： self-supervised clustering graph convolutional network feature correspondence semantic feature guidance confusion matrix evaluation indicator

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Instability and bifurcation analysis of a diffusive predator-prey model with fear effect and delay

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International Journal of Biomathematics 2024年第8期17卷 245-290页

作者： Hongyan Sun Jianzhi Cao Pengmiao Hao Lijie Zhang School of Management Hebei University Baoding 071002P.R.China College of Mathematics and Information Science Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding 071002P.R.China School of Mathematical Sciences Zhejiang Normal University Jinhua 321004P.R.China

In this paper,a delayed diffusive predator-prey model with fear effect under Neumann boundary conditions is *** the system without diffusion and delay,the conditions for the existence and local stability of equilibria are obtained by analyzing the ***,the instability induced by diffusion and delay-diffusion of the positive constant stationary solutions are discussed,***,the regions of instability and pattern formation can be achieved with respect to diffusion and delay ***,the existence and direction of Hopf bifurcation and the properties of the homogeneous/nonhomogeneous bifurcated periodic solutions are driven by using the center manifold theorem and the normal form ***,some numerical simulations are carried out to verify the theoretical results.

关键词： Delayed diffusive system Hopf bifurcation Turing instability pattern formation delay-diffusion-driven instability fear effect

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A Multiscale Global-Local Transformer for Long-Sequence PV Power Generation Forecasting 25th

A Multiscale Global-Local Transformer for Long-Sequence PV ...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Deng, Tian-Yang Li, Wen-Li Zhang, Feng Hua, Qiang Dong, Chun-Ru Lim, Boon Han Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics and Information Science Hebei University Baoding China Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia

ISBN: (纸本)9789819642069

The intermittent and fluctuating solar irradiance makes photovoltaic (PV) power generation unstable, which brings great challenges to the power grid system. Existing deep learning-based PV power generation prediction models usually employ Long Short-Term Memory (LSTM) or Convolutional Neural Networks (CNNs) to predict the future PV power generation. However, the recurrent structure-based methods usually failed to model the long-term dependence due to the accumulated error caused by backpropagation through time. To overcome these issues, an improved Transformer-based prediction model, which is called GLformer, is proposed in this work. Specifically, the proposed GLformer decomposes an input series into global pattern subsequences and local pattern subsequences, where the global pattern subsequences represent the trends and seasonality and the local pattern subsequences are used to describe the fluctuations. The global and local features are extracted and fused using a Transformer-based global-local feature extraction module, which enhances the model’s capability of modeling long-term dependence, and obtaining complete time features for prediction. Extensive experimental results show that the proposed method is superior to most SOTA forecasting models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning PV power forecasting Solar energy Transformer

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An improved particle swarm optimization algorithm for task scheduling in cloud computing

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Journal of Ambient intelligence and Humanized Computing 2023年第4期14卷 4313-4327页

作者： Pirozmand, Poria Jalalinejad, Hoda Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Li, Yingqiu Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding071002 China Department of Mathematics and Computer Science Bandar Abbas Branch Islamic Azad University Bandar Abbas Iran Department of Computer Science University of Regina Regina Canada Tehran Iran

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cloud computing

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Bifurcation and Turing instability for genetic regulatory networks with diffusion

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International Journal of Biomathematics 2023年第2期16卷 1-30页

作者： Hongyan Sun Jianzhi Cao Peiguang Wang Haijun Jiang School of Management Hebei University Baoding 071002P.R.China College of Mathematics and Information Sciences Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding 071002P.R.China College of Mathematics and System Sciences Xinjiang University Urumqi 830046P.R.China

In this paper,a diffusive genetic regulatory network under Neumann boundary conditions is ***,the criteria for the local stability and diffusion-driven instability of the positive stationary solution without and with diffusion are investigated,***,Turing regions and pattern formation are obtained in the plane of diffusion ***,the existence and multiplicity of spatially homogeneous/nonhomogeneous non-constant steady-states are studied by using the Lyapunov-Schmidt ***,some numerical simulations are carried out to illustrate the theoretical results.

关键词： Genetic regulatory networks diffusion Turing instability pattern formation bifurcation Lyapunov-Schmidt reduction.

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