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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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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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Global well-posedness and decay estimates for the 2D chemotaxis model with the supercritical case

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Journal of Mathematical Analysis and Applications 2025年第1期552卷

作者： Peiyi Liu Qian Zhang Chinese Academy of Inspection & Quarantine Greater Bayarea Zhongshan 528000 PR China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding 071002 PR China

In this paper, we investigate the Cauchy problem for the 2D chemotaxis model. By considering an external force term of the form − ρ q for 1 ≤ q < 2 , we establish the existence of a global classical solution and ...

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Adaptive-weighted One-Class Support Vector machine for Outlier Detection 29

Adaptive-weighted One-Class Support Vector Machine for Outli...

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第29届中国控制与决策会议

作者： Man Ji Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information ScienceHebei University

ISBN: (纸本)9781509046584

The classification performances of the traditional one-class support vector machine(OCSVM) and its variants are often not satisfying when outliers are *** deal with this case,assigning smaller weights to these outliers may alleviate their influence upon the classification boundary and enhance the robustness of *** this paper,a novel adaptive-weighted one-class support vector machine(AWOCSVM) is proposed for dealing with the outlier detection *** appropriate weights are assigned to training samples by considering both their local densities and distances between them to their *** results on two synthetic data sets and eight benchmark data sets demonstrate that the proposed AWOCSVM achieves more compact classification boundary and superior performance in comparison with the traditional OCSVM and one related approach.

关键词： One-Class Support Vector machine Classification Boundary Weighted One-Class Support Vector machine Support Vector machine

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Locality Correlation Preserving Based One-Class Support Vector machine 29

Locality Correlation Preserving Based One-Class Support Vect...

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第29届中国控制与决策会议

作者： Jian-Di Chang Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information ScienceHebei University

ISBN: (纸本)9781509046584

In order to fully utilize the local geometric information of the given training set consisting of the normal data,locality correlation preserving(LCP) is introduced into the traditional one-class support vector machine(OCSVM).The proposed method,named as locality correlation preserving based one-class support vector machine(LCP-OCSVM),inherits the merits of LCP and *** can keep locality correlation of the normal data and margin maximization between the normal data and the origin in the high-dimensional feature *** results on one synthetic data set and ten benchmark data sets demonstrate that the proposed method is superior to the traditional OCSVM and two related approaches.

关键词： Locality Correlation Preserving One-Class Support Vector machine One-Class Classification Local Geometric Structure

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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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A mathematical formula retrieval method using structure sub-tree

A mathematical formula retrieval method using structure sub-...

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3rd International Conference on Materials Science and Information Technology, MSIT 2013

作者： Guan, Ming Jie Tian, Xue Dong Yang, Fang Yang, Song Qiang College of Mathematics and Computer Science Hebei University Baoding China Hebei key laboratory of Machine Learning and Computational Intelligence Hebei University China

ISBN: (纸本)9783037857700

It is quite inadequate in providing formula retrieval function by traditional retrieval techniques used in full-text information retrieval system. The main reason is that there are many difficulties to extract the keywords of the mathematical formulas. In this paper, a detailed analysis of the structural characteristics of mathematical formulas and existing index mechanism of mathematical formula searching engine is fulfilled. Then a full-text index (named SLIndex) of mathematical formulas with B+ tree structure is designed and implemented which extracts the structured logic sub-tree feature as keywords of formulas and employs inverted index. Finally, a formula search engine model based on SLIndex is implemented in Apache 2.0 web server. © (2013) Trans Tech Publications, Switzerland.

关键词： Search engines

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An image retrieval method about ancient Chinese characters within local area

An image retrieval method about ancient Chinese characters w...

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2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Tian, Xue-Dong Huang, Juan Yang, Fang Ha, Yan College of Mathematics and Computer Science Hebei University Baoding Hebei China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Hebei China

ISBN: (纸本)9781457717017

Ancient Chinese characters normally have complex structures which are composed of many strokes. Different characters may show a similar shape which results in unsatisfactory answers for their image retrieving using the traditional methods. In this paper, an image retrieval algorithm for ancient Chinese characters is proposed based on the local area of characters. First of all, according to the need of users, the regions needed to be matched are marked on the sample of ancient Chinese character image. Then we match the image by comparing the Euclidean distance of feature parameters between sample image and database images. Finally, the ancient images which users need are found. The experimental results show that the proposed method could improve the efficiency of correlative research. © 2011 IEEE.

关键词： Image retrieval

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Multimodal multi-objective differential evolution algorithm based on spectral clustering

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International Journal of Innovative Computing and Applications 2022年第5-6期13卷 303-313页

作者： Wang, Shenwen Chu, Xiaokai Zhang, Jiaxing Gao, Na Zhou, Yao School of Information Engineering Hebei GEO University Hebei Shijiazhuang050031 China Laboratory of Artificial Intelligence and Machine Learning Hebei GEO University Hebei Shijiazhuang050031 China

In recent years, in the face of the same problem in industrial production and life, decision-makers often hope to have a variety of different solutions to deal with. In other words, we hope to locate more different Pareto solutions under the condition of finding Pareto front. However, there are few researches in this field. For this reason, we propose a multimodal multi-objective differential evolution algorithm based on spectral clustering (SC-MMODE), which mainly uses some mechanisms to divide the solutions in the decision space into several mutually independent subpopulations. First, SC-MMODE uses a spectral clustering algorithm to control the decision space and form multiple sub-populations with good neighbourhood relations. Secondly, a special crowding distance mechanism is used to balance the distribution of solutions in the decision space and objective space. In addition, the classical differential evolution algorithm can effectively prevent premature convergence. Then, in 17 test problems, the SC-MMODE algorithm and some new multimode multi-objective algorithms are tested simultaneously. Finally, through the analysis of experimental data, the SC-MMODE algorithm can find more Pareto optimal sets in the decision space, so it can effectively solve such problems. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Multiobjective optimization

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Research on location and similar comments in point-of-interest recommendation system for users

Research on location and similar comments in point-of-intere...

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2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020

作者： Liu, Yu Wei, Ning College of Mathematics and Information Science Hebei University Baoding Hebei071002 China Hebei Key Laboratory of Machine Learning and Computational Intelligence China

By combining deep learning algorithms and potential probabilistic topic models, this paper proposes a new modeling strategy for individual users and constructs an effective point of interest recommendation system. First, Doc2vec is used to vectorize the user's comments, and the Bow model is used to match the similarity of the points of interest, and then the potential probability model is used to cluster the positions of the points of interest. Finally, relying on the user's historical comment data as a basis, the Top-K ranking recommendation scheme is obtained. The experimental results show that for large-scale location-based social network data sets, the algorithm in this paper has an ideal final effect on users' personalized recommendations. The recommendation model for user location information and similar comment points of interest proposed in this paper is more subjectively and objectively in line with user behavior needs. On the premise of ensuring the real-time performance of recommendation, the model proposed in this paper effectively improves the accuracy of recommendation compared with traditional point-of-interest recommendation algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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