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arXiv 2024年

作者： Zhang, Ruoyu Wang, Lihui Tang, Kun Xu, Jingwen Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m

关键词： Spheres

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A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms

arXiv

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arXiv 2023年

作者： Jiang, Meiyu Shen, Jun Jiang, Xuetao Luo, Lihui Zhou, Rui Zhou, Qingguo School of Information Science and Engineering Lanzhou University Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Gansu Provincial Industry Technology Center of Intelligent Equipment & Big Data for Disaster Prevention Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Gansu Lanzhou730000 China

Wind energy is a widely distributed, renewable, and environmentally friendly energy source that plays a crucial role in mitigating global warming and addressing energy shortages. Nevertheless, wind power generation is characterized by volatility, intermittence, and randomness, which hinder its ability to serve as a reliable power source for the grid. Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources. However, traditional wind power forecasting systems primarily focus on ultra-short-term or short-term forecasts, limiting their ability to address the diverse adjustment requirements of the power system simultaneously. To overcome these challenges, We propose an automatic framework capable of forecasting wind power across multi-time scale. The framework based on the tree-structured Parzen estimator (TPE) and temporal fusion transformer (TFT) that can provide ultra-short-term, short-term and medium-term wind power forecasting power. Our approach employs the TFT for wind power forecasting and categorizes features based on their properties. Additionally, we introduce a generic algorithm to simultaneously fine-tune the hyperparameters of the decomposition method and model. We evaluate the performance of our framework by conducting ablation experiments using three commonly used decomposition algorithms and six state-of-the-art models for forecasting multi-time scale. The experimental results demonstrate that our proposed method considerably improves prediction accuracy on the public dataset Engie https://***. Compared to the second-best state-of-the-art model, our approach exhibits a reduction of 31.75% and 28.74% in normalized mean absolute error (nMAE) for 24-hour forecasting, and 20.79% and 16.93% in nMAE for 48-hour forecasting, respectively. © 2023, CC BY.

关键词： Wind power

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computing optimal partition problems via Lagrange multiplier approach

arXiv

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arXiv 2024年

作者： Cheng, Qing Guo, Jing Wang, Dong Department of Mathematics Tongji University Shanghai200092 China Key Laboratory of Intelligent Computing and Applications Tongji University Ministry of Education China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China

In this paper, we consider numerical approximations for the optimal partition problem using Lagrange multipliers. By rewriting it into constrained gradient flows, three and four steps numerical schemes based on the Lagrange multiplier approach [16, 17] are proposed to solve the constrained gradient system. Numerical schemes proposed for the constrained gradient flows satisfy the nice properties of orthogonality-preserving, norm-preserving, positivity-preserving and energy dissipating. The proposed schemes are very efficient in which only linear Poisson equations are solved at each time step. Extensive numerical results in 2D and 3D for optimal partition problem are presented to validate the effectiveness and accuracy of the proposed numerical schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Pheromone Matrix Eigenvalue-based Convergence Assessment Indicator for Ant Colony Optimization

Pheromone Matrix Eigenvalue-based Convergence Assessment Ind...

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Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Xu Li Lin Shi Jian-Yu Li Jing Xu Zhi-Hui Zhan School of Computer Science and Engineering South China University of Technology Guangzhou P. R. China Research Center of Intelligent Computing & Big Data Technology Guangdong University of Finance and Economics Guangzhou Guangdong P. R. China College of Artificial Intelligence Nankai University Tianjin P. R. China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

As a crucial member of the evolutionary computation (EC), ant colony optimization (ACO) converges to the optimal result through a continuous process of iterative optimization. However, determining the convergence status of an ant colony remains a challenging task without well-established methods. To this end, this paper proposes a global evaluation indicator—average absolute eigenvalue of pheromone matrix (AAEPM) by constructing a pheromone matrix and analyzing its eigenvalue variations. This indicator aims to assess the convergence status of the ant colony during the evolutionary process. Experimental results show that the AAEPM is correlated with the convergence status of ACO and is not significantly influenced by algorithm parameters and problem scale.

关键词： Measurement Ant colony optimization Heuristic algorithms Evolutionary computation Eigenvalues and eigenfunctions Robustness Iterative methods Optimization Machine intelligence Convergence

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Petri Net Control Method for Pipe-line Systems and Its Implementation via CIF3 ⁎

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IFAC-PapersOnLine 2019年第24期52卷 261-266页

作者： YuHao Fu JiLiang Luo Huifeng Wu Jianhong Ye Yi-sheng Huang College Information Science and Engineering Huaqiao University Xiamen 361021 China College Information Science and Engineering Huaqiao University Xiamen 361021 China and also with Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen 361021 China Institute of Intelligent and Software Technology Hangzhou Dianzi University Hangzhou 310018 China Computer Science and Technology Huaqiao University Xiamen 361021 China and also with Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000 China Department of Electrical Engineering National Ilan University Taiwan

As for a pipe-line system subjected to complex operations, an approach is proposed to synthesize the controller via Petri nets (PNs) such that the plant is run as concurrently as possible and the loads of equipments are balanced. A P-timed and labeled PN is designed to model the whole process of a pipe-line system, where tasks, including transporting material from one tank to another and cleaning tank, are represented by operational places, and level sensors amounted in tanks are represented by labels assigned to transitions. Further, monitor places are designed to resolve the conflict relations among tasks due to the shared valves and pipes. An method is presented to translate an PN controller into a CIF3 (Compositional Interchange Format) model, which can be converted into a PLC program in the CIF3 tool. A beer filtration plant is taken as an example to illustrate the approach, and its simulation experiments are carried out to verify the theoretic results.

关键词： Chemical plant Valve control Petri net CIF3

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Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism

SSRN

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SSRN 2023年

作者： Jiang, Xuetao Jiang, Meiyu Luo, Lihui Shen, Jun Zhou, Qingguo Zhou, Rui School of Information Science and Engineering Lanzhou University Tianshui South Road No.222 Gansu Lanzhou730000 China Gansu Provincial Industry Technology Center of Intelligent Equipment & Big Data for Disaster Prevention Northwest Institute of Eco-Environment and Resources Chinese Academy of Science Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Accurate photovoltaic (PV) generation forecasts can reduce power redeploy from the grid, thus increasing the supplier's profit in the day-ahead electricity market. However, the PV process is affected differently by various factors under different weather conditions, resulting in significantly different energy output curves. In this context, this paper proposes a day-ahead PV power forecasting method with weather conditioned attention mechanism. We propose a multi-stream attention fusion network (MSAFN) which utilizes an algorithm to derive the optimal decomposition algorithm for different weather conditions. The proposed conditional decomposition (CD) algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model, aiming to achieve the optimal prediction performance. The MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather conditions. Notably, the attention modules adeptly learn patterns under diverse conditions, while simultaneously, the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training time. We compare the state-of-the-art decomposition algorithms (VMD, EEMD and MSTL, etc.) and prediction models (BPN, LSTM, XGBoost and transformer, etc.) commonly used in PV prediction. The results show that the MSAFN model is more accurate than the models above, which has a noticeable improvement compared to other recent day-ahead PV predictions on DKASC. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Research on the relationship between new business talents' ability and its thinking mode in the era of big data

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Journal of Physics: Conference Series 2021年第1期1774卷

作者： Ying Qu Ruoyu An Hebei University of Science and Technology School of Economics and Management Data science and Intelligent Computing Research Center Shijiazhuang City Hebei Province China

In the era of big data, the development of 'new business' really needs talents who use new ways of thinking to solve economic management ***, it is very important to comprehensively analyze the relationship between the ability of new business talents and the way of thinking in the era of big *** combing the literature, we have collected and sorted out the thinking mode in the era of big data and the ability requirements of new business for talents. This article selects 9 ability elements, including 4 ways of thinking and 5 talent abilities, to present the structural model of the relationship between the ability of new business talents and their thinking mode in the era of big data. Analyzing the relationship between talent abilities and thinking styles from a new perspective of big data, supplementing and expanding the existing research on new business talent's abilities, is conducive to the update of training programs, and has certain significance for integrating teaching resources and optimizing curriculum structure.

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A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

arXiv

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arXiv 2023年

作者： Gui, Jie Chen, Tuo Zhang, Jing Cao, Qiong Sun, Zhenan Luo, Hao Tao, Dacheng School of Cyber Science and Engineering Southeast University with Purple Mountain Laboratories Nanjing210000 China School of Cyber Science and Engineering Southeast University China School of Computer Science The University of Sydney CamperdownNSW2050 Australia College of Computing & Data Science Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue Singapore639798 Singapore JD Explore Academy Center for Research on Intelligent Perception and Computing Chinese Academy of Sciences Beijing100190 China Alibaba Group Hangzhou310052 China

Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. Firstly, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Secondly, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain. A curated collection of valuable resources can be accessed at https://***/guijiejie/SSL. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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Provably secure fog-based authentication protocol for VANETs

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Computer Networks 2024年 246卷

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Badar, Hafiz Muhammad Sanaullah Kharel, Rupak Das, Ashok Kumar School of Microelectronics and Communication Engineering Chongqing University Chongqing China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Emerging Computing Technologies Emerson University Multan Pakistan School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India

The integration of fog computing and vehicular ad-hoc networks (VANETs) has become increasingly important due to advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs poses a significant challenge. To address this challenge, we propose a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for confidential communication, and ensures privacy protection without relying on bilinear pairing. We provide rigorous security proofs for our protocol, specifically tailored for fog-based VANETs, demonstrating its ability to meet their stringent security requirements. Simulations using NS3 allow for a comprehensive performance evaluation, providing insights into the protocol's efficiency and scalability. Additionally, our protocol is highly efficient, featuring low computation and communication costs, thereby offering a practical and scalable solution for secure communication in fog-based VANETs. © 2024

关键词： Fog

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

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