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STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting

Tsinghua science and Technology 2024年第4期29卷 1232-1247页

作者： Zhuolun Jiang Zefei Ning Hao Miao Li Wang College of Computer Science and Technology(College of Data Science) Taiyuan University of TechnologyJinzhong 030600China

Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.

关键词： time series forecasting multivariate time series spatio-temporal decomposition

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Route selection for opportunity-sensing and prediction of waterlogging

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Frontiers of computer science 2024年第4期18卷 147-159页

作者： Jingbin WANG Weijie ZHANG Zhiyong YU Fangwan HUANG Weiping ZHU Longbiao CHEN College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Department of Computer Science and Technology Xiamen UniversityXiamen 361005China

Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlogging monitoring impossible.A common method is to forecast the city’s global waterlogging status using its partial waterlogging *** method has two challenges:first,existing predictive algorithms are either driven by knowledge or data alone;and second,the partial waterlogging data is not collected selectively,resulting in poor *** overcome the aforementioned challenges,this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus *** framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and *** predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural *** combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming *** selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget *** experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost,high accuracy,wide coverage,and fine granularity.

关键词： waterlogging prediction sparse crowdsensing active learning route selection graph convolutional network

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Distributed stochastic source seeking and formation control based on delayed measurements

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science China(Information sciences) 2025年第3期68卷 319-337页

作者： Pei-Pei ZHANG Lin-Yu YANG Shu-Jun LIU School of Mathematics Sichuan University School of Data and Computer Science Shandong Women's University

In this paper, we design a distributed stochastic source seeking algorithm based on time-delay measurements to implement source seeking and formation control, so that vehicles can achieve and maintain a specific formation during the source seeking process. First, we present continuous-time stochastic averaging theorems for nonlinear delay-differential systems with stochastic perturbations. Then, based on the stochastic extremum seeking method and the leaderless formation strategy,we design a distributed stochastic source seeking algorithm based on time-delay measurements to navigate multiple velocity-actuated vehicles to search for an unknown source while achieving and maintaining a predefined formation, and the effect of the delay is eliminated by adopting the one-stage sequential predictor approach. Moreover, based on our developed stochastic averaging theorems, we prove that the average position of vehicles exponentially converges to a small neighborhood of the source in the almost sure sense, and vehicles can achieve and maintain a predefined formation. Finally, we provide numerical examples to verify the effectiveness of our proposed algorithm.

关键词： distributed source seeking formation control stochastic extremum seeking stochastic averaging time delays

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Automation of AD-OHC Dashbord and Monitoring of Cloud Resources using Genrative AI to Reduce Costing and Enhance Performance

Automation of AD-OHC Dashbord and Monitoring of Cloud Resour...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Chavan, Parikshit Chavan, Peeyusha Be Computer Science Data Science

ISBN: (纸本)9798350319019

In this extensive review, the incorporation of Generative Artificial Intelligence (AI) into ad-hoc dashboards and cloud resource monitoring is investigated in depth. A purpose of this work is to investigate a current research and highlight the transformational potential of generative artificial intelligence for optimising costs, automating tasks, and improving overall cost efficiency. This article includes a comprehensive analysis of a development of ad-hoc cloud computing, a relevance of cloud resource monitoring, and the role that generative artificial intelligence plays in reducing costs. Notable advantages include better user satisfaction, resource optimisation, adaptive learning, and efficient automation. Additionally, precise decision-making and resource optimisation are highlighted. By presenting actual evidence, the study highlights the association between the quality of generative artificial intelligence and the influence it has on society. It finishes with an analysis of generative artificial intelligence applications in ad-hoc dashboards, with a focus on increased resource utilisation, scalability, and timely consistency of cloud resources. In addition, the article addresses constraints and makes suggestions for future paths. These include models that protect users' privacy, efficient resource utilisation, explainable artificial intelligence, dynamic autonomy, and security-driven generative models. The paper's goal is to provide the groundwork for further study and improvement. © 2024 IEEE.

关键词： Cost reduction

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data Migration between on prim to Cloud using Generative AI to Reduce Costing and Overheads

Data Migration between on prim to Cloud using Generative AI ...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Chavan, Parikshit Chavan, Peeyusha Be Computer Science Data Science

ISBN: (纸本)9798350319019

'Cloud data migration' describes the method of transferring digital data to new cloud services or garage structures. data have to be transferred in a way that guarantees it remains available, secure, and uncompromised. Businesses might also easily adjust to new needs and trends in era by way of transferring their statistics, apps, or workloads to the cloud. Scalability, adaptability, and optimising charges are all made feasible by this. Generative AI can help us evaluate existing applications and provide insights into how they can be optimized for the target cloud environments. In the ever-evolving world of technology, Generative AI (GenAI) has emerged as a transformative force, poised to revolutionize various industries and domains, including cloud migration. As the global landscape of migration continues to shift, GenAI is prepared to play an essential role in streamlining processes, enhancing accuracy, and improving overall migration outcomes. The review present paper investigates the utilisation of generative AI in order to facilitate the transfer of data from on-premises to the cloud. While highlighting the potential of generative AI to automate transfer operations, it tackles obstacles such as economic implications and security concerns. Migration techniques, the benefits of cloud migration, and pertinent literature are examined in this study, which also provides a summary of key findings and recommendations for future research. © 2024 IEEE.

关键词： data transfer

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SGformer:Boosting transformers for indoor lighting estimation from a single image

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Computational Visual Media 2024年第4期10卷 671-686页

作者： Junhong Zhao Bing Xue Mengjie Zhang Centre for Data Science and Artificial Intelligence&School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Estimating lighting from standard images can effectively circumvent the need for resourceintensive high-dynamic-range(HDR)lighting ***,this task is often ill-posed and challenging,particularly for indoor scenes,due to the intricacy and ambiguity inherent in various indoor illumination *** propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian(SG)distributions—a compact yet expressive lighting *** from previous approaches,we explore underlying local and global dependencies in lighting features,which are crucial for reliable lighting ***,we investigate the structural relationships spanning various resolutions of SG distributions,ranging from sparse to dense,aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component *** harnessing the synergy of local–global lighting representation learning and incorporating consistency constraints from various SG resolutions,the proposed method yields more accurate lighting estimates,allowing for more realistic lighting effects in object relighting and *** code and model implementing our work can be found at https://***/junhong-jennifer-zhao/SGformer.

关键词： lighting estimation transformer spherical Gaussian(SG) augmented reality

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On hierarchical clustering-based identification of PWA model with model structure selection and application to automotive actuators for HiL simulation

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Soft Computing 2025年第2期29卷 543-558页

作者： Ren, Zhenxing College of Computer Science and Technology and College of Data Science Taiyuan University of Technology Shanxi Jinzhong China

The application of the electronic control unit (ECU) motivates dynamic models with high precision to simulate mechatronic systems for various analysis and design tasks like hardware-in-the-loop (HiL) simulation. Unlike traditional physical models which are established based on the research experience or the mechanics mechanism, in this study, a novel data-driven modeling approach is presented based on the piecewise affine (PWA) identification method. In this work, the highly nonlinear dynamic of automotive actuators is well approximated by the PWA model. To obtain experimental data that can accurately reflect the characteristics of actuators, a test bench was first built. On this basis, the PWA identification of automotive is composed of the data clustering, the model structure selection, and the model parameter estimation. The proposed clustering method improves the widely used balanced iterative reducing and clustering using hierarchies (BIRCH) by introducing a refinement phase for handling clusters with arbitrary shapes. The model structure selection and the parameter estimation are jointly solved by using the optimization method. The presented method is demonstrated with an academic example and an automotive throttle, and the results show that the proposed method can achieve a high model quality, which means that the normalized root mean squared error (NRMSE) is 0.03 and the absolute maximal prediction error can reach 2.43°. Compared to other models like a physical model or a fuzzy model, the improvement using the proposed model can be up to 50%. Thus, the quality of the proposed PWA model is sufficient for HiL simulation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Hardware-in-the-loop simulation

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Using Multi-Scale Convolution Fusion and Memory-Augmented Adversarial Autoencoder to Detect Diverse Anomalies in Multivariate Time Series

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Tsinghua science and Technology 2025年第1期30卷 234-246页

作者： Zefei Ning Hao Miao Zhuolun Jiang Li Wang College of Computer Science and Technology(College of Data Science) Taiyuan University of TechnologyJinzhong 030600China

Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a *** and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle *** this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly *** is an encoder-decoder-based framework with four main ***-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal ***-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context *** module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical ***,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly ***,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids *** on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.

关键词： multivariate time series anomaly detection AutoEncoder(AE) multi-scale fusion

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Adaptive Luminance Enhancement and High-Fidelity Color Correction for Low-Light Image Enhancement

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 732-747页

作者： Niu, Yuzhen Li, Fusheng Li, Yuezhou Chen, Siling Chen, Yuzhong Fuzhou University College of Computer and Data Science Fuzhou350108 China

It is a challenging task to obtain high-quality images in low-light scenarios. While existing low-light image enhancement methods learn the mapping from low-light to clear images, such a straightforward approach lacks the targeted design for real-world scenarios, hampering their practical utility. As a result, issues such as overexposure and color distortion are likely to arise when processing images in uneven luminance or extreme darkness. To address these issues, we propose an adaptive luminance enhancement and high-fidelity color correction network (LCNet), which adopts a strategy of enhancing luminance first and then correcting color. Specifically, in the adaptive luminance enhancement stage, we design a multi-stage dual attention residual module (MDARM), which incorporates parallel spatial and channel attention mechanisms within residual blocks. This module extracts luminance prior from the low-light image to adaptively enhance luminance, while suppressing overexposure in areas with sufficient luminance. In the high-fidelity color correction stage, we design a progressive multi-scale feature fusion module (PMFFM) that combines progressively stage-wise multiscale feature fusion with long/short skip connections, enabling thorough interaction between features at different scales across stages. This module extracts and fuses color features with varying receptive fields to ensure accurate and consistent color correction. Furthermore, we introduce a multi-color-space loss to effectively constrain the color correction. These two stages together produce high-quality images with appropriate luminance and high-fidelity color. Extensive experiments on both low-level and high-level tasks demonstrate that our LCNet outperforms state-of-the-art methods and achieves superior performance for low-light image enhancement in real-world scenarios. © 2015 IEEE.

关键词： Photointerpretation

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Integer wavelet transform-based secret image sharing using rook polynomial and hamming code with authentication

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1435-1450页

作者： Sara Charoghchi Zahra Saeidi Samaneh Mashhadi Cryptography and Data Security Laboratory School of Mathematics and Computer ScienceIran University of Science&TechnologyTehranIran

As an effective way to securely transfer secret images,secret image sharing(SIS)has been a noteworthy area of *** in a SIS scheme,a secret image is shared via shadows and could be reconstructed by having the required number of them.A major downside of this method is its noise-like shadows,which draw the malicious users'*** order to overcome this problem,SIS schemes with meaningful shadows are introduced in which the shadows are first hidden in innocent-looking cover images and then *** most of these schemes,the cover image cannot be recovered without distortion,which makes them useless in case of utilising critical cover images such as military or medical ***,embedding the secret data in Least significant bits of the cover image,in many of these schemes,makes them very fragile to steganlysis.A reversible IWT-based SIS scheme using Rook polynomial and Hamming code with authentication is *** order to make the scheme robust to steganalysis,the shadow image is embedded in coefficients of Integer wavelet transform of the cover *** Rook polynomial makes the scheme more secure and moreover makes authentication very easy and with no need to share private key to ***,utilising Hamming code lets us embed data with much less required modifications on the cover image which results in high-quality stego images.

关键词： hamming code homogeneous linear recursion IWT rook polynomial (t n)-secret image sharing

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