Tsinghua Science and Technology最新文献_第3页

Cooperative Digital Healthcare Task Scheduling and Resource Management in Edge Intelligence Systems

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010140

Xing Liu;Jianhui Lv;Byung-Gyu Kim;Keqin Li;Hongkai Jin;Wei Gao;Jiayuan Bai

{"title":"Cooperative Digital Healthcare Task Scheduling and Resource Management in Edge Intelligence Systems","authors":"Xing Liu;Jianhui Lv;Byung-Gyu Kim;Keqin Li;Hongkai Jin;Wei Gao;Jiayuan Bai","doi":"10.26599/TST.2024.9010140","DOIUrl":"https://doi.org/10.26599/TST.2024.9010140","url":null,"abstract":"The rapid growth of digital healthcare applications has led to an increasing demand for efficient and reliable task scheduling and resource management in edge computing environments. However, the limited resources of edge servers and the need to process delay-sensitive healthcare tasks pose significant challenges. Existing solutions often need help to balance the trade-off between system cost and quality of service, particularly in resource-constrained scenarios. To address these challenges, we propose a novel cooperative task scheduling and resource management framework for digital healthcare applications in edge intelligence systems. Our approach leverages a two-step optimization strategy that combines the Multi-armed Combinatorial Selection Problem (MCSP) for task scheduling and the Sequential Markov Decision Process (SMDP) with alternative reward estimation for computation offloading. The MCSP-based scheduling algorithm efficiently explores the combinatorial task scheduling space to minimize healthcare task completion time and costs. The SMDP-based offloading strategy incorporates alternative reward estimation to improve robustness against dynamic variations in the system environment. Extensive simulations using real-world healthcare data demonstrate the superior performance of our proposed framework compared to state-of-the-art baselines, achieving significant improvements in cost, task success rate, and fairness. The proposed approach enables reliable and efficient digital healthcare services in resource-constrained edge computing environments.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"926-945"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Hyperparameter Prediction Framework for Distributed Energy Trading on Photovoltaic Network 光伏网络分布式能源交易的多参数预测框架

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010150

Chun Chen;Yong Zhang;Boon Han Lim;Li Ning;Shengzhong Feng;Peng Xie

{"title":"A Multi-Hyperparameter Prediction Framework for Distributed Energy Trading on Photovoltaic Network","authors":"Chun Chen;Yong Zhang;Boon Han Lim;Li Ning;Shengzhong Feng;Peng Xie","doi":"10.26599/TST.2024.9010150","DOIUrl":"https://doi.org/10.26599/TST.2024.9010150","url":null,"abstract":"The rapid evolution of distributed energy resources, particularly photovoltaic systems, poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing costs. The integrated nature of distributed markets, blending centralized and decentralized elements, holds the promise of maximizing social welfare and significantly reducing overall costs, including computational and communication expenses. However, achieving this balance requires careful consideration of various hyperparameter sets, encompassing factors such as the number of communities, community detection methods, and trading mechanisms employed among nodes. To address this challenge, we introduce a groundbreaking neural network-based framework, the Energy Trading-based Artificial Neural Network (ET-ANN), which excels in performance compared to existing algorithms. Our experiments underscore the superiority of ET-ANN in minimizing total energy transaction costs while maximizing social welfare within the realm of photovoltaic networks.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"864-874"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Time-Frequency Denoising Transform Defense for Spectrum Monitoring in Integrated Networks

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010045

Sicheng Zhang;Yandie Yang;Songlin Yang;Juzhen Wang;Yun Lin

{"title":"Deep Time-Frequency Denoising Transform Defense for Spectrum Monitoring in Integrated Networks","authors":"Sicheng Zhang;Yandie Yang;Songlin Yang;Juzhen Wang;Yun Lin","doi":"10.26599/TST.2024.9010045","DOIUrl":"https://doi.org/10.26599/TST.2024.9010045","url":null,"abstract":"The Space-Air-Ground-Sea Integrated Networks (SAGSIN) significantly enhance global communication by merging satellite, aviation, terrestrial, and marine networks. Crucial to SAGSIN's functionality and security is spectrum monitoring using deep learning-based Automatic Modulation Classification (AMC), essential for processing and classifying complex modulation signals. However, these AMC models are susceptible to adversarial attacks. Thus, we introduce the Deep Time-Frequency Denoising Transformation (DTFDT) defense method to mitigate the impact of adversarial attacks. The DTFDT method is comprised of a deep denoising module and a transformation module. The denoising module maps signals into the time-frequency domain, amplifying the differences between benign and adversarial examples, aiding in the elimination of adversarial perturbations. Concurrently, the transformation module develops a learnable network, generating example-specific transformation matrices suited for signal data, which diminishes the effectiveness of attacks. Extensive evaluations on two datasets, RML2016.10a and DMRadio09.real, demonstrate the superior defense capabilities of DTFDT against various attacks.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"851-863"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Machine Learning Rapid Prediction of the Aerothermodynamic Environment for Near-Space Hypersonic Unmanned Aircraft 近空间高超音速无人驾驶飞机空气热动力环境的机器学习快速预测

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010018

Xujia Chen;Wenhui Fan

{"title":"A Machine Learning Rapid Prediction of the Aerothermodynamic Environment for Near-Space Hypersonic Unmanned Aircraft","authors":"Xujia Chen;Wenhui Fan","doi":"10.26599/TST.2024.9010018","DOIUrl":"https://doi.org/10.26599/TST.2024.9010018","url":null,"abstract":"Near-space hypersonic unmanned aircrafts (NHUA) encounter significant aerodynamic heating effects when flying at high velocities in extreme conditions. This leads to the generation of extremely high temperatures, reaching several thousand degrees, posing a substantial risk to the safety of NHUA. Accurate and rapid prediction of the aerothermodynamic environment is crucial for the thermal protection of NHUA. Conventional approaches exhibit some limitations, including the need for extensive pre-processing, long calculation time, inadequate precision, and reliance on expert knowledge, making them ill-suited for online intelligent prediction. This study proposes a novel “flying state-pressure and heat flux-temperature” data-driven prediction theoretical framework, considering both efficiency and accuracy. Our approach entails a prediction model for high-dimensional pressure and heat flux fields, employing principal component analysis (PCA) and multi-layer perceptron (MLP) models. A temperature time series model is also constructed using recurrent neural networks (RNN). The experimental results suggest that the prediction error falls within a narrow margin of approximately 5%. It takes around 0.1 seconds to forecast a high-dimensional field and 1 second to predict the temperature time series, which satisfies both speed and accuracy requirements.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"682-694"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786937","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Network-Based Folk Culture Propagation in the Digital Age: Analyzing Dissemination Mechanisms and Influential Factors 数字时代基于社交网络的民间文化传播：分析传播机制和影响因素

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010049

Zhaojin Li;Fugao Jiang;Weiyi Zhong;Chao Yan

{"title":"Social Network-Based Folk Culture Propagation in the Digital Age: Analyzing Dissemination Mechanisms and Influential Factors","authors":"Zhaojin Li;Fugao Jiang;Weiyi Zhong;Chao Yan","doi":"10.26599/TST.2024.9010049","DOIUrl":"https://doi.org/10.26599/TST.2024.9010049","url":null,"abstract":"The rapid development of the internet has ushered the real world into a “media-centric” digital era where virtually everything serves as a medium. Leveraging the new attributes of interactivity, immediacy, and personalization facilitated by online communication, folklore has found a broad avenue for dissemination. Among these, online social networks have become a vital channel for propagating folklore. By using social network theory, we devise a comprehensive approach known as SocialPre. Firstly, we utilize embedding techniques to capture users' low-level and high-level social relationships. Secondly, by applying an automatic weight assignment mechanism based on the embedding representations, multi-level social relationships are aggregated to assess the likelihood of a social interaction between any two users. These experiments demonstrate the ability to classify different social groups. In addition, we delve into the potential directions of folklore evolution, thus laying a theoretical foundation for future folklore communication.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"894-907"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a Holistic and Intelligent Model of Care for Gender Dysphoria 迈向全面、智能的性别失调症护理模式

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010151

Maya Krayneva

{"title":"Towards a Holistic and Intelligent Model of Care for Gender Dysphoria","authors":"Maya Krayneva","doi":"10.26599/TST.2023.9010151","DOIUrl":"https://doi.org/10.26599/TST.2023.9010151","url":null,"abstract":"The gender-affirmative model of care has proven unsuccessful in many cases of gender dysphoria. There is a pressing need to continue research and develop and implement alternative models of care. Personalised models of care need to replace the standardised ones to reflect the unique nature of internal issues that exist within every individual. A holistic care model that includes Physical, Mental, Emotional, Social, and Spiritual (PMESS) aspects of a person's wellbeing indicates an effective way forward. Such a multidimensional model enables a greater understanding of complex relationships between different factors and their effects on health and overall wellbeing. Empowered by intelligent technologies, such as data mining and Artificial Intelligence (AI), the PMESS model can systematically capture, analyse, and evaluate data across the multiple dimensions of the holistic model of care. It can help identify patterns within the data, generate useful insights, and support the development of effective prevention and personalized treatment strategies. The PMESS-AI model supports the collaboration between multiple stakeholders, and the machine learning aspects of it can usher in the discovery of new knowledge and breakthroughs in research.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"624-632"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Task ConvMixer Networks with Triplet Attention for Low-Resource Keyword Spotting

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-09-24 DOI: 10.26599/TST.2024.9010088

Alexander Rogath Kivaisi;Qingjie Zhao;Yuanbing Zou

{"title":"Multi-Task ConvMixer Networks with Triplet Attention for Low-Resource Keyword Spotting","authors":"Alexander Rogath Kivaisi;Qingjie Zhao;Yuanbing Zou","doi":"10.26599/TST.2024.9010088","DOIUrl":"https://doi.org/10.26599/TST.2024.9010088","url":null,"abstract":"Customized keyword spotting needs to adapt quickly to small user samples. Current methods primarily solve the problem under moderate noise conditions. Recent work increases the level of difficulty in detecting keywords by introducing keyword interference. However, the current solution has been explored on large models with many parameters, making it unsuitable for deployment on small devices. When applying the current solution to lightweight models with minimal training data, the performance degrades compared to the baseline model. Therefore, we propose a light-weight multi-task architecture (<9.0×10>4</sup>\u0000parameters) created from integrating the triplet attention module in the ConvMixer networks and a new auxiliary mixed labeling encoding to address the challenge. The results of our experiment show that the proposed model outperforms similar light-weight models for keyword spotting, with accuracy gains ranging from 0.73% to 2.95% for a clean set and from 2.01% to 3.37% for a mixed set under different scales of training set. Furthermore, our model shows its robustness in different low-resource language datasets while converging faster.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"875-893"},"PeriodicalIF":6.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10691379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scheduling of Low-Latency Medical Services in Healthcare Cloud with Deep Reinforcement Learning 利用深度强化学习调度医疗保健云中的低延迟医疗服务

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2024.9010033

Hongfei Du;Ming Liu;Nianbo Liu;Deying Li;Wenzhong Li;Lifeng Xu

{"title":"Scheduling of Low-Latency Medical Services in Healthcare Cloud with Deep Reinforcement Learning","authors":"Hongfei Du;Ming Liu;Nianbo Liu;Deying Li;Wenzhong Li;Lifeng Xu","doi":"10.26599/TST.2024.9010033","DOIUrl":"https://doi.org/10.26599/TST.2024.9010033","url":null,"abstract":"In the current landscape of online data services, data transmission and cloud computing are often controlled separately by Internet Service Providers (ISPs) and cloud providers, resulting in significant cooperation challenges and suboptimal global data service optimization. In this study, we propose an end-to-end scheduling method aimed at supporting low-latency and computation-intensive medical services within local wireless networks and healthcare clouds. This approach serves as a practical paradigm for achieving low-latency data services in local private cloud environments. To meet the low-latency requirement while minimizing communication and computation resource usage, we leverage Deep Reinforcement Learning (DRL) algorithms to learn a policy for automatically regulating the transmission rate of medical services and the computation speed of cloud servers. Additionally, we utilize a two-stage tandem queue to address this problem effectively. Extensive experiments are conducted to validate the effectiveness for our proposed method under various arrival rates of medical services.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"100-111"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest Recommendation 用于兴趣点推荐的异构时空图对比学习

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010148

Jiawei Liu;Haihan Gao;Cheng Yang;Chuan Shi;Tianchi Yang;Hongtao Cheng;Qianlong Xie;Xingxing Wang;Dong Wang

{"title":"Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest Recommendation","authors":"Jiawei Liu;Haihan Gao;Cheng Yang;Chuan Shi;Tianchi Yang;Hongtao Cheng;Qianlong Xie;Xingxing Wang;Dong Wang","doi":"10.26599/TST.2023.9010148","DOIUrl":"https://doi.org/10.26599/TST.2023.9010148","url":null,"abstract":"As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks (GNNs) have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social relationships or user attributes to alleviate the data sparsity problem, such auxiliary information is not always available for privacy reasons. Self-supervised learning gives a new idea to alleviate the data sparsity problem, but most existing self-supervised recommendation methods cannot be directly used in the spatio-temporal graph of POI recommendations. In this paper, we propose a novel heterogeneous spatio-temporal graph contrastive learning method, HestGCL, to compensate for existing GNN-based methods' shortcomings. To model spatio-temporal information, we generate spatio-temporally specific views and design view-specific heterogeneous graph neural networks to model spatial and temporal information, respectively. To alleviate data sparsity, we propose a cross-view contrastive strategy to capture differences and correlations among views, providing more supervision signals and boosting the overall performance collaboratively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HestGCL, which significantly outperforms existing methods.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"186-197"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Multi-Scale Convolution Fusion and Memory-Augmented Adversarial Autoencoder to Detect Diverse Anomalies in Multivariate Time Series 利用多尺度卷积融合和记忆增强对抗式自动编码器检测多元时间序列中的各种异常现象

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010095

Zefei Ning;Hao Miao;Zhuolun Jiang;Li Wang

{"title":"Using Multi-Scale Convolution Fusion and Memory-Augmented Adversarial Autoencoder to Detect Diverse Anomalies in Multivariate Time Series","authors":"Zefei Ning;Hao Miao;Zhuolun Jiang;Li Wang","doi":"10.26599/TST.2023.9010095","DOIUrl":"https://doi.org/10.26599/TST.2023.9010095","url":null,"abstract":"Time series anomaly detection is an important task in many applications, and deep learning based time series anomaly detection has made great progress. However, 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 challenge. Fusion and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics, and contribute to the discovery of complex and subtle anomalies. In this paper, we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder (MCFMAAE) for multivariate time series anomaly detection. It is an encoder-decoder-based framework with four main components. Multi-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal information. Self-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context information. Memory module is introduced to explore the internal structure of normal samples, capturing it into the latent space, and thus remembering the typical pattern. Finally, the decoder is used to reconstruct the signals, and then a process is coming to calculate the anomaly score. Moreover, an additional discriminator is added to the model, which enhances the representation ability of autoencoder and avoids overfitting. Experiments 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.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"234-246"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0