IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-07-25 DOI: 10.1049/cmu2.70062

Yuze Guo, Liang Jin, Yangming Lou, Xiaoming Xu, Qinlong Li, Boming Li, Shuaiyin Wang

{"title":"Multi-Stream Signal Separation Based on Asynchronous Control Meta-Surface Antenna","authors":"Yuze Guo, Liang Jin, Yangming Lou, Xiaoming Xu, Qinlong Li, Boming Li, Shuaiyin Wang","doi":"10.1049/cmu2.70062","DOIUrl":"https://doi.org/10.1049/cmu2.70062","url":null,"abstract":"<p>The real-time reconfigurable characteristics of meta-surface antennas can be used to separate multi-stream signals under the condition of single radio frequency (RF). However, with the increase of the symbol rate and the number of antenna arrays in the future, it will face the problem that the state switch rate of the electromagnetic unit is not enough to reach the upper limit of array effective degrees of freedom (DOF) of the meta-surface antenna. To solve this problem, a theory of asynchronous control meta-surface antenna is proposed in this paper. By designing the starting time of different element state switching, different electromagnetic element states are staggered to improve the array effective DOF of the meta-surface antenna. Then, an electromagnetic unit state design algorithm of asynchronous control meta-surface antenna based on the minimum condition number of equivalent channel matrix is proposed. We improve the sparrow search algorithm to solve the condition number minimization problem in order to obtain the better multi-stream signal separation performance. The simulation results show that compared with the synchronous control meta-surface antenna, theory proposed in this paper can improve the effective DOF of array under the condition of limited switch rate, and can effectively reduce the receiving bit error rate and improve spectral efficiency when separating multi-stream signals.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 short-term load forecasting method considering multiple feature factors based on long short-term memory and an improved temporal convolutional network 基于长短期记忆和改进时间卷积网络的多特征因素短期负荷预测方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-25 DOI: 10.1016/j.engappai.2025.111649

Yu Mu, Lingrui Kong, Guoqiang Zheng, Zhonge Su, Guodong Wang

{"title":"A short-term load forecasting method considering multiple feature factors based on long short-term memory and an improved temporal convolutional network","authors":"Yu Mu, Lingrui Kong, Guoqiang Zheng, Zhonge Su, Guodong Wang","doi":"10.1016/j.engappai.2025.111649","DOIUrl":"10.1016/j.engappai.2025.111649","url":null,"abstract":"<div><div>In order to address the problems of multi-factor coupling difficulties and low prediction efficiency of existing short-term electricity load forecasting methods, in this paper a short-term load forecasting method is proposed that combines the maximum mutual information coefficient (MIC) algorithm and the Long Short-Term Memory (LSTM)-Improved Temporal Convolutional Network (ITCN) model. Second, based on the problem of low prediction efficiency of the Temporal Convolutional Network (TCN), the TCN was improved (ITCN) by using the single residual block structure and the parallel activation function structure. Finally, the LSTM-ITCN model is designed to extract the short-term temporal features of the given data using LSTM first, and extract the long-term temporal features of the given data using ITCN and make the final prediction. Comparison experiments with Convolutional Neural Network (CNN)-LSTM, CNN-Bidirectional Gated Recurrent Unit (BIGRU), and other prediction methods on different datasets are conducted, and the findings indicate that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>), and Running times values of the proposed method are improved by 10.56%, 10.48%, 8.45%, and 25.64%, respectively, which significantly improves the prediction accuracy and prediction efficiency.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retraction 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-07-25 DOI: 10.1111/coin.70111

引用次数: 0

Retraction 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-07-25 DOI: 10.1111/coin.70110

引用次数: 0

Retraction 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-07-25 DOI: 10.1111/coin.70109

引用次数: 0

Retraction 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-07-25 DOI: 10.1111/coin.70106

引用次数: 0

Corrigendum to “MA-FSAR: Multimodal Adaptation of CLIP for few-shot action recognition” [Pattern Recognition 169 (2026) 111902] “MA-FSAR: CLIP的多模态适应用于少量动作识别”的勘误表[模式识别169 (2026)111902]

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-07-24 DOI: 10.1016/j.patcog.2025.112160

Jiazheng Xing , Jian Zhao , Chao Xu , Mengmeng Wang , Guang Dai , Yong Liu , Jingdong Wang , Xuelong Li

引用次数: 0

IEEE Internet of Things Journal Information for Authors IEEE物联网期刊信息作者

IF 8.2 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-07-24 DOI: 10.1109/JIOT.2025.3587572

引用次数: 0

Online Adaptable Offline RL With Guidance Model. 带引导模型的在线自适应离线强化学习。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-07-24 DOI: 10.1109/tnnls.2025.3589418

Xun Wang,Jingmian Wang,Zhuzhong Qian,Bolei Zhang

{"title":"Online Adaptable Offline RL With Guidance Model.","authors":"Xun Wang,Jingmian Wang,Zhuzhong Qian,Bolei Zhang","doi":"10.1109/tnnls.2025.3589418","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3589418","url":null,"abstract":"Reinforcement learning (RL) has emerged as a promising approach across various applications, yet its reliance on repeated trial-and-error learning to develop effective policies from scratch poses significant challenges for deployment in scenarios where interaction is costly or constrained. In this work, we investigate the offline-to-online RL paradigm, wherein policies are initially pretrained using offline historical datasets and subsequently fine-tuned with a limited amount of online interaction. Previous research has suggested that efficient offline pretraining is crucial for achieving optimal final performance. However, it is challenging to incorporate appropriate conservatism to prevent the overestimation of out-of-distribution (OOD) data while maintaining adaptability for online fine-tuning. To address these issues, we propose an effective offline RL algorithm that integrates a guidance model to introduce suitable conservatism and ensure seamless adaptability to online fine-tuning. Our rigorous theoretical analysis and extensive experimental evaluations demonstrate better performance of our novel algorithm, underscoring the critical role played by the guidance model in enhancing its efficacy.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"34 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-Satellite Links-Enabled Cooperative Edge Computing in Satellite-Terrestrial Networks 卫星-地面网络中基于卫星间链路的协同边缘计算

IF 10.4 1区计算机科学

IEEE Transactions on Wireless Communications Pub Date : 2025-07-24 DOI: 10.1109/twc.2025.3590005

Bodong Shang, Caiguo Li, Junchao Ma, Pingzhi Fan

引用次数: 0

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