检索结果-内蒙古大学图书馆

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Hao Peng, Zhiyuan Li, Xu Li, Yukai Shao, Mingjie Kong, Qiuqiang Liu, Ju School of Information Science and Engineering Shandong University Qingdao China Department of Computer Science North Carolina State University NC United States ARC Lab Tencent PCG China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability. © 2025 IEEE.

关键词： contrastive language-audio pre-training retrieval-augmented strategy target sound extraction uni-modal training for multi-modal tasks

来源：评论

学校读者我要写书评

暂无评论

Semantic Gaussian Mixture Variational Autoencoder for Sequential Recommendation

arXiv

引用

arXiv 2025年

作者： Li, Beibei Xiang, Tao Jin, Beihong Zheng, Yiyuan Zhao, Rui College of Computer Science Chongqing University Chongqing China Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China

Variational AutoEncoder (VAE) for Sequential Recommendation (SR), which learns a continuous distribution for each user-item interaction sequence rather than a determinate embedding, is robust against data deficiency and achieves significant performance. However, existing VAE-based SR models assume a unimodal Gaussian distribution as the prior distribution of sequence representations, leading to restricted capability to capture complex user interests and limiting recommendation performance when users have more than one interest. Due to that it is common for users to have multiple disparate interests, we argue that it is more reasonable to establish a multimodal prior distribution in SR scenarios instead of a unimodal one. Therefore, in this paper, we propose a novel VAE-based SR model named SIGMA. SIGMA assumes that the prior of sequence representation conforms to a Gaussian mixture distribution, where each component of the distribution semantically corresponds to one of a user’s multiple interests. For multi-interest elicitation, SIGMA includes a probabilistic multi-interest extraction module that learns a unimodal Gaussian distribution for each interest according to implicit item hyper-categories. Additionally, to incorporate the multimodal interests into sequence representation learning, SIGMA constructs a multi-interest-aware ELBO, which is compatible with the Gaussian mixture prior. Extensive experiments on public datasets demonstrate the effectiveness of SIGMA. The code is available at https://***/libeibei95/SIGMA. acceptance. © 2025, CC BY-NC-SA.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Improved High-Speed Thermal Analysis Based on Two-Step Simulation for High-Frequency Transformers 14

Improved High-Speed Thermal Analysis Based on Two-Step Simul...

引用

14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Yi, Zheyuan Sun, Kai Li, Qiang Liu, Zengyang Tsinghua University State Key Laboratory of Power System Department of Electrical Engineering Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China University Blacksburg Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State BlacksburgVA United States

ISBN: (纸本)9798331516116

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process. © 2025 IEEE.

关键词： Thermal modeling

来源：评论

学校读者我要写书评

暂无评论

Image Computation for Quantum Transition systems

Image Computation for Quantum Transition Systems

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Xin Hong Dingchao Gao Sanjiang Li Shenggang Ying Mingsheng Ying Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checking quantum systems by providing efficient image computation algorithms for quantum transition systems, which play a fundamental role in model checking. In our approach, we represent quantum circuits as tensor networks and design algorithms by leveraging the properties of tensor networks and tensor decision diagrams. Our experiments demonstrate that our contraction partition-based algorithm can greatly improve the efficiency of image computation for quantum transition systems.

关键词： Quantum system Tensors Europe Model checking Software Hardware Partitioning algorithms Computational efficiency Quantum circuit

来源：评论

学校读者我要写书评

暂无评论

S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

引用

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

In-situ NAS: A Plug-and-Search Neural architecture Search framework across hardware platforms

引用

IEEE Transactions on computers 2025年

作者： Lv, Hao Zhang, Lei Wang, Ying Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China State Key Laboratory of Computer Architecture China

Hardware-aware Neural architecture Search (HW-NAS) has garnered significant research interest due to its ability to automate the design of neural networks for various hardware platforms. Prevalent HW-NAS frameworks often use fast predictors to estimate network performance, bypassing the time-consuming actual profiling step. However, the resource-intensive nature of building these predictors and their accuracy limitations hinder their practical use in diverse deployment scenarios. In response, we emphasize the indispensable role of actual profiling in HW-NAS and explore efficiency optimization possibilities within the HW-NAS framework. We provide a systematic analysis of profiling overhead in HW-NAS and identify many redundant and unnecessary operations during the search phase. We then optimize the workflow and present Insitu NAS, which leverages similarity features and exploration history to eliminate redundancy and improve runtime efficiency. In-situ NAS also offers simplified interfaces to ease the user’s effort in managing the complex device-dependent profiling flow, enabling plug-and-search functionality across diverse hardware platforms. Experimental results show that In-situ NAS achieves an average 10x speedup across different hardware platforms while reducing the search overhead by 8x compared to predictor-based approaches in various deployment scenarios. Additionally, In-situ NAS consistently discovers networks with better accuracy (about 1.5%) across diverse hardware platforms compared to predictor-based NAS. © 1968-2012 IEEE.

关键词： AutoML Deep Learning Hardware-Aware Neural architecture Search Optimization Performance evaluation

来源：评论

学校读者我要写书评

暂无评论

An active power control of DFIG-based wind turbine generators for frequency regulation with expected dynamic performance

引用

Applied Energy 2025年 391卷

作者： Zhao, Teyang Liu, Hui Wang, Ni Su, Jinshuo Luo, Zhiqiang Ma, Siyu School of Electrical Engineering Guangxi University Nanning530004 China Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning530004 China The State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of Macau 519000 China

The escalating wind power penetration presents a substantial challenge in ensuring the system frequency stability. Although wind turbine generators (WTGs) can offer rapid frequency support by the presetting power trajectory response (PPTR), it will result in a secondary frequency dip (SFD) while facing the demand of restoring rotor speed after frequency regulation. Moreover, the frequency overshoot will occur due to the fixed power increment provided by PPTR under minor disturbances. In this paper, an active power control strategy of DFIG-based WTGs is proposed for eliminating frequency overshoot and mitigating SFD while providing power support for the system frequency. A rotor-speed-based power increment and power attenuation trajectory are designed to enhance the primary frequency nadir while ensuring the operation stability of WTGs. After that, SFD will be mitigated by the proposed parabolic-formed compensation power. Moreover, a power increment control mode is proposed to eliminate the frequency overshoot by a membership-function-based global controller, which can smoothly switch the combined inertial response and the improved PPTR to provide a flexible power increment. Finally, the validity of the proposed strategy in improving frequency nadir, eliminating frequency overshoot, and mitigating SFD are verified by simulations under different wind power penetration levels, disturbances, and wind conditions. © 2025

关键词： Frequency stability

来源：评论

学校读者我要写书评

暂无评论

Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Continual Reinforcement Learning for Digital Twin Synchronization Optimization

引用

IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Improving meteorological drought simulation in Iran using wavelet-enhanced deep learning models

引用

Theoretical and Applied Climatology 2025年第5期156卷 1-15页

作者： Heydarizad, Mojtaba Liu, Zhongfang Stojanovic, Milica Sorí, Rogert Mohammadabadi, Hamid Ghalibaf Ali, Aamir Minaei, Masoud State Key Laboratory of Marine Geology Tongji University Shanghai 200092 China Environmental Physics Laboratory (EPhysLab) Centro de Investigación Mariña Universidade de Vigo Campus As Lagoas s/n Ourense 32004 Spain Department of Computer Engineering Ferdowsi University of Mashhad Mashhad *** Iran School of Ocean and Earth Science Tongji University Shanghai 200092 China Department of Geography Ferdowsi University of Mashhad Mashhad *** Iran Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab) Ferdowsi University of Mashhad Mashhad *** Iran

In recent decades, the intensification of meteorological droughts has significantly impacted semi-arid and arid regions, including Iran. This study aims to develop a high-accuracy simulation framework for the Standardized Precipitation Index (SPI) at 1-, 3-, and 6-month timescales (SPI-1, SPI-3, and SPI-6), using 40 years (1980–2019) of ERA5 precipitation data. Fourteen climatic predictors were employed to simulate SPI values using two machine learning approaches: a Deep Neural Network (DNN) and a hybrid model combining DNN with the Discrete Wavelet Transform (DNN–Wavelet). Evaluation across multiple metrics confirmed that the DNN–Wavelet model consistently outperformed the standalone DNN model by effectively capturing both temporal and frequency-domain features. Predictor importance analysis using Shapley Additive Explanations (SHAP) identified dew point temperature, mean sea level pressure, and evaporation rate as the primary drivers of SPI variations. In addition, multiscale wavelet analysis and Bivariate Wavelet Coherence (BWC) revealed strong time–frequency correlations between SPI drought indices and key climatic variables. The proposed DNN–Wavelet model shows strong potential as a reliable approach for simulating drought conditions, particularly in data-scarce and drought-prone regions such as Iran. Its application can enhance understanding of drought dynamics and support strategic and sustainable water resource planning. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：