检索结果-内蒙古大学图书馆

Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

引用

Science China(Information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and Information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

来源：评论

学校读者我要写书评

暂无评论

Phase Retrieval System Based on Refractive Index Change 4

Phase Retrieval System Based on Refractive Index Change

引用

4th International Computational Imaging Conference, CITA 2024

作者： Cheng, Hong Zhang, Hongyi Wang, Li Fenzhang Li, Shuhan Key Laboratory of Intelligent Computing & Signal Processing Anhui University Anhui Province Hefei230601 China

ISBN: (纸本)9781510688834

The phase recovery method based on the Transport of Intensity Equation is widely used in the field of microscopic imaging of biological cells. When using this method for phase recovery, the CCD is always moved to acquire intensity images at different out-of-focus positions, which introduces mechanical errors, it also causes errors when using the traditional FFT method. Based on this, this paper proposes a phase recovery system based on refractive index change: firstly, an image acquisition system with a refractive index medium is built, and two intensity images with different refractive index are acquired;the intensity-phase equation based on refractive index change is established, and the "maximum intensity" assumption method is introduced to solve this equation. Then, we can obtain high-precision phase results without moving the CCD. In this paper, the feasibility and accuracy of this technique are confirmed by numerical simulation and optical experiment respectively. © 2025 SPIE.

关键词： Refractive index

来源：评论

学校读者我要写书评

暂无评论

HyFit: Hybrid Fine-Tuning With Diverse Sampling for Abstractive Summarization

引用

IEEE Transactions on Big Data 2025年第3期11卷 1054-1065页

作者： Zhao, Shu Cheng, Yuanfang Zhang, Yanping Chen, Jie Duan, Zhen Sun, Yang Wang, Xinyuan Anhui University Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing Hefei230601 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230601 China

Abstractive summarization has made significant progress in recent years, which aims to generate a concise and coherent summary that contains the most important facts from the source document. Current fine-tuning approaches based on pre-training models typically rely on autoregressive and maximum likelihood estimation, which may result in inconsistent historical distributions generated during the training and inference stages, i.e., exposure bias problem. To alleviate this problem, we propose a hybrid fine-tuning model(HyFit), which combines contrastive learning and reinforcement learning in a diverse sampling space. Firstly, we introduce reparameterization and probability-based sampling methods to generate a set of summary candidates called candidates bank, which improves the diversity and quality of the decoding sampling space and incorporates the potential for uncertainty. Secondly, hybrid fine-tuning with sampled candidates bank, upweighting confident summaries and downweighting unconfident ones. Experiments demonstrate that HyFit significantly outperforms the state-of-the-art models on SAMSum and DialogSum. HyFit also shows good performance on low-resource summarization, on DialogSum dataset, using only approximate 8% of the examples exceed the performance of the base model trained on all examples. © 2015 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Dual Mach-Zehnder Interferometer Based on DCF and FCF for Temperature and Strain Measurement

引用

Photonic Sensors 2025年第1期15卷 39-46页

作者： Ya GAO Shu JIN Cheng ZUO Benli YU Shenglai ZHEN Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education AnhuiUniversityHefei 230601China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui UniversityHefei 230601China

In this paper,a dual Mach-Zehnder interferometer for measuring both temperature and strain is proposed and verified by *** sensor configuration involves cascading a four-core fiber and a double-clad fiber between two single-mode *** exploiting the different responses of the two Mach-Zehnder interferometers to temperature and strain,we construct a matrix using two selected resonance dips from the transmission spectra,so that both temperature and strain can be measured *** experimental results show the sensor’s remarkable performance,with the maximum temperature sensitivity of-94.2 pm/℃and the maximum strain sensitivity of 2.68 pm/με.The maximum temperature error and strain error are found to be±0.35℃and±4.8με,*** with other optical fiber sensors,the sensor has high sensitivity,a simple structure,and ease to manufacture and implement,making it a structure choice for applications in quality inspection of materials.

关键词： Dual Mach-Zehnder interferometer temperature strain

来源：评论

学校读者我要写书评

暂无评论

An Improved Compressive Sensing-Based Model for Efficient Analysis of Monostatic Electromagnetic Scattering Problems

引用

IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 388-391页

作者： Qi, Qi Fan, Yunuo Cao, Xinyuan Liu, Yi Kong, Meng Huang, Zhixiang Wu, Xianliang Hefei Normal University Anhui Province Key Laboratory of Simulation Calculation and Design for Electronic Information System Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

To further accelerate the analysis of monostatic electromagnetic (EM) scattering problems of objects, an improved compressive sensing (CS)-based model is proposed. First, an orthogonal subspace spanned by the wide-angle induced currents is identified by means of the wide-angle incident sources. Then, the sparse transform of the induced currents is replaced by the subspace projection. In addition, an optimal number of measurements is determined, only slightly larger than the dimension of the subspace. Finally, the recovery algorithm is simplified to the least-squares method to solve the resulting overdetermined system. Compared with the traditional CS-based model, the proposed model significantly improves the efficiency of the recovery step and optimizes both the requirement for and the way of determining the number of measurements. © 2023 IEEE.

关键词： Least squares approximations

来源：评论

学校读者我要写书评

暂无评论

Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction

引用

IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2333-2344页

作者： Zhao, Jia-Le Zhang, Xian-Jie Ding, Xiao Zhang, Xingyi Zhang, Hai-Feng Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China

Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on pairwise graphs often limits their capacity to capture latent higher-order topological semantic information. Thus, it is crucial to find a way to extract the latent higher-order information of graphs without missing the lower-order information of the original graph. To address this issue, we here develop a method to construct hypergraph based on motifs, and then a novel neural network framework, named MD-HGNN, is proposed for enhanced graph learning. Specifically, we first utilize motifs of the original graph to construct the hypergraph and eliminate nested structures within the hypergraph to prevent information redundancy. Subsequently, GNNs and hypergraph neural networks (HGNNs) are employed separately to extract the lower-order and higher-order topological semantic information of the graph. Finally, the lower-order and higher-order information are integrated to obtain an embedded representation of graph. Extensive experimental results demonstrate that MD-HGNN preserves the original lower-order graph structure information while effectively extracting higher-order features. Moreover, its performance and robustness are validated across different downstream tasks. © 2013 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Few-shot Object Detection Using Adaptive Feature Modification and Gaussian Dynamic Dilated Convolution 16

Remote Sensing Few-shot Object Detection Using Adaptive Feat...

引用

2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Zhiwei, Zhang Jiangtao, Wang Penglei, Chen Cheng, He School of Physics and Electronic Information Huaibei Normal University Anhui Province Huaibei City China Anhui Province Key Laboratory of Intelligent Computing and Applications Anhui Province Huaibei City China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Object detection in remote sensing images relies on a large amount of annotated data for training, but adequately annotating novel classes is difficult. Few-shot object detection(FSOD) address this problem by fine-tuning on finitely annotated novel classes, this results in its within-class variation being limited, making it difficult to adequately fit the within-class distribution. In order to alleviate this problem, this paper proposed a few-shot object detection method in remote sensing based on adaptive feature modification and guided by Gaussian dynamic dilated convolution. Firstly, in view of the difficulty of novel classes recognition, an adaptive feature modification algorithm is proposed. By using the characteristics of the two-branch network architecture of meta-learning itself, the support features are integrated into the query feature extraction network as convolutional bias to adaptively modify the query features. Secondly, in order to simulate intra-class variation of noise (feature perturbation caused by noise in the foreground), the backbone network weights were reparameterized to make them fit Gaussian distribution variables during fine-tuning, and the feature perturbation was achieved with parameter variation. Finally, in order to simulate intra-class scale variation (different foreground sizes), the convolution is changed into a dynamic dilation convolution, and different dilation rates are used to replace feature scale variation. Extensive experiments on two widely used remote sensing datasets (NWPU.V2, dior) demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Utilizing machine learning models to grasp water quality dynamic changes in lake eutrophication through phytoplankton parameters

引用

Frontiers of Environmental Science & Engineering 2025年第2期19卷 1-16页

作者： Yong Fang Ruting Huang Yeyin Zhang Jun Zhang Wenni Xi Xianyang Shi Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration School of Resources and Environmental EngineeringAnhui UniversityHefei 230601China

Phytoplankton serve as vital indicators of eutrophication ***,relying solely on phytoplankton parameters,such as chlorophyll-a,limits our comprehensive understanding of the intricate eutrophication conditions in natural lakes,particularly in terms of timely analysis of changes in limiting nutrients and their *** study presents machine learning(ML)models for predicting and identifying lake *** tree-based ML models were developed using the latest data on hydrological,water quality,and meteorological parameters obtained from 34 sites in the Huating Lake basin over 5 *** extreme gradient boosting model exhibited high accuracy in predicting the total nitrogen/total phosphorus ratio(TN/TP)(R^(2)=0.88;RMSE=24.60;MAPE=26.14%).Analysis of the TN/TP ratio and output eigenvalue weight revealed that phosphorus plays a crucial role in eutrophication,probably because of the low-flow and deep-water characteristics of the ***,the light gradient boosting machine model exhibited outstanding performance and high accuracy in predicting phytoplankton parameters,especially the Shannon index(H′)(R^(2)=0.92;RMSE=0.11;MAPE=4.95%).The mesotrophic classification of the Huating Lake determined using the H′threshold,coincided with the findings from the H′*** research should cover a wider range of pollution sources and spatiotemporal dimensions to further validate our ***,this study highlights the potential of incorporating the TN/TP ratio and phytoplankton parameters into ML techniques for effective monitoring and management of environmental conditions.

关键词： machine learning Lake Phytoplankton Water quality

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-aware coarse-to-fine alignment for text-image person retrieval

引用

Visual Intelligence 2025年第1期3卷 1-14页

作者： Deng, Yifei Chen, Zhengyu Li, Chenglong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei 230601 China

Text-to-image person retrieval, a fine-grained cross-modal retrieval problem, aims to search for person images from an image library that match a given textual caption. Existing text-to-image person retrieval methods usually use fixed-point embedding to express the semantics of the two modalities and perform multi-granularity alignment between modalities in the embedding space. However, owing to the inherent mutual one-to-many correspondence between images and texts, it is often difficult for fixed-point embedding methods to adequately capture this relationship, leading to erroneous retrieval results. To address this problem, we propose a novel uncertainty-aware coarse-to-fine alignment method, which first maps fixed-point embedding to probability distributions and then aligns two modalities in terms of distributions and sampling points at a coarse-to-fine granularity, for accurate text-to-image person retrieval. Specifically, we first introduce two contrastive learning tasks of distribution contrast learning and point contrast learning, to achieve coarse-grained inter-modal alignment with uncertainty-aware. The distribution contrast learning task ensures that distributions with the same identity are as similar as possible across modalities through distribution-based contrastive learning. The point contrast learning task performs the contrastive learning of inter-modal and intra-modal sampling points, which not only models rich and diverse cross-modal associations, but also optimizes the learning of distributions. For the fine-grained association requirements of text-to-image person retrieval, we design the task of uncertainty-aware attribute masking language reconstruction, which achieves fine-grained alignment by randomly masking attribute words in the text and reconstructing them via inter-modal sample point interactions. Extensive experiments on two public datasets demonstrate the superior performance of our method. © The Author(s) 2025.

关键词： Coarse-to-fine alignment Contrastive learning Cross-modal retrieval Probalility distribution Uncertainty-aware

来源：评论

学校读者我要写书评

暂无评论

LightGBM-Based Audio Watermarking Robust to Recapturing and Hybrid Attacks

引用

IEEE Transactions on Information Forensics and Security 2025年 20卷 4212-4227页

作者： Su, Zhaopin Weng, Zhaofang Zhang, Guofu Lian, Chensi Wang, Niansong Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China Joint Laboratory of Intelligent Prevention and Recognition of Audio and Video Anhui Provincial Public Security Department Hefei230000 China

Digital audio watermarking is a critical technology widely used for copyright protection, content authentication, and broadcast monitoring. However, its robustness is significantly challenged by recapturing and hybrid attacks, which can easily remove watermarks. To address this issue, this work proposes a novel scheme based on the light gradient boosting machine (LightGBM), named LRAW (LightGBM-based Robust Audio Watermarking), which is designed to increase the robustness of audio watermarking against various attacks. Specifically, the scheme begins by analysing coefficients derived from the discrete wavelet transform (DWT), graph-based transform (GBT), and singular value decomposition (SVD). The extracted singular values consistently maintain a stable descending order even under recapturing attacks at a slightly greater distance. Leveraging this stability, the watermark information is implicitly embedded into the audio signal using a quantization rule. To simulate a hybrid attack scenario, a comprehensive feature dataset comprising 396,000 pieces of DWT-GBT-SVD feature data is constructed based on 60 original recordings and 9 types of attack. Furthermore, considering the distinct influences of embedding watermark bits 0 and 1 on the quantization of singular values, the watermark extraction process is formulated as a binary classification problem. LightGBM is trained using Bayesian optimization and the feature dataset to classify the watermark bits accurately. Finally, the complete watermark is recovered using a watermark sequence matching algorithm. Theoretical analysis and experimental results demonstrate that the proposed LRAW scheme outperforms state-of-the-art watermarking methods in robustness against various recapturing and hybrid attacks, even when the distance between the acoustic source and the receiver is considerable. © 2005-2012 IEEE.

关键词： Audio watermarking

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：