检索结果-内蒙古大学图书馆

IEEE Conference on Global Communications (GLOBECOM)

作者： Mingqing Li Li Ping Qian Qianru Wang Yuan Wu Bin Lin Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China Zhuhai UM Science and Technology Research Institute Zhuhai China Department of Computer and Information Science University of Macau Macao SAR China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Department of Communication Engineering Dalian Maritime University Dalian China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hierarchical computing offloading with the assistance of high altitude platforms (HAPs), and a hybrid transmission scheme of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) is designed for achieving efficient computation offloading. Specifically, the offshore sensing devices (SDs) initially perform computation offloading to the HAPs by forming NOMA groups, and the HAPs further offload partial workload to the onshore base station (BS) via FDMA. For efficient calculation, we aim to minimize the overall delay in completing all the workload processing of these SDs by jointly optimizing the durations of NOMA and FDMA transmission as well as the hierarchical computation offloading workload. Though the problem is in the form of non-convexity, we design an efficient SCA-based algorithm to tackle it. Finally, numerical results demonstrate the optimality and convergence of the proposed algorithm, as well as the performance gains of the proposed scheme.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

引用

science China(Information sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

来源：评论

学校读者我要写书评

暂无评论

A Point-to-distribution Degeneracy Detection Factor for LiDAR SLAM using Local Geometric Models

A Point-to-distribution Degeneracy Detection Factor for LiDA...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Sehua Ji Weinan Chen Zerong Su Yisheng Guan Jiehao Li Hong Zhang Haifei Zhu Biomimetic and Intelligent Robotics Lab (BIRL) School of Electromechanical Engineer Guangdong University of Technology Guangzhou China JT-Innovation (Guangdong) Intelligent Technology Co. Ltd. Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China College of Engineering South China Agricultural University China Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://***/jisehua/***. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.

关键词： Point cloud compression Accuracy Simultaneous localization and mapping Sensitivity Laser radar Geometric modeling Noise

来源：评论

学校读者我要写书评

暂无评论

MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE TRANSFER

MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE T...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Huaji Zhou Xu Wang Jing Bai Zhu Xiao Science and Technology on Communication Information Security Control Laboratory Jiaxing China School of Artificial Intelligence Xidian University Xi'an China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781665435413

Deep learning-based recognition of radio signal modulation has emerged as a current research hotspot with significant practical potential. However, in practical applications, radio modulation signal data acquisition is complicated to obtain, and label samples are costly and time-consuming to meet the data dependence of deep learning. Transfer learning allows pretrained networks to be reused on large-scale datasets, making it a kind of solution for modulation signal recognition in limited data. The method of suppressing small singular values in the feature vector is employed in this paper to realize selective knowledge transfer for modulation signal recognition, while stochastic normalization is employed to replace the batch normalization layer to avoid over-fitting. We tested the stochastic normalized selective knowledge transfer method on the RML2016.10A and RML2016.04C datasets, with an SNR of 6dB signal samples, and found that it can lead to average growth of 15.77% and 10.32% when compared to direct training, and 6.1% and 2.73% when compared to vanilla fine-tuning. In addition, we check up under a variety of SNR conditions to ensure that our method is effective.

关键词： Training Deep learning Transfer learning Data acquisition Modulation Global communication Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

A Joint Matrix Factorization and Clustering Scheme for Irregular Time Series Data

SSRN

引用

SSRN 2022年

作者： He, Shiming Guo, Meng Li, Zhuozhou Zhou, Siyuan Xie, Kun Xiong, Naixue Key Laboratory of Safety Control of Bridge Engineering Ministry of Education School of Computer and Communication Engineering Changsha University of Science and Technology Hunan Changsha410114 China College of Computer Science and Electronics Engineering Hunan University Hunan Changsha410082 China Department of Mathematics and Computer Science Northeastern State University TahlequahOK74464 United States

Key Performance Indicator (KPI) clustering plays an important role in Artificial Intelligence for IT Operations when facing large-scale KPIs. It can effectively reduce the overhead by dividing KPIs into several classes, then applying the same anomaly detection or prediction model to all KPIs in a class. However, KPI sampling strategies vary according to the various environments, which produces irregular KPIs. Few existing works have considered the clustering of KPIs with irregular sampling. Matrix factorization (MF) is widely applied in low-rank data recovery and can be used to align and fill the irregular KPIs. However, the clustering performance after recovering and filling by MF remains unknown. These two problems interact with each other and should be solved together. Therefore, we formulate the joint MF and clustering problem for irregular KPIs and design an iterative clustering scheme based on MF. The iterative clustering scheme comprises alignment and pre-filling, the loop of clustering and subclass filling by MF, which can work with two pre-filling methods and any type of clustering algorithms. Extensive experiments on two real-world datasets show that the iterative clustering scheme can obtain higher normalized mutual information (NMI) than non-iterative clustering, and moreover consumes less computational time than Dynamic Time Warping. © 2022, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Time Series Anomaly Detection under Data Contamination

Unsupervised Time Series Anomaly Detection under Data Contam...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiaohui Lin Zuoyong Li Xunhua Huang Xinwei Chen Haoyi Fan College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China Fujian Engineering and Research Center of New Chinese Lacquer Materials Minjiang University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE.

关键词： Training Dimensionality reduction Time series analysis Training data Mean square error methods Gaussian distribution Robustness

来源：评论

学校读者我要写书评

暂无评论

A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Ruiheng Yang, Biwen Xu, Lixin Huang, Yan Xu, Xiaofeng Zhang, Qi Jiang, Zhizhuo Liu, Yu Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control Beijing100081 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Anhui Polytechnic University School of Computer and Information Wuhan241000 China Technical University of Munich Data Science in Earth Observation Munich85521 Germany Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Infrared few-shot object detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial features reveals that sparse annotations introduce spectral bias in the frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the frequency compression few-shot detection (FC-fsd) method, which incorporates a frequency compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://***/RuihengZhang/IFSOD-dataset. © 2025 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

A Prediction Approach Based on Long Short-Term Memory Networks for Dynamic Multiobjective Optimization

SSRN

引用

SSRN 2024年

作者： Xu, Biao Rang, Gejie Li, Wenji Fan, Zhun Gong, Dunwei Yang, Shengxiang He, Jie College of Engineering Shantou University Shantou515063 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Energy and Mechanical Engineering Nanchang Institute of Technology Nanchang330013 China Minjiang University China

Dynamic multiobjective optimization problems (DMOPs) have posed considerable hurdles for traditional evolutionary optimization techniques due to the perpetual variations in their Pareto-optimal sets (PSs) and fronts (PFs). Prediction-based methods have shown promise in swiftly tracking these alterations. However, the majority of current methods rely on linear models to anticipate changing PSs, which can be limiting. When PSs across different environments exhibit nonlinear patterns, accurately predicting the PS in a novel problem becomes challenging. To address this issue, this study introduces a dynamic multiobjective evolutionary optimization algorithm leveraging long short-term memory (LSTM) network predictions. In our approach, the PS of each problem is first divided into several clusters, and the center of each cluster can then be obtained. Thereafter, the cluster centers for the PSs of the problems with different environments form a number of time series. Subsequently, the LSTM network models are trained using the time seriesdata as training samples. Furthermore, the cluster centers of the new PS are predicted utilizing the trained models. LSTM networks excel at capturing historical information and modeling nonlinear relationships, ensuring high prediction accuracy. To enhance the algorithm’s performance, a novel population-generation strategy is employed, ensuring a well-converged and diverse initial population. The proposed algorithm is rigorously tested using benchmark functions, and the results demonstrate its effectiveness in handling DMOPs, outperforming state-of-the-art algorithms. © 2024, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

引用

arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

来源：评论

学校读者我要写书评

暂无评论

A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

引用

computer Modeling in engineering & sciences 2020年第11期125卷 579-596页

作者： Xing Deng Haijian Shao Liang Shi Xia Wang Tongling Xie School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China Department of Electrical and Computer Engineering National University of Singapore117583Singapore The People’s Hospital of RUGAO Nantong226500China

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff *** of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation *** this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of *** benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

关键词： COVID-19 detection deep learning transfer learning pre-trained models

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：