检索结果-内蒙古大学图书馆

Towards a Theoretical Understanding of Semi-Supervised Learning Under Class Distribution Mismatch

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4853-4868页

作者： Pan Du Suyun Zhao Puhui Tan Zisen Sheng Zeyu Gan Hong Chen Cuiping Li School of Information Renmin University of China Beijing China Engineering Research Center of Database and Business Intelligence Ministry of Education (MOE) Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education (MOE) Beijing China School of Statistics Renmin University of China Beijing China

Semi-supervised learning (SSL) confronts a formidable challenge under class distribution mismatch, wherein unlabeled data contain numerous categories absent in the labeled dataset. Traditional SSL methods undergo performance deterioration in such mismatch scenarios due to the invasion of those instances from unknown categories. Despite some technical efforts to enhance SSL by mitigating the invasion, the profound theoretical analysis of SSL under class distribution mismatch is still under study. Accordingly, in this work, we propose Bi- Objective Optimization Mechanism (BOOM) to theoretically analyze the excess risk between the empirical optimal solution and the population-level optimal solution. Specifically, BOOM reveals that the SSL error is the essential contributor behind excess risk, resulting from both the pseudo-labeling error and invasion error. Meanwhile, BOOM unveils that the optimization objectives of SSL under mismatch are binary: high-quality pseudo-labels and adaptive weights on the unlabeled instances, which contribute to alleviating the pseudo-labeling error and the invasion error, respectively. Moreover, BOOM explicitly discovers the fundamental factors crucial for optimizing the bi-objectives, guided by which an approach is then proposed as a strong baseline for SSL under mismatch. Extensive experiments on benchmark and real datasets confirm the effectiveness of our proposed algorithm.

关键词： Training Labeling Semisupervised learning Predictive models Optimization Perturbation methods Entropy data models Annotations Upper bound

来源：评论

学校读者我要写书评

暂无评论

Distributed predefined-time robust adaptive control design for attitude consensus of multiple spacecraft

引用

Advances in Space Research 2025年第10期75卷 7473-7486页

作者： Yao, Qijia Li, Qing Xie, Shumin Jahanshahi, Hadi School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing100083 China School of Mechanics and Civil Engineering and Architecture Northwestern Polytechnical University Xi'an710072 China Institute of Electrical and Electronics Engineers TorontoM5V3T9 Canada

This article investigates the distributed predefined-time attitude consensus of multiple rigid spacecraft under inertia uncertainties and external perturbations over directed communication topology. First, a distributed predefined-time observer is constructed for each follower spacecraft to estimate the virtual leader's attitude, velocity, and acceleration. Then, based on the recovered information, a predefined-time nonsingular terminal sliding mode (NTSM) controller is developed for each follower spacecraft to fulfill the local attitude and velocity tracking. The predefined-time NTSM controller is strongly robust against uncertainties and perturbations but owns the undesirable chattering phenomena. To overcome such problem, an adaptive version of the predefined-time NTSM controller is carried out by incorporating with the parametric adaptation mechanism to identify the total disturbance. In this manner, the predefined-time adaptive NTSM controller has no chattering phenomenon and can maintain the high tracking accuracy simultaneously. Lastly, comparative simulations verify and highlight the main results. © 2025 COSPAR

关键词： Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

UARC:Unsupervised Anomalous Traffic Detection with Improved U-Shaped Autoencoder and RetNet Based Multi-clustering 26th

UARC:Unsupervised Anomalous Traffic Detection with Improved ...

引用

26th International Conference on Information and Communications Security, ICICS 2024

作者： Xie, Yunyang Chen, Kai Li, Shenghui Li, Bingqian Zhang, Ning Hubei Key Laboratory of Distributed System Security Hubei Engin eering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University Technology Wuhan430074 China

ISBN: (纸本)9789819787975

With the ongoing advancement of deep learning, modern network intrusion detection systems increasingly favor utilizing deep learning networks to improve their ability to learn traffic characteristics. To address the challenge of obtaining a substantial amount of labeled training data, many intrusion detection systems now focus on unsupervised anomaly detection methods. Despite this shift, researchers still face the daunting task of distinguishing a significant volume of anomalous traffic and dealing with data imbalance. To address these real-world challenges, we introduce UARC, a system capable of achieving unsupervised anomaly traffic detection through multi-clustering. UARC utilizes an enhanced U-shaped autoencoder and a feature fusion method incorporating Masked Retnet to effectively extract spatiotemporal features from network traffic. It combines these techniques with the HDBSCAN algorithm for multi-clustering of traffic, providing a form of reverse guidance for network learning. Experimental results on multiple datasets demonstrate that UARC can cluster various types of traffic with an impressive accuracy rate of up to 97.96%, while achieving a 99.70% AUC value for anomaly detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

arXiv

引用

arXiv 2025年

作者： Hu, Yuxuan Zhang, Jing Chen, Xiaodong Zhao, Zhe Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China Engineering Research Center of Database and Business Intelligence Beijing China Tencent AI Lab Beijing China

Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA techniques with additional masking mechanisms. Despite these successes, such approaches suffer from an increased memory and computation overhead, which affects efficiency of LoRA methods. In response to this limitation, we introduce LoRS, an innovative method designed to achieve both memory and computation efficiency when fine-tuning sparse LLMs. To mitigate the substantial memory and computation demands associated with preserving sparsity, our approach incorporates strategies of weight recompute and computational graph rearrangement. In addition, we also improve the effectiveness of LoRS through better adapter initialization. These innovations lead to a notable reduction in memory and computation consumption during the fine-tuning phase, all while achieving performance levels that outperform existing LoRA approaches. © 2025, CC BY.

关键词： Problem oriented languages

来源：评论

学校读者我要写书评

暂无评论

A leaded sine–cosine artificial protozoa optimizer algorithm for solving multi-objective optimization problems

引用

Cluster Computing 2025年第5期28卷 1-65页

作者： Liao, Junjie Melethil Sethumadhavan, Syam Ke, Rui Gao, Zheng-Ming Zhao, Juan College of Computer and Information Technology China Three Gorges University Yichang443002 China School of Computer Engineering Jingchu University of Technology Jingmen448000 China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang443002 China Big Data Research Center Jingchu University of Technology Jingmen448000 China School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China

Many real-world engineering problems require simultaneous optimization of multiple objectives. The artificial protozoa optimizer (APO), a recently proposed and efficient optimization method, has demonstrated potential in solving multi-objective problems. In order to solve multi-objective problems, this paper proposes a novel algorithm based on the APO framework, the Leaded Sine Cosine Multi-objective APO (LSCMOAPO) algorithm. LSCMOAPO mainly integrates the sine–cosine algorithm (SCA) and a leader selection strategy to guide the population towards the true Pareto front. The algorithm was validated through extensive simulations involving 41 benchmark test functions (ZDT-series, UF-series, CF-series, and CEC2020-series) and five practical engineering problems. Performance was evaluated using the inverted generational distance, generational distance, Hypervolume, and spacing metrics. Comparative analysis with ten other multi-objective optimization algorithms (MOPSO, MOSSA, MOGWO, MODA, MOCryStAl, MOALO, MOGOA, MOSMA, MOGNDO, and MOAPO) also validated the better performance of LSCMOAPO in more than 86% occasions (The average of the evaluation indicators) in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. It will set a new benchmark for new algorithm proposed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Design, Performance, and Applications of AMMIS: A Novel Airborne Multimodular Imaging Spectrometer for High-Resolution Earth Observations

引用

engineering 2025年第4期47卷 38-56页

作者： Jia, Jianxin Wang, Yueming Zheng, Xiaorou Yuan, Liyin Li, Chunlai Cen, Yi Si, Fuqi Lv, Gang Wang, Chongru Wang, Shengwei Zhang, Changxing Zhang, Dong He, Daogang Zhuang, Xiaoqiong Han, Guicheng Zhang, Mingyang Hyyppä, Juha Wang, Jianyu Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis Guangdong School of Computer Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Department of Mechanical Engineering Aalto University EspooFI-02150 Finland

Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades. Despite the high sensitivity of push-broom hyperspectral imagers, they experience limited swath and wavelength coverage. In this study, we report the development of a push-broom airborne multimodular imaging spectrometer (AMMIS) that spans ultraviolet (UV), visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) wavelengths. As an integral part of China's High-Resolution Earth Observation Program, AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads. It has been mounted on aircraft platforms such as Y-5, Y-12, and XZ-60. Since 2016, AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data. This study describes the system design, calibration techniques, performance tests, flight campaigns, and applications of the AMMIS. The system integrates UV, VNIR, SWIR, and TIR modules, which can be operated in combination or individually based on the application requirements. Each module includes three spectrometers, utilizing field-of-view (FOV) stitching technology to achieve a 40° FOV, thereby enhancing operational efficiency. We designed advanced optical systems for all modules, particularly for the TIR module, and employed cryogenic optical technology to maintain optical system stability at 100 K. Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data. The AMMIS features more than 1400 spectral bands, with spectral sampling intervals of 0.1 nm for UV, 2.4 nm for VNIR, 3 nm for SWIR, and 32 nm for TIR. In addition, the instantaneous fields of view (IFoVs) for the four modules were 0.5, 0.25, 0.5, and 1 mrad, respectively, with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode. This study reports on land

关键词： Fighter aircraft

来源：评论

学校读者我要写书评

暂无评论

A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network

引用

IEEE Sensors Journal 2025年第9期25卷 15548-15563页

作者： Ye, Miao Jiang, Zhibang Xue, Xingsi Li, Xingwang Wen, Peng Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo454099 China

Attention-based transformers have played an important role in wireless sensor network (WSN) timing anomaly detection due to their ability to capture long-term dependencies. However, there are several issues that must be addressed, such as the fact that their ability to capture long-term dependencies is not completely reliable, their computational complexity levels are high, and the spatiotemporal features of WSN timing data are not sufficiently extracted for detecting the correlation anomalies of multinode WSN timing data. To address these limitations, this article proposes a WSN anomaly detection method that integrates frequency-domain features with dynamic graph neural networks (GNNs) under a designed self-encoder reconstruction framework. First, the discrete wavelet transform (DWT) effectively decomposes trend and seasonal components of time series to solve the poor long-term reliability of transformers. Second, a frequency-domain attention mechanism (FDAM) is designed to make full use of the difference between the amplitude distributions of normal data and anomalous data in this domain. Finally, a multimodal fusion-based dynamic graph convolutional network (MFDGCN) is designed by combining an attention mechanism and a graph convolutional network (GCN) to adaptively extract spatial correlation features. A series of experiments conducted on public datasets and their results demonstrate that the anomaly detection method designed in this article exhibits superior precision and recall than the existing methods do, with an F1 score of 93.5%, representing an improvement of 2.9% over that of the existing models. © 2025 IEEE. All rights reserved.

关键词： Normal distribution

来源：评论

学校读者我要写书评

暂无评论

data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

引用

Computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

来源：评论

学校读者我要写书评

暂无评论

Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

引用

2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Li, Zhenyu Wu, Ruixin Wang, Jin Hu, Chuanqi Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture. © 2025 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

引用

Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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：