检索结果-内蒙古大学图书馆

A Fused Multiscale Pictorial Sequence Learning Mechanism Applied to Pavement Detection

IEEE ACCESS 2025年 13卷 7502-7511页

作者： Zhang, Jiazhen Wang, Xiulai Zhao, Shuxu Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

Considering that most pavement anomaly detection algorithms are difficult to play a stable role in data related to different distributions of pavement anomalies, this paper proposes a pavement anomaly detection algorithm based on multi-scale fusion time series information. The algorithm iteratively extracts autoregressive latent variables at different time series scales to quantify dynamic regularity information. Then iterative feature set is embedded into the pictographic subsequence learning module to rapidly extract most representative subsequence features through deep learning. The static statistical evaluation value is retained as one of the specific data to assist in identifying the sample, and the two angle data complement each other to maximize the feature information entropy. Using the SVM classifier with the global sequence alignment algorithm as the main kernel function, the data classification module detects anomalies, so that the algorithm can mine valuable feature attributes from multiple angles as much as possible while stably exerting its classification function. The experimental results show that the multi-scale fusion features capture more complete information about the temporal trajectory than the single latent variable feature information, suggesting that the proposed algorithm exhibits better comprehensive performance and generalization ability than unimproved machine learning algorithms and typical deep learning classifiers.

关键词： Feature extraction Heuristic algorithms Roads Mathematical models Anomaly detection classification algorithms Time series analysis Deep learning Vectors Optimization Time series pavement anomaly detection algorithm deep learning model optimization

来源：评论

学校读者我要写书评

暂无评论

A Novel Rust Damage Diagnosis Method for Reinforced Concrete Based on WAESN and GADF-AlexNet-Multihead-Attention

引用

IEEE SENSORS JOURNAL 2025年第1期25卷 1579-1592页

作者： Wu, Yin Che, Yiyue Liu, Yanyi Liu, Wenbo Nanjing Forestry Univ Coll Informat Sci & Technol & Artificial Intellige Nanjing 210037 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 211106 Peoples R China

Upon corrosion of the rebar within concrete, there is a substantial reduction in the effective cross section of the rebar, leading directly to decreased strength of the rebar itself, cracking of the concrete, weakened bond strength between the rebar and concrete, and spalling of the concrete cover layer. These issues pose a significant threat to public safety and property. This article presents the design and implementation of a reinforced concrete corrosion diagnosis system based on a wireless acoustic emission sensor network (WAESN), primarily aimed at nondestructive testing of rebar corrosion. Initially, WAESN nodes perform high-speed sampling of the acoustic emission (AE) signals from rebar concrete specimens under electrified accelerated corrosion, calculate characteristic parameters, and transmit them wirelessly to a gateway. Subsequently, the Gramian angular difference field (GADF) is utilized to transform 1-D time series data into polar coordinates, effectively capturing the nonstationary time-variant characteristics and the differences between modes, thus maximizing the distinction among various features and profiling the deeper aspects of concrete rebar corrosion. Finally, by integrating Multihead-Attention, the classification accuracy of AlexNet is enhanced, allowing for precise detection of different corrosion stages. The proposed GADF-AlexNet-Multihead-Attention model achieved accuracies, precision, recall, and F1 -scores of 97.42%, 97.68%, 98.13%, and 97.55%, respectively, surpassing other comparative algorithms and demonstrating superior monitoring capabilities.

关键词： Concrete Corrosion Steel Sensors Wireless sensor networks Acoustic emission Wireless communication Acoustics Monitoring classification algorithms AlexNet-Multihead-Attention Gramian angular difference field (GADF) reinforced concrete corrosion damage wireless acoustic emission sensor network (WAESN) system WAESN

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Framework for Cluster-Based Side-Channel Analysis on Public-Key Cryptosystems

引用

IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 1962-1973页

作者： Wei, Congming He, Shulin Wang, An Sun, Shaofei Ding, Yaoling Zhang, Jingqi Zhu, Liehuang Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China

Classical cluster-based side-channel analysis (SCA) uses clustering algorithms to analyze power traces and often, principal component analysis to reduce the dimension of data, resulting in that clustering may not deal well with high-dimensional traces, such as cryptographic algorithm implementations with countermeasures. In this article, we propose an intelligent framework for cluster-based SCA, which includes three steps of clustering, classification and correction, for processing large high-dimensional data. By combining unsupervised clustering and supervised deep learning techniques, the framework succeeds in mining the data for additional in-depth information. In addition, unlike traditional cluster-based SCA, our approach focuses on deep learning and deliberately avoids over-reliance on cluster labels during classification. And metrics for correction are adopted to achieve a high level of reliability in key recovery. Experiments on the RSA smart card based on Montgomery ladder implementation and FPGA-based ECC with random delay demonstrate that our framework can significantly improve the success rate with strong robustness.

关键词： Clustering algorithms Deep learning Noise Convolutional neural networks classification algorithms Cryptography Computational modeling Cluster deep learning public-key cryptosystems side-channel analysis (SCA)

来源：评论

学校读者我要写书评

暂无评论

Revisiting the Control Systems of Autonomous Vehicles in the Agricultural Sector: A Systematic Literature Review

引用

IEEE ACCESS 2025年 13卷 54686-54721页

作者： Bhat, Vinayambika S. Wang, Yong Mangalore Inst Technol & Engn Dept Elect & Commun Engn Mangalore 574225 Karnataka India Binghamton Univ Sch Syst Sci & Ind Engn Binghamton NY 13902 USA

The primary objective of this article is to systematically review and categorize the diverse control algorithms applied in autonomous vehicles within the agricultural sector from 2000 to 2023. This systematic literature review (SLR) was conducted using Scopus and Web of Science databases to ensure a comprehensive coverage of peer-reviewed research. The geographical scope of this review is global, encompassing studies from various regions to present a holistic perspective on the technological advancements in autonomous agricultural vehicles. By employing a systematic literature review (SLR) methodology, this study meticulously analyzed published articles to identify, extract, and synthesize data on various control algorithms, which include their application and effectiveness in enhancing agricultural productivity and sustainability. The findings reveal a significant evolution in autonomous vehicle control systems, highlighting a trend towards integrating artificial intelligence (AI)-based control algorithms. These advancements suggest potential navigation and operational efficiency improvements, contributing towards sustainable development goals (SDGs) related to sustainable agriculture. This research presents a novel systematic categorization of control algorithms for autonomous agricultural vehicles by integrating control strategies into a multi-dimensional framework based on algorithmic type (linear, nonlinear, AI-based), application context (path tracking, stability control, obstacle avoidance), and agricultural field type (dry, paddy). Unlike previous reviews that primarily classify algorithms based on technical specifications alone, this study uniquely maps these algorithms to real-world agricultural challenges, providing a structured framework that aligns control methodologies with practical implementation scenarios. This approach enhances clarity in understanding algorithm suitability, adaptability, and scalability across different agricultural settings. The

关键词： Systematic literature review Agriculture Autonomous vehicles Farming Productivity classification algorithms Control systems Real-time systems Machine learning algorithms Data mining Agricultural technology artificial intelligence autonomous vehicle control algorithm systematic literature review

来源：评论

学校读者我要写书评

暂无评论

A Multisource Data Approach for Change and Disturbance Mapping of Ontario's Clay Belt Toward More Accurate Carbon and Emissions Estimation

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 38-60页

作者： Ituen, Ima Hu, Baoxin York Univ Dept Earth & Space Sci & Engn Toronto ON M3J 1P3 Canada

This article is the first of a two-part study on disturbance-informed land use/land cover changes in the Clay Belt region of Northern Ontario, Canada. Despite the drive to convert forests to agricultural land, detailed information on land use changes and the resulting impacts on soil carbon and greenhouse gas (GHG) emissions in the region is lacking. Therefore, this work aims to address the information gap by estimating the amount of land cover changes. The study is driven by the urgent need to develop suitable methodologies for detecting and mapping land cover dynamics in Northern Ontario for forest and agricultural lands. Predominant land cover classes in the study area are mapped in order to quantify the changes from 2002 to 2022. Drawing on nascent technology and tools such as machine learning and Google Earth Engine's cloud computing, the satellite images from Landsat and Sentinel are used to examine the trend of land cover and land use changes. This study proposes a reliable methodology of multisensor fusion with data free of cloud contamination-a method which can be deployed anywhere for large-scale monitoring-yielding high accuracy results for regional or national accounting of ecosystem carbon stocks and GHG emissions.

关键词： Accuracy Carbon Spatial resolution Land surface Forests Earth MODIS Landsat Data models classification algorithms Change detection classification Google Earth Engine (GEE) land cover

来源：评论

学校读者我要写书评

暂无评论

RRT-Based Optimizer: A Novel Metaheuristic Algorithm Based on Rapidly-Exploring Random Trees Algorithm

引用

IEEE ACCESS 2025年 13卷 42744-42776页

作者： Lai, Guang-Jin Li, Tao Shi, Bao-Jun Hebei Univ Technol Sch Mech Engn State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China Hebei Univ Technol Sch Mech Engn Key Lab Hebei Prov Scale Span Intelligent Equipmen Tianjin 300401 Peoples R China

Real-world optimization problems are becoming increasingly complex and require effective and versatile algorithms to provide reliable solutions. However, the no-free-lunch theorem indicates that no single optimization algorithm can solve all optimization problems accurately. Consequently, new optimization methods are required. Inspired by the search mechanism of the Rapidly-exploring Random Trees (RRT) algorithm commonly used in robot path planning, we propose a novel metaheuristic algorithm called RRT-based Optimizer (RRTO). This is the first time that the concept of the RRT algorithm has been integrated with metaheuristic algorithms. The key innovation of RRTO is its three position update strategies: adaptive step size wandering, absolute difference-based adaptive step size, and boundary-based adaptive step size. These strategies enable RRTO to efficiently explore the search space while guiding the population toward high-quality solutions. To evaluate its effectiveness, the RRTO is tested on 23 standard benchmark functions, the CEC2017 test suite, and six constrained optimization problems. In comparison with more than eight peer metaheuristic algorithms, RRTO achieves competitive results across diverse problems. Specifically, among the 35 metrics across the six constrained optimization problems, RRTO ranks first in 26 and second in 4.

关键词： Optimization Metaheuristics Convergence Path planning Space exploration Mathematical models classification algorithms Benchmark testing Standards Heuristic algorithms Exploitation exploration metaheuristic optimization RRT-based optimizer

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Across Time-Space-Domain Perception Method for Rescue Robots Based on DN-CycleGAN and MR-Xception Transfer Learning

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhao, Wen Wang, Yingqi Li, Liqiao Zhan, Hanwen Fu, Yiqi Northwestern Polytech Univ Sch Civil Aviat Xian 710072 Shaanxi Peoples R China Waseda Univ Fac Sci & Technol Tokyo Japan

Recently, rescue robots have been used in scenarios such as earthquakes, chemical explosions, etc. The challenges for these robots lie in terrain awareness and target recognition by machine vision. The rescue environments are complex, with harsh weather changes and challenging terrain, leading to differences in the temporal and spatial domains between the visual training and test data. Traditional cross-domain sample set collection methods are time-consuming and heavily rely on large sample sizes;furthermore, obtaining a comprehensive training sample set under extreme weather conditions, including various time domains (e.g., ice, snow) and spatial domains (e.g., fog, poison gas), is difficult. This article proposes an across time-space environment adaptive perception method for rescue robots based on an improved CycleGAN termed "DenseNet CycleGAN" (DN-CycleGAN). With the support of a terrain diversified-sample synthesis method based on style transfer, it can enhance the transition layer and discriminator of the cycle network generator. Compared with the traditional CycleGAN, the DN-CycleGAN in this article has a 5.26% higher PSNR score and a 9.27% higher SSIM, and the DN-CycleGAN obtains a more realistic image to a certain extent, with color characteristics closer to the target style image. In this article, multiple recognition Xception (MR-Xception), a terrain classification algorithm based on Xception transfer learning, is proposed, which can cope with the low accuracy of traditional deep neural networks in terrain recognition and training time, which can deal with the low accuracy of traditional deep neural networks in terrain recognition and training time. This method enables the diverse cross-domain recognition of small sample terrains and enhances the terrain recognition accuracy to 99.54%. It also ensures the transfer of the fundamental terrain style across various time-space domains;this method enables the cross-domain migration of terrain dataset for the ta

关键词： Robots Training Rescue robots Accuracy Legged locomotion Generators Disasters classification algorithms Transfer learning Visualization Cross-domain deep-transfer learning model (DTLM) DenseNet CycleGAN (DN-CycleGAN) environment awareness multiple recognition Xception (MR-Xception) rescue robot

来源：评论

学校读者我要写书评

暂无评论

A Music classification Approach Based on the Trajectory of Fifths

引用

IEEE ACCESS 2022年 10卷 73494-73502页

作者： Lukaszewicz, Tomasz Kania, Dariusz Silesian Tech Univ Fac Automat Control Elect & Comp Sci PL-44100 Gliwice Poland

In this paper we examine the applicability of the trajectory of fifths as a source of knowledge in automated music classification processes. The study shows that such trajectories provide valuable information concerning the harmonic structure of a given piece of music. The results of the conducted experiments indicate that even basic coefficients quantifying the trajectory of fifths (e.g. the length of the trajectory) can be utilized as feature variables in music classification algorithms. The performed experiments involved the computation of the trajectories of fifths for the music pieces from two genre groups - rock/pop and jazz, calculation of their basic coefficients, and using these coefficients as feature variables for various rock/pop - jazz classifiers based on popular machine learning algorithms, i.e. Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Linear Discriminant Analysis, Gaussian Naive Bayes, and C-SVM. The tests evaluating the performance of the established classifiers have shown that in all of the considered cases the mean balanced accuracies were greater than 0.900, reaching the maximum of 0.936 (std=0.019) for the C-SVM classifier and the minimum of 0.910 (std=0.017) for the classification using the Decision Tree algorithm. Given the basic nature of the utilized coefficients and the relative simplicity of the applied classification models, the achieved results seem very promising. They strongly encourage further research concerning the use of the trajectory of fifths in different music classification algorithms - especially evaluation of its potential in the multi-class or multi-label genre classification scenarios.

关键词： Trajectory Music classification algorithms Harmonic analysis Aggregates Signal processing algorithms Instruments Music classification computer music analysis music information retrieval music data mining

来源：评论

学校读者我要写书评

暂无评论

Semisupervised Domain Adaptation for Wafer Map Defect Recognition via Cross-Alignment Network

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Yiru Wu, Peng Fang, Senlin Cao, Bozhan Wu, Xinyu Lu, Xu Yi, Zhengkun Chinese Acad Sci Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100049 Peoples R China City Univ Macau Fac Data Sci Macau Peoples R China Xinyang Agr & Forestry Univ Sch Informat Engn Xinyang 464400 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen 518055 Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China Guangdong Prov Key Lab Intellectual Property & Big Guangzhou 510665 Peoples R China

Wafer map defect recognition is a crucial step in semiconductor manufacturing and measurement. Semisupervised domain adaptation (SSDA) aims to leverage abundant labeled data in the source domain to achieve accurate classification in the target domain with limited labeled data, effectively addressing the challenge of insufficient labels in wafer map datasets. However, a significant obstacle lies in the label mismatch among wafer map datasets, a problem that most current SSDA methods struggle to mitigate. To solve this problem, this article proposes a new SSDA algorithm named cross-alignment network (CAN). CAN decouples the feature subspace and label space using different encoders and classifiers. To facilitate knowledge transfer between the source and target domains, we propose cross-alignment learning (CAL). CAL consists of sample-wise cross-alignment and class-wise cross-alignment, designed to ensure the consistency of encoders' outputs and facilitate the formation of robust clusters. We also integrate consistency regularization to enforce uniformity in outputs across different views of a sample. Additionally, we introduce a dynamic threshold strategy that tailors thresholds for each class based on its learning effect, thereby enhancing the model's efficiency and data utilization. Empirically, our method outperforms the current state-of-the-art SSDA methods on public wafer map datasets. Furthermore, detailed experiments are conducted to verify the effectiveness of each component in CAN.

关键词： Deep learning Production Semiconductor device modeling Training Knowledge transfer Data models classification algorithms Accuracy Training data Service robots Defect recognition domain alignment label mismatch semiconductor manufacturing semisupervised domain adaptation (SSDA) wafer map

来源：评论

学校读者我要写书评

暂无评论

Performance Optimization and Interpretability of Recurrent Sigma-Pi-Sigma Neural Networks on Application of IoE Data

引用

IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3639-3653页

作者： Deng, Fei Zhang, Liqing Kunming Univ Coll Informat Engn Kunming 650208 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Comp Technol Applicat Kunming 650500 Peoples R China Univ Yunnan Key Lab Data Governance & Intelligent Decis Kunming 650106 Peoples R China

With the exponential growth of Internet of Things (IoT) devices in the era of Internet of Everything (IoE), two major issues arise: 1) data processing speed and 2) interpretability in neural networks. Specifically, training neural networks to handle IoE data often results in poor convergence speed, overfitting of the weights, and fluctuations in the error function. To address these challenges, this article introduces a novel neural network, the recurrent sigma-pi-sigma neural network (RSPSNN), trained using a batch gradient algorithm enhanced with smoothing L1 lasso regularization and an adaptive momentum term. This approach not only improves convergence speed but also enhances generalization capabilities and reduces oscillations. Furthermore, the interpretability of RSPSNN is theoretically demonstrated through characteristics of monotonicity, strong/weak convergence, and stability. Finally, the theoretical findings are supported by experiment results in classification, recognition, and prediction tasks.

关键词： Neural networks Convergence Internet of Things Smoothing methods Training Heuristic algorithms classification algorithms Adaptation models Accuracy Vectors Adaptive momentum Internet of Everything (IoE) interpretability recurrent sigma-pi-sigma neural network (RSPSNN) smoothing lasso regularization

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：