检索结果-内蒙古大学图书馆

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第1期9卷 31-45页

作者： Naji, Abdulbary Hawbani, Ammar Wang, Xingfu Al-Gunid, Haithm M. Al-Dhabi, Yunes Al-Dubai, Ahmed Hussain, Amir Zhao, Liang Alsamhi, Saeed Hamood Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH11 4BN Scotland Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Univ Galway Insight Ctr Data Analyt Galway H91 TK33 Ireland IBB Univ Ibb 70270 Yemen

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate the importance of nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks' assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes' density, and queuing processing.

关键词： Task analysis Path planning Network topology clustering algorithms Heuristic algorithms Topology Time factors Hexagonal-clustering path planning scheduling wireless power transfer wireless sensor networks wireless rechargeable sensor networks

来源：评论

学校读者我要写书评

暂无评论

Hadoop Processing Methods for Large Scale Video Data

引用

IEEE ACCESS 2024年 12卷 176247-176258页

作者： Wu, Guangzhi Guangzhou Univ Party Propaganda Dept Guangzhou 510000 Peoples R China

As the continuous advancement of the We-media era, there are more and more short video users. However, there are still problems with low data processing efficiency and serious video damage in the current processing framework for large-scale video data. To address this issue, research is conducted to improve the artificial fish swarm algorithm using particle swarm optimization algorithm, and the improved algorithm is applied to the distributed system architecture Hadoop to enhance Hadoop's computation speed and accuracy of data. The study first conducts comparative experiments on the improved algorithm. The outcomes denoted that the accuracy of the improved algorithm was as high as 98.7%, while the error rate was only 0.9%. The recall and precision of video data were 92.1% and 96.5%, respectively, and the computational performance was significantly better than other comparative algorithms. The Hadoop data processing framework optimized after the improved algorithm was further tested, and the outcomes denoted that the response accuracy of the optimized data processing framework was as high as 98.6%, and the response time was only 0.32 seconds. From the above outcomes, the proposed improved algorithm can optimize the processing capability of the data processing framework for large-scale video data. Moreover, the optimized data processing framework can not only be used for large-scale video data processing, but also widely used in other fields to raise computational accuracy.

关键词： Fish Data processing Mathematical models Distributed databases Accuracy clustering algorithms Particle swarm optimization Tail Linear programming Flowcharts Video data particle swarm optimization algorithm artificial fish swarm algorithm Hadoop framework data processing

来源：评论

学校读者我要写书评

暂无评论

Partially-Supervised Metric Learning via Dimensionality Reduction of Text Embeddings Using Transformer Encoders and Attention Mechanisms

引用

IEEE ACCESS 2024年 12卷 77536-77554页

作者： Hodgson, Ryan Wang, Jingyun Cristea, Alexandra I. Graham, John Univ Durham Dept Comp Sci Durham DH1 3LE England Reveela Technol Newcastle Upon Tyne NE1 6UF Northumberland England

Real-world applications of word embeddings to downstream clustering tasks may experience limitations to performance, due to the high degree of dimensionality of the embeddings. In particular, clustering algorithms do not scale well when applied to highly dimensional data. One method to address this is through the use of dimensionality reduction algorithms (DRA). Current state of the art algorithms for dimensionality reduction (DR) have been demonstrated to contribute to improvements in clustering accuracy and performance. However, the impact that a neural network architecture can have on the current state of the art Parametric Uniform Manifold Approximation and Projection (UMAP) algorithm is yet unexplored. This work investigates, for the first time, the effects of using attention mechanisms in neural networks for Parametric UMAP, through the application of network architectures that have had considerable effect upon the wider machine learning and natural language processing (NLP) fields - namely, the transformer-encoder, and the bidirectional recurrent neural network. We implement these architectures within a semi-supervised metric learning pipeline, with results demonstrating an improvement in the clustering accuracy, compared to conventional DRA techniques, on three out of four datasets, and comparable SoA accuracy on the fourth. To further support our analysis, we also investigate the effects of the transformer-encoder metric-learning pipeline upon the individual class accuracy of downstream clustering, for highly imbalanced datasets. Our analyses indicate that the proposed pipeline with transformer-encoder for parametric UMAP confers a significantly measurable benefit to the accuracy of underrepresented classes.

关键词： Dimensionality reduction Task analysis clustering algorithms Neural networks Transformers Measurement Supervised learning attention mechanisms clustering transformer networks metric learning

来源：评论

学校读者我要写书评

暂无评论

Dual-Task GPR Method: Improved Generative Adversarial Clutter Suppression Network and Adaptive Target Localization Algorithm in GPR Image

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Hou, Feifei Fang, Mengqi Luo, Tess Xianghuan Fan, Xinyu Guo, Ying Cent South Univ Sch Automat Changsha 410083 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing 100876 Peoples R China Hefei Natl Lab Hefei 230088 Peoples R China

Ground-penetrating radar (GPR), as a geophysical survey method, offers high resolution and fast detection capabilities obtaining rich information about the underground structure. However, under real conditions, GPR is susceptible to interference and clutter. Therefore, there is an urgent need to develop methods for suppressing clutter, highlighting and positioning targets in GPR images. This article proposed an integrated method consisting of two modules: GPR image decluttering and underground target localization, which are used for clutter suppression and hyperbola vertex estimation from GPR image. In the first module, an unsupervised generative adversarial network (GAN) model is developed to map cluttered GPR images to clutter-free GPR images, where the CycleGAN is adapted as the main network architecture. To further improve the performance of the model, the first module proposes a novel loss function that introduces a perceptual loss into the previous CycleGAN loss. It utilizes a multiscale structural similarity index (MS-SSIM) loss to obtain richer and more realistic context information. In the second module, based on the obtained decluttered image, a novel adaptive target characterization algorithm, called gradient points clustering-based localization (GPCL), is proposed for the recognition and localization of GPR target signatures. This algorithm is designed to leverage changes in target position and local characteristics, thereby achieving efficient target vertex evaluation. Experimental results conducted on field datasets demonstrate that the proposed methods can significantly improve the quality and clarity of various cluttered images and exhibit strong applicability for the localization of underground targets.

关键词： Clutter Location awareness Feature extraction clustering algorithms Transforms Generative adversarial networks Data mining clustering algorithm clutter elimination generative adversarial network (GAN) ground-penetrating radar (GPR) perceptual loss subsurface target localization

来源：评论

学校读者我要写书评

暂无评论

A Blockchain System for QoS Monitoring in Decentralized Edge Computing

引用

IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第1期17卷 263-276页

作者： Wang, Puwei Li, Haoran Fu, Hang Sun, Zhouxing Chen, Jinchuan Du, Xiaoyong Renmin Univ China Sch Informat Minist Educ Beijing 100872 Peoples R China Renmin Univ China Key Lab Data Engn & Knowledge Engn Minist Educ Beijing 100872 Peoples R China

In edge computing, applications are usually delivered as services, each of which runs independently and cooperates to construct complicated applications. QoS (Quality of Service) monitoring is an important way to detect and locate faulty services. In a decentralized environment, QoS monitoring will face trust problem because it is difficult to guarantee the trustworthiness of monitoring results. This article builds a blockchain system for QoS monitoring. However, there are two challenges. First, although the blockchain consensus ensures the consistency of on-chain data among nodes, there is no guarantee that the monitoring data collected in the decentralized environment are authentic, because malicious nodes may report falsified data. Second, in order to handle service faults in time, the real-time query is usually required for obtaining monitoring data. But, blockchains suffer from inefficient querying, because the sequential data storage of blockchain is designed for write intensive applications at the expense of some read performance. To address these challenges, this article proposes a clustering-based algorithm for validating the authenticity of monitoring data collected in the decentralized environment, and proposes a probabilistic threshold query over blockchain, which supports efficient querying and guarantees the probability that the query results are correct is not less than a given threshold. This article implements the proposed blockchain system based on the blockchain platform Hyperledger Fabric and the edge computing platform KubeEdge. The experiment results demonstrate the proposed blockchain system provides high-throughput and low-latency monitoring ability, and can efficiently obtain monitoring results close to real QoS data.

关键词： Monitoring Blockchains Quality of service Edge computing Probabilistic logic Measurement clustering algorithms QoS monitoring edge computing blockchain probabilistic threshold query

来源：评论

学校读者我要写书评

暂无评论

DACA: Distributed adaptive grid decision graph based clustering algorithm

引用

SOFTWARE-PRACTICE & EXPERIENCE 2022年第5期52卷 1199-1215页

作者： He, Jing Zhou, Jun Wang, Haoyu Cai, Li Yunnan Univ Natl Pilot Sch Software Kunming Yunnan Peoples R China

clustering algorithms play a very important role in machine learning. With the development of big-data artificial intelligence, distributed parallel algorithms have become an important research field. To reduce the computational complexity and running time of large-scale datasets in the clustering process, this study proposes a distributed clustering algorithm DACA (distributed adaptive grid decision graph based clustering algorithm). In a distributed environment, DACA uses relative entropy to adaptively mesh the data to form an obvious sparse grid and dense grid. Then, the decision graph is used to determine the cluster center mesh object. Finally, the KD-tree is used to accelerate the determination of the cluster center of sparse points to complete clustering. The algorithm is implemented using the popular Apache Spark computing framework, compared with other distributed clustering algorithms, DACA can adaptively divide the grid according to the data distribution to obtain better clustering effect. At the same time, KD tree algorithm is used to speed up the decision-making of clustering center. Numerous experiments show that the DACA algorithm has excellent performance and accuracy on six standard datasets and real GPS trajectory datasets.

关键词： adaptive grid division clustering algorithms decision graphs distributed KD-tree

来源：评论

学校读者我要写书评

暂无评论

Automatic Dual-Polarized Ground Penetrating Radar for Enhanced 3-D Tree Roots System Architecture Reconstruction

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Luo, Wenhao Lee, Yee Hui Hao, Tong Yusof, Mohamed Lokman Mohd Yucel, Abdulkadir C. Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Tongji Univ Ctr Spatial Informat Sci & Sustainable Dev Applica Shanghai 200092 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Natl Pk Board Singapore 259569 Singapore

Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.

关键词： Three-dimensional displays Image reconstruction Radar antennas Vegetation Signal processing algorithms Accuracy Surveys Permittivity Ground penetrating radar clustering algorithms 3-D reconstruction dual-polarized ground-penetrating radar (GPR) optimized slice relation clustering (OSRC) radar signal processing framework root system architecture (RSA)

来源：评论

学校读者我要写书评

暂无评论

SVM-Assisted Adaptive Kernel Power Density clustering Algorithm for Millimeter Wave Channels

引用

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第6期70卷 4014-4026页

作者： Du, Fei Zhao, Xiongwen Zhang, Yu Wen, Yang Fu, Zihao Geng, Suiyan Qin, Peng Zhou, Zhenyu Xu, Chen Liu, Yongsheng Fan, Wei North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Sch Elect & Elect Engn Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Power Internet Things Technol Baoding 071003 Hebei Peoples R China China Res Inst Radiowave Propagat Qingdao 266107 Peoples R China Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

Cluster-based channel modeling has gradually become a trend in the development of a channel model, since it is a good compromise between accuracy and complexity. However, most of the existing clustering algorithms require prior knowledge of clusters, initialization, and threshold choices. An accurate and automatic cluster identification algorithm is therefore highly desirable for channel modeling. In this article, adaptive kernel-power-density (AKPD) and support vector machine-assisted AKPD (SVM-AKPD) algorithms are proposed. First, a new distance-based metric is proposed to calculate an adaptive-K for each multipath component (MPC), in which the AKPD can be used in scenarios where we have a complex distribution of MPCs, especially for the cluster with small MPCs. Furthermore, the SVM is applied in clustering by the full partition of MPCs' feature space to overcome the limitation of the AKPD, where the MPCs lying at a large distance from the cluster centroids will be clustered into surrounding clusters when the clusters are closely spaced in the AKPD. Finally, the performance of the proposed AKPD and SVM-AKPD is validated with measured and simulated channels data at millimeter waveband, respectively. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm. The proposed algorithms enable applications in multiple-input-multiple-output (MIMO) channels with no prior knowledge about the clusters, such as number and initial locations. It also does not need to adjust cluster parameters manually and can be implemented for cluster-based channel modeling with a fairly low complexity.

关键词： clustering algorithms Delays Support vector machines MIMO communication Channel models Adaptation models Wireless communication Channel measurement and modeling cluster kernel density multipath component (MPC) multiple-input-multiple-output (MIMO) support vector machine (SVM) wireless channel

来源：评论

学校读者我要写书评

暂无评论

Deep Fuzzy Envelope Sample Generation Mechanism for Imbalanced Ensemble Classification

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第3期32卷 1248-1262页

作者： Li, Fan Li, Yongming Shen, Yinghua Pedrycz, Witold Zhang, Xiaoheng Wang, Pin Li, Pufei Zhou, Chuanyan Cheng, Huan Chongqing Univ Sch Microelectorn & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing 400044 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton T6G 2R3 AB Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn TR-34010 Sariyer Turkiye

Ensemble methods are widely used to tackle class imbalance problem. However, for existing imbalanced ensemble (IE) methods, the samples in each subset are resampled from the same dataset, and are directly input to the classifier for training, so the quality (diversity and separability) of the subsets is unsatisfactory usually. To solve the problem, a deep fuzzy envelope sample generation mechanism is proposed. First, the fuzzy C-means clustering based deep sample envelope prenetwork (DSEN) is designed to mine correlation information among samples, thereby increasing the quality of the subsets. Second, the local manifold structure metric and global structure distribution metric are designed to construct local-global structure consistency mechanism (LGSCM) to enhance distribution consistency of interlayer samples of DSEN. Third, the DSEN and LGSCM are combined to form the final deep sample envelope network-DSENLG to refresh the existing subsets. Finally, base classifiers are applied on the new subsets generated by the DSENLG and then fused, thereby realizing a new IE algorithm. The experimental results show that the proposed algorithm is significantly better than existing representative IE algorithms and it achieves the highest improvement of 10.64%, 19.5%, 18.67% and 22.33% on four criteria over the state-of-the-art methods. The originality of the article is threefold: proposing the concept of "deep fuzzy samples" or "envelope samples", which comprehensively considers the correlation information among original samples;proposing the LGSCM to resolve the distribution inconsistency of interlayer samples;and forming an fuzzy envelope sample based IE algorithm.

关键词： Ensemble learning Correlation Classification algorithms Training Manifolds Measurement clustering algorithms Class imbalance problem domain adaption ensemble approach envelope learning fuzzy C-means (FCMs) clustering

来源：评论

学校读者我要写书评

暂无评论

Semantic Maps for Knowledge Graphs: A Semantic-Based Summarization Approach

引用

IEEE ACCESS 2024年 12卷 6729-6744页

作者： Camarillo-Ramirez, Pablo Cervantes-Alvarez, Francisco Gutierrez-Preciado, Luis Fernando Western Inst Technol & Higher Educ Guadalajara 45601 Jalisco Mexico

Knowledge Graphs (KGs) have emerged as a powerful tool for representing semantic structured information and enabling the development of intelligent systems. This paper focuses on the generation of semantic maps as summarization method for KGs. We propose a strategy that utilizes centroid-based clustering algorithms, namely Affinity Propagation and Partitioning Around Medoids (PAM), to capture the semantic distance between nodes in the KG and generate meaningful clusters. Our experiments demonstrate divergent results between the two clustering algorithms, with Affinity Propagation showing qualitative coherence and meaningfulness, while PAM performs well in terms of internal validation metrics. We leverage the computed centroids to infer a main term of the semantic map, which contributes to the visually appealing and informative representation of the KG. The combination of semantic distance capture, clustering algorithms, and centroid-based inference facilitates a comprehensive understanding of the KG. Our findings highlight the importance of considering both qualitative and quantitative evaluation measures in assessing clustering results. The effectiveness of semantic maps is showcased in visualizing KGs and advancing the field of knowledge graph visualization. The integration of centroid-based clustering algorithms, qualitative evaluation, and inference methods offers improved clarity and interpretability for complex KG analysis.

关键词： Semantics Knowledge graphs Visualization clustering algorithms Data visualization Ontologies Measurement knowledge graph visualization semantic distance semantic mapping visual data exploration clustering

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：