检索结果-内蒙古大学图书馆

Real-Time On-Site Ground-Motion Prediction Using ResNet

Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

引用

Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

来源：评论

学校读者我要写书评

暂无评论

Digital Currency Abnormal Detection Based on Graph Neural Network

Digital Currency Abnormal Detection Based on Graph Neural Ne...

引用

2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Que, Xingle Cheng, Yang Bai, Yunfei Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

In recent years, with the rapid development of the digital currency market, the security issues of digital currency transactions have become increasingly prominent, and abnormal transaction detection has become an important technical means to ensure the security of the financial system. Traditional detection methods face many challenges in dealing with complex blockchain data, as they cannot fully utilize the inherent structural information of transaction data, and are difficult to handle the problem of imbalanced categories. Accordingly, this study proposes a GNN (Graph Neural Network)-based abnormal transaction detection method, which models transaction data as a graph structure, extracts transaction features using Graph Convolutional Network (GCN) and uses small sample processing techniques to alleviate data imbalance. The proposed method showed that the experimental results could effectively improve the detection performance of abnormal transactions after multiple rounds of training, especially in terms of AUC evaluation indicators, which verifies its potential application in the detection of abnormal transactions in digital currencies. This study provides a novel and effective solution for anomaly detection in complex network data and points out the direction for future research. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

引用

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

来源：评论

学校读者我要写书评

暂无评论

XGCN:a library for large-scale graph neural network recommendations

引用

Frontiers of computer science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

来源：评论

学校读者我要写书评

暂无评论

Quantum Run-length Encoding: Optimizing Data Compression on Quantum computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jiale Che, Xilong Jin, Shiyong Pan, Kaifan Peng, Shun Hu, Juncheng College of Computer Science and Technology Jilin University Changchun China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (纸本)9798350368741

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE's application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM's superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware. © 2025 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Dijkstra Algorithm Based on Generalized Rule Tree for Crowdsourced Express Delivery 24

Hierarchical Dijkstra Algorithm Based on Generalized Rule Tr...

引用

8th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2024

作者： Huang, Junni Ding, Cong Wang, Ruofei Liu, Guanyou Xu, Chuan Song, Mei Yang, Maoyun Jiangsu Normal University Research Center of Management Science and Technology China School of Computer Science and Technology Jiangsu Normal University China Jiangsu Normal University School of Computer Science and Technology China

ISBN: (纸本)9798400707971

Various of algorithms were developed for solving the shortest path problem with a set of mandatory points in crowdsourced express delivery. However, the existing approaches cannot effectively solve the problem of user-specified vertices with certain partial order rules. This study proposes a hierarchical Dijkstra algorithm based on the generalized rule tree for adding mandatory points with ordered categories and ordered nodes. First, the order relations between mandatory points are stored in the form of a generalized rule tree. Then the algorithm can make appropriate pruning and jumps to reduce the time wasted on unnecessary computations by combining the hierarchical structure and the generalized rule tree. Finally, experimental simulations demonstrate that the proposed algorithm can generate a shortest path that passes through mandatory points in the specified order efficiently, thereby improving the solution of related problems. © 2024 ACM.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7593-7620页

作者： Han, Yuhang Xiang, Hongyu Cao, Jianing Yang, Xiaohua Pan, Nan Huang, Linhai Mechanical Engineering Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Yunnan China Computer Science and Technology Metrology Center Yunnan Power Grid Co. Ltd. Yunnan China Computer Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs’ impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Automatic text summarization using deep reinforced model coupling contextualized word representation and attention mechanism

引用

Multimedia Tools and Applications 2024年第1期83卷 733-762页

作者： Aliakbarpour, Hassan Manzuri, Mohammad Taghi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

With the rapid and unprecedented growth of textual data in recent years, there is a remarkable need for automatic text summarization models to retrieve useful information from these large numbers of textual documents without human intervention within a reasonable time. Text summarization is commonly performed based on extractive and abstractive paradigms. Although different machine learning and deep learning based methods have been proposed for the task of text summarization during the last decades, they are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a new summarization model is proposed in this paper which takes advantage of both extractive and abstractive text summarization models as a single unified model based on the strategy gradient of reinforcement learning. The proposed model also employs the combination of convolutional neural network and gated recurrent unit in both extraction and abstraction modules besides attention mechanism. Moreover, language models, namely Word2Vec and BERT, are used as the backbone of the proposed model to better express sentence semantics as a word vector. We conducted our experiments on widely-studied text summarization datasets (CNN\Daily Mail and DUC-2004) and according to the empirical results, not only the proposed model achieved higher accuracy compared to both extractive and abstractive summarization models in terms of ROUGE metric but also its generated summaries presented higher saliency and readability based on human evaluation. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Trajectory Design and Resource Allocation for UAV-Assisted Computation Offloading 7

Trajectory Design and Resource Allocation for UAV-Assisted C...

引用

7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Xu, Yunxin Wang, Jiale Wang, Junhao Que, Xingle Lu, Dandan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

This paper studies air-ground cooperative communication based on unmanned aerial vehicles (UAVs) and device-to-device (D2D) technology. This field has become a research hotspot due to its susceptibility to passive eavesdropping and privacy leakage. To enhance data transmission security, this paper proposes a full-duplex UAV-assisted scheme to address potential ground eavesdropping risks in UAV-assisted computation offloading tasks. The scheme transmits interference signals while receiving computation tasks to counteract eavesdroppers. Given the limited energy of UAVs and the inadequacies of existing energy optimization research, this paper establishes a UAV-assisted mobile edge computing (MEC) communication system model. To address the non-convex optimization problem within the model, a successive convex approximation algorithm is employed for simplification and decomposition. User scheduling, power allocation, and UAV trajectory design subproblems are solved through an alternating iteration method. Simulation results validate that the proposed method can effectively improve the system's secure communication performance even in the presence of uncertain eavesdropper locations. ©2024 IEEE.

关键词： Convex optimization

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：