检索结果-内蒙古大学图书馆

An improved branching algorithm for the proper interval edge deletion problem

Frontiers of Computer science 2022年第2期16卷 178-180页

作者： Wenjun LI Xiaojing TANG Yongjie YANG School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha 410015China School of Computer Science and Engineering Central South UniversityChangsha 410083China

1 Introduction For a graph class G,the G-EDGE DELETION problem is to determine whether a given graph can be transformed into a graph in G by deleting at most k *** G-EDGE DELETION problem for a large body of graph cla... 详细信息

关键词： branching problem transformed

来源：评论

学校读者我要写书评

暂无评论

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for big data

引用

Computers, Materials & Continua 2024年第10期81卷 1409-1432页

作者： Hongjun Zhang Zeyu Zhang Yilong Ruan Hao Ye Peng Li Desheng Shi School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China Ministry of Science and Technology Innovation China Communications Services Corporation LimitedBeijing100071China School of Artificial Intelligence The University of ManchesterManchesterM139PLUK Ministry of Technology Innovation China Telecom Artificial Intelligence Technology(Beijing)Corporation LimitedBeijing100032China Ministry of Science and Technology Innovation Zhongbo Information Technology Research Institute Corporation LimitedNanjing210012China Jiangsu Postal Big Data Technology and Application Engineering Research Center Nanjing University of Posts and TelecommunicationsNanjing210003China

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering *** address this issue,this paper introduces a new method named big data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering ***,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing ***,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary *** new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in ***,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic *** experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most ***,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering *** not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through *** method shows great potential in real-world applications where dynamic data growth is common,and it is of significant imp

关键词： Tensor incremental update distributed clustering processing big data

来源：评论

学校读者我要写书评

暂无评论

Implementation of Mask Wearing Recognition 10

Implementation of Mask Wearing Recognition

引用

10th International Conference on Orange technology, ICOT 2022

作者： Lin, Yuh-Chung Kuan, Ta Wen Tseng, Shih-Pang Ma, Wan Bo School of Information Science and Technology Sanda University Shanghai China School of Software and Big Data Changzhou College of Information Technology Changzhou China

ISBN: (纸本)9781665454193

Because of the pandemic of COVID-19 since 2020, it seriously affects people's daily life and causes huge economic loss. Recently, the international community has mostly adopted an attitude of coexisting with Covid-19. We cannot ignore the harm the virus can bring to us. In order to effectively protect everyone from the virus, the most basic and effective way is to wear a mask to keep you away from exposure to the virus when going to public areas. Vision intelligence can play an important role in public health issues. In this paper, we utilize the object detection method to implement an actual mask wearing recognition system which can detect if people have a face mask on their face, and send a warning message if not wearing a mask. YOLOv3 is the basic framework for our implementation. After training and fine-tuning processes, the implemented model can perform effectively and correctly. © 2022 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

引用

IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Student Comprehensive Assessment System 12

Design and Implementation of Student Comprehensive Assessmen...

引用

12th International Conference on Orange technology, ICOT 2024

作者： Liu, Zhongjie Cai, Jiachen Xu, Meifang Tao, Dawei Tseng, Shih-Pang School of Software and Big Data Changzhou College of Information Technology Changzhou China School of Cyberspace Security Changzhou College of Information Technology Changzhou China School of Electronic Engineering Changzhou College of Information Technology Changzhou China School of Information Science and Technology Sanda University Shanghai China

ISBN: (纸本)9798331523602

With the continuous deepening of educational informatization, student comprehensive assessment has become increasingly important as a core link in evaluating academic achievements and promoting the improvement of teaching quality. However, some schools still lack sufficient educational informatization and rely on Excel spreadsheets to analyze students comprehensive assessment data, resulting in low work efficiency. This paper proposes a student comprehensive assessment system based on the Django framework, which deeply investigates the functional requirements of existing student comprehensive assessments and designs corresponding functional modules such as honor declaration, moral education evaluation, academic performance inquiry, dormitory performance accounting, and comprehensive performance inquiry. Based on the different weights of the entered data, the system can quickly calculate the comprehensive assessment scores for each student. The system also integrates a large language model from Qianfan, developing an intelligent question assistant to deeply learn the school's policy knowledge base to answer questions encountered by teachers and students in their learning and daily lives. The deployment and implementation of the system have verified the accuracy of the student comprehensive assessment results, improved the level of educational informatization management in student work, and promoted the development of the student comprehensive assessment system. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Evolving Effective Drug Therapies with Multi-objective Genetic Algorithm 16th

Evolving Effective Drug Therapies with Multi-objective Genet...

引用

16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Weng, Shaowei Chu, Shu-Chuan Nguyen, Trong-The Dao, Thi-Kien School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

The development of effective drug therapies is a complex and multifaceted problem involving various biological, chemical, and computational challenges. Traditional drug development methods are often time-consuming and costly, necessitating innovative approaches. Multi-objective genetic algorithms (MOGAs) have emerged as a powerful tool for optimizing drug design by simultaneously considering multiple conflicting objectives, such as efficacy, safety, and cost. This paper reviews the application of MOGAs in drug discovery, highlighting their principles, advantages, and case studies, and discussing future directions in the field. Enhancing MOGAs in drug discovery are also outlined, emphasizing the integration with machine learning, advancements in computational infrastructure, and the potential for personalized medicine. The paper concludes by affirming the significant promise of MOGAs in accelerating the development of safe and effective drug therapies, thereby addressing critical needs in modern pharmacology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drug discovery

来源：评论

学校读者我要写书评

暂无评论

A human-in-the-loop method for pulmonary nodule detection in CT scans

引用

Visual Intelligence 2024年第1期2卷 227-239页

作者： Qingjie Zeng Yutong Xie Zilin Lu Yong Xia National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Australian Institute for Machine Learning The University of AdelaideAdelaide SA 5000Australia

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung *** extensive well-performed methods have been proposed for this task,they suffer from the domain shift issue between training and test *** domain adaptation(UDA)methods provide a promising means to mitigate the domain variance;however,their performance is still limited since no target domain supervision is *** make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains,we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target ***,we first train a detector model on source domain,and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme(USSS)to find a few target domain samples worth annotating most and obtain their human ***,the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint(ZZC)*** evaluate our method on the Nodule Analysis 2016(LUNA16)and TianChi *** results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain.

关键词： Pulmonary nodule detection Human-in-the-loop Semi-supervised learning Domain adaptation

来源：评论

学校读者我要写书评

暂无评论

An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

引用

22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

来源：评论

学校读者我要写书评

暂无评论

Analysis of Schema Formation in Genetic Algorithms: A Review 16th

Analysis of Schema Formation in Genetic Algorithms: A Review

引用

16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A Time Series Forecast Method for Vessel Trajectory Prediction 4

A Time Series Forecast Method for Vessel Trajectory Predicti...

引用

4th International Conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Li, Shaobin Tan, Zehan Chen, Yanyu Yang, Weidong Lei, Siyuan Zhang, Jiale University of Macau Data Science Research Institute City China Fudan University College of Computer Science and Technology Shanghai China Big Data Centers Gree Electric Appliances Inc. of Zhuhai Zhuhai China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In recent years, alongside the progress of marine vessel information technology, the scale of vessel-related data has grown exponentially. At the same time, maritime monitoring based on vessel data has achieved unprecedented development, so how to effectively manage and supervise the marine operations of vessels is now a widely concerned issue. By predicting the future trajectories of vessels, vessel behavior can be assessed to avoid potential hazards. This paper establishes a pre-processing model of vessel data based on the time series data of vessels. According to the characteristics of vessel data, cleaning, noise reduction, and trajectory extraction are conducted to the data followed by interpolation. Trajectory similarity evaluation is conducted with a time series similarity measurement method, and a trajectory clustering model based on DBSCAN is constructed from the trajectory similarity information. Later, this paper proposes a time series prediction model based on Attention and LSTM. The prediction model adopts an Encoder-Decoder structure. The model takes the time series characteristics of a vessel trajectory as the input to predict the future trajectory of a vessel. © 2022 IEEE.

关键词： Trajectories

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：