检索结果-内蒙古大学图书馆

International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Ks Adarsha Krishna M. Sivalingam Gauravdeep Shami Marc Lyonnais Rodney Wilson Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India External Research Global Research and Development Ciena Corporation Ottawa Canada

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In data networks carrying large numbers of flows, Heavy Hitters (HHs) or Elephant flows are the flows exceeding pre-determined thresholds (e.g. wrt. number of packets or bytes) in a given time window. Such HH flows need to be handled differently in order to minimize their impact on other smaller flows. In recent years, HH detection techniques were shown to be effectively implementable in programmable data plane switches. In recent work, it was shown that the inter-packet gap can be used to identify heavy hitters. In order to conserve memory, such schemes use a limited-size hash table for storing flow state information and using this for the detection. However, when hash collisions occur, it is possible that a valid HH flow in the table can be replaced by a non-HH flow resulting in missing detection of HH flows. To address this problem, this paper incorporates a flow’s medium term Packet Count feature. In order to limit the packet count field size in the hash table, counting is done only till Hash Collision occurs so as to the reduce the range of values to be stored and thus, the required number of bits. The proposed scheme has been implemented in the P4 language and run on Intel Tofino hardware. Performance evaluation has been done using MAWI-based real-life traffic traces. The results shows that in several scenarios cases, we can significantly reduce the False Negatives for HHs by using the packet count data effectively and efficiently.

关键词： Performance evaluation Flow production systems Telecommunication traffic Feature extraction Hardware Computer networks Telecommunications detection algorithms

来源：评论

学校读者我要写书评

暂无评论

An Effective Image Copy-Move Forgery detection Using Entropy Information

An Effective Image Copy-Move Forgery Detection Using Entropy...

引用

International Conference on Image, Vision and Computing (ICIVC)

作者： Li Jiang Zhaowei Lu School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Image forensics has become increasingly crucial in our daily lives. Among various types of forgeries, copy-move forgery detection has received considerable attention within the academic community. Keypoint-based algorithms, particularly those based on Scale Invariant Feature Transform, have achieved promising outcomes. However, most of keypoint detection algorithms failed to generate sufficient matches when tampered patches were occurred in smooth areas, leading to insufficient matches. Therefore, this paper introduces entropy images to determine the coordinates and scales of keypoints based on Scale Invariant Feature Transform detector, which make the pre-processing more suitable for solving the above problems. Furthermore, an overlapped entropy level clustering algorithm is developed to mitigate the increased matching complexity caused by the non-ideal distribution of gray values in keypoints. Experimental results demonstrate that our algorithm achieves a good balance between performance and time efficiency.

关键词： Image forensics Clustering algorithms Transforms Detectors Gray-scale Feature extraction Entropy Forgery Complexity theory detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Localizing and Assessing Node Significance in Default Mode Network Using Sub-Community detection in Mild Cognitive Impairment

Localizing and Assessing Node Significance in Default Mode N...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ameiy Acharya Chakka Sai Pradeep Neelam Sinha Centre for Brain Research Indian Institute of Science Bengaluru International Institute of Information Technology Bangalore

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Our study aims to utilize fMRI to identify the affected brain regions within the Default Mode Network (DMN) in subjects with Mild Cognitive Impairment (MCI), using a novel Node Significance Score (NSS). We construct subject-specific DMN graphs by employing partial correlation of Regions of Interest (ROIs) that make-up the DMN. For the DMN graph, ROIs are the nodes and edges are determined based on partial correlation. Four popular community detection algorithms (Clique Percolation Method (CPM), Louvain algorithm, Greedy Modularity and Leading Eigenvectors) are applied to determine the largest sub-community. NSS ratings are derived for each node, considering (I) frequency in the largest sub-community within a class across all subjects and (II) occurrence in the largest sub-community according to all four methods. After computing the NSS of each ROI in both healthy and MCI subjects, we quantify the score disparity to identify nodes most impacted by MCI. The results reveal a disparity exceeding 20% for 10 DMN nodes, maximally for PCC and Fusiform, showing 45.69% and 43.08% disparity. This aligns with existing medical literature, additionally providing a quantitative measure that enables the ordering of the affected ROIs. These findings offer valuable insights and could lead to treatment strategies aggressively targeting the affected nodes.

关键词： Correlation Atmospheric measurements Image edge detection Biomedical measurement Functional magnetic resonance imaging Particle measurements detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on the $\beta{-}$ VAE-DDRRN-BFA Tool Wear detection Algorithm

Research on the $\beta{-}$ VAE-DDRRN-BFA Tool Wear Detection...

引用

2024 International Symposium on Computer Science and Educational Technology (ISCSET)

作者： Kaige Xu Xiaoming Sun Yan Chen Yongliang Wang Bochao Su Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen China

ISBN: (数字)9798350390803

ISBN: (纸本)9798350390810

With the advent of the industry 4.0 era, the manufacturing industry has become an integral part of the national economy. In machining processes, it is necessary to continuously cut metals using tools. During tool operation, wear occurs continuously. Over time, this wear gradually affects the quality of workpieces, and in severe cases, it may pose the risk of machine tool damage. In this context, tool wear detection has become an indispensable aspect of the manufacturing industry. However, at present, tool wear detection methods face several challenges, including complex model structures, insufficient detection accuracy, low efficiency, and poor generalization capabilities. These factors can impact the effectiveness of tool wear detection, leading to delayed tool replacement and increased industrial losses. This paper addresses the issue of poor generalization capability in existing deep models and proposes a tool wear detection algorithm based on the combination of $\beta{-}$ V AE (Beta Variational Autoencoder) and Deep Dense Recursive Residual Network (DDRRN). Additionally, a Bayesian Fusion Algorithm (BFA) module is integrated at the signal input end to enhance the reliability of tool wear detection. Finally, experimental validation is conducted using the PHM2010 public dataset. The results indicate that the β-VAE-DDRRN-BFA algorithm outperforms other classical methods in the reconstruction of tool wear, and the model's computational efficiency is higher than other methods.

关键词： Manufacturing industries Autoencoders Metals Machining Bayes methods Machine tools Reliability detection algorithms Faces Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Modelling Individual Experts in Neonatal Seizure detection Algorithm Development

Modelling Individual Experts in Neonatal Seizure Detection A...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Sergio Morant Gálvez Mark O'Sullivan Brian Henry Walsh Alison O'Shea NeuroBell Ltd. Cork Ireland INFANT Research Centre University College Cork Cork Ireland Department of Paediatrics and Child Health University College Cork Cork Ireland

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Developing algorithms to detect seizures in neonatal electroencephalogram (EEG) signals is an important area of research. Identifying neonatal seizures is a time-consuming process that requires specially trained experts. Most neonatal seizure detection algorithms use supervised learning and require large datasets of labelled EEG for training. However, EEG is a complex physiological signal, and expert annotators often have disagreements when identifying seizures in infants. Most studies with multiple expert annotators compress the annotations down to one ‘ground truth’ set of labels during algorithm training, this may lead to a loss of valuable information. This study investigates if preserving the disagreement of multiple expert annotators during training improves model performance. Three variations of a deep learning architecture are compared experimentally; each one varies in how annotator disagreements are accounted for. The results indicate that there is value in modelling expert annotations separately in supervised learning algorithms. This study proposes architectures that harness expert variability by learning from both the agreement and disagreement in an open-source dataset of neonatal EEGs. Clinical Relevance-This work demonstrates how a more holistic approach to neonatal seizure detection algorithm development, incorporating opinions of all annotators, improves algorithm results and better reflects the standards of clinical care.

关键词： Training Deep learning Pediatrics Annotations Supervised learning Brain modeling Electroencephalography Physiology detection algorithms Standards

来源：评论

学校读者我要写书评

暂无评论

Impolite Words detection Algorithm for Standard Thai and Thai Regional Dialects

Impolite Words Detection Algorithm for Standard Thai and Tha...

引用

International Computer Science and Engineering Conference (ICSEC)

作者： Kanida Charungchit Nannapas Abhiratthanarath Chouvalit Khancome Department of Information Technology Faculty of Science and Technology Rajabhat Rajanagarindra University Chachoengsao Thailand Department of Computer Scienc Faculty of Science Ramkhamhaeng University Bangkok Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

Impolite word detection, also known as rude word detection, is a natural language processing (NLP) technique designed to identify inappropriate or offensive language in texts and various media. This technique aims to prevent the use of language that could be considered inappropriate or cause discomfort in online communities and communication applications. This research article introduces a novel approach for detecting impolite Thai words, encompassing various Thai dialects, and identifying the regional origin of the offensive words. The new algorithm employs multiple dictionaries; each stored using inverted lists based on character positions rather than direct word storage. This dictionary structure enables the search process to achieve 100% efficiency with no errors. Experimental results from a program developed to detect impolite words in standard Thai, as well as Northern, Southern, and Northeastern Thai dialects, demonstrated 100% accuracy, significantly outperforming previous detection algorithms. Additionally, the new algorithm outperformed current Generative AI models, even without considering sentence context or utilizing AI.

关键词： Computer science Dictionaries Accuracy Generative AI Computational modeling Media Natural language processing detection algorithms Standards Context modeling

来源：评论

学校读者我要写书评

暂无评论

Heuristic Algorithm of Multi-Constraint MHT in Ground-Moving Target Track detection

Heuristic Algorithm of Multi-Constraint MHT in Ground-Moving...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Xiaofu Sun Zhongfei Deng Fei Wang Qinghua Han Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.) Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China School of Artificial Intelligence Zaozhuang University Zaozhuang China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The track detection technology of the ultra-wideband (UWB) radar for ground-moving targets is studied in this paper. A heuristic algorithm based on multi-constraint multi-hypothesis tracking (H-MCMHT) is proposed. Some constraints are added to the initial track generation of multi-hypothesis tracking according to the short-time motion characteristics of ground-moving targets. Then a heuristic track association method is proposed. The aim of the algorithm is to obtain as complete a real track as possible in a short time. Simulation results show that compared with MHT, H-MCMHT can effectively reduce the occurrence of false tracks and ensure the detection rate of real tracks. The physical verification results also confirm the simulation conclusions.

关键词： Measurement Target tracking Heuristic algorithms Simulation Radar detection Radar tracking Data processing Computational efficiency detection algorithms Ultra wideband radar

来源：评论

学校读者我要写书评

暂无评论

Pedestrian detection algorithm for scenic spots based on improved YOLOv8

Pedestrian detection algorithm for scenic spots based on imp...

引用

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Peng Yang Daisheng Xu Lihua Luo Yaowu Xu Haolong Wu School of Optoelectronics and Communication Engineering Xiamen University of Technology Xiamen China Hanhe Tianchen (Xiamen) Tech Co. Ltd Xiamen China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Aiming at the current pedestrian target detection in the scenic area, there are issues like inadequate detection precision and the challenge of identifying small-sized pedestrians, an improved YOLOv8 scenic area pedestrian detection algorithm is proposed. Firstly, the C2fNEW convolution module is designed to expand the detection field of view, which improves the detection ability of the model; secondly, the BiFormer attention mechanism and the replacement of the DyHead dynamic detection head are used to improve the detection of the pedestrian features as well as the expression ability of the detection head; lastly, the improved channel enhancement module is designed to enhance the model's ability of extracting the features of the channel, which effectively improves the model's ability to detect pedestrians in scenic spots. Through the analysis of experimental results, the improved YOLOv8 algorithm performs well on the pedestrian label data set in PASCAL VOC2007+2012, with an increase of 3.1% at mAP@0.5 and 2.9% at mAP@0.5:0.95, which represents a notable enhancement over the initial algorithm. It can be better applied in pedestrian detection tasks in scenic spots.

关键词： Pedestrians Attention mechanisms Convolution Computational modeling Heuristic algorithms Object detection Feature extraction Data models Data mining detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Label Propagation Algorithm Based on 2-Hop Neighbor and Weighted Path for Community detection

Label Propagation Algorithm Based on 2-Hop Neighbor and Weig...

引用

Data Science and Information Technology (DSIT), International Conference on

作者： Boya Feng Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Community detection is a research focus in the field of complex networks and has wide applications in various domains. However, traditional label propagation algorithms and modularity maximization algorithms often fail to achieve optimal results in many community detection tasks. By observing the characteristics of real-world social networks, this paper proposes a label propagation algorithm based on 2-hop neighbor and weighted path (LPANW) for community detection. The proposed algorithm is compared with three other algorithms on both synthetic networks and real-world datasets, demonstrating better NMI index and robustness.

关键词： Social networking (online) Heuristic algorithms Complex networks Data science Robustness Iterative algorithms Indexes Time complexity Information technology detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Ramose RAIM: A Multiple-Failure detection Algorithm Design for Civil Aviation

引用

Chinese Journal of Electronics 2023年第2期19卷 356-359页

作者： Xinyuan Zhang Zhigang Huang Rui Li School of Electronic and Information Engineering Beihang University Beijing China

With the advent of Galileo and Chinese Compass, GNSS navigation technologies will develop rapidly. However, as the number of navigation satellites in view will increase, the fault probability will rise. This paper calculates the multi-fault risk of multi-constellation satellite navigation system in terms of MOPS. It is highlighted that the probability of either one-fault or double-faults is higher than integrity requirement. The paper also describes a snap-shot ramose RAIM technology to protect civil aviation users in multiple-fault case. In this condition, a series of FDE calculation is adopted to separate satellites in sight into several groups. Therefore the problem will transform into several single-fault case, which current RAIM can solve. Simulation results verify that this technique is proper to solve both single-fault and double-fault problem of civil aviation caused by application of multi-constellation GNSS.

关键词： Fault diagnosis Global navigation satellite system Satellites Simulation Transforms detection algorithms Compass

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：