检索结果-南通市图书馆

Retrieval-and-alignment based large-scale indoor point cloud semantic segmentation

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第4期67卷 164-180页

作者： Zongyi XU Xiaoshui HUANG Bo YUAN Yangfu WANG Qianni ZHANG Weisheng LI Xinbo GAO school of computer science and Technology Chongqing University of Posts and Telecommunications Shanghai Artificial Intelligence Laboratory school of Electronic engineering and computer science Queen Mary University of London

Current methods for point cloud semantic segmentation depend on the extraction of descriptive features. However, unlike images, point clouds are irregular and often lack texture information, making it demanding to extract discriminative features. In addition, noise, outliers, and uneven point distribution are commonly present in point clouds, which further complicates the segmentation task. To address these problems, a novel architecture is proposed for direct and accurate large-scale point cloud segmentation based on point cloud retrieval and alignment. The proposed approach involves using a feature-based point cloud retrieval method for searching for reference point clouds with annotations from a dataset. In the following segmentation stage, an overlap-based point cloud registration method has been developed to align the target and reference point clouds. For accurate and robust alignment, an overlap region estimation module is trained to locate the optimal overlap region between two pieces of point clouds in a coarse-to-fine manner. In the detected overlap region, the global and local features of the points are extracted and combined for featuremetric registration to obtain accurate transformation parameters between the target and reference point clouds. After alignment, the annotated segmentation of the reference is transferred to the target point clouds to obtain accurate segmentation results. Extensive experiments are conducted to show that the developed method outperforms the state-of-the-art approaches in terms of both accuracy and robustness against noise and outliers.

关键词： point cloud semantic segmentation large-scale indoor point clouds point cloud alignment overlap estimation label transfer

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

China Communications 2024年第5期21卷 261-279页

作者： Sun Yi Wang Zhouyang Beijing University of Posts and telecommunications School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and telecommunications International School

The distribution of data has a significant impact on the results of *** the distribution of one class is insignificant compared to the distribution of another class,data imbalance *** will result in rising outlier values and ***,the speed and performance of classification could be greatly *** the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification *** with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone *** we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector *** introduce the cost control to solve the problem of sample ***,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is *** can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

关键词： fuzzy ordered regression(OR) relaxing variables twin support vector machine

维普期刊数据库

FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 139-152页

作者： Ruru ZHANG Haihong E Meina SONG school of computer science Beijing University of Posts and Telecommunications Education Department Information Network engineering Research Center Beijing University of Posts and Telecommunications

The ability to learn incrementally is critical to the long-term operation of AI systems. Benefiting from the power of few-shot class-incremental learning(FSCIL), deep learning models can continuously recognize new classes with only a few samples. The difficulty is that limited instances of new classes will lead to overfitting and exacerbate the catastrophic forgetting of the old classes. Most previous works alleviate the above problems by imposing strong constraints on the model structure or parameters, but ignoring embedding network transferability and classifier adaptation(CA), failing to guarantee the efficient utilization of visual features and establishing relationships between old and new classes. In this paper, we propose a simple and novel approach from two perspectives: embedding bias and classifier bias. The method learns an embedding augmented(EA) network with cross-class transfer and class-specific discriminative abilities based on self-supervised learning and modulated attention to alleviate embedding bias. Based on the adaptive incremental classifier learning scheme to realize incremental learning capability,guiding the adaptive update of prototypes and feature embeddings to alleviate classifier bias. We conduct extensive experiments on two popular natural image datasets and two medical datasets. The experiments show that our method is significantly better than the baseline and achieves state-of-the-art results.

关键词： Training Adaptation models Visualization Image recognition Prototypes Self-supervised learning Power capacitors

FlowGANAnomaly: Flow-Based Anomaly Network Intrusion Detection with Adversarial Learning

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 58-71页

作者： Zeyi LI Pan WANG Zixuan WANG school of computer science Nanjing University of Posts and Telecommunications school of Modern Posts Nanjing University of Posts and Telecommunications school of Internet of Things Nanjing University of Posts and Telecommunications

In recent years, low recall rates and high dependencies on data labelling have become the biggest obstacle to developing deep anomaly detection(DAD) techniques. Inspired by the success of generative adversarial networks(GANs) in detecting anomalies in computer vision and imaging, we propose an anomaly detection model called Flow GANAnomaly for detecting anomalous traffic in network intrusion detection systems(NIDS). Unlike traditional GAN-based approaches, which are composed of a flow encoder, a convolutional encoder-decoder-encoder, a flow decoder and a convolutional encoder, the architecture of this model consists of a generator(G) and a discriminator(D).Flow GANAnomaly maps the different types of traffic feature data from separate datasets to a uniform feature space,thus can capture the normality of network traffic data more accurately in an adversarial manner to mitigate the problem of the high dependence on data labeling. Moreover, instead of simply detecting the anomalies by the output of D, we proposed a new anomaly scoring method that integrates the deviation between the output of two Gs’ convolutional encoders with the output of D as weighted scores to improve the low recall rate of anomaly detection. We conducted several experiments comparing existing machine learning algorithms and existing deep learning methods(Auto Encoder and VAE) on four public datasets(NSL-KDD, CIC-IDS2017, CIC-DDo S2019, and UNSW-NB15). The evaluation results show that Flow GANAnomaly can significantly improve the performance of anomaly-based NIDS.

关键词： Deep learning Machine learning algorithms Computational modeling Network intrusion detection Imaging Telecommunication traffic Generative adversarial networks

Malware Evasion Attacks Against IoT and Other Devices: An Empirical Study

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Tsinghua science and Technology 2024年第1期29卷 127-142页

作者： Yan Xu Deqiang Li Qianmu Li Shouhuai Xu school of computer science and engineering Nanjing University of Science and TechnologyNanjing 210094China school of computer science Nanjing University of Posts and TelecommunicationsNanjing 210023China Department of computer science University of Colorado Colorado SpringsColorado SpringsCO 80918USA

The Internet of Things(loT)has grown rapidly due to artificial intelligence driven edge *** enabling many new functions,edge computing devices expand the vulnerability surface and have become the target of malware ***,attackers have used advanced techniques to evade defenses by transforming their malware into functionality-preserving *** systematically analyze such evasion attacks and conduct a large-scale empirical study in this paper to evaluate their impact on *** specifically,we focus on two forms of evasion attacks:obfuscation and adversarial *** the best of our knowledge,this paper is the first to investigate and contrast the two families of evasion attacks *** apply 10 obfuscation attacks and 9 adversarial attacks to 2870 malware *** obtained findings are as follows.(1)Commercial Off-The-Shelf(COTS)malware detectors are vulnerable to evasion attacks.(2)Adversarial attacks affect COTS malware detectors slightly more effectively than obfuscated malware examples.(3)Code similarity detection approaches can be affected by obfuscated examples and are barely affected by adversarial attacks.(4)These attacks can preserve the functionality of original malware examples.

关键词： Android malware obfuscation adversarial examples

A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

computer Modeling in engineering & sciences 2024年第7期140卷 255-273页

作者： Zhong Qu Guoqing Mu Bin Yuan school of computer science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China school of Software engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr

关键词： Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection

Design of tightly linked dual ring antenna and imaging of magnetic field distribution using a diamond fiber probe

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Physics B 2024年第2期33卷 347-352页

作者：叶青云薛雅文何飞越赵旭彤卞雨辰卢文韬王金旭陈鸿浩夏圣开曾明菁杜关祥 College of telecommunications and Information engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China College of science Nanjing University of Posts and TelecommunicationsNanjing 210003China school of computer science Nanjing University of Posts and TelecommunicationsNanjing 210023China Bell Honors school Nanjing University of Posts and TelecommunicationsNanjing 210023China

A tightly linked dual ring antenna is designed,and it is specifically tailored for uniformly coupling the microwave magnetic field to the nitrogen-vacancy(NV)*** designed antenna operates at a center frequency of about 2.87 GHz,with a bandwidth of around 200 MHz,allowing it to address multiple resonance peaks in the optically detected magnetic resonance(ODMR)spectrum in an external magnetic ***,the antenna generates a fairly uniform magnetic field in a range with a radius of 0.75 *** resolution imaging of the magnetic field distribution on the surface of the antenna is conducted by using a fiber diamond *** also investigate the effect of magnetic field uniformity on the linewidth of ODMR,so as to provide insights into reducing the inhomogeneous broadening of ODMR.

关键词： nitrogen vacancy(NV) microwave(MW)imaging inhomogeneous broadening

维普期刊数据库

High-resolution imaging of magnetic fields of banknote anti-counterfeiting strip using fiber diamond probe

在线全文

学校读者我要写书评

暂无评论

Chinese Physics B 2024年第4期33卷 720-727页

作者：赵旭彤何飞越薛雅文马文豪殷筱晗夏圣开曾明菁杜关祥 College of telecommunications and Information engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China College of science Nanjing University of Posts and TelecommunicationsNanjing 210003China school of computer science Nanjing University of Posts and TelecommunicationsNanjing 210023China Bell Honors school Nanjing University of Posts and TelecommunicationsNanjing 210023China

Counterfeiting of modern banknotes poses a significant challenge,prompting the use of various preventive *** such measure is the magnetic anti-counterfeiting ***,due to its inherent weak magnetic properties,visualizing its magnetic distribution has been a longstanding *** this work,we introduce an innovative method by using a fiber optic diamond probe,a highly sensitive quantum sensor designed specifically for detecting extremely weak magnetic *** employ this probe to achieve high-resolution imaging of the magnetic fields associated with the RMB 50denomination anti-counterfeiting ***,we conduct computer simulations by using COMSOL Multiphysics software to deduce the potential geometric characteristics and material composition of the magnetic region within the anti-counterfeiting *** findings and method presented in this study hold broader significance,extending the RMB 50 denomination to various denominations of the Chinese currency and other items that employ magnetic anti-counterfeiting *** advances have the potential to significantly improve and promote security measures in order to prevent the banknotes from being counterfeited.

关键词： banknote anti-counterfeiting strip nitrogen-vacancy(NV)centers magnetic field imaging numerical simulation

维普期刊数据库

A Task Scheduling Algorithm Based on Clustering Pre-processing in Space-Based Information Network

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 217-230页

作者： Yufei WANG Jun LIU Shengnan ZHANG Sai XU Jingyi WANG school of computer science and engineering Northeastern University

With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. To address the problems of a limited number of resources and resource heterogeneity in the space-based information network, we propose a bilateral pre-processing model for tasks and resources in the scheduling pre-processing stage. We use an improved fuzzy clustering method to cluster tasks and resources and design coding rules and matching methods to match similar categories to improve the clustering effect. We propose a space-based information network task scheduling strategy based on an ant colony simulated annealing algorithm for the problems of high latency of space-based information network communication and high resource dynamics. The strategy can efficiently complete the task and resource matching and improve the task scheduling performance. The experimental results show that our proposed task scheduling strategy has less task execution time and higher resource utilization than other algorithms under the same experimental conditions. It has significantly improved scheduling performance.

关键词： Scheduling algorithms Heuristic algorithms Clustering methods Clustering algorithms Simulated annealing Dynamic scheduling Search problems