检索结果-内蒙古大学图书馆

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

来源：评论

学校读者我要写书评

暂无评论

SteadySketch: Finding Steady Flows in data Streams 31

SteadySketch: Finding Steady Flows in Data Streams

引用

31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Li, Xiaodong Fan, Zhuochen Li, Haoyu Zhong, Zheng Guo, Jiarui Long, Sheng Yang, Tong Cui, Bin School of Computer Science Peking University National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350399738

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub. © 2023 IEEE.

关键词： Steady flow

来源：评论

学校读者我要写书评

暂无评论

SketchConf: A Framework for Automatic Sketch Configuration 39

SketchConf: A Framework for Automatic Sketch Configuration

引用

39th IEEE International Conference on data engineering, ICDE 2023

作者： Miao, Ruijie Dong, Fenghao Zhao, Yikai Zhao, Yiming Wu, Yuhan Yang, Kaicheng Yang, Tong Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github. © 2023 IEEE.

关键词： Frequency estimation

来源：评论

学校读者我要写书评

暂无评论

Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

来源：评论

学校读者我要写书评

暂无评论

Framework for Fuzzy Temporal Association Rule Mining Based on Density Clustering Optimization 9

Framework for Fuzzy Temporal Association Rule Mining Based o...

引用

9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Cao, Yang Ma, Pengzhi Shenyang China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (纸本)9798350376548

This paper proposes a framework for fuzzy temporal association rule mining based on density clustering optimization, aimed at improving the fuzzification of continuous attributes and enhancing the efficiency of mining temporal association rules. The first part of the framework effectively handles continuous attribute data and reduces data redundancy by adopting a density peak clustering algorithm and designing an optimized membership function. The second part introduces a fuzzy list ranking structure combined with item lifespan to mine fuzzy temporal association rules. Experimental results demonstrate that the framework exhibits superior performance in mining fuzzy temporal association rules and outperforms advanced algorithms in handling continuous attributes. © 2024 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

ABC-DE hybrid evolutionary algorithm based on individual preference population selection 5

ABC-DE hybrid evolutionary algorithm based on individual pre...

引用

5th International Conference on Computer Communication and Network Security, CCNS 2024

作者： Song, XiaoYu Li, Wen Zhao, Ming Shenyang China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (纸本)9781510681842

To fully leverage the complementary advantages of the Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms for various optimization problems, this paper introduces an ABC-DE hybrid evolutionary algorithm based on individual preference for population selection. This method divides the population into subpopulations using a preference-based selection mechanism and employs ABC and DE for diverse subpopulation search strategies. Additionally, it implements a new resource allocation mechanism that increases computational efforts for promising solutions. Comparative analysis on the CEC2014 benchmark suite with five other current ABC and DE improved algorithms confirms that the proposed hybrid evolutionary algorithm effectively enhances convergence speed, accuracy, and robustness. © 2024 SPIE.

关键词： Evolutionary algorithms Chromium Analytical research Mathematical optimization Evolutionary optimization Statistical analysis Algorithm development Chemical elements Lithium Reflection

来源：评论

学校读者我要写书评

暂无评论

Research on traffic vehicle target detection method based on improved YOLOv7 24

Research on traffic vehicle target detection method based on...

引用

7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Zhang, Feng Sun, Quan Yuan, Shuai School of Electrical and Control Engineering Shenyang Jianzhu University China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center China

ISBN: (纸本)9798400716720

With the increase in the number of vehicles in our country, traffic accidents have become frequent. The real-time detection of dense traffic vehicles is particularly important, which can promote the development of autonomous driving and lay a good foundation for intelligent transportation. Aiming at the problems such as high vehicle overlap rate, high small target missing rate and poor real-time detection in traffic vehicle data set, a traffic vehicle target detection method based on improved YOLOv7 was proposed. Firstly, the neck network structure of the original YOLOv7 is lightweight and introduced into the GhostSlimPAN paradigm structure to reduce the number of model parameters and operation cost, and improve the accuracy of detection of small target vehicles. Then, the original loss function CIoU is improved to replusion loss function to reduce the missed and false detection rate caused by mutual occlusion between vehicles. In order to verify the effect of the improved model, the vehicle dataset was selected for testing and verification. The experimental results show that compared with the original YOLOv7 algorithm, the mAP@0.5 of the improved YOLOv7 algorithm on this dataset reaches 88.2%, which is 1.8% higher than that of the benchmark network. The improved algorithm can be applied to vehicle target detection in daily environment, providing key technical support for intelligent transportation and autonomous driving. © 2024 ACM.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

HyperCalm Sketch: One-Pass Mining Periodic Batches in data Streams 39

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

引用

39th IEEE International Conference on data engineering, ICDE 2023

作者： Liu, Zirui Kong, Chaozhe Yang, Kaicheng Yang, Tong Miao, Ruijie Chen, Qizhi Zhao, Yikai Tu, Yaofeng Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation China

ISBN: (纸本)9798350322279

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced. © 2023 IEEE.

关键词： Error analysis

来源：评论

学校读者我要写书评

暂无评论

Resilience-oriented Valuation for Energy Storage Amidst Extreme Events

引用

Chinese Journal of Electrical engineering 2023年第3期9卷 15-25页

作者： Youzhen Wu Jianxiao Wang Yiyang Song Yunyun Xie College of Engineering Peking UniversityBeijing 100871China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

In power grids,the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate *** has sparked discussions on the resilience of power ***-storage systems(ESSs)are critical for enhancing the resilience of power ***,with their mechanism of flexible charging and discharging,adjust energy usage as needed during disasters,thereby mitigating the impact on the grid and enhancing security and ***,in turn,ensures the power system’s stable ***,there is limited systematic research quantifying the economic value of energy storage in resilience ***,a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events.A two-stage stochastic optimization mathematical model was *** first stage involves pre-deployment based on day-ahead expectations,and the second stage involves simulating potential failure scenarios through real-time *** the temporal dimension,the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of ***,a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity,as reflected in the emergency dispatch *** index helps determine the balance between the energy storage investment cost and resilience ***,an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed *** findings revealed a significant improvement in the resilience value,with a 23.49%increase observed when energy storage systems were implemented compared to the scenario without energy storage *** optimal capacity configurations for the flywheel,lithium-ion batteries,and pumped hydro storage were 10 MW,11 MW,and 12 MW,respectively,highligh

关键词： Energy storage dispatch model power system resilience resilience-oriented valuation two-stage optimization model

来源：评论

学校读者我要写书评

暂无评论

A method for correcting characteristic X-ray net peak count from drifted shadow peak

引用

Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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：