检索结果-内蒙古大学图书馆

5th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Han, Rongze Dalian University Dalian China

ISBN: (纸本)9798331507992

Biometrics are widely used in the field of information security. Compared with other biometric technologies (such as fingerprint and iris recognition), electroencephalograph (EEG) rely on their high degree of individual specificity and are difficult to forge. EEG have gradually become a focus of identification research. However, the existing EEG recognition technology still faces problems such as complex equipment and inconvenient operation in its practical application. In particular, there are significant shortcomings in terms of portability and utility. To solve these problems, this paper innovatively proposes a multi-scale convolutional neural network based on a single-channel portable ear-EEG device. Combining existing feature extraction algorithms, computing multi-dimensional features of single-channel EEG to ultimately optimize classification performance. The experiment invited 14 subjects to participate in the identification test, and the results showed that the model achieved an average classification accuracy of 86.48%• The experimental results verify the effectiveness of ear EEG in identity recognition applications. Expanding the Application Prospects of Ear-Electroencephalography Identity Recognition Technology. © 2024 IEEE.

关键词： A single-channel portable ear-EEG device Biometric identification EEG multi-scale convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Fault prediction of rolling bearings using a multi-scale convolutional neural network with parallel BiLSTM for noise environment

引用

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第11期38卷 5867-5883页

作者： Li, Junxing Xu, Hang Fan, Jiahui Zhuang, Jichao Henan Univ Sci & Technol Sch Mechatron Engn Luoyang 471003 Peoples R China Collaborat Innovat Ctr Hennan Prov Adv Mfg Machin Luoyang 471003 Peoples R China Collaborat Innovat Ctr Hennan Prov High End Beari Luoyang 471003 Peoples R China Natl Univ Singapore Ind Syst Engn & Management Singapore Singapore Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China

This study has developed a multi-scale convolutional neural network (MSCNN) with a parallel bi-directional long short-term memory (PBiLSTM) model for improving the prediction accuracy under noise environment. Firstly, the signals are pre-processed using an ensemble empirical mode decomposition (EEMD) method to solve the problem of mode mixing caused by environmental noise. Subsequently, a MSCNN frame-work is designed to extract bearing feature information and predict faults. Additionally, to tackle the problem of overfitting and vanishing gradients in backpropagation, residual connections are incorporated between layers to dynamically adjust the layer count in the MSCNN. To further mitigate the impact of noise, temporal sequence feature information from vibration signals is extensively utilized by introducing a parallel bidirectional long short-term memory (BiLSTM) model at the output layer of the MSCNN structure. The results show that, compared to traditional methods, the proposed model achieves higher fault prediction accuracy under different levels of noise environment.

关键词： Ensemble empirical mode decomposition Fault prediction multi-scale convolutional neural network Parallel bidirectional long short-term memory rolling bearing

来源：评论

学校读者我要写书评

暂无评论

Microblog Negative Comments Data Analysis Model Based on multi-scale convolutional neural network and Weighted Naive Bayes Algorithm

引用

neural PROCESSING LETTERS 2024年第5期56卷 229页

作者： Zhou, Chunliang Meng, Xiangpei Shen, Zhaoqiang Ningbo Univ Finance & Econ Coll Finance & Informat Ningbo 315175 Peoples R China Ningbo Univ Finance & Econ Acad Coll Ningbo 315175 Zhejiang Peoples R China

As a form of public supervision, Microblog's negative reviews allow people to share their opinions and experiences and express dissatisfaction with unfair and unreasonable phenomena. This form of supervision has the potential to promote social fairness, drive governments, businesses, and individuals to correct mistakes and enhance transparency. To characterize the sentiment trend and determine the influence of Microblog negative reviews, we propose a multi-scale convolutional neural network and weighted naive bayes algorithm (MCNN-WNB). We define the feature vector characterization index for Microblog negative review data and preprocess the data accordingly. We quantify the relationship between attributes and categories using the weighted Naive Bayes method and use the quantification value as the weighting coefficient for the attributes, addressing the issue of decreased classification performance in traditional methods. We introduce a sentiment classification model based on word vector representation and a multi-scale convolutional neural networks to filter out Microblog negative review data. We conduct simulation experiments using real data, analyzing key influencing parameters such as convergence time, training set sample size, and number of categories. By comparing with K-means, Naive Bayes algorithm, Spectral Clustering algorithm and Autoencoder algorithm, we validate the effectiveness of our proposed method. We discover that the convergence time of the MCNN-WNB algorithm increases as the number of categories increases. The average classification accuracy of the algorithm remains relatively stable with varying test iterations. The algorithm's precision increases with the number of training set samples and eventually stabilizes.

关键词： Influence on public opinion Sentiment classification multi-scale convolutional neural network Weighted Naive Bayes

来源：评论

学校读者我要写书评

暂无评论

Side-channel attacks based on attention mechanism and multi-scale convolutional neural network

引用

COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： He, Pengfei Zhang, Ying Gan, Han Ma, Jianfei Zhang, Hongxin Yantai Univ Sch Phys & Elect Informat Yantai 264005 Peoples R China Yantai Univ Shandong Data Open Innovat Applicat Lab Smart Grid Yantai 264005 Peoples R China Shandong Business & Technol Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100088 Peoples R China

One widely utilized attack technique for side-channel attacks is deep learning. However, the efficiency of deep learning attacks can be impacted by the differences in data collected from various devices or environmental conditions. These differences arise from factors such as hardware discrepancies, environmental noise, and countermeasures. Such differences can disrupt the correlation between the leaked information from the target device during operations and sensitive data, leading to failures in the attacks. In order to better extract the feature information and temporal information of traces in this challenge, we propose an Attention Mechanism and multiscale convolutional neural network (AMCNNet). The critical components of the AMCNNet are the multi-scale convolution module and the feature extraction module. The multi-scale convolution module extracts feature of different scales from traces, improving the network's ability to capture high-level meanings and details. Then, the feature extraction module dynamically weights and rescales the features to better captures the long-term dependencies in the traces.. Through experiments conducted on publicly available datasets such as DPA Contest v4, ASCAD, and AES_HD, as well as analyzing the guessing entropy, the experimental results demonstrate that the network exhibits strong generalization capability and robustness across datasets with diverse features.

关键词： multi-scale convolutional neural network multi-head self-attention mechanisms Squeeze and excitation residual Side-channel attacks

来源：评论

学校读者我要写书评

暂无评论

A high precision intrusion detection system for network security communication based on multi-scale convolutional neural network

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 129卷 399-406页

作者： Yu, Jing Ye, Xiaojun Li, Hongbo Tsinghua Univ Sch Software Beijing 100084 Peoples R China Southwest Univ Coll Elect Informat Engn Chongqing 400700 Peoples R China

The openness of network data makes it vulnerable to hackers, viruses and other attacks, which seriously threatens the privacy and property security of users. In order to improve the accuracy of the intrusion detection for network security communication, based on the traditional intrusion detection system, combining with the deep learning theory and shortcomings, this paper proposed an intrusion detection system for network security communication based on multi-scale convolutional neural network, and conducted the corresponding experiments on public data sets. The experimental results perform that compared to the intrusion detection system based on Adaboost model and Recurrent neural network model, the convergence speed of multi-scale convolutional neural network system is faster, the average error detection rate is reduced by 4.02%, and the average accuracy is improved by 4.37%. The results prove that the intrusion detection system based on multi-scale convolution neural network has a high detection accuracy. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Intrusion detection Detection accuracy multi-scale convolutional neural network network security communication

来源：评论

学校读者我要写书评

暂无评论

An efficient fire detection algorithm based on multi-scale convolutional neural network

引用

FIRE AND MATERIALS 2022年第7期46卷 981-992页

作者： Cheng, Yanying Chen, Ke Bai, Hui Mou, Chunjie Zhang, Yuchun Yang, Kai Gao, Yunji Liu, Yu Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Key Lab Transportat Tunnel Engn Minist Educ Chengdu Peoples R China

Video fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial stage is the best time for firefighting, it's crucial to develop a robust algorithm for early warning. In this paper, an efficient VFD fusion algorithm is presented. First, the fire candidate areas (FCA) are located quickly based on low-level visual features to guarantee well timeliness. Furthermore, a multi-scale convolutional neural network with spatial pyramid pooling is built and trained on the dedicated flame data set without the requirement of sample labeling. In this case, FCA with different aspect ratios and scales can be accurately identified. The method is fully tested on various databases of arbitrary-sized images and videos. Experimental results show that the proposed fusion algorithm, in addition to improving the detection efficiency, also ensures the accurate identification of flames on different scales.

关键词： multi-scale convolutional neural network rapid fire location scene recognition special data set for fire video fire detection

来源：评论

学校读者我要写书评

暂无评论

Image Classification Method Based on multi-scale convolutional neural network

引用

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第10期33卷 2450186-2450186页

作者： Du, Shaobo Li, Jing Guizhou Univ Commerce Sch Comp & Informat Engn Guiyang 550014 Guizhou Peoples R China

Traditional convolutional neural networks (CNNs) typically use fixed scale convolutional kernels for feature extraction when processing image classification tasks, while ignoring the multi-scale information present in the image. To overcome this limitation, we propose an algorithm based on multi-scale CNNs, which capture features at different levels by introducing convolutional kernels of different scales into the convolutional layer. In this study, we first designed a multi-scale convolutional layer consisting of multiple convolutional kernels of different scales to extract multi-scale features of the image. To further enhance classification performance, we introduced a multi-scale feature fusion module that can effectively fuse features of different scales and classify them through a fully connected layer. Then we conducted extensive experiments on several commonly used image classification datasets. The experimental results show that this network can not only effectively identify and locate hyperspectral image targets in different scenarios, but also reduce missed detections and false positives during the detection process. The average accuracy of the improved model has been improved, and the recognition accuracy of some small markers affected by external factors such as occlusion and lighting has also been improved. In addition, by comparing the detection effect of a single image, the progressiveness and anti-leakage ability of the improved model are proved. The image classification method based on multi-scale CNNs has broad application prospects in image recognition and feature extraction, and can provide valuable reference and reference for research in related fields.

关键词： multi-scale convolutional neural network image classification object detection BIFPN structure NMS optimization

来源：评论

学校读者我要写书评

暂无评论

Developing a multi-scale convolutional neural network for Spatiotemporal Fusion to Generate MODIS-like Data Using AVHRR and Landsat Images

引用

REMOTE SENSING 2024年第6期16卷 1086页

作者： Zhang, Zhicheng Ao, Zurui Wu, Wei Wang, Yidan Xin, Qinchuan Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Peoples R China South China Normal Univ Beidou Res Inst Fac Engn Guangzhou 510631 Peoples R China Guizhou Univ Min Coll Guiyang 550025 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong 999077 Peoples R China

Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications.

关键词： spatiotemporal fusion multi-scale convolutional neural network MODIS-like data image correction retrospective reconstruction

来源：评论

学校读者我要写书评

暂无评论

Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural network

引用

ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2021年第14期413卷 3801-3811页

作者： Ding, Jingyu Lin, Qingqing Zhang, Jiameng Young, Glenn M. Jiang, Chun Zhong, Yaoguang Zhang, Jianhua Shanghai Ocean Univ Coll Food Sci & Technol Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Minist Educ China Res Design & Electromagnet Comp Key Lab Shanghai 200240 Peoples R China Univ Calif Davis Dept Food Sci & Technol Davis CA 95616 USA Shanghai Jiao Tong Univ Bor S Luh Food Safety Res Ctr Sch Agr & Biol Shanghai 200240 Peoples R China Shanghai Inst Food & Drug Control NMPA Key Lab Testing Technol Pharmaceut Microbiol Shanghai 201203 Peoples R China

Salmonella is a prevalent pathogen causing serious morbidity and mortality worldwide. There are over 2600 serovars of Salmonella. Among them, Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Paratyphi were reported to be the most common foodborne pathogenic serovars in the EU and China. In order to provide a more efficient approach to detect and distinguish these serovars, a new analytical method was developed by combining surface-enhanced Raman spectroscopy (SERS) with multi-scale convolutional neural network (CNN). We prepared 34-nm gold nanoparticles (AuNPs) as the label-free Raman substrate, measured 1854 SERS spectra of these three Salmonella serovars, and then proposed a multi-scale CNN model with three parallel CNNs to achieve multi-dimensional extraction of SERS spectral features. We observed the impact of the number of iterations and training samples on the recognition accuracy by changing the ratio of the number of the training and testing sets. By comparing the calculated data with experimental one, it was shown that our model could reach recognition accuracy more than 97%. These results indicate that it was not only feasible to combine SERS spectroscopy with multi-scale CNN for Salmonella serotype identification, but also for other pathogen species and serovar identifications.

关键词： Surface-enhanced Raman scattering (SERS) multi-scale convolutional neural network Salmonella serovars Identification

来源：评论

学校读者我要写书评

暂无评论

DeepnsSNPs: Accurate prediction of non-synonymous single-nucleotide polymorphisms by combining multi-scale convolutional neural network and residue environment information

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 215卷 104326-104326页

作者： Ge, Fang Muhammad, Arif Yu, Dong-Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiaolingwe Nanjing 210094 Peoples R China

Non-synonymous single-nucleotide polymorphisms (nsSNPs) is a typical kind of genetic variant, and more than 6000 diseases have been detected to be caused by nsSNPs. Accordingly, the accurate prediction of nsSNPs is of great importance for a better understanding of their functional mechanisms and disease treatment. Till now, many computational studies have been developed to identify disease-causing nsSNPs from the neutral ones;however, there is still some gap existing for further improvement in terms of overall prediction performance. In this work, we proposed a novel deep learning model, called multi-scale convolutional neural network (MSCNN). It utilized multi-scale convolution with different kernel sizes for feature processing, which can collect more effective characteristics than using a single convolution kernel size. Moreover, we applied three types of nominal structural features for further improving the nsSNPs prediction performance. Notably, the nsSNPs sequence and structural features were extracted based on the "residue environment" method we proposed, which has proved to be effective for protein nsSNPs prediction in our previous research. Based on the proposed MSCNN model and the extracted informative feature matrix, we implemented a new nsSNPs predictor, named DeepnsSNPs. The DeepnsSNPs was tested on three nsSNPs datasets collected from the PredictSNP1 website and achieved an average Matthews correlation coefficient of 0.507, which is 18.28% higher than the individual classifiers and 11.37% higher than the consensus classifier on average. Detailed dataset analyses have demonstrated that the DeepnsSNPs would be useful in the nsSNPs prediction. We provide the source python codes and benchmark datasets at htt ps://***/sera616/*** for academic use.

关键词： Single nucleotide variants Disease-causing nsSNPs multi-scale convolutional neural network Residue environment information Non-synonymous single-nucleotide poly-morphisms

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：