检索结果-南通市图书馆

Water source identification in mines combining LIF technology and resnet

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Mountain Science 2023年第11期20卷 3392-3401页

作者： YAN Peng-cheng ZHAO Yu-ting LI Guo-dong WANG Jing-bao WANG Wen-chang State Key Laboratory of Deep Coal Mine Exploitation Response and Disaster Prevention Anhui University of Science andTechnologyHuainan 232001China School of Electrical and Information Engineering Anhui University of Science and TechnologyHuainan 232001China Collaborative Innovation Center of Mine Intelligent Equipment and Technology Anhui University of Science&TechnologyHuainan 232001China

The problem of mine water source has always been an important hidden danger in mine safety *** water source under the mine working face may lead to geological disasters,such as mine collapse and water *** research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological *** is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine ***,timely and accurate identification and control of mine water source is very important to ensure mine production ***-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical *** this experiment,sandstone water and old air water were collected from the Huainan mining area as original *** types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for *** preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and *** was determined as the optimal preprocessing method based on class discrimination,data distribution,and data *** maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality *** comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction ***,two neural network models,CT+PCA+CNN and CT+PCA+resnet,were constructed by combining Convolutional Neural Networks(

关键词： Water source identification Mine safety LIF technology CT PCA resnet

Facial expression recognition based on improved resnet

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 28-38页

作者： Wang Xianlun Wang Guangyu Cui Yuxia College of Electromechanical Engineering Qingdao University of Science and TechnologyQingdao 266061China

Facial expression recognition(FER)is a vital application of image processing *** this paper,a FER model based on the residual network is *** proposed model introduces the idea of the DenseNet,in which the outputs of the residual blocks are not simply added but are linked to the channel *** addition,transfer learning is used to reduce training costs and accelerate training *** accuracy and robustness of the proposed FER model were tested by K-fold *** results show that the proposed method has competitive performances on FER2013,FER plus(FERPlus),and the real-world affective faces database(RAF-DB).

关键词： facial expression recognition convolutional neural networks resnet transfer learning K-fold cross-validation

A resnet-based approach for accurate radiographic diagnosis of knee osteoarthritis

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2022年第3期7卷 512-521页

作者： Yu Wang Shibo Li Baoliang Zhao Jianwei Zhang Yuanyuan Yang Bing Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhenChina Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina TAMS Department of InformaticsUniversity of HamburgHamburgGermany

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence(KL)scoring ***,individual scoring is subjective and error *** study proposes an approach for automated knee osteoarthritis classification based on deep neural *** knee X-ray images are first preprocessed with frequency-domain filtering and histogram normalisation,making the trabecular bone texture more obvious and benefiting the subsequent classification ***,a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the resnet-50 *** addition,a rebalance operation is proposed to deal with the dataset unbalance problem,and a quick search technique is proposed to improve the iterative search efficiency for the joint *** all of these techniques,a classification accuracy of 81.41%is obtained,which is higher compared to the state-of-the-art approaches.

关键词： AMSGrad knee osteoarthritis recognition resnet VGG

NFHP-RN:AMethod of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-resnet

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 929-955页

作者： Tao Yi Xingshu Chen Mingdong Yang Qindong Li Yi Zhu School of Cyber Science and Engineering Sichuan UniversityChengdu610065China CyberScience Research Institute Sichuan UniversityChengdu610065China Chengdu Fengwei Technology Co. Ltd.Chengdu610041China

Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective *** the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has ***,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract *** address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(resnet)is ***,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in *** addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background ***,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack *** results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail *** state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.

关键词： APT attacks spatial pyramid pooling NFHP(network flow holo-graphic picture) resnet self-attention mechanism meta-learning

A Modified Method for Scene Text Detection by resnet

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2020年第12期65卷 2233-2245页

作者： Shaozhang Niu Xiangxiang Li Maosen Wang Yueying Li School of Computer Science and Technology Beijing University of Posts and TelecommunicationsBeijing100876China College of Arts and Sciences Boston UniversityBoston UniversityBoston02215USA

In recent years,images have played a more and more important role in our daily life and social *** some extent,the textual information contained in the pictures is an important factor in understanding the content of the scenes *** more accurate the text detection of the natural scenes is,the more accurate our semantic understanding of the images will ***,scene text detection has also become the hot spot in the domain of computer *** this paper,we have presented a modified text detection network which is based on further research and improvement of Connectionist Text Proposal Network(CTPN)proposed by previous *** extract deeper features that are less affected by different images,we use Residual Network(resnet)to replace Visual Geometry Group Network(VGGNet)which is used in the original ***,to enhance the robustness of the models to multiple languages,we use the datasets for training from multi-lingual scene text detection and script identification datasets(MLT)of 2017 International Conference on Document Analysis and Recognition(ICDAR2017).And apart from that,the attention mechanism is used to get more reasonable weight *** found the proposed models achieve 0.91 F1-score on ICDAR2011 test,better than CTPN trained on the same datasets by about 5%.

关键词： CTPN scene text detection resnet attention

An Improved SCMA Detector Based on resnet Perception Fusion

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Communications 2024年第12期21卷 152-165页

作者： Jin Libiao Feng Yuwei Li Shufeng Sun Yao Yin Fangfang State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing 100024China James Watt School of Engineering University of GlasgowScotland G128QQUK

The received signals used for sparse code multiple access(SCMA)detection are usually contaminated with noise during transmission,which exposes an issue of low decoding *** address this issue,a novel detector based on a residual network(resnet)perception fusion framework(RSMPA)is proposed for uplink SCMA system in this ***,we first formulate a joint design of perception system and traditional communication module.A perception framework based on resnet is applied to cancel the noise component and enhance the communication system *** resnet model is designed and trained using the clean and noisy SCMA signal,*** on the denoised output,information iteration process is executed for multi-user *** results indicate that the perception model achieves an excellent denoising performance for SCMA system and the proposed scheme outperforms the conventional detection algorithms in terms of SER performance.

关键词： perception fusion resnet SCMA signal detection

RBEBT: A resnet-Based BA-ELM for Brain Tumor Classification

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第1期74卷 101-111页

作者： Ziquan Zhu Muhammad Attique Khan Shui-Hua Wang Yu-Dong Zhang University of Leicester LeicesterLE17RHUK HITEC University Taxila Taxila47080Pakistan

Brain tumor refers to the formation of abnormal cells in the *** can be divided into benign and *** main diagnostic methods for brain tumors are plain X-ray film,Magnetic resonance imaging(MRI),and so ***,these artificial diagnosis methods are easily affected by external *** have made such impressive progress in brain tumors classification by using convolutional neural network(CNN).However,there are still some problems:(i)There are many parameters in CNN,which require much calculation.(ii)The brain tumor data sets are relatively small,which may lead to the overfitting problem in *** this paper,our team proposes a novel model(RBEBT)for the automatic classification of brain *** use fine-tuned resnet18 to extract the features of brain tumor *** RBEBT is different from the traditional CNN models in that the randomized neural network(RNN)is selected as the ***,our team selects the bat algorithm(BA)to opti7mize the parameters of *** use fivefold cross-validation to verify the superiority of the *** accuracy(ACC),specificity(SPE),precision(PRE),sensitivity(SEN),and F1-score(F1)are 99.00%,95.00%,99.00%,100.00%,and 100.00%.The classification performance of the RBEBT is greater than 95%,which can prove that the RBEBT is an effective model to classify brain tumors.

关键词： Brain tumor randomized neural network bat algorithm resnet

Realtime Object Detection Through M-resnet in Video Surveillance System

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2023年第2期35卷 2257-2271页

作者： S.Prabu J.M.Gnanasekar Department of Computer Science&Engineering Karpaga Vinayaga College of Engineering and TechnologyChengalpattuTamilnadu603308India Department of Computer Science&Engineering Sri Venkateshwara College of Engineering&TechnologyChennai602001TamilnaduIndia

Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as trafﬁc signals,roadways,retail malls,train stations,and ***,monitor-ing the video continually at a quicker pace is a challenging *** a consequence,security cameras are useless and need human *** primary difﬁculty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful *** anomalous action does not occur at a high-er rate than usual *** detect the object in a video,ﬁrst we analyze the images pixel by *** digital image processing,segmentation is the process of segregating the individual image parts into *** performance of segmenta-tion is affected by irregular illumination and/or low *** factors highly affect the real-time object detection process in the video surveillance *** this paper,a modiﬁed resnet model(M-resnet)is proposed to enhance the image which is affected by insufﬁcient *** results provide the comparison of existing method output and modiﬁcation architecture of the resnet model shows the considerable amount improvement in detection objects in the video *** proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andﬁnds a reasonable improvement in the object detection.

关键词： Object detection resnet video survilence image processing object quality

Detection method of absence seizures based on resnet and bidirectional GRU

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Acta Epileptologica 2023年第1期6卷 34-42页

作者： Lijun Li Hengxing Zhang Xiaomei Liu Jie Li Lei Li Dan Liu Jieqing Min Ping Zhu Huan Xia Shangkun Wang Li Wang Kunming children‘s hospital Kunming 650000China Zhengzhou Zoneyet Technology Corp. LtdZhengzhou 450000China

Background Epilepsy is a common chronic neurological *** repeated seizure attacks have a great negative impact on patients’physical and mental *** diagnosis of epilepsy mainly depends on electroencephalogram(EEG)signals detection and *** are two main EEG signals detection methods for *** is the detection based on abnormal waveform,the other is the analysis of EEG signals based on the traditional machine *** feature extraction method of the traditional machine learning is difcult to capture the high-dimension information between adjacent *** In this paper,redundant information was removed from the data by Gaussian fltering,downsampling,and short-time Fourier *** Neural Networks(CNN)was used to extract the high-dimensional features of the preprocessed data,and then Gate Recurrent Unit(GRU)was used to combine the sequence information before and after,to fully integrate the adjacent information EEG signals and improve the accuracy of the model *** Four models were designed and *** experimental results showed that the prediction model based on deep residual network and bidirectional GRU had the best efect,and the test accuracy of the absence epilepsy test set reached 92%.Conclusions The prediction time of the network is only 10 sec when predicting four-hour EEG *** can be efectively used in EEG software to provide reference for doctors in EEG analysis and save doctors’time,which has great practical value.

关键词： resnet Epilepsy GRU Electroencephalogram