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SEFormer:A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis 认领 引用 被引量：4
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作者 Hongxing Wang Xilai Ju +1 位作者 Hua Zhu Huafeng Li 《Computers, Materials & Continua》 SCIE EI 2025年第1期1417-1437,共21页
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine... Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment. 展开更多
关键词 CNN-Transformer separable multiscale depthwise convolution efficient self-attention fault diagnosis
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PokerNet:Expanding Features Cheaply via Depthwise Convolutions 认领 引用 被引量：1
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作者 Wei Tang Yan Huang Liang Wang 《International Journal of Automation and computing》 CSCD 2021年第3期432-442,共11页
Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models.However,it takes up most of the overall computational cost(usually more than 90%).This paper proposes a novel P... Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models.However,it takes up most of the overall computational cost(usually more than 90%).This paper proposes a novel Poker module to expand features by taking advantage of cheap depthwise convolution.As a result,the Poker module can greatly reduce the computational cost,and meanwhile generate a large number of effective features to guarantee the performance.The proposed module is standardized and can be employed wherever the feature expansion is needed.By varying the stride and the number of channels,different kinds of bottlenecks are designed to plug the proposed Poker module into the network.Thus,a lightweight model can be easily assembled.Experiments conducted on benchmarks reveal the effectiveness of our proposed Poker module.And our Poker Net models can reduce the computational cost by 7.1%-15.6%.Poker Net models achieve comparable or even higher recognition accuracy than previous state-of-the-art(SOTA)models on the Image Net ILSVRC2012 classification dataset.Code is available at http://gffzz188fe103f8f1460askp6fv9qucf5f6bxv.ffgz.tsg.suse.edu.cn/diaomin/pokernet. 展开更多
关键词 Deep learning depthwise convolution lightweight deep model model compression model acceleration
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Underwater Image Enhancement Based on Depthwise Separable Convolution-Based Generative Adversarial Network 认领 引用
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作者 ZENG Jun-yang SI Zhan-jun 《印刷与数字媒体技术研究》 CAS 北大核心 2026年第1期60-66,共7页
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver... The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics. 展开更多
关键词 Underwater image enhancement Generating adversarial network Depthwise separable convolution
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Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3 认领 引用 被引量：10
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作者 Yue-Yan Qin Jiang-Tao Cao Xiao-Fei Ji 《International Journal of Automation and computing》 CSCD 2021年第2期300-310,共11页
Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in dee... Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate. 展开更多
关键词 Fire detection depthwise separable convolution fire classification You Only Look Once version 3(YOLOv3) target regression
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A MCG-GFAM-MRDCM Model for Accurate Building Electricity Load Forecasting 认领 引用
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作者 Chuan Lin Weixian Chen Guangtao Hao 《Energy Engineering》 EI 2026年第8期208-237,共30页
Accurate building electricity load forecasting(BELF)can provide a regulatory basis for building energy management systems and promote the transition of buildings toward low-carbon and intelligent operation modes.Howev... Accurate building electricity load forecasting(BELF)can provide a regulatory basis for building energy management systems and promote the transition of buildings toward low-carbon and intelligent operation modes.However,building electricity load is influenced by historical loads,as well as outside environmental conditions such as humidity and temperature,which reduces the prediction accuracy of models.To tackle these challenges,this study presents a BELF model,which consists of a modal component grouping approach,grouped feature attention mechanism,and multi-scale residual depthwise convolution memory module.First,the modal component grouping method analyzes building electricity load in the time domain,frequency domain(via fast fourier transform,FFT),and complexity(via sample entropy,SE),and then performs clustering to achieve precise decomposition of load components with different fluctuation characteristics.Second,the grouped feature attention mechanism assigns suitable importance to various input features to emphasize key factors affecting prediction accuracy.Third,the multi-scale residual depthwise convolution memory module mitigates the impact of long and short-term load variations on BELF by employing residual blocks of depthwise convolution layers with different kernel sizes.Meanwhile,gated recurrent units are used to identify the time-dependent trends of building load.Experimental results on public buildings show that the proposed model outperforms existing models,achieving more than 2.4%improvement in MAPE prediction performance. 展开更多
关键词 Building electricity load forecasting modal component grouping method depthwise convolutional neural network attention mechanism
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A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification 认领 引用 被引量：2
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作者 Adama Dembele Ronald Waweru Mwangi Ananda Omutokoh Kube 《Journal of Computer and Communications》 2024年第2期173-200,共28页
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware reso... Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline. 展开更多
关键词 MobileNet Image Classification Lightweight Convolutional Neural Network Depthwise Dilated Separable Convolution Hierarchical Multi-Scale Feature Fusion
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A Framework of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines 认领 引用
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作者 Qi Wang Zhaoying Liu +3 位作者 Ting Zhang Shanshan Tu Yujian Li Muhammad Waqas 《Journal on Artificial Intelligence》 2022年第1期37-48,共12页
Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classifi... Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification.However,the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters.To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters,this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines(LC-CKMSVM).The framework consists of a feature extraction module and a classification module.The feature extraction module first maps the data from low-dimensional to high-dimensional space by fusing the representations of different dimensional spaces through cross-connections;then,it uses depthwise separable convolution to replace part of the original convolution to reduce the number of parameters in the module;The classification module uses a soft margin support vector machine for classification.The results on 6 different visual datasets show that LC-CKMSVM obtains better classification accuracies on most cases than the other five models. 展开更多
关键词 Convolutional neural network cross-connected lightweight framework depthwise separable convolution
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Intelligent Suppression of Marine Seismic Multiples Using Deep Learning Methods 认领 引用 被引量：1
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作者 HU Guang LI Yan +4 位作者 YANG Shengxiong ZHANG Heng LIU Xin LI Yuanheng TIAN Dongmei 《Journal of Ocean University of China》 SCIE CAS CSCD 2025年第4期967-978,共12页
Multiple suppression is an important element of marine seismic data processing.Intelligent suppression of multiples us-ing artificial intelligence reduces labor costs,minimizes dependence on unknown prior information,... Multiple suppression is an important element of marine seismic data processing.Intelligent suppression of multiples us-ing artificial intelligence reduces labor costs,minimizes dependence on unknown prior information,and improves data processing ef-ficiency.In this study,we propose an intelligent method for suppressing marine seismic multiples using deep learning approaches.The proposed method enables the intelligent suppression of free-surface-related multiples from seismic records.Initially,we construct a multi-category marine seismic multiple dataset through finite difference forward modeling under different boundary conditions.We use various models and data augmentation methods,including sample rotation,noise addition,and random channel omission.Then,we apply depthwise separable convolution to develop our deep learning Mobilenet-Unet model.The Mobilenet-Unet framework sig-nificantly reduces the number of operations required for multiple elimination without sacrificing model performance,ultimately reali-zing the optimal multiple suppression model.The trained Mobilenet-Unet is applied to the test set for verification.Moreover,to deter-mine its generalization ability,it is implemented to seismic records containing multiples generated by two marine geophysical models that were not included in the training process.The performance of Mobilenet-Unet is also compared with that of different network structures.The results indicate that,despite its small size,our proposed Mobilenet-Unet deep learning model can rapidly and effective-ly separate multiples in marine seismic data,possessing reasonable generalization ability. 展开更多
关键词 multiple suppression marine seismic surveys artificial intelligence deep learning depthwise separable convolution
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Intelligent Detection of Abnormal Traffic Based on SCN-BiLSTM 认领 引用
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作者 Lulu Zhang Xuehui Du +3 位作者 Wenjuan Wang Yu Cao Xiangyu Wu Shihao Wang 《Computers, Materials & Continua》 SCIE EI 2025年第7期1901-1919,共19页
To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study p... To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study proposed an intelligent detection method that combines a Stacked Convolutional Network(SCN),Bidirectional Long Short-Term Memory(BiLSTM)network,and Equalization Loss v2(EQL v2).This method was divided into two components:a feature extraction model and a classification and detection model.First,SCN was constructed by combining a Convolutional Neural Network(CNN)with a Depthwise Separable Convolution(DSC)network to capture the abstract spatial features of traffic data.These features were then input into the BiLSTM to capture temporal dependencies.An attention mechanism was incorporated after SCN and BiLSTM to enhance the extraction of key spatiotemporal features.To address class imbalance,the classification detection model applied EQL v2 to adjust the weights of the minority classes,ensuring that they received equal focus during training.The experimental results indicated that the proposed method outperformed the existing methods in terms of accuracy,FPR,and F1-score and significantly improved the identification rate of minority classes. 展开更多
关键词 Convolutional neural network depthwise separable convolution bidirectional long and short-term memory network class imbalance abnormal traffic detection
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Straw Segmentation Algorithm Based on Modified UNet in Complex Farmland Environment 认领 引用 被引量：3
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作者 Yuanyuan Liu Shuo Zhang +4 位作者 Haiye Yu Yueyong Wang Yuehan Feng Jiahui Sun Xiaokang Zhou 《Computers, Materials & Continua》 SCIE EI 2021年第1期247-262,共16页
Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment.Traditional pattern recognition has some problems,such as low precision and a long processing time... Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment.Traditional pattern recognition has some problems,such as low precision and a long processing time,when segmenting complex farmland,which cannot meet the conditions of embedded equipment deployment.Based on these problems,we proposed a novel deep learning model with high accuracy,small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution(RADw–UNet).This algorithm is based on the UNet symmetric codec model.All the feature extraction modules of the network adopt the residual structure,and the whole network only adopts 8 times the downsampling rate to reduce the redundant parameters.To better extract the semantic information of the spatial and channel dimensions,the depthwise convolutional residual block is designed to be used in feature maps with larger depths to reduce the number of parameters while improving the model accuracy.Meanwhile,the multi–level attention mechanism is introduced in the skip connection to effectively integrate the information of the low–level and high–level feature maps.The experimental results showed that the segmentation performance of RADw–UNet outperformed traditional methods and the UNet algorithm.The algorithm achieved an mIoU of 94.9%,the number of trainable parameters was only approximately 0.26 M,and the running time for a single picture was less than 0.03 s. 展开更多
关键词 Straw segmentation convolutional neural network residual structure depthwise convolution attention mechanism
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Lightweight Method for Plant Disease Identification Using Deep Learning 认领 引用
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作者 Jianbo Lu Ruxin Shi +3 位作者 Jin Tong Wenqi Cheng Xiaoya Ma Xiaobin Liu 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期525-544,共20页
In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal d... In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources.In this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed.In the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large gradients.Experiments are conducted on the public dataset PlantVillage.The results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model.Compared to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals. 展开更多
关键词 Plant disease identification mixed depthwise convolution lightweight ShuffleNetV2 attention mechanism
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Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network 认领 引用 被引量：4
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作者 Guanghua Yi Xinhong Hao +3 位作者 Xiaopeng Yan Jian Dai Yangtian Liu Yanwen Han 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第3期364-373,共10页
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ... Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR. 展开更多
关键词 Automatic modulation recognition Radiation source signals Two-dimensional data matrix Residual neural network Depthwise convolution
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Probability-Based Channel Pruning for Depthwise Separable Convolutional Networks 认领 引用 被引量：3
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作者 Han-Li Zhao Kai-Jie Shi +4 位作者 Xiao-Gang Jin Ming-Liang Xu Hui Huang Wang-Long Lu Ying Liu 《Journal of Computer Science & Technology》 SCIE EI CSCD 2022年第3期584-600,共17页
Channel pruning can reduce memory consumption and running time with least performance damage,and is one of the most important techniques in network compression.However,existing channel pruning methods mainly focus on ... Channel pruning can reduce memory consumption and running time with least performance damage,and is one of the most important techniques in network compression.However,existing channel pruning methods mainly focus on the pruning of standard convolutional networks,and they rely intensively on time-consuming fine-tuning to achieve the performance improvement.To this end,we present a novel efficient probability-based channel pruning method for depthwise separable convolutional networks.Our method leverages a new simple yet effective probability-based channel pruning criterion by taking the scaling and shifting factors of batch normalization layers into consideration.A novel shifting factor fusion technique is further developed to improve the performance of the pruned networks without requiring extra time-consuming fine-tuning.We apply the proposed method to five representative deep learning networks,namely MobileNetV1,MobileNetV2,ShuffleNetV1,ShuffleNetV2,and GhostNet,to demonstrate the efficiency of our pruning method.Extensive experimental results and comparisons on publicly available CIFAR10,CIFAR100,and ImageNet datasets validate the feasibility of the proposed method. 展开更多
关键词 network compression channel pruning depthwise separable convolution batch normalization
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Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes 认领 引用 被引量：3
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作者 Eko Prasetyo Rani Purbaningtyas +2 位作者 Raden Dimas Adityo Nanik Suciati Chastine Fatichah 《Information Processing in Agriculture》 EI CSCD 2022年第4期485-496,共12页
Image classification using Convolutional Neural Network(CNN)achieves optimal perfor-mance with a particular strategy.MobileNet reduces the parameter number for learning features by switching from the standard convolut... Image classification using Convolutional Neural Network(CNN)achieves optimal perfor-mance with a particular strategy.MobileNet reduces the parameter number for learning features by switching from the standard convolution paradigm to the depthwise separable convolution(DSC)paradigm.However,there are not enough features to learn for identify-ing the freshness of fish eyes.Furthermore,minor variances in features should not require complicated CNN architecture.In this paper,our first contribution proposed DSC Bottle-neck with Expansion for learning features of the freshness of fish eyes with a Bottleneck Multiplier.The second contribution proposed Residual Transition to bridge current feature maps and skip connection feature maps to the next convolution block.The third contribu-tion proposed MobileNetV1 Bottleneck with Expansion(MB-BE)for classifying the freshness of fish eyes.The result obtained from the Freshness of the Fish Eyes dataset shows that MB-BE outperformed other models such as original MobileNet,VGG16,Densenet,Nasnet Mobile with 63.21%accuracy. 展开更多
关键词 Depthwise separable convolution Bottleneck Classification Freshness Fish eye Residual transition
Image Semantic Segmentation for Autonomous Driving Based on Improved U-Net 认领 引用 被引量：2
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作者 Chuanlong Sun Hong Zhao +2 位作者 Liang Mu Fuliang Xu Laiwei Lu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第7期787-801,共15页
Image semantic segmentation has become an essential part of autonomous driving.To further improve the generalization ability and the robustness of semantic segmentation algorithms,a lightweight algorithm network based... Image semantic segmentation has become an essential part of autonomous driving.To further improve the generalization ability and the robustness of semantic segmentation algorithms,a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism(SE)and Depthwise Separable Convolution(DSC)is designed.Meanwhile,Adam-GC,an Adam optimization algorithm based on Gradient Compression(GC),is proposed to improve the training speed,segmentation accuracy,generalization ability and stability of the algorithm network.To verify and compare the effectiveness of the algorithm network proposed in this paper,the trained networkmodel is used for experimental verification and comparative test on the Cityscapes semantic segmentation dataset.The validation and comparison results show that the overall segmentation results of the algorithmnetwork can achieve 78.02%MIoU on Cityscapes validation set,which is better than the basic algorithm network and the other latest semantic segmentation algorithms network.Besides meeting the stability and accuracy requirements,it has a particular significance for the development of image semantic segmentation. 展开更多
关键词 Deep learning semantic segmentation attention mechanism depthwise separable convolution gradient compression
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BEVGGC:Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images 认领 引用 被引量：2
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作者 Junding Sun Xiang Li +1 位作者 Chaosheng Tang Shixin Chen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第11期729-753,共25页
Purpose:As to January 11,2021,coronavirus disease(COVID-19)has caused more than 2 million deaths worldwide.Mainly diagnostic methods of COVID-19 are:(i)nucleic acid testing.This method requires high requirements on th... Purpose:As to January 11,2021,coronavirus disease(COVID-19)has caused more than 2 million deaths worldwide.Mainly diagnostic methods of COVID-19 are:(i)nucleic acid testing.This method requires high requirements on the sample testing environment.When collecting samples,staff are in a susceptible environment,which increases the risk of infection.(ii)chest computed tomography.The cost of it is high and some radiation in the scan process.(iii)chest X-ray images.It has the advantages of fast imaging,higher spatial recognition than chest computed tomography.Therefore,our team chose the chest X-ray images as the experimental dataset in this paper.Methods:We proposed a novel framework—BEVGG and three methods(BEVGGC-I,BEVGGC-II,and BEVGGC-III)to diagnose COVID-19 via chest X-ray images.Besides,we used biogeography-based optimization to optimize the values of hyperparameters of the convolutional neural network.Results:The experimental results show that the OA of our proposed three methods are 97.65%±0.65%,94.49%±0.22%and 94.81%±0.52%.BEVGGC-I has the best performance of all methods.Conclusions:The OA of BEVGGC-I is 9.59%±1.04%higher than that of state-of-the-art methods. 展开更多
关键词 Biogeography-based optimization convolutional neural networks depthwise separable convolution dilated
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Vehicle Head and Tail Recognition Algorithm for Lightweight DCDSNet 认领 引用
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作者 Chao Wang Kaijie Zhang +3 位作者 Xiaoyong Yu Dejun Li Wei Xie Xinqiao Wang 《Computers, Materials & Continua》 SCIE EI 2024年第9期4451-4473,共23页
In the model of the vehicle recognition algorithm implemented by the convolutional neural network,the model needs to compute and store a lot of parameters.Too many parameters occupy a lot of computational resources ma... In the model of the vehicle recognition algorithm implemented by the convolutional neural network,the model needs to compute and store a lot of parameters.Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance.Therefore,obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research.In this paper,a lightweight densely connected,and deeply separable convolutional network(DCDSNet)algorithmis proposed to achieve this goal.Visual Geometry Group(VGG)model is improved by utilizing the convolution instead of the fully connected module,the deeply separable convolution module,and the densely connected network module,with the first two modules reducing the parameters and the third module allowing the algorithm to have more features in a limited number of parameters.The algorithm achieves better results in the mine vehicle recognition dataset.Experiments show that the recognition accuracy is improved by 4.41% compared to VGG19 and the amount of parameters is reduced by 71% compared to VGG19. 展开更多
关键词 VGGNet vehicle head and tail recognition densely connected depthwise separable convolutional
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Optimizing depthwise separable convolution on DCU 认领 引用 被引量：3
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作者 Zheng Liu Meng Hao +8 位作者 Weizhe Zhang Gangzhao Lu Xueyang Tian Siyu Yang Mingdong Xie Jie Dai Chenyu Yuan Desheng Wang Hongwei Yang 《CCF Transactions on High Performance Computing》 EI 2024年第6期646-664,共19页
The integration of Large Language Models(LLMs)with Convolutional Neural Networks(CNNs)is significantly advancing the development of large models.However,the computational cost of large models is high,necessitating opt... The integration of Large Language Models(LLMs)with Convolutional Neural Networks(CNNs)is significantly advancing the development of large models.However,the computational cost of large models is high,necessitating optimization for greater efficiency.One effective way to optimize the CNN is the use of depthwise separable convolution(DSC),which decouples spatial and channel convolutions to reduce the number of parameters and enhance efficiency.In this study,we focus on porting and optimizing DSC kernel functions from the GPU to the Deep Computing Unit(DCU),a computing accelerator developed in China.For depthwise convolution,we implement a row data reuse algorithm to minimize redundant data loading and memory access overhead.For pointwise convolution,we extend our dynamic tiling strategy to improve hardware utilization by balancing resource allocation among blocks and threads,and we enhance arithmetic intensity through a channel distribution algorithm.We implement depthwise and pointwise convolution kernel functions and integrate them into PyTorch as extension modules.Experiments demonstrate that our optimized kernel functions outperform the MIOpen library on the DCU,achieving up to a 3.59×speedup in depthwise convolution and up to a 3.54×speedup in pointwise convolution.These results highlight the effectiveness of our approach in leveraging the DCU’s architecture to accelerate deep learning operations. 展开更多
关键词 Depthwise separable convolution Deep computing unit Dynamic tiling Channel distribution
Lightweight Malicious Code Classification Method Based on Improved Squeeze Net 认领 引用 被引量：2
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作者 Li Li Youran Kong Qing Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第1期551-567,共17页
With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the netw... With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious code.Therefore,it is important to identify malicious codes on computer systems efficiently.However,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited resources.Therefore,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module.The feature slicing module reduces the number of parameters by grouping features.The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes.In addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet.The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,respectively.It proves that LCMISNet has a powerful malicious code recognition performance.In addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations. 展开更多
关键词 Lightweight neural network malicious code classification feature slicing feature splicing multi-size depthwise separable convolution
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PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform 认领 引用 被引量：1
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作者 Wenbo Li Qi Wang Shang Gao 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期921-938,共18页
Infrared target detection models are more required than ever before to be deployed on embedded platforms,which requires models with less memory consumption and better real-time performance while considering accuracy.T... Infrared target detection models are more required than ever before to be deployed on embedded platforms,which requires models with less memory consumption and better real-time performance while considering accuracy.To address the above challenges,we propose a modified You Only Look Once(YOLO)algorithm PF-YOLOv4-Tiny.The algorithm incorpo-rates spatial pyramidal pooling(SPP)and squeeze-and-excitation(SE)visual attention modules to enhance the target localization capability.The PANet-based-feature pyramid networks(P-FPN)are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy.To lighten the network,the standard convolutions other than the backbone network are replaced with depthwise separable convolutions.In post-processing the images,the soft-non-maximum suppression(soft-NMS)algorithm is employed to subside the missed and false detection problems caused by the occlusion between targets.The accuracy of our model can finally reach 61.75%,while the total Params is only 9.3 M and GFLOPs is 11.At the same time,the inference speed reaches 87 FPS on NVIDIA GeForce GTX 1650 Ti,which can meet the requirements of the infrared target detection algorithm for the embedded deployments. 展开更多
关键词 Infrared target detection visual attention module spatial pyramid pooling dual-path feature fusion depthwise separable convolution soft-NMS
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