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Channel-Attention DenseNet with Dilated Convolutions for MRI Brain Tumor Classification 认领 引用
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作者 Abdu Salam Mohammad Abrar +5 位作者 Raja Waseem Anwer Farhan Amin Faizan Ullah Isabel de la Torre Gerardo Mendez Mezquita Henry Fabian Gongora 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期2457-2479,共23页
Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intell... Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI. 展开更多
关键词 Artificial intelligence MRI analysis deep learning dilated convolution DenseNet brain tumor detection brain tumor segmentation
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Multi-Head Attention Enhanced Parallel Dilated Convolution and Residual Learning for Network Traffic Anomaly Detection 认领 引用 被引量：2
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作者 Guorong Qi Jian Mao +2 位作者 Kai Huang Zhengxian You Jinliang Lin 《Computers, Materials & Continua》 SCIE EI 2025年第2期2159-2176,共18页
Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract loc... Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features;Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection model based on parallel dilated convolution and residual learning (Res-PDC). To better explore the interactive relationships between features, the traffic samples are converted into two-dimensional matrix. A module combining parallel dilated convolutions and residual learning (res-pdc) was designed to extract local and global features of traffic at different scales. By utilizing res-pdc modules with different dilation rates, we can effectively capture spatial features at different scales and explore feature dependencies spanning wider regions without increasing computational resources. Secondly, to focus and integrate the information in different feature subspaces, further enhance and extract the interactions among the features, multi-head attention is added to Res-PDC, resulting in the final model: multi-head attention enhanced parallel dilated convolution and residual learning (MHA-Res-PDC) for network traffic anomaly detection. Finally, comparisons with other machine learning and deep learning algorithms are conducted on the NSL-KDD and CIC-IDS-2018 datasets. The experimental results demonstrate that the proposed method in this paper can effectively improve the detection performance. 展开更多
关键词 Network traffic anomaly detection multi-head attention parallel dilated convolution residual learning
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An improved deep dilated convolutional neural network for seismic facies interpretation 认领 引用 被引量：6
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作者 Na-Xia Yang Guo-Fa Li +2 位作者 Ting-Hui Li Dong-Feng Zhao Wei-Wei Gu 《Petroleum Science》 SCIE EI CAS CSCD 2024年第3期1569-1583,共15页
With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural network... With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information. 展开更多
关键词 Seismic facies interpretation Dilated convolution Spatial pyramid pooling Internal feature maps Compound loss function
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DcNet: Dilated Convolutional Neural Networks for Side-Scan Sonar Image Semantic Segmentation 认领 引用 被引量：2
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作者 ZHAO Xiaohong QIN Rixia +3 位作者 ZHANG Qilei YU Fei WANG Qi HE Bo 《Journal of Ocean University of China》 SCIE CAS CSCD 2021年第5期1089-1096,共8页
In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS... In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS image in real time can realize online submarine geomorphology or target recognition,which is conducive to submarine detection.However,because of the complexity of the marine environment,various noises in the ocean pollute the sonar image,which also encounters the intensity inhomogeneity problem.In this paper,we propose a novel neural network architecture named dilated convolutional neural network(DcNet)that can run in real time while addressing the above-mentioned issues and providing accurate semantic segmentation.The proposed architecture presents an encoder-decoder network to gradually reduce the spatial dimension of the input image and recover the details of the target,respectively.The core of our network is a novel block connection named DCblock,which mainly uses dilated convolution and depthwise separable convolution between the encoder and decoder to attain more context while still retaining high accuracy.Furthermore,our proposed method performs a super-resolution reconstruction to enlarge the dataset with high-quality im-ages.We compared our network to other common semantic segmentation networks performed on an NVIDIA Jetson TX2 using our sonar image datasets.Experimental results show that while the inference speed of the proposed network significantly outperforms state-of-the-art architectures,the accuracy of our method is still comparable,which indicates its potential applications not only in AUVs equipped with SSS but also in marine exploration. 展开更多
关键词 side-scan sonar(SSS) semantic segmentation dilated convolutions super-resolution
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Long Text Classification Algorithm Using a Hybrid Model of Bidirectional Encoder Representation from Transformers-Hierarchical Attention Networks-Dilated Convolutions Network 认领 引用 被引量：1
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作者 ZHAO Yuanyuan GAO Shining +1 位作者 LIU Yang GONG Xiaohui 《Journal of Donghua University(English Edition)》 CAS 2021年第4期341-350,共10页
Text format information is full of most of the resources of Internet,which puts forward higher and higher requirements for the accuracy of text classification.Therefore,in this manuscript,firstly,we design a hybrid mo... Text format information is full of most of the resources of Internet,which puts forward higher and higher requirements for the accuracy of text classification.Therefore,in this manuscript,firstly,we design a hybrid model of bidirectional encoder representation from transformers-hierarchical attention networks-dilated convolutions networks(BERT_HAN_DCN)which based on BERT pre-trained model with superior ability of extracting characteristic.The advantages of HAN model and DCN model are taken into account which can help gain abundant semantic information,fusing context semantic features and hierarchical characteristics.Secondly,the traditional softmax algorithm increases the learning difficulty of the same kind of samples,making it more difficult to distinguish similar features.Based on this,AM-softmax is introduced to replace the traditional softmax.Finally,the fused model is validated,which shows superior performance in the accuracy rate and F1-score of this hybrid model on two datasets and the experimental analysis shows the general single models such as HAN,DCN,based on BERT pre-trained model.Besides,the improved AM-softmax network model is superior to the general softmax network model. 展开更多
关键词 long text classification dilated convolution BERT fusing context semantic features hierarchical characteristics BERT_HAN_DCN AM-softmax
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Multi⁃Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification 认领 引用
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作者 Shanshan Zheng Wen Liu +3 位作者 Rui Shan Jingyi Zhao Guoqian Jiang Zhi Zhang 《Journal of Harbin Institute of Technology(New Series)》 CAS 2021年第4期25-32,共8页
Aiming at the problem of image information loss,dilated convolution is introduced and a novel multi⁃scale dilated convolutional neural network(MDCNN)is proposed.Dilated convolution can polymerize image multi⁃scale inf... Aiming at the problem of image information loss,dilated convolution is introduced and a novel multi⁃scale dilated convolutional neural network(MDCNN)is proposed.Dilated convolution can polymerize image multi⁃scale information without reducing the resolution.The first layer of the network used spectral convolutional step to reduce dimensionality.Then the multi⁃scale aggregation extracted multi⁃scale features through applying dilated convolution and shortcut connection.The extracted features which represent properties of data were fed through Softmax to predict the samples.MDCNN achieved the overall accuracy of 99.58% and 99.92% on two public datasets,Indian Pines and Pavia University.Compared with four other existing models,the results illustrate that MDCNN can extract better discriminative features and achieve higher classification performance. 展开更多
关键词 multi⁃scale aggregation dilated convolution hyperspectral image classification(HSIC) shortcut connection
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Hard-rock tunnel lithology identification using multiscale dilated convolutional attention network based on tunnel face images 认领 引用 被引量：5
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作者 Wenjun ZHANG Wuqi ZHANG +5 位作者 Gaole ZHANG Jun HUANG Minggeng LI Xiaohui WANG Fei YE Xiaoming GUAN 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2023年第12期1796-1812,共17页
For real-time classification of rock-masses in hard-rock tunnels,quick determination of the rock lithology on the tunnel face during construction is essential.Motivated by current breakthroughs in artificial intellige... For real-time classification of rock-masses in hard-rock tunnels,quick determination of the rock lithology on the tunnel face during construction is essential.Motivated by current breakthroughs in artificial intelligence technology in machine vision,a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed.The method benefits from residual learning for training a deep convolutional neural network(DCNN),and a multi-scale dilated convolutional attention block is proposed.The block with different dilation rates can provide various receptive fields,and thus it can extract multi-scale features.Moreover,the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model.In this study,an initial image data set made up of photographs of tunnel faces consisting of basalt,granite,siltstone,and tuff was first collected.After classifying and enhancing the training,validation,and testing data sets,a new image data set was generated.A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators,including accuracy,precision,recall,F1-score,and computing time.Finally,a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction.Overall,this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face. 展开更多
关键词 hard-rock tunnel face intelligent lithology identification multi-scale dilated convolutional attention network image classification deep learning
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High performance dilated convolutions on multi-core DSPs 认领 引用 被引量：2
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作者 Yang Wang Qinglin Wang +3 位作者 Xiangdong Pei Songzhu Mei Rongchun Li Jie Liu 《CCF Transactions on High Performance Computing》 EI 2024年第1期78-93,共16页
Dilated convolutions are widely used to accomplish wide receptive fields while keeping the resolution of feature maps in deep learning applications,such as semantic segmentation and object detection.However,the data l... Dilated convolutions are widely used to accomplish wide receptive fields while keeping the resolution of feature maps in deep learning applications,such as semantic segmentation and object detection.However,the data locality in dilated convo-lutions deteriorates rapidly with the increase of dilation rate,which brings a great challenge to the high-performance direct implementation of convolutions.Multi-core digital signal processors(DSPs)with software-controlled on-chip memories allow programmers to move data between on-chip and off-chip memories by hand so that it may be very friendly to the direct implementation of dilated convolutions.In this paper,we introduce a high-performance parallel direct implementation of dilated convolutions on multi-core DSPs in a CPU-DSP heterogeneous prototype processor,which can effectively capture the data locality in dilated convolutions.The experimental results demonstrate that the direct implementation achieves much better performance than GEMM-based ones on multi-core DSPs for all the tested layers,and gets much higher efficiency than the high-performance libraries on three other architectures in cases with large feature maps.In addition,the direct implementation also exhibits good scalability. 展开更多
关键词 CNNs Dilated convolutions DSPs Parallel algorithm
DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting 认领 引用 被引量：1
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作者 Zhuangzhuang Miao Yong Zhang +2 位作者 Yuan Peng Haocheng Peng Baocai Yin 《Computational Visual Media》 SCIE EI CSCD 2023年第4期859-873,共15页
Crowd counting provides an important foundation for public security and urban management.Due to the existence of small targets and large density variations in crowd images,crowd counting is a challenging task.Mainstre... Crowd counting provides an important foundation for public security and urban management.Due to the existence of small targets and large density variations in crowd images,crowd counting is a challenging task.Mainstream methods usually apply convolution neural networks(CNNs)to regress a density map,which requires annotations of individual persons and counts.Weakly-supervised methods can avoid detailed labeling and only require counts as annotations of images,but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually ignored.We propose a weakly-supervised method,DTCC,which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd counting.Its main components include a recursive swin transformer and a multi-level dilated convolution regression head.The recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features,including global features.The multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction module.This module can capture both low-and high-level features simultaneously to enhance the receptive field.In addition,two regression head fusion mechanisms realize dynamic and mean fusion counting.Experiments on four well-known benchmark crowd counting datasets(UCF_CC_50,ShanghaiTech,UCF_QNRF,and JHU-Crowd++)show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods. 展开更多
关键词 crowd counting transformer dilated convolution global perspective field pyramid
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Magnetic Resonance Imaging Reconstruction Based on Butterfly Dilated Geometric Distillation 认领 引用
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作者 DUO Lin XU Boyu +1 位作者 REN Yong YANG Xin 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第3期590-599,共10页
In order to improve the reconstruction accuracy of magnetic resonance imaging(MRI),an accurate natural image compressed sensing(CS)reconstruction network is proposed,which combines the advantages of model-based and de... In order to improve the reconstruction accuracy of magnetic resonance imaging(MRI),an accurate natural image compressed sensing(CS)reconstruction network is proposed,which combines the advantages of model-based and deep learning-based CS-MRI methods.In theory,enhancing geometric texture details in linear reconstruction is possible.First,the optimization problem is decomposed into two problems:linear approximation and geometric compensation.Aimed at the problem of image linear approximation,the data consistency module is used to deal with it.Since the processing process will lose texture details,a neural network layer that explicitly combines image and frequency feature representation is proposed,which is named butterfly dilated geometric distillation network.The network introduces the idea of butterfly operation,skillfully integrates the features of image domain and frequency domain,and avoids the loss of texture details when extracting features in a single domain.Finally,a channel feature fusion module is designed by combining channel attention mechanism and dilated convolution.The attention of the channel makes the final output feature map focus on the more important part,thus improving the feature representation ability.The dilated convolution enlarges the receptive field,thereby obtaining more dense image feature data.The experimental results show that the peak signal-to-noise ratio of the network is 5.43 dB,5.24 dB and 3.89 dB higher than that of ISTA-Net+,FISTA and DGDN networks on the brain data set with a Cartesian sampling mask CS ratio of 10%. 展开更多
关键词 butterfly geometric distillation dilation convolution channel attention image reconstruction
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Convolution-Transformer for Image Feature Extraction 认领 引用 被引量：2
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作者 Lirong Yin Lei Wang +10 位作者 Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Xiaobing Chen Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期87-106,共20页
This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual structures.Compared to Convolutional Neural Networks(CNNs),the Transformers a... This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual structures.Compared to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow convergence.To tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer architecture.Our model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer framework.Additionally,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved stability.This results in an efficient convolution combined Transformer image feature extraction network.Experimental results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational speed.It achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline models.When integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)scores.This research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency. 展开更多
关键词 Transformer E-Attention depth convolution dilated convolution CEFormer
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1D-CNN:Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features 认领 引用 被引量：7
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作者 Mustaqeem Soonil Kwon 《Computers, Materials & Continua》 SCIE EI 2021年第6期4039-4059,共21页
Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Re... Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Recently,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not convincing.Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations.In this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called HFLBs.Each HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation strategy.Furthermore,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal cues.The final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the emotions.The proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively. 展开更多
关键词 Affective computing one-dimensional dilated convolutional neural network emotion recognition gated recurrent unit raw audio clips
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Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction 认领 引用
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作者 Yu Zhuang Zhongyi Zhang +5 位作者 Jin Tao Yi Li Fan Li Yu Wang Lei Zhang Jian Du 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第1期132-144,共13页
Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling... Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R2 value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach. 展开更多
关键词 Multi-scale dilated causal convolution Neural networks Soft sensor Systems engineering attention mechanism Biochemical engineering
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基于空洞因果卷积的学生成绩预测及分析方法 认领 引用
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作者 赖英旭 张亚薇 +1 位作者 庄俊玺 刘静 《北京工业大学学报》 CAS CSCD 北大核心 2026年第3期252-267,共16页
针对使用循环神经网络对学生长序列行为数据进行特征提取存在梯度消失或爆炸、长期依赖关系提取能力不足、深度学习模型缺乏可解释性等问题,提出一种面向长序列数据的空洞因果卷积(dilated causal convolution,DCC)成绩预测及分析方法... 针对使用循环神经网络对学生长序列行为数据进行特征提取存在梯度消失或爆炸、长期依赖关系提取能力不足、深度学习模型缺乏可解释性等问题,提出一种面向长序列数据的空洞因果卷积(dilated causal convolution,DCC)成绩预测及分析方法。首先,采用生成对抗网络(generative adversarial network,GAN)生成符合少数类学生原始行为数据分布规律的新样本,并将新样本加入学生数据集中以达到均衡数据集的目的;然后,提出一种基于DCC的成绩预测模型,DCC和门控循环单元(gated recurrent unit,GRU)相结合的结构提高了模型对长序列数据依赖关系的提取能力;最后,使用沙普利加性解释(Shapley additive explanations,SHAP)方法并结合三因素理论对影响学生成绩的因素进行重要性分析和解释。在公开数据集上的实验结果表明,在成绩预测任务中提出的方法与基线方法相比,加权F1分数提高了约6个百分点,并进一步验证了所提方法中关键模块的有效性和模型的泛化能力。此外,通过对比优秀学生和风险学生的学习特点发现,良好的学习习惯、课堂学习的主动性以及不同行为环境等因素会对学生成绩产生重要影响。 展开更多
关键词 学生成绩预测 空洞因果卷积(dilated causal convolution,DCC) 不均衡数据 生成对抗网络(generative adversarial network,GAN) 沙普利加性解释(Shapley additive explanations,SHAP)方法 成绩影响因素分析
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TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting 认领 引用 被引量：1
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作者 Haoran Huang Weiting Chen Zheming Fan 《Computers, Materials & Continua》 SCIE EI 2024年第3期3665-3681,共17页
Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in t... Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN. 展开更多
关键词 Difference data prediction time series temporal convolutional network dilated convolution
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A Novel Fish Counting Method Based on Multiscale and Multicolumn Convolution Group Network 认领 引用
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作者 Yuxuan Zhang Junfeng Wu +3 位作者 Hong Yu Shihao Guo Yizhi Zhou Jing Li 《国际计算机前沿大会会议论文集》 2022年第1期339-353,共15页
An accurate grasp of the number of fish in the breeding pond or fixed waters can provide an important basis for bait placement and reasonable fishing,and these data can also provide the necessary data support for accu... An accurate grasp of the number of fish in the breeding pond or fixed waters can provide an important basis for bait placement and reasonable fishing,and these data can also provide the necessary data support for accurate breeding.Due to the high density of fish in the real underwater environment,the strong occlusion and the large amount of adhesion,it is difficult to count fish,and the accuracy is low.Considering the above issues,we present a new approach to a fish counting method based on a multiscale multicolumn convolution group network.To enhance the counting accuracy and reduce the complexity of the network,this method uses an asymmetric convolution kernel to change the traditional convolution kernel,which increases our network depth and appreciably reduces the size of the network.In the backbone network,a convolutional group is used to replace a single convolutional layer to enhance the learning capacity of the network.The back of the net introduces the spatial structure of the pyramid and the multicolumn dilated convolution,which preserves the different scaling properties of fish data and improves the capabilities of the fish counting algorithm.To check the performance of the algorithm,this work collects and labels the DLOU3 fish dataset suitable for counting fish and conducts simulation experiments on the DLOU3 fish dataset using our algorithm.The experiments are comparedwith other popular fish counting algorithms in terms of the mean absolute error(MAE)and mean square error(MSE).The MAE and MSE of the final experimental results of our method are 5.36 and 6.56 and 23.67 and 32.52 in the two test sets,respectively,and the best performance among the five groups of algorithms is obtained. 展开更多
关键词 Fish counting Neural networks Asymmetric convolution Dilated convolution
Improved multi-scale feature fusion for infrared small target detection based on YOLOv8 认领 引用
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作者 DING Shangsi YANG Guiqin GAN Bingkun 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第2期208-218,共11页
Aiming at the problems of low target pixels and intricate background in small target detection in infrared scenes,a target detection model based on multi-scale feature extraction with YOLOv8 was proposed.Firstly,all d... Aiming at the problems of low target pixels and intricate background in small target detection in infrared scenes,a target detection model based on multi-scale feature extraction with YOLOv8 was proposed.Firstly,all downsampling convolutions in the network were replaced with the Haar wavelet downsampling(HWD)module to better preserve fine-grained details in infrared imagery during downsampling.Secondly,the spatial pyramid pooling-fast(SPPF)module was improved by introducing separable convolutions,which expanded the receptive field in both horizontal and vertical directions,enabling more comprehensive spatial information capture.Furthermore,a novel C2f_CDWR module was designed using dilated convolutions with varying dilation rates to achieve adaptive feature extraction across multiple receptive fields,thus enhancing detection performance for objects of different sizes.Finally,to improve localization accuracy,the original CIoU loss in YOLOv8 was replaced with Inner-SIoU,which effectively improved bounding box regression accuracy and significantly boosted the model’s capability in detecting small infrared targets.The experimental evaluation on the HIT-UAV dataset shows that the precision of the enhanced YOLOv8 model is 90.5%,the recall rate is 75.9%,and the mean average precision is 85.7%.In terms of infrared target detection,its performance was significantly better than that of the baseline YOLOv8 model and other benchmark models. 展开更多
关键词 infrared image small object detection multi-scale feature extraction dilation convolution YOLOv8v8
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TSMS-InceptionNeXt:A Framework for Image-Based Combustion State Recognition in Counterflow Burners via Feature Extraction Optimization 认领 引用
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作者 Huiling Yu Xibei Jia +1 位作者 Yongfeng Niu Yizhuo Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第6期4329-4352,共24页
The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitate... The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion efficiency.Among existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt.It has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction capabilities.However,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction optimization.First,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic features.Secondly,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical features.Next,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual information.Finally,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input features.To validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image dataset.The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71%on the dataset,improving by 2.38%over the baseline model and outperforming the baseline model’s performance.It achieved accuracy improvements of 10.47%,4.76%,11.19%,and 9.28%compared to the Reparameterized Visual Geometry Group(RepVGG),Squeeze-erunhanced Axial Transoformer(SeaFormer),Simplified Graph Transformers(SGFormer),and VanillaNet models,respectively,effectively enhancing the recognition performance for combustion states in counterflow burners. 展开更多
关键词 Counterflow burner combustion state recognition InceptionNeXt dilated convolution channel shuffling
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Prediction of turned surface roughness based on GADF of multi-channel signal fusion and MA-ResNet 认领 引用
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作者 SHI Lichen LIU Tengfei WANG Haitao 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2025年第2期302-312,共11页
In order to achieve high precision online prediction of surface roughness during turning process and improve cutting quality,a prediction method of turned surface roughness based on Gramian angular difference field(GA... In order to achieve high precision online prediction of surface roughness during turning process and improve cutting quality,a prediction method of turned surface roughness based on Gramian angular difference field(GADF)of multi-channel signal fusion and multi-scale attention residual network(MA-ResNet)was proposed.Firstly,the multi-channel vibration signals were subdivided into various frequency bands using wavelet packet decomposition,and the sensitive channels were selected for signal fusion by doing correlation analysis between the signals of various frequency bands and the surface roughness.Then the fused signals were converted into pictures using GADF image encoding.Finally,the pictures were inputted into the residual network model combining the parallel dilation convolution and attention module for training and verifying the effectiveness of the model performance.The proposed method has a root mean square error of 0.0187,a mean absolute error of 0.0143,and a coefficient of determination of 0.8694 in predicting the surface roughness,which is close to the actual value.Therefore,the proposed method had good engineering significance for high-precision prediction and was conducive to on-line monitoring of surface quality during workpiece processing. 展开更多
关键词 signal fusion Gramian angular difference field dilated convolution residual network roughness prediction
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Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method 认领 引用 被引量：1
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作者 Shaohan Wang Xiangyu Wang Xin Guo 《Journal of Software Engineering and Applications》 2023年第1期1-19,共19页
A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask... A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%. 展开更多
关键词 Face Mask Detection Object Detection Hybrid Dilation Convolution Computer Vision
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