Long Text Classification Algorithm Using a Hybrid Model of Bidirectional Encoder Representation from Transformers-Hierarchical Attention Networks-Dilated Convolutions Network

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.

DcNet: Dilated Convolutional Neural Networks for Side-Scan Sonar Image Semantic Segmentation

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.

TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

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.

A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification

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.

1D-CNN:Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features

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.

Multi⁃Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification

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.

Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method

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%.

A Model for Helmet-Wearing Detection of Non-Motor Drivers Based on YOLOv5s

Detecting non-motor drivers’helmets has significant implications for traffic control.Currently,most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection,which are unsuitable for practical application scenar-ios.Therefore,this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5(YOLOv5).First,the Dilated convolution In Coordinate Attention(DICA)layer is added to the backbone network.DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer,which can increase the perceptual field of the network to get more contextual information.Also,it can reduce the network’s learning of unnecessary features in the background and get attention to small objects.Second,the Rebuild Bidirectional Feature Pyramid Network(Re-BiFPN)is used as a feature extraction network.Re-BiFPN uses cross-scale feature fusion to combine the semantic information features at the high level with the spatial information features at the bottom level,which facilitates the model to learn object features at different scales.Verified on the proposed“Helmet Wearing dataset for Non-motor Drivers(HWND),”the results show that the proposed model is superior to the current detection algorithms,with the mean average precision(mAP)of 94.3%under complex background.

A Hybrid CNN for Image Denoising

Deep convolutional neural networks(CNNs)with strong learning abilities have been used in the field of image denoising.However,some CNNs depend on a single deep network to train an image denoising model,which will have poor performance in complex screens.To address this problem,we propose a hybrid denoising CNN(HDCNN).HDCNN is composed of a dilated block(DB),RepVGG block(RVB),feature refinement block(FB),and a single convolution.DB combines a dilated convolution,batch normalization(BN),common convolutions,and activation function of ReLU to obtain more context information.RVB uses parallel combination of convolution,BN,and ReLU to extract complementary width features.FB is used to obtain more accurate information via refining obtained feature from the RVB.A single convolution collaborates a residual learning operation to construct a clean image.These key components make the HDCNN have good performance in image denoising.Experiment shows that the proposed HDCNN enjoys good denoising effect in public data sets.

An image compressed sensing algorithm based on adaptive nonlinear network

Traditional compressed sensing algorithm is used to reconstruct images by iteratively optimizing a small number of measured values.The computation is complex and the reconstruction time is long.The deep learning-based compressed sensing algorithm can greatly shorten the reconstruction time,but the algorithm emphasis is placed on reconstructing the network part mostly.The random measurement matrix cannot measure the image features well,which leads the reconstructed image quality to be improved limitedly.Two kinds of networks are proposed for solving this problem.The first one is ReconNet’s improved network IReconNet,which replaces the traditional linear random measurement matrix with an adaptive nonlinear measurement network.The reconstruction quality and anti-noise performance are greatly improved.Because the measured values extracted by the measurement network also retain the characteristics of image spatial information,the image is reconstructed by bilinear interpolation algorithm(Bilinear)and dilate convolution.Therefore a second network USDCNN is proposed.On the BSD500 dataset,the sampling rates are 0.25,0.10,0.04,and 0.01,the average peak signal-noise ratio(PSNR)of USDCNN is 1.62 dB,1.31 dB,1.47 dB,and 1.95 dB higher than that of MSRNet.Experiments show the average reconstruction time of USDCNN is 0.2705 s,0.3671 s,0.3602 s,and 0.3929 s faster than that of ReconNet.Moreover,there is also a great advantage in anti-noise performance.

A Deep Learning Approach for Crowd Counting in Highly Congested Scene

With the rapid progress of deep convolutional neural networks,several applications of crowd counting have been proposed and explored in the literature.In congested scene monitoring,a variety of crowd density estimating approaches has been developed.The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task,as a large number of individuals stand nearby and,it is hard for detection techniques to recognize them,as the crowd can vary from low density to high density.To deal with such highly congested scenes,we have proposed the Congested Scene Crowd Counting Network(CSCC-Net)using VGG-16 as a core network with its first ten layers due to its strong and robust transfer learning rate.A hole dilated convolutional neural network is used at the back end to widen the relevant field to extract a large range of information from the image without losing its original resolution.The dilated convolution neural network is mainly chosen to expand the kernel size without changing other parameters.Moreover,several loss functions have been applied to strengthen the evaluation accuracy of the model.Finally,the entire experiments have been evaluated using prominent data sets namely,ShanghaiTech parts A,B,UCF_CC_50,and UCF_QNRF.Our model has achieved remarkable results i.e.,68.0 and 9.0 MAE on ShanghaiTech parts A,B,199.1 MAE on UCF_CC_50,and 99.8 on UCF_QNRF data sets respectively.

Application on Anomaly Detection of Geoelectric Field Based on Deep Learning

The deep learning method has made nurnerials achievements regarding anomaly detection in the field of time series.We introduce the speech production model in the field of artificial intelligence,changing the convolution layer of the general convolution neural network to the residual element structure by adding identity mapping,and expanding the receptive domain of the model by using the dilated causal convolution.Based on the dilated causal convolution network and the method of log probability density function,the anomalous events are detected according to the anomaly scores.The validity of the method is verified by the simulation data,which is applied to the actual observed data on the observation staion of Pingliang geoeletric field in Gansu Province.The results show that one month before the Wenchuan M_S8.0,Lushan M_S7.0 and Minxian-Zhangxian M_S6.6 earthquakes,the daily cumulative error of log probability density of the predicted results in Pingliang Station suddenly decreases,which is consistent with the actual earthquake anomalies in a certain time range.After analyzing the combined factors including the spatial electromagnetic environment and the variation of micro fissures before the earthquake,we explain the possible causes of the anomalies in the geoelectric field of before the earthquake.The successful application of deep learning in observed data of the geoelectric field may behefit for improving the ultilization rate both the data and the efficiency of detection.Besides,it may provide technical support for more seismic research.

DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting

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.

Hard-rock tunnel lithology identification using multiscale dilated convolutional attention network based on tunnel face images

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.

Automatic detection of sow estrus using a lightweight real-time detector and thermal images

Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.

Automatic Detection of Weld Defects in Pressure Vessel X-Ray Image Based on CNN

The visual automatic detection method based on artificial intelligence has attracted more and more attention. In order to improve the performance of weld nondestructive defect detection,we propose DRepDet(Dilated RepPoints Detector). First, we analyze the weld defect dataset in detail and summarize the distribution characteristics of weld defect data, that is, the defect scale is very different and the aspect ratio distribution range is large. Second, according to the distribution characteristics of defect data, we design DResBlock module, and introduce dilated convolution with different dilated rates in the process of feature extraction to expand the receptive field and improve the detection performance of large-scale defects. Based on DResBlock and anchor-free detection framework RepPoints, we design DRepDet. Extensive experiments show that our proposed detector can detect 7 types of defects. When using combined dilated rate convolution network in detection, the AP50 and Recall50 of big defects are improved by 3.1% and 3.3% respectively, while the performance of small defects is not affected, almost the same or slightly improved. The final performance of the whole network is improved a large margin,with 6% AP50 and 4.2% Recall50 compared with Cascade RCNN and 1.4% AP50 and 2.9% Recall50 compared with RepPoints.

Semantic segmentation for remote sensing images based on an AD-HRNet model

Semantic segmentation for remote sensing images faces challenges of unbalanced category weight,rich context causing difficulties of recognition,blurred boundaries of multi-scale objects,and so on.To address these problems,we propose a new model by combining HRNet with attention mechanisms and dilated convolution,denoted as:AD-HRNet for the semantic segmentation of remote sensing images.In the framework of AD-HRNet,we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight imbalance.The Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of computation.To address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation,we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular objects.Taking Postdam,Vaihingen,and SAMA-VTOL datasets as materials,we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models.Experimental results shown that AD-HRNet increases the mIoUs to 75.59%and 71.58%based on the Postdam and Vaihingen datasets,respectively.

Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D

The charging load of electric vehicles(EVs)has a strong spatiotemporal randomness.Predicting the dynamic spatiotemporal distribution of the charging load of EVs is of great significance for the grid to cope with the access of large-scale EVs.Existing studies lack a prediction model that can accurately describe the dual dynamic changes of EVs charging the load time and space.Therefore,a spatial-temporal dynamic load forecasting model,dilated causal convolution-2D neural network(DCC-2D),is proposed.First,a hole factor is added to the time dimension of the three-dimensional convolutional convolution kernel to form a two-dimensional hole convolution layer so that the model can learn the spatial dimension information.The entire network is then formed by stacking the layers,ensuring that the network can accept long-term historical input,enabling the model to learn time dimension information.The model is simulated with the actual data of the charging pile load in a certain area and compared with the ConvLSTM model.The results prove the validity of the proposed prediction model.

A Novel Fish Counting Method Based on Multiscale and Multicolumn Convolution Group Network

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.