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.

Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

Chinese named entity recognition with multi-network fusion of multi-scale lexical information

Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.

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.

基于多尺度与注意力机制的两阶段风暴单体外推研究

风暴是一种生命周期短、发生突然、空间尺度小的自然现象,常用雷达回波外推方法进行预测,但时序预测模型难以在众多特征中定位风暴关键信息,导致预测精度低,模型无法充分学习图像高频信息,导致预测细节缺失,结果模糊。为了提升预测性能,提出两阶段风暴单体外推框架。第一阶段使用多尺度模块提取多尺度信息,注意力机制挖掘影响预测的重要特征,使用时空长短期记忆单元进行序列预测。第二阶段对一阶段结果进行偏差矫正,使用频域损失丰富外推细节。实验结果表明,在雷达回波数据集上,与主流模型PredRNN-V2相比,该模型均方误差降低11.4%,SSIM提升4.3%,在风暴单体外推任务中表现优越。在Moving MNIST数据集上,均方误差降低4.95%,感知损失降低12.67%,SSIM提升至0.898,具有良好的时序预测能力。

基于多尺度门控膨胀卷积网络的时间序列预测算法

针对当前时间序列预测任务存在的高维特征、大规模数据以及对预测准确性高要求等问题,提出一种基于多尺度趋势-周期分解的多头门控膨胀卷积网络模型。该模型采用多尺度分解方法,将原始协变量序列和预测变量序列分解为各自的周期项和趋势项,从而实现独立的预测。对于周期项,引入多头门控膨胀卷积网络的编码器,以提取各自的周期信息;在解码器阶段,使用交叉注意力机制进行通道信息的交互融合,并将预测变量的周期信息采样对齐后通过时间注意力与通道融合信息进行周期预测。对趋势项则采用自回归方式进行趋势预测。最后将趋势预测与周期预测的结果相加得到预测序列。与长短期记忆(LSTM)、Informer等多个主流基准模型进行比较,所提模型在ETTm1、ETTh1等5个数据集上的均方误差(MSE)平均下降了19.2%~52.8%,平均绝对误差(MAE)平均下降了12.1%~33.8%。通过消融实验验证了所提出的多尺度分解模块、多头门控膨胀卷积以及时间注意力模块能提升时序预测的准确度。

基于季节分解的混合神经网络的时间序列预测

近年来,时间序列预测已经在金融、气象、军事等多个领域得到广泛应用。深度学习已开始在时间序列预测任务中展现巨大的潜力和应用前景。其中,循环神经网络在跨度较大的时间序列预测中容易出现信息丢失和梯度爆炸等问题。而Transformer模型及其变种在使用注意力机制时通常忽略了时间序列变量之间的时序关系。为了应对这些问题,提出了一种基于季节分解的混合神经网络时间序列预测模型。该模型利用季节分解模块来捕获时间序列中不同周期频率分量的变化,同时通过融合多头注意力机制和复合扩张卷积层,利用全局信息和局部信息的交互获取数据之间的多尺度时序位置信息。最终,在4个领域的公开数据集上进行了实验,结果表明模型的预测性能优于当前的主流方法。

基于多尺度串联空洞卷积的轻量化UNet香蕉图像分割

针对香蕉果串识别系统中传统的UNet网络存在实时性差、参数量多、下采样后丢失空间信息等问题,该研究提出基于UNet模型的轻量化分割网络,构造一个轻量级的主干特征提取模块,在降低模型参数量和计算量的同时增强网络提取特征的能力,使用膨胀率为[2,1,2]锯齿波形的多尺度串联空洞卷积组合在增大感受野的同时保持对细节的敏感度。该研究算法在自建香蕉果串数据集上的试验结果表明,网络参数量为0.45 M时,香蕉果串识别分割速度可达41.0帧/s,平均像素分类准确率为97.32%、交并比为92.57%。相比于其他模型具有准确率高、参数量小等优点,能够较好地实现精度和速度的均衡。该算法对自然种植环境下的香蕉果串具有良好的识别效果,可为智能化香蕉采摘等应用提供视觉识别技术支持。

基于时空信息增强的科技论文主题趋势预测

近年来,随着社会对科研投入的增大,各种领域的研究成果数量显著增加,准确有效地预测未来研究主题的趋势可以帮助科研人员发现未来研究热点,各种领域的研究成果数量显著增加,准确有效地预测未来研究主题的趋势可以帮助科研人员发现未来研究热点。然而由于各个研究主题之间关联逐渐紧密,大量研究主题之间存在一定的依赖关系,孤立地看待单个研究主题并采用传统的序列问题处理方法,无法有效地挖掘这些研究主题之间的空间依赖。为了同时捕获研究主题之间的空间依赖以及时间变化,提出了一种基于时空信息增强的科技论文主题趋势预测模型,该模型结合了图卷积神经网络(GCN)和时间卷积网络(TCN),具体来说,GCN用于学习研究主题的空间表示,并利用空间依赖加强空间特征,TCN用于学习研究主题趋势的动态变化,并根据时间距离计算加权损失进行优化。在论文数据集以及公开数据集上与当前主流的序列预测模型以及类似的时空模型进行了对比,实验结果表明,在研究主题预测任务以及其他动态图任务中,该模型可以有效捕获时空关系并且优于当前最新的基准算法。

基于时间卷积网络的刀具磨损在线监测

在刀具磨损监测领域中,传统卷积神经网络难以选择合适的卷积核大小,循环神经网络容易发生梯度消失和梯度爆炸,为克服以上缺点,引入时间卷积网络构建在线监测模型对刀具磨损量进行监测。考虑到原始数据量过大且每次走刀过程所采集数据量不同,对数据进行降采样处理,获得了大小为(7,5000)的网络输入数据。通过一维卷积神经网络和时间卷积块的依次叠加,对数据进行特征提取,使用全连接网络将特征映射到刀具磨损值。最后,使用PHM大赛中铣刀磨损的数据验证了模型的效果。实验结果证明,基于时间卷积网络的刀具磨损在线监测模型具有较强的泛化能力,在验证集上均方误差和平均绝对误差分别仅为65.16与6.21,相较于隐马尔科夫、梯度提升树等模型具有较大的提升。

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.

基于一维空洞卷积神经网络的故障电弧检测方法

串联故障电弧因多样性、相似性和隐蔽性而难以被检测,容易引发故障电弧保护装置误报和漏报。采用一维空洞卷积神经网络(one-dimensional dilated convolutional neural network,1D-DCNN)提取以高采样率采集的故障电弧电流特征,引入扩展型指数线性单元(scaled exponential linear unit, SeLU)激活函数和残差连接解决梯度消失和网络退化问题,并结合平均集成学习和Softmax多分类器建立故障电弧检测模型。实验结果表明:所提方法对单负载和混合负载故障电弧的检测准确率达99.67%,相应负载识别准确率达99.95%,总体预测结果准确率达99.62%,优于传统卷积神经网络(convolutional neural network, CNN),满足故障电弧检测要求,有助于串联故障电弧检测和负载识别。