Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain

Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.

Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.

Neighborhood fusion-based hierarchical parallel feature pyramid network for object detection

In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy.

RealFuVSR:Feature enhanced real-world video super-resolution

Background Recurrent recovery is a common method for video super-resolution(VSR)that models the correlation between frames via hidden states.However,the application of this structure in real-world scenarios can lead to unsatisfactory artifacts.We found that in real-world VSR training,the use of unknown and complex degradation can better simulate the degradation process in the real world.Methods Based on this,we propose the RealFuVSR model,which simulates real-world degradation and mitigates artifacts caused by the VSR.Specifically,we propose a multiscale feature extraction module(MSF)module that extracts and fuses features from multiple scales,thereby facilitating the elimination of hidden state artifacts.To improve the accuracy of the hidden state alignment information,RealFuVSR uses an advanced optical flow-guided deformable convolution.Moreover,a cascaded residual upsampling module was used to eliminate noise caused by the upsampling process.Results The experiment demonstrates that RealFuVSR model can not only recover high-quality videos but also outperforms the state-of-the-art RealBasicVSR and RealESRGAN models.

A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings

Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data.

并行特征提取和渐进特征融合的计算机主板装配缺陷检测

针对计算机主板装配缺陷检测中的元器件位置分布复杂、缺陷目标不显著及多尺度等问题,本文提出了一种并行特征提取和互交叉渐进特征融合的端到端的缺陷检测算法。首先,结合部分卷积和视觉Transformer提出了一种并行残差特征提取网络,利用部分卷积的低计算复杂度的优势提取局部特征,同时利用视觉Transformer的长距离建模能力扩大模型的感受野,增强网络的特征提取能力。其次,引入注意力机制和特征渐进融合机制,提出了一种多尺度注意力互交叉的渐进特征融合网络,增强检测模型的特征融合能力。在公开数据集上的实验结果表明,该算法的平均精度均值(mAP)达到了94.63%,相较于基线模型YOLOv5提升了4.62%,并优于其他几种先进模型,检测速度达到了25 FPS。实现了较好的检测精度与速度的平衡,为实际工业环境下计算机主板表面装配缺陷检测自动化和智能化的实现提供了一种快速、有效的方法。

大数据场景下用户评论聚类文本挖掘算法

因传统文本数据挖掘算法在大数据场景下的文本聚类挖掘效果较差,提出一种大数据场景下基于文本数据挖掘的用户评论聚类算法。首先,通过设计改进的信息增益算法提取用户评论数据特征,根据信息熵提取文本关键字和不平衡数据项形成特征数据。之后,使用改进的聚类数据挖掘算法对特征数据进行聚类挖掘。最后,基于Spark框架将改进的聚类数据挖掘算法进行并行化改造。设计实验验证分析所提特征提取算法与聚类挖掘算法的性能,结果表明在大数据场景下所提算法的运行时间、准确率和加速比方面优于传统算法。

RESEARCH ON SATELLITE IMAGE PROCESSING AND RECOGNITION WITH PARALLEL ALGORITHM

Using the method of mathematical morphology,this paper fulfills filtration,segmentation and extraction of morphological features of the satellite cloud image.It also gives out the relative algorithms,which is realized by parallel C programming based on Transputer networks.It has been successfully used to process the typhoon and the low tornado cloud image.And it will be used in weather forecast.

融合多重多尺度特征的高分辨率遥感影像建筑物提取网络

针对高分辨率遥感影像因复杂背景信息导致的建筑物边界、角点以及内部信息出现的错分、漏分问题,提出了一种融合多重多尺度目标特征的DPRS-Net深度学习网络。DPRS-Net采用Resnet50与Swin-T(Tiny)的并行编码结构以结合两种编码优势,进而获取特征图的大范围深层信息;利用跳跃连接降低建筑物的边界特征损失;引入特征金字塔注意力模块和密集空洞空间特征金字塔池化模块,使采样过程中的建筑物细节特征损失减弱。为验证模型的优势性、分析性能提升原因,在WHU和自建Changchun3建筑物数据集上进行对比和消融实验。结果表明,DPRS-Net在两种数据集上均取得更高的精度,提取的建筑物信息更为完整,且模型各结构均能有效提升预测效果。

嵌入注意力机制的并行多尺度点云上采样方法

目前,基于深度学习的点云上采样方法缺失对局部区域特征关联性的关注和对全局特征的多尺度提取,导致输出的密集点云存在异常值过多、细粒度不高等问题。为解决上述问题,提出了嵌入注意力机制的并行多尺度点云上采样网络(Parallel Multi-scale with Attention mechanism for Point cloud Upsampling),网络由特征提取器、特征拓展器、坐标细化器和坐标重建器4个模块级联组成。首先给定一个N×3的稀疏点云作为输入,为了获得点云的全局和局部特征信息,设计了一个嵌入注意力机制的并行多尺度特征提取模块(PMA)用于将三维空间的点云映射到高维特征空间。其次使用边缘卷积特征拓展器拓展点云特征维度,得到高维点云特征,以更好地保留点云特征的边缘信息,将高维点云特征通过坐标重建器转换回三维空间中。最后使用坐标细化器精细调整输出点云细节。在合成数据集PU1K上的对比实验结果表明,PMA-PU生成的密集点云在倒角距离(CD)、豪斯多夫距离(HD)和点面距离(P2F)上都有显著提升,分别比性能次优的网络模型优化了7.863%,21.631%,14.686%。可视化结果证明了PMA-PU具有性能更好的特征提取器,能够生成细粒度更高、形状更接近真实值的密集点云。

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

Enhancing Tea Leaf Disease Identification with Lightweight MobileNetV2

Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring equipment.This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these challenges.The proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions accurately.Secondly,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease scales.Finally,the AutoML for Model Compression(AMC)is used to compress themodel and reduce computational complexity.Experimental results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.

多路并行多尺度特征复用的遥感图像超分辨率

遥感图像内部物体尺寸小、分布不均匀、耦合程度高,针对目前遥感图像超分辨率模型特征提取信息单一且利用不足的现状,文中提出一种多路并行多尺度特征复用网络模型以改进图像重建的性能。该模型使用局部特征级联和全局特征融合的结构融合多个网络残差块提取的特征信息,其中每个残差块由两个多尺度卷积单元串行连接。多尺度卷积单元通过对特征信息进行交叉融合,构建多路并行的分支提取图像特征。同时引入短跳跃连接加强不同分支之间的特征复用,通过长跳跃连接加强网络不同深度的特征融合。当放大因子为4时,在两个测试集上该模型的峰值信噪比分别为29.6531 dB、29.0374 dB,相对于其他模型的测试结果具有明显提升,因此所提模型在遥感图像超分辨率重建上具有较好的效果。

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

属性关联模型下大数据集群查询仿真

在数据查询过程中,易受冗余数据、服务器异常、虚拟信息等问题的干扰,导致查询时间长、查询稳定性差等现象产生。为了解决上述问题,提出基于属性关联模型的大数据集群查询算法。采用扩展t-SNE算法对大数据集群中的数据节点做降维处理,避免冗余数据对查询过程产生干扰。将降维后的数据输入到属性关联模型中,实现大数据集群的特征提取,并将提取的特征输入到分布式并行架构中,通过查询负载量的计算完成大数据集群的查询。实验结果表明,所提算法的响应时间短,查询开销小于50Mb,且查询稳定性强。

Multi-core based parallel computing technique for content-based image retrieval

In this paper, we propose a parallel computing technique for content-based image retrieval （CBIR） system. This technique is mainly used for single node with multi-core processor, which is different from those based on cluster or network computing architecture. Due to its specific applications （such as medical image processing） and the harsh terms of hardware resource requirement, the CBIR system has been prevented from being widely used. With the increasing volume of the image database, the widespread use of multi-core processors, and the requirement of the retrieval accuracy and speed, we need to achieve a retrieval strategy which is based on multi-core processor to make the retrieval faster and more convenient than before. Experimental results demonstrate that this parallel architecture can significantly improve the performance of retrieval system. In addition, we also propose an efficient parallel technique with the combinations of the cluster and the multi-core techniques, which is supposed to gear to the new trend of the cloud computing.

Parallel solving model for quantified boolean formula based on machine learning

A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance in QBF parallel solving system,and the experimental evaluation scheme was also designed.It shows that the characterization factor of clause and cube influence the solving performance markedly in our experiment.At the same time,the heuristic machine learning algorithm was applied,support vector machine was chosen to predict the performance of QBF parallel solving system based on clause sharing and cube sharing.The relative error of accuracy for prediction can be controlled in a reasonable range of 20%30%.The results show the important and complex role that knowledge sharing plays in any modern parallel solver.It shows that the parallel solver with machine learning reduces the quantity of knowledge sharing about 30%and saving computational resource but does not reduce the performance of solving system.

金属表面缺陷检测方法YOLOv3I

提出一种金属表面缺陷检测方法的改进模型.首先,基于YOLOv3(you only look once v3)目标检测模型,使用多尺度卷积并行结构,提取、融合多尺度特征;其次,使用高效下采样,在保留特征信息的同时减少特征升维的计算量;最后,使用空间可分离卷积,在保持感受野不变的前提下增加模型的宽度与深度,从而得到模型参数量减少、同时提升了模型性能的改进模型YOLOv3I(you only look once v3 inception).改进模型提高了对复杂缺陷的特征提取能力,并进一步降低了对硬件配置的要求.实验结果表明,改进模型在精度与计算效率上均有明显提升.平均准确率在公开数据集上约提高5%,在企业提供的轴承数据集上约提高3%,模型参数量下降超过20%,两个数据集上模型浮点计算量分别减少1.6×10^(9)和1.2×10^(10)次.