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MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement
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作者 Tao Zhou Yujie Guo +3 位作者 Caiyue Peng Yuxia Niu Yunfeng Pan Huiling Lu 《Computers, Materials & Continua》 SCIE EI 2024年第6期4863-4882,共20页
Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the f... Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis. 展开更多
关键词 PNEUMONIA X-ray image ResNet multi-scale feature direction feature TRANSFORMER
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Few-shot image recognition based on multi-scale features prototypical network
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作者 LIU Jiatong DUAN Yong 《High Technology Letters》 EI CAS 2024年第3期280-289,共10页
In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract i... In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively. 展开更多
关键词 few-shot learning multi-scale feature prototypical network channel attention label-smoothing
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A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification
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作者 Adama Dembele Ronald Waweru Mwangi Ananda Omutokoh Kube 《Journal of Computer and Communications》 2024年第2期173-200,共28页
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware reso... Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline. 展开更多
关键词 MobileNet Image Classification Lightweight Convolutional Neural Network Depthwise Dilated Separable Convolution Hierarchical multi-scale feature Fusion
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A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics 被引量:2
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作者 Qian Zhang Wen-Dong Wang +4 位作者 Yu-Liang Su Wei Chen Zheng-Dong Lei Lei Li Yong-Mao Hao 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期327-342,共16页
A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes... A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation. 展开更多
关键词 multi-scale coupled flow Stress sensitivity Shale oil Micro-scale effect fractal theory
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Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data
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作者 Yongqiang Xi Zhen Ye 《Journal of Beijing Institute of Technology》 EI CAS 2023年第1期13-22,共10页
With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)da... With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods. 展开更多
关键词 hyperspectral image(HSI) light detection and ranging(LiDAR) multi-scale feature classification
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Multi-scale pore fractal characteristics of differently ranked coal and its impact on gas adsorption 被引量:3
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作者 Zhongbei Li Ting Ren +4 位作者 Xiangchun Li Ming Qiao Xiaohan Yang Lihai Tan Baisheng Nie 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2023年第4期389-401,共13页
Well-developed pores and cracks in coal reservoirs are the main venues for gas storage and migration.To investigate the multi-scale pore fractal characteristics,six coal samples of different rankings were studied usin... Well-developed pores and cracks in coal reservoirs are the main venues for gas storage and migration.To investigate the multi-scale pore fractal characteristics,six coal samples of different rankings were studied using high-pressure mercury injection(HPMI),low-pressure nitrogen adsorption(LPGA-N2),and scanning electron microscopy(SEM)test methods.Based on the Frankel,Halsey and Hill(FHH)fractal theory,the Menger sponge model,Pores and Cracks Analysis System(PCAS),pore volume complexity(D_(v)),coal surface irregularity(Ds)and pore distribution heterogeneity(D_(p))were studied and evaluated,respectively.The effect of three fractal dimensions on the gas adsorption ability was also analyzed with high-pressure isothermal gas adsorption experiments.Results show that pore structures within these coal samples have obvious fractal characteristics.A noticeable segmentation effect appears in the Dv1and Dv2fitting process,with the boundary size ranging from 36.00 to 182.95 nm,which helps differentiate diffusion pores and seepage fractures.The D values show an asymmetric U-shaped trend as the coal metamorphism increases,demonstrating that coalification greatly affects the pore fractal dimensions.The three fractal dimensions can characterize the difference in coal microstructure and reflect their influence on gas adsorption ability.Langmuir volume(V_(L))has an evident and positive correlation with Dsvalues,whereas Langmuir pressure(P_(L))is mainly affected by the combined action of Dvand Dp.This study will provide valuable knowledge for the appraisal of coal seam gas reservoirs of differently ranked coals. 展开更多
关键词 multi-scale pore structure fractal theory fractal characteristics Differently ranked coal Coalbed gas adsorption
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Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation
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作者 Yuchun Li Mengxing Huang +1 位作者 Yu Zhang Zhiming Bai 《Computers, Materials & Continua》 SCIE EI 2024年第2期1649-1668,共20页
The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prosta... The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation. 展开更多
关键词 Prostate segmentation multi-scale attention 3D Transformer feature fusion MRI
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Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model
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作者 Dongmei Chen Peipei Cao +5 位作者 Lijie Yan Huidong Chen Jia Lin Xin Li Lin Yuan Kaihua Wu 《Phyton-International Journal of Experimental Botany》 SCIE 2024年第2期261-275,共15页
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... 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. 展开更多
关键词 Tea shoots attention mechanism multi-scale feature extraction instance segmentation deep learning
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Ship recognition based on HRRP via multi-scale sparse preserving method
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作者 YANG Xueling ZHANG Gong SONG Hu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第3期599-608,共10页
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) ba... 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. 展开更多
关键词 ship target recognition high-resolution range profile(HRRP) multi-scale fusion kernel sparse preserving projection(MSFKSPP) feature extraction dimensionality reduction
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Underwater Image Enhancement Based on Multi-scale Adversarial Network
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作者 ZENG Jun-yang SI Zhan-jun 《印刷与数字媒体技术研究》 CAS 北大核心 2024年第5期70-77,共8页
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 ea... 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. 展开更多
关键词 Underwater image enhancement Generative adversarial network multi-scale feature extraction Residual dense block
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Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting 被引量:16
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作者 李一兵 葛娟 +1 位作者 林云 叶方 《Journal of Central South University》 SCIE EI CAS 2014年第11期4254-4260,共7页
In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on m... In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value. 展开更多
关键词 emitter recognition multi-scale wavelet entropy feature weighting uneven weight factor stability weight factor
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Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain 被引量:2
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作者 Shengkun Xie Anna T. Lawnizak +1 位作者 Pietro Lio Sridhar Krishnan 《Engineering(科研)》 2013年第10期268-271,共4页
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 (... 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. 展开更多
关键词 multi-scale Principal Component Analysis Discrete WAVELET TRANSFORM feature Extraction Signal CLASSIFICATION Empirical CLASSIFICATION
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Fast Face Detection with Multi-Scale Window Search Free from Image Resizing Using SGI Features
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作者 Masayuki Miyama 《Journal of Computer and Communications》 2016年第10期22-29,共9页
Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. Th... Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation. 展开更多
关键词 Face Detection multi-scale Window Search Resizing Free SGI feature
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Long memory of price-volume correlation in metal futures market based on fractal features 被引量:3
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作者 程慧 黄健柏 +1 位作者 郭尧琦 朱学红 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2013年第10期3145-3152,共8页
An empirical test on long memory between price and trading volume of China metals futures market was given with MF-DCCA method. The empirical results show that long memory feature with a certain period exists in price... An empirical test on long memory between price and trading volume of China metals futures market was given with MF-DCCA method. The empirical results show that long memory feature with a certain period exists in price-volume correlation and a fittther proof was given by analyzing the source of multifractal feature. The empirical results suggest that it is of important practical significance to bring the fractal market theory and other nonlinear theory into the analysis and explanation of the behavior in metal futures market. 展开更多
关键词 metal futures price-volume correlation long memory MF-DCCA method MULTIfractal fractal features multifractalspectrum
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New Algorithm for Image Target Recognition Based on Fractal Feature Fusion 被引量:2
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作者 潘秀琴 侯朝桢 苏利敏 《Journal of Beijing Institute of Technology》 EI CAS 2002年第4期342-345,共4页
By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Com... By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid. 展开更多
关键词 fractal feature fusion target recognition
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Incorporation ofκ-carrageenan improves the practical features of agar/konjac glucomannan/κ-carrageenan ternary system 被引量:2
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作者 Dongling Qiao Hao Li +3 位作者 Fatang Jiang Siming Zhao Sheng Chen Binjia Zhang 《Food Science and Human Wellness》 SCIE CSCD 2023年第2期512-519,共8页
Three materials(agar,konjac glucomannan(KGM)andκ-carrageenan)were used to prepare ternary systems,i.e.,sol-gels and their dried composites conditioned at varied relative humidity(RH)(33%,54%and 75%).Combined methods,... Three materials(agar,konjac glucomannan(KGM)andκ-carrageenan)were used to prepare ternary systems,i.e.,sol-gels and their dried composites conditioned at varied relative humidity(RH)(33%,54%and 75%).Combined methods,e.g.,scanning electron microscopy,small-angle X-ray scattering,infrared spectroscopy(IR)and X-ray diffraction(XRD),were used to disclose howκ-carrageenan addition tailors the features of agar/KGM/κ-carrageenan ternary system.As affirmed by IR and XRD,the ternary systems withκ-carrageenan below 25%(agar/KGM/carrageenan,50:25:25,m/m)displayed proper component interactions,which increased the sol-gel transition temperature and the hardness of obtained gels.For instance,the ternary composites could show hardness about 3 to 4 times higher than that for binary counterpart.These gels were dehydrated to acquire ternary composites.Compared to agar/KGM composite,the ternary composites showed fewer crystallites and nanoscale orders,and newly-formed nanoscale structures from chain assembly.Such multi-scale structures,for composites withκ-carrageenan below 25%,showed weaker changes with RH,as revealed by especially morphologic and crystalline features.Consequently,the ternary composites with lessκ-carrageenan(below 25%)exhibited stabilized elongation at break and hydrophilicity at different RHs.This hints to us that agar/KGM/κ-carrageenan composite systems can display series applications with improved features,e.g.,increased sol-gel transition point. 展开更多
关键词 Agar/konjac glucomannan/κ-carrageenan ternary system Component interaction multi-scale structure Practical features
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Multi-scale Fractal Characteristics of Atmospheric Boundary-Layer Turbulence 被引量:3
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作者 李昕 胡非 +1 位作者 刘罡 洪钟祥 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2001年第5期787-792,共6页
The turbulence data are decomposed to multi-scales and its respective fractal dimensions are computed. The conclusions are drawn from investigating the variation of fractal dimensions. With the level of decomposition ... The turbulence data are decomposed to multi-scales and its respective fractal dimensions are computed. The conclusions are drawn from investigating the variation of fractal dimensions. With the level of decomposition increasing, the low-frequency part extracted from the turbulence signals tends to be simple and smooth, the dimensions decrease; the high-frequency part shows complex, the dimensions are fixed, about 1.70 on the average, which indicates clear self-similarity characteristics. 展开更多
关键词 discrete wavelet fractal dimension multi-scale turbulence data
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Experimental validation of a signal-based approach for structural earthquake damage detection using fractal dimension of time frequency feature 被引量:2
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作者 Tao Dongwang Mao Chenxi +1 位作者 Zhang Dongyu Li Hui 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2014年第4期671-680,共10页
This article extends a signal-based approach formerly proposed by the authors, which utilizes the fractal dimension of time frequency feature (FDTFF) of displacements, for earthquake damage detection of moment resis... This article extends a signal-based approach formerly proposed by the authors, which utilizes the fractal dimension of time frequency feature (FDTFF) of displacements, for earthquake damage detection of moment resist frame (MRF), and validates the approach with shaking table tests. The time frequency feature (TFF) of the relative displacement at measured story is defined as the real part of the coefficients of the analytical wavelet transform. The fractal dimension (FD) is to quantify the TFF within the fundamental frequency band using box counting method. It is verified that the FDTFFs at all stories of the linear MRF are identical with the help of static condensation method and modal superposition principle, while the FDTFFs at the stories with localized nonlinearities due to damage will be different from those at the stories without nonlinearities using the reverse-path methodology. By comparing the FDTFFs of displacements at measured stories in a structure, the damage-induced nonlinearity of the structure under strong ground motion can be detected and localized. Finally shaking table experiments on a 1:8 scale sixteen-story three-bay steel MRF with added frictional dampers, which generate local nonlinearities, are conducted to validate the approach. 展开更多
关键词 earthquake damage detection time frequency feature fractal dimension signal-based shaking table test frictional damper
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Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions
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作者 Chih-Ta Yen Tz-Yun Chen +1 位作者 Un-Hung Chen Guo-Chang WangZong-Xian Chen 《Computers, Materials & Continua》 SCIE EI 2023年第1期83-99,共17页
A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.M... A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.Multiple kernel sizes were used in convolutional neural network(CNN)to evaluate their performance for extracting features.Moreover,a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner.The CNN achieved recognition of the four table tennis strokes.Experimental data were obtained from20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment.The data were collected to verify the performance of the proposed models for wearable devices.Finally,the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58%and 99.16%,respectively,for the four strokes.The accuracy for five-fold cross validation was 99.87%.This result also shows that the multi-scale convolutional neural network has better robustness after fivefold cross validation. 展开更多
关键词 Wearable devices deep learning six-axis sensor feature fusion multi-scale convolutional neural networks action recognit
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RealFuVSR:Feature enhanced real-world video super-resolution
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作者 Zhi LI Xiongwen PANG +1 位作者 Yiyue JIANG Yujie WANG 《Virtual Reality & Intelligent Hardware》 EI 2023年第6期523-537,共15页
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 t... 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. 展开更多
关键词 Video super-resolution Deformable convolution Cascade residual upsampling Second-order degradation multi-scale feature extraction
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