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.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

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.

Pedestrian attribute classification with multi-scale and multi-label convolutional neural networks

Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.

基于多尺度Scale-Unet的单样本图像翻译

随着生成对抗网络(GAN)的发展,基于单样本的无监督图像到图像翻译(UI2I)取得了重大进展。然而,以前方法无法捕获图像中的复杂纹理并保留原始内容信息。为解决这个问题,提出了一种基于尺度可变U-Net结构(Scale—Unet)的新型单样本图像翻译结构SUGAN。所提出的SUGAN使用Scale—Unet作为生成器,利用多尺度结构和渐进方法不断改进网络结构,以从粗到细地学习图像特征。同时,提出了尺度像素损失scale-pixel来更好地约束保留原始内容信息,防止信息丢失。实验表明,与SinGAN、TuiGAN、TSIT、StyTR2等公共数据集Summer■Winter、Horse■Zebra上的方法相比,该方法生成图像的SIFID值平均降低了30%。所提方法可更好地保留图像内容信息,同时生成详细逼真的高质量图像。

High Dimension Multivariate Data Analysis for Small Group Samples of Chemical Volatile Profiles of African Nightshade Species

Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.

改进Multi-scale ResNet的蔬菜叶部病害识别

基于深度网络的蔬菜叶部病害图像识别模型虽然性能显著,但由于存在参数量巨大、训练时间长、存储成本与计算成本过高等问题,仍然难以部署到农业物联网的边缘计算设备、嵌入式设备、移动设备等硬件资源受限的领域。该研究在残差网络(ResNet18)的基础上,提出了改进型的多尺度残差(Multi-scale ResNet)轻量级病害识别模型,通过增加多尺度特征提取模块,改变残差层连接方式,将大卷积核分解,进行群卷积操作,显著减少了模型参数、降低了存储空间和运算开销。结果表明,在PlantVillage和AI Challenge2018中15种病害图像数据集中取得了95.95%的准确率,在自采集的7种真实环境病害图像数据中取得了93.05%的准确率,在准确率较ResNet18下降约3.72%的情况下,模型的训练参数减少93%左右,模型总体尺寸缩减约35%。该研究提出的改进型Multi-scale ResNet使蔬菜叶部病害识别模型具备了在硬件受限的场景下部署和运行的能力,平衡了模型的复杂度和识别精度,为基于深度网络模型的病害识别系统进行边缘部署提供了思路。

融合Multi-scale CNN和Bi-LSTM的人脸表情识别研究

为了有效改善现有人脸表情识别模型中存在信息丢失严重、特征信息之间联系不密切的问题,提出一种融合多尺度卷积神经网络(Multi-scale CNN)和双向长短期记忆(Bi-LSTM)的模型。Bi-LSTM可以增强特征信息间的联系与信息的维持,在Multi-scale CNN中通过不同尺度的卷积核可以提取到更加丰富的特征信息,并通过加入批标准化(BN)层与特征融合处理,从而加快网络的收敛速度,有利于特征信息的重利用,再将两者提取到的特征信息进行融合,最后将改进的正则化方法应用到目标函数中,减小网络复杂度和过拟合。在JAFFE和FER-2013公开数据集上进行实验,准确率分别达到了95.455%和74.115%,由此证明所提算法的有效性和先进性。

基于改进Multi-Scale AlexNet的番茄叶部病害图像识别

番茄同种病害在不同发病阶段表征差异明显,不同病害又表现出一定的相似性,传统模式识别方法不能体现病害病理表征的动态变化,实用性较差。针对该问题,基于卷积神经网络提出一种适用于移动平台的多尺度识别模型,并基于此模型开发了面向农业生产人员的番茄叶部病害图像识别系统。该文详细描述了AlexNet的结构,分析其不足,结合番茄病害叶片图像特点,去除局部响应归一化层、修改全连接层、设置不同尺度卷积核提取特征,设计了基于AlexNet的多感受野识别模型,并基于Android实现了使用此模型的番茄叶部病害图像识别系统。Multi-ScaleAlexNet模型运行所耗内存为29.9MB,比原始AlexNet的内存需求652MB降低了95.4%,该模型对番茄叶部病害及每种病害早中晚期的平均识别准确率达到92.7%,基于此模型的Andriod端识别系统在田间的识别率达到89.2%,能够满足生产实践中移动平台下的病害图像识别需求。研究结果可为基于卷积神经网络的作物病害图像识别提供参考,为作物病害的自动化识别和工程化应用参考。

Morphology Similarity Distance for Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy

Bearings are crucial components in rotating machines,which have direct effects on industrial productivity and safety.To fast and accurately identify the operating condition of bearings,a novel method based on multi⁃scale permutation entropy(MPE)and morphology similarity distance(MSD)is proposed in this paper.Firstly,the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization.Then,the MSD was employed to measure the distance among test samples from different fault types and the reference samples,and achieved classification with the minimum MSD.Finally,the proposed method was verified with two experiments concerning artificially seeded damage bearings and run⁃to⁃failure bearings,respectively.Different categories were considered for the two experiments and high classification accuracies were obtained.The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.

Fundamental Theories of Spatial Similarity Relations in Multi-scale Map Spaces

Similarity relation is one of the spatial relations in the community of geographic information science and cartography.It is widely used in the retrieval of spatial databases, the recognition of spatial objects from images, and the description of spatial features on maps.However, little achievements have been made for it by far.In this paper, spatial similarity relation was put forward with the introduction of automated map generalization in the construction of multi-scale map databases;then the definition of spatial similarity relations was presented based on set theory, the concept of spatial similarity degree was given, and the characteristics of spatial similarity were discussed in detail, in-cluding reflexivity, symmetry, non-transitivity, self-similarity in multi-scale spaces, and scale-dependence.Finally a classification system for spatial similarity relations in multi-scale map spaces was addressed.This research may be useful to automated map generalization, spatial similarity retrieval and spatial reasoning.

Application of Multi-Scale Tracking Radar Echoes Scheme in Quantitative Precipitation Nowcasting

A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation （MTREC） was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation （TREC） technique with a large ＂box＂ to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small ＂box＂ to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies： the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.

Multi-scale Cyclone Activity in the Changjiang River–Huaihe River Valleys during Spring and Its Relationship with Rainfall Anomalies

Based on the recognition framework of the outermost closed contours of cyclones, an automated identification algorithm capable of identifying the multi-scale cyclones that occur during spring in the Changjiang River-Huaihe River valleys （CHV） were developed. We studied the characteristics of the multi-scale cyclone activity that affects CHV and its relationship with rainfall during spring since 1979. The results indicated that the automated identification algorithm for cyclones proposed in this paper could intuitively identify multi-scale cyclones that affect CHV. The algorithm allows for effectively describing the shape and coverage area of the closed contours around the periphery of cyclones. We found that, compared to the meso- and sub-synoptic scale cyclone activities, the synoptic-scale cyclone activity showed more intimate correlation with the overall activity intensity of multi-scale CHV cyclones during spring. However, the frequency of occurrence of sub-synoptic scale cyclones was the highest, and their effect on changes in CHV cyclone activity could not be ignored. Based on the area of impact and the depth of the cyclones, the sub-synoptic scale, synoptic scale and comprehensive cyclone intensity indices were further defined, which showed a positive correlation with rainfall in CHV during spring. Additionally, the comprehensive cyclone intensity index was a good indicator of strong rainfall events.

Crustal structure beneath the Qilian Orogen Zone from multiscale seismic tomography

The Qilian Orogen Zone(QOZ), located in the north margin of the Tibetan Plateau, is the key area for understanding the deformation and dynamics process of Tibet. Numerous geological and geophysical studies have been carried out on the mechanics of the Tibetan Plateau deformation and uplift; however, the detailed structure and deformation style of the Qilian Orogen Zone have remained uncertain due to poor geophysical data coverage and limited resolution power of inversion algorithms. In this study, we analyze the P-wave velocity structure beneath the Qilian Orogen Zone, obtained by applying multi-scale seismic tomography technique to P-wave arrival time data recorded by regional seismic networks. The seismic tomography algorithm used in this study employs sparsity constraints on the wavelet representation of the velocity model via L1-norm regularization. This algorithm can deal efficiently with uneven-sampled volumes, and can obtain multi-scale images of the velocity model. Our results can be summarized as follows:(1) The crustal velocity structure is strongly inhomogeneous and consistent with the surface geological setting. Significant low-velocity anomalies exist in the crust of northeastern Tibet, and slight high-velocity anomalies exist beneath the Qaidam Basin and Alxa terrane.(2)The Qilian Orogen Zone can be divided into two main parts by the Laji Shan Faults: the northwestern part with a low-velocity feature, and the southeastern part with a high-velocity feature at the upper and middle crust.(3) Our tomographic images suggest that northwestern and southeastern Qilian Orogen Zones have undergone different tectonic processes. In the northwest Qilian Orogen Zone, the deformation and growth of the Northern Tibetan Plateau has extended to the Heli Shan and Beida Shan region by northward overthrusting at the upper crust and thickening in the lower crust. We speculate that in the southeast Qilian Orogen Zone the deformation and growth of the Northern Tibet Plateau were of strike-slip style at the upper crust; in the lower crust, the evidence suggests ductile shear extrusion style and active frontage extension to the Alxa terrane.(4) The multi-scale seismic tomography technique provides multiscale analysis and sparse constraints, which has allowed to us obtain stable, high-resolution results.

A Multi-Scale Urban Atmospheric Dispersion Model for Emergency Management

To assist emergency management planning and prevention in case of hazardous chemical release into the atmosphere,especially in densely built-up regions with large populations,a multi-scale urban atmospheric dispersion model was established.Three numerical dispersion experiments,at horizontal resolutions of 10 m,50 m and 3000 m,were performed to estimate the adverse effects of toxic chemical release in densely built-up areas.The multi-scale atmospheric dispersion model is composed of the Weather Forecasting and Research （WRF） model,the Open Source Field Operation and Manipulation software package,and a Lagrangian dispersion model.Quantification of the adverse health effects of these chemical release events are given by referring to the U.S.Environmental Protection Agency＇s Acute Exposure Guideline Levels.The wind fields of the urban-scale case,with 3 km horizontal resolution,were simulated by the Beijing Rapid Update Cycle system,which were utilized by the WRF model.The sub-domain-scale cases took advantage of the computational fluid dynamics method to explicitly consider the effects of buildings.It was found that the multi-scale atmospheric dispersion model is capable of simulating the flow pattern and concentration distribution on different scales,ranging from several meters to kilometers,and can therefore be used to improve the planning of prevention and response programs.

Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System

An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.

The Multi-Scale Numerical Modeling System for Research on the Relationship between Urban Planning and Meteorological Environment

Considering the urban characteristics, a customized multi-scale numerical modeling system is established to simulate the urban meteorological environment. The system mainly involves three spatial scales: the urban scale, urban sub-domain scale, and single to few buildings scale. In it, different underlying surface types are employed, the building drag factor is used to replace its roughness in the influence on the urban wind field, the effects of building distribution, azimuth and screening of shortwave radiation are added, and the influence of anthropogenic heating is also taken into account. All the numerical tests indicate that the simulated results are reasonably in agreement with the observational data, so the system can be used to simulate the urban meteorological environment. Making use of it, the characteristics of the meteorological environment from the urban to urban sub-domain scales, even the among-buildings scale, can be recognized. As long as the urban planning scheme is given, the corresponding simulated results can be obtained so as to meet the need of optimizing urban planning.