Multi-scale Components Research Articles

Concurrent optimization of continuous carbon fiber-reinforced composites with multi-scale components considering the manufacturing constraint

Concurrent optimization of continuous carbon fiber-reinforced composites with multi-scale components considering the manufacturing constraint

Nanoparticle-Empowered Core-Shell Microcapsules: From Architecture Design to Fabrication and Functions.

Compartmentalization is a powerful concept to integrate multiscale components with diverse functionalities into miniature architectures. Inspired by evolution-optimized cell compartments, synthetic core-shell capsules enable storage of actives and on-demand delivery of programmed functions, driving scientific progress across various fields including adaptive materials, sustainable electronics, soft robotics, and precision medicine. To simultaneously maximize structural stability and environmental sensitivity, which are the two most critical characteristics dictating performance, diverse nanoparticles are incorporated into microcapsules with a dense shell and a liquid core. Recent studies have revealed that these nano-additives not only enhance the intrinsic properties of capsules including mechanical robustness, optical behaviors, and thermal conductivity, but also empower dynamic features such as triggered release, deformable structures, and fueled mobility. In this review, the physicochemical principles that govern nanoparticle assembly during microencapsulation are examined in detail and the architecture-controlled functionalities are outlined. Through the analysis of how each primary method implants nanoparticles into microcapsules, their distinct spatial organizations within the core-shell structures are highlighted. Following a detailed discussion of the specialized functions enabled by specific nanoparticles, the vision of the required fundamental insights and experimental studies for this class of microcarriers to fulfill its potential are sketched.

Impacts of Multiscale Components of Initial Perturbations on Error Growth Characteristics and Ensemble Forecasting Skill

Abstract To compare the roles of two kinds of initial perturbations in a convection-permitting ensemble prediction system (CPEPS) and reveal the effects of the differences in large-scale/small-scale perturbation components on the CPEPS, three initial perturbation schemes are introduced, including a dynamical downscaling (DOWN) scheme originating from a coarse-resolution model, a multiscale ensemble transform Kalman filter (ETKF) scheme, and a filtered ETKF (ETKF_LARGE) scheme. First, the comparisons between the DOWN and ETKF schemes reveal that they behave differently in many ways. Specifically, the ensemble spread and forecast error for precipitation in the DOWN scheme are larger than those in the ETKF; the probabilistic forecasting skill for precipitation in the DOWN scheme is better than that in the ETKF at small neighborhood radii, whereas the advantages of the ETKF begin to appear as the neighborhood radius increases; DOWN possesses better spread–skill relationships than ETKF and has comparable probabilistic forecasting skills for nonprecipitation. Second, the comparisons between DOWN and ETKF_LARGE indicate that the differences in the large-scale initial perturbation components are key to the differences between DOWN and ETKF. Third, the comparisons between ETKF and ETKF_LARGE demonstrate that the small-scale initial perturbations are important since they can increase the precipitation spread in the early times and decrease the forecast errors while simultaneously improving the probabilistic forecasting skill for precipitation. Given the advantages of the DOWN and ETKF schemes and the importance of both large-scale and small-scale initial perturbations, multiscale initial perturbations should be constructed in future research.

Multiscale feature analysis of forecast errors of 500 hPa geopotential height for the CMA‐GFS model

AbstractUsing ERA5 reanalysis data from March 2021 to February 2022 and the China Meteorological Administration Global Forecasting System (CMA‐GFS) operational forecast dataset of 500 hPa geopotential height in the Northern Hemisphere in the same period, the multiscale features of forecast errors are analyzed. The results indicate that the anomaly correlation coefficient (ACC) of 500 hPa geopotential height and its multiscale components in the Northern Hemisphere keep decreasing with the extension of forecast lead time, and there are no seasonal differences in the evolution of the ACC. The effective forecast skills by season for the CMA‐GFS model are above 6 days at multiscale, with the highest skills in winter and the planetary‐scale components. In space, significant seasonal differences are observed in the locations of the extreme values of multiscale forecast errors for 500 hPa geopotential height, and the spatial distribution of forecast errors reflects the inadequate prediction of the intensity of large‐scale trough and ridge systems at middle and high latitudes and the phase‐shift prediction of small troughs and ridges at middle latitudes. Generally, the forecast errors of the original field and planetary‐scale component show wavelike or banded distribution, and the synoptic‐scale forecast errors are always distributed in latitudinal wavelike patterns alternating between positive and negative, without significant differences in the distribution of land, sea, and terrain. The first empirical orthogonal function modes of multiscale forecast errors almost retain their respective feature. In temporal, the spring, summer, and autumn time series all have quasi‐biweekly positive and negative phase transitions within the monthly scale, and the significant phase transition in winter only occurs around January 1st. These results deepen the understanding of the distribution and possible causes of forecast errors of the CMA‐GFS model and provide ideas for the improvement and revision of the model.

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to jointly represent them. According to the resulted reconstruction errors of different classes, the target label of test sample can be classified. The moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up the standard operating condition (SOC) and several extended operating conditions (EOCs) including configuration variants, depression angle variances, noise corruption, and partial occlusion to test and validate the proposed method. The results confirm the effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR references.

Environmental drivers of paraglacial time duration and intensity for scree slopes dynamics in forested environments

AbstractThe highly fissile lithology of the rockwalls and the diversity of mass‐wasting processes provide a specific character to the active talus slopes of the northern Gaspé Peninsula since deglaciation. At a regional scale, the geology of the rockwalls, the patterns and modalities of deglaciation and the evolution towards a cold temperate morphoclimatic regime in a maritime context still influence the geomorphological dynamics of scree slopes today. At a local scale, the south–north orientation of the main coastal valleys influences insolation and exposure to prevailing winds, which in turn influence the snow cover regime and the occurrence of freeze–thaw cycles. The statistical analyses carried out from the mapping of 43 talus slopes and their geometric variables allowed the identification of significant environmental factors for the characterization of the dominant geomorphic processes: snow avalanches, frost‐coasted clast flows, debris flows and rockfalls. Slope aspect appears to be a key parameter in the nature of the processes acting on the talus slopes. East‐ and north‐facing talus slopes are generally covered by a significant snowpack in winter and the dominant processes are snow avalanches and debris flows. West‐ and south‐facing talus slopes face prevailing winds and insolation and are subject to frost‐coated clast flows, the main driver for forest regression, and rockfalls. However, the evolution of scree slopes in forested environments remains extremely complex due to the multiscale components that affect their evolution in the short, medium and long term.

MEDIA-Net: Multi-scale Feature Extraction and DepthwIse Attention Network for Accurate Carotid Artery Segmentation

Accurate segmentation of the carotid artery in digital subtraction angiography (DSA) imaging is crucial for the diagnosis, prognosis and therapy planning of chronic carotid artery occlusion (CCAO). However, general segmentation methods often produce misclassifications and fuzzy boundaries, making it challenging to obtain precise segmentation results. This paper suggests a solution to these problems by proposing a Multi-scale Feature Extraction and DepthwIse Attention Network (MEDIA-Net) consisting of the Multi-scale Feature Extraction (MFE) module and the DepthwIse Attention (DA) module. The MFE module extracts and blends features at different scales to capture dependencies between multi-scale components. In contrast, the DA module further utilizes depthwise convolution, gated attention and spatial attention to enhance the network's classification and boundary delineation accuracy. Experimental results show that MEDIA-Net outperforms general methods regarding mIoU and Dice score, producing fewer misclassifications and s harper boundaries. The proposed method offers a promising solution for carotid artery segmentation in DSA imaging. It highlights the importance of densely combining multi-scale feature extraction and depthwise attention in general medical image segmentation.

Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox.

This article presents an intelligent fault diagnosis method for wind turbine (WT) gearbox by using wavelet packet decomposition (WPD) and deep learning. Specifically, the vibration signals from the gearbox are decomposed using WPD and the decomposed signal components are fed into a hierarchical convolutional neural network (CNN) to extract multiscale features adaptively and classify faults effectively. The presented method combines the multiscale characteristic of WPD with the strong classification capacity of CNNs, and it does not need complex manual feature extraction steps as usually adopted in existing results. The presented CNN with multiple characteristic scales based on WPD (WPD-MSCNN) has three advantages: 1) the added WPD layer can legitimately process the nonstationary vibration data to obtain components at multiple characteristic scales adaptively, it takes full advantage of WPD and, thus, enables the CNN to extract multiscale features; 2) the WPD layer directly sends multiscale components to the hierarchical CNN to extract rich fault information effectively, and it avoids the loss of useful information due to hand-crafted feature extraction; and 3) even if the scale changes, the lengths of components remain the same, which shows that the proposed method is robust to scale uncertainties in the vibration signals. Experiments with vibration data from a production wind farm provided by a company using condition monitoring system (CMS) show that the presented WPD-MSCNN method is superior to traditional CNN and multiscale CNN (MSCNN) for fault diagnosis.

Bitcoin Price Forecasting: An Integrated Approach Using Hybrid LSTM-ELM Models

In recent years, digital currencies have flourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole financial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and financial systems. Considering the multiscale attributes of cryptocurrency price, we match the different machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. The Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. The empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in upward and downward market conditions. Meanwhile, the different algorithms are applicable for components with varying time scales.

Data-driven and topological design of structural metamaterials for fracture resistance

Data science as a promising paradigm provides novel and diverse opportunities for structural metamaterials attaining exceptional mechanical properties. It is demonstrated here that porous structures composed of brittle constitutive materials can be strong and tough through topological optimization and data-driven techniques. We show that brittle fracture properties can be tailored through the linear control of the homogenized stress and non-periodic microstructures from a multiscale perspective. These tough advanced structural metamaterials pave the way to multiscale components with exceptional fracture resistance.

Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network

Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.

Short-Term Load Forecasting and Temperature Load Extraction Based on CEEMDAN and TDIC

With the intensification of urbanization in various countries worldwide, the temperature load which is greatly affected by ambient temperature, such as summer cooling loads and winter heating loads, accounts for a rising proportion of the total urban load. It causes an increasing peak-to-valley load difference. However, due to the complex composition and strong randomness of the load, it is necessary to study the multi-scale and multi-period correlation between temperature. Based on this, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the temperature and load into multi-scale components. The time-dependent intrinsic correlation (TDIC) is proposed to analyze the local correlation between temperature and load in multiple periods under a multi-scale framework, and obtain the dynamic change characteristics of the correlation between temperature and load. Based on the TDIC analysis results, a suitable sample period for short-term load forecasting (STLF) and input temperature data can be selected. Finally, extreme learning machine optimized by particle swarm optimization (PSO-ELM) is used to forecast each component of the load. The proposed STLF method is validated on real-time data from the Pennsylvania-New Jersey-Maryland (PJM) Company in the United States. The proposed method has greatly reduced in both mean absolute percentage error (MAPE) and root mean square error (RMSE) compared with other traditional methods, and the temperature load that fluctuates with temperature in the day to be forecasted is extracted.

Graph wavelet transform for image texture classification

Graph is a data structure that can represent complex relationships among data. Graph signal processing, unlike traditional signal processing, explicitly considers the structure and relationship among the signal samples. Graph wavelet transform can provide a multiscale analysis for the graph signal. It is well known that texture is a region property in an image, which is characterized with the intensity and relationship among pixels. In this context of the graph signal processing framework, an image texture can be considered as the signal on the graph. Therefore, a texture classification method based on graph wavelet transform is proposed. Specifically, image textures are decomposed into multiscale components by using two-channel graph wavelet filter banks. Then the local singular value decomposition is applied to each subband. In order to improve the noise-resistant ability, the maximum, mean and median values of the local singular values of graph-wavelet transformation coefficients are extracted. Finally, the Weibull distributions are used to model those extracted values to describe the image textures. The experiments on the benchmark texture datasets are conducted to demonstrate the effectiveness of the proposed method.

Synthetic aperture radar target recognition based on joint classification of selected monogenic components by nonlinear correlation information entropy

A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods.

Morphology-controllable wrinkled hierarchical structure and its application to superhydrophobic triboelectric nanogenerator

Abstract Hierarchical structures allow one to improve device performance by exploiting the synergistic effects of micro/nano multiscale components. However, the structural complexity of hierarchical structures places limits on their fabrication and applications. Herein, a novel morphology-controllable wrinkled micro/nano hierarchical structure (WHS) was developed by integrating micropatterns, nanopatterns, and wrinkles on a single substrate to overcome these limitations. Each structure could be individually controlled, which offers unlimited design diversity. The produced WHS was used as a superhydrophobic triboelectric nanogenerator. Compared to a nanogenerator with on a film structure, the WHS-based nanogenerator showed a superior contact angle of 152.5°, which is indicative of high hydrophobicity, and an enhanced (by 608%) triboelectric effect, which was ascribed to the highly rough surface of the WHS. The WHS-based nanogenerator was used to fabricate a self-powered and water-repellent cough detection sensor with an entirely superhydrophobic structure and stable superior sensing performance during repeated water spraying.

Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model.

Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

A novel two-stage approach for cryptocurrency analysis

Modelling and quantifying the underlying characteristics of the cryptocurrency market has drawn increasing attention since Bitcoin went online in 2009. This study proposes a two-stage decomposition and composition method (2SDC) that begins with a Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) for better interpreting cryptocurrency formations. This study involves daily closing price data from six cryptocurrencies (i.e., Bitcoin, Ethereum, Bitcoin Cash, Litecoin, Monero and Dash) from July 23rd, 2017 to July 23rd, 2019. In the first stage, six time series are jointly decomposed into 10 independent intrinsic mode functions (IMF) from high to low frequency plus one residual. In the second stage, the IMFs for each cryptocurrency are composed into three components based on Wilcoxon signed-rank test, including high and low frequency components and a long-term trend. These three multi-scale components can be interpreted as short-term fluctuations caused by investor sentiment and micro-structure, the effect of significant events and fundamental values. Furthermore, we demonstrated that the low and high frequency compositions are determining factors of cryptocurrency prices, which supports for the existing evidence (e.g. Bouoiyour, Selmi, Tiwari, & Olayeni, 2016; Ji, Bouri, Lau, & Roubaud, 2019).

Beyond Sustainability in Food Systems: Perspectives from Agroecology and Social Innovation

Food security faces many multifaceted challenges, with effects ranging far beyond the sectors of agriculture and food science and involving all the multiscale components of sustainability. This paper puts forward our point of view about more sustainable and responsible approaches to food production research underlying the importance of knowledge and social innovation in agroecological practices. Increased demand for food worldwide and the diversification of food choices would suggest the adoption of highly productive, but low-resilient and unsustainable food production models. However, new perspectives are possible. These include the revitalization and valorization of family-based traditional agriculture and the promotion of diversified farming systems as a social and economic basis to foster social-ecological conversion. Additionally, they encompass the forecasting of the Participatory Guarantee Systems (PGS) and the drafting of a new agenda for food sovereignty. Thanks to a desk analysis, the study describes and discusses these perspectives, their trajectories and action research implications. The results suggest the need to adopt a more inclusive and systemic approach to the described problems, as the solutions require the promotion of responsibility within decision makers, professionals and consumers. This appears essential for reading, analyzing and understanding the complex ecological-functional, social and economic relations that characterize farming systems, as well as mobilizing local communities.

Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes

In industrial processes, the noise and high dimension of process signals usually affect the performance of those methods in fault detection and diagnosis. A predominant property of a fault diagnosis model is to extract effective features from process signals. Wavelet transform is capable of extracting multiscale information that provides effective fault features in time and frequency domain of process signals. In this paper, a new deep neural network (DNN), multichannel one-dimensional convolutional neural network (MC1-DCNN), is proposed to investigate feature learning from high-dimensional process signals. Wavelet transform is used to extract multiscale components with fault features from process signals. MC1-DCNN is able to learn discriminative time–frequency features from these multiscale process signals. Tennessee Eastman process and fed-batch fermentation penicillin process are adopted to verify performance of the proposed method. The experimental results demonstrate remarkable feature extraction and fault diagnosis performance of MC1-DCNN and show prosperous possibility of applying this method to industrial processes.

Experimental investigation of wall-bounded turbulence drag reduction by active control of double piezoelectric vibrator

In order to manipulate large-scale coherent structures in the wall-bounded turbulence and reduce the skin-friction, an active-control experimental investigation was performed through synchronous and asynchronous vibrations of double piezoelectric vibrators embedded spanwisely on a smooth flat plate surface. A TSI-IFA300 hot-wire anemometer and TSI-1621A-T1.5 hot-wire probe were used to measure the time series of instantaneous velocity at different locations. Influence of the vibrations on wall-bounded turbulence are compared in a multi-scale point of view. A disturbance Reynolds Number Red = ρd2f/μ is introduced to represent the disturbance. A probability density functions (PDFs) of the multi-scale components of the turbulence velocity and multi-scale conditional phase-averaged waveform are studied in detail using the Wavelet transform. The results show that the maximum drag reduction rate 18.54% was obtained at 100V/160Hz and Red = 0.54 case with an asynchronous vibration mode. The disturbances generated by the vibrators have a significant influence on the sweep events of burst. The asynchronous vibration model is more effective than synchronous vibration one. A possible physical mechanism is recognized to explain why the disturbance frequency of 160Hz leads to an optimal parameter set for drag reduction.