Large-scale Factors Research Articles

RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment

The bearings have been exposed to a noisy environment for an extended period, making it challenging to identify fault characteristics accurately and resulting in low accuracy. In this study, we proposed a technique for diagnosing train-bearing faults in noisy environments using refined time-shift multiscale fractional order fuzzy dispersion entropy and hybrid kernel ridge regression (RTSMFFDE-HKRR) to enhance the precision of fault detection. Firstly, RTSMFFDE is proposed based on multiscale fuzzy dispersion entropy by combining the theories of refinement, time-shifting, and fractional order. Revising the refinement process helps to stabilize the entropy value when dealing with a large scale factor. Implementing time-shifting techniques can help preserve the original signal features more effectively. The theory of fractional order strengthens the noise-resistant performance of the algorithm, and the proposed RTSMFFDE has a superior feature extraction capability. Subsequently, a hybrid kernel function is formulated through the combination of the radial basis kernel function (RBF) and the linear kernel function, aiming to improve the nonlinear mapping ability and anti-noise ability of ridge regression classification algorithm. Finally, two sets of experimental cases were used to verify the proposed RTSMFFDE-HKRR. The results show that the fault feature information extracted by RTSMFFDE in noisy environment is more comprehensive, and the classification effect of HKRR is more significant. In the fault diagnosis of two bearing simulation test beds, the accuracy rate of RTSMFFDE-HKRR is up to 100%. In the noisy environment, the accuracy rate is 98.84% and 97.32%, which is much higher than other diagnostic models. RTSMFFDE-HKRR is suitable for train bearing fault diagnosis in noisy environment.

Improved Similarity Law for Scaling Dynamic Responses of Stiffened Plates with Distorted Stiffener Configurations

Experimental analysis on small-scale models is widely used to predict the dynamic responses of full-scale structures subjected to impact loads. However, due to manufacturing constraints, achieving a perfectly scaled model under a large scaling factor is challenging, leading to the use of distorted scaled models as a compromise. This paper introduces an improved similarity law that ensures distorted scaled models accurately replicate the dynamic responses of prototype stiffened plates under impact loads. The proposed similarity law meticulously considers both the distorted attached plate thickness and variations in stiffener configuration. Double input parameters involving the load case and geometry are formulated to govern the dynamic responses of integrated stiffeners. Additionally, an approximate method rooted in elastic–plastic theory is developed to assess the dominant behaviors of stiffened plates during impact. Consequently, the distorted stiffener configuration of scaled models is designed through a methodology primarily centered on capturing dominant behaviors. Comprehensive numerical simulations are conducted to evaluate the behavior of stiffened plates subjected to impact loads. The results compellingly demonstrate that the proposed similarity law adeptly compensates for geometric distortions, ensuring reliable predictions of dynamic responses in distorted scaled models.

BIRD COMMUNITIES RESPOND TO THE SEASONAL FRUIT AND FLOWER AVAILABILITY IN A FRAGMENTED TROPICAL ANDEAN LANDSCAPE

Fire is one of the main causes of fragmentation in tropical Andean forests. Fragmentation can influence plant reproductive phenology, which in turn affects bird communities. In the Bolivian Yungas, we investigated how the seasonal availability of fruits and flowers affects bird richness, abundance and bird community composition in forest edges and adjacent bracken (Pteridium ferns) dominated areas. We captured birds with mist nets at eight sites during the dry, transition and wet seasons, and recorded fruit and flower availability through phenological observations and plant collections. We examined variation in species richness and abundance using generalized linear mixed-effects models, and bird species composition in each season using multivariate analyses. Fruit availability was higher in the transition and wet seasons, and lowest in the dry season, while flower availability did not differ among seasons. Bird species richness and abundance were significantly higher in bracken areas, although there was significantly lower fruit availability than at the forest edge, and these were mainly generalist bird species that may be tolerant to disturbance. Total bird abundance increased with higher fruit availability in both habitats, and with flower availability at the forest edge. Bird species composition differed between forest edges and bracken areas in the transition and the wet seasons, being influenced by fruiting and flowering phenology in the dry and transition seasons, and elevation in all seasons. Our results show the varying seasonal influence of plant reproductive phenology on avian communities of disturbed habitats, although other large scale factors associated with fragmentation may also shape bird communities.

High-frequency channel attention and contrastive learning for image super-resolution

AbstractOver the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general, recovering high-frequency features is crucial for high-performance models. High-frequency features suffer more serious damages than low-frequency features during downscaling, making it hard to recover edges and textures. In this paper, we attempt to guide the network to focus more on high-frequency features in restoration from both channel and spatial perspectives. Specifically, we propose a high-frequency channel attention (HFCA) module and a frequency contrastive learning (FCL) loss to aid the process. For the channel-wise perspective, the HFCA module rescales channels by predicting statistical similarity metrics of the feature maps and their high-frequency components. For the spatial perspective, the FCL loss introduces contrastive learning to train a spatial mask that adaptively assigns high-frequency areas with large scaling factors. We incorporate the proposed HFCA module and FCL loss into an EDSR baseline model to construct the proposed lightweight high-frequency channel contrastive network (HFCCN). Extensive experimental results show that it can yield markedly improved or competitive performances compared to the state-of-the-art networks of similar model parameters.

Biotic and abiotic factors influencing macrofungal diversity and biomass in Mediterranean forests with a focus on the Porcini group

Macrofungi are among the most crucial ecological forest resources and essential components of terrestrial ecosystems. Despite growing socio-economic interest, knowledge of their production is not understood because of many factors that can affect their natural growth. The aim of this study is to analyze which biotic and abiotic factors can influence the diversity and biomass of macrofungal fruiting bodies, at small and large scales. We worked in broadleaf Mediterranean forests, with a special focus on wild edible species (Porcini). The mycological observations were focused on epigeous macrofungi. To investigate con­nections between the occurrence of Boletus edulis species distribution modeling was used. Contrary to previ­ous studies, the results reveal that, at a small (local) scale, the soil properties and the geochemical content (traces and minor elements) are more strongly correlated with macrofungal communities than vegetation (tree richness, dead wood, and litter volume) and climatic parameters (air temperatures and rainfall). At large scale, both edaphic and climatic factors, are considered essential for fungal fruiting and distribution across landscapes. The quantity of precipitation of the driest month is the crucial climatic factor influenc­ing the occurrence of Porcini. The different results highlight a high variability and site dependence of both biotic and abiotic factors. Further studies appear to be necessary to increase knowledge on which factors have the most influence on edible and non-edible mushroom yield in various habitats.

Spatial and Temporal Characteristics of Winter Diel Vertical Migration of Zooplankton and Nekton in the East China Shelf Seas Based on Multi‐Frequency Echosounder

AbstractThe diel vertical migration (DVM) of plankton and nekton plays an important role in the energy flow and material cycling of marine ecosystems. Especially in shelf seas, seasonal environmental variations may distinguish DVM characteristics in winter mixed water from summer pattern. Therefore, to investigate the patterns and influencing factors of winter DVM in East China Shelf Seas (ECSS), we carried out comprehensive research based on high‐resolution multi‐frequency acoustic data during the winter of 2019/2020. The results showed that: (a) acoustic and zooplankton sampling data can effectively capture the widely existence of winter DVM of different biological community in ECSS and their spatio‐temporal variabilities. (b) In East China Sea, the migration was mainly dominated by the Gas‐bearing class (GB), which had fast migration speed, compared to the Yellow Sea where the migration was dominated by the Fluid‐like class (FL). In addition, GB descends earlier than FL. (c) For large scale factors, during the full moon, the GB will aggregate deeper and the FL shallower, compared to the new moon, and regional temperature is an important factor affecting the distribution of organisms at meridional direction; for small scale processes, cold and warm currents and ocean fronts can also affect migration behavior. In the muddy area with full moon and spring tide, GB and FL show different inconsistent migration compared to normal relatively consistent pattern. These results will help us to better understand the physiological, ecological and environmental elements that control winter DVM, facilitating the management of ecosystem‐based fisheries resources.

CLSR: Cross-Layer Interaction Pyramid Super-Resolution Network

Convolutional neural network (CNN) achieves impressive success in image super-resolution (SR), where global context interaction is critical for reconstructing reliable edge and texture details. However, most CNN-based SR models focus on modeling the global contextual information within a single feature map by using attention mechanisms, and ignore the dependencies among hierarchical features, resulting in blurred or even distorted detail restoration, especially for SR tasks with large scaling factors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., ×4, ×8). To tackle the above issue, we propose a cross-layer interaction pyramid super-resolution (CLSR) network that reconstructs the desired SR images progressively in a coarse-to-fine fashion. Specifically, we propose a novel cross-layer non-local attention (CLNL) for accurate detail restoration. Through explicitly modeling the long-range feature-wise similarities within and between layers, the proposed CLNL is able to discriminatively explore complementary patches from hierarchical features to reconstruct the target LR patches. Then, to further strengthen the information interaction among hierarchical features at different scales, we propose a novel gradient consistency-aware learning framework (GCA) by constructing a closed loop (LR→HR→LR) on the gradient space. The proposed GCA is able to effectively capture the interdependence between LR and HR gradient maps to guide our CLSR for reliable detail restoration. Extensive experiments validate that our CLSR outperforms the state-of-the-art methods in terms of both reconstruction accuracy and visual quality.

Anisotropic Segre [(11)(1,1)] dark energy following a particular equation of state

A generally anisotropic equation of state originally derived in the context of Newman-Janis rotating systems allows for vacuum energy at a specific density. In this paper we examine the possibility of using that equation of state for cosmological dark energy. We treat the case of large scale ordering of the directions of the energy-momentum tensor eigenvectors with a Bianchi cosmological model, and treat the case where the ordering is random on small scales with an effectively isotropic FLRW system. We find particular spacetimes which evolve towards a vacuum energy/ de Sitter like configuration in either case. In the anisotropic Bianchi case, the system can have behavior reminiscent of big bounce cosmologies, in which the matter content approaches vacuum energy at large scale factor and can behave in a variety of ways at small scale factor. For particular conditions in the effectively isotropic case, we can evolve between true and false vacuum configurations, or between radiation like and vacuum energy configurations. We also show how some simpler equations of state behave under the same assumptions to elucidate the methods for analysis.

A novel feature extraction method based on symbol-scale diversity entropy and its application for fault diagnosis of rotary machines

Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information, and poor robustness to noises. Thus, this work proposes a symbol-scale analysis method to deal with the above problems. In one aspect, to capture fault features from the time series over multiple time scales, time-delay process of different intervals is utilized to obtain long-term features and short-term features. In the other aspect, symbol-scale analysis introduces a symbolization procedure and maps time series into a corresponding sequence of symbols to overcome the limitation of weak fault extraction under a low-signal-to-noise ratio environment. Moreover, the symbol-scale entropy approach is developed by integrating with diversity entropy, called symbol-scale diversity entropy. The effectiveness of the proposed strategy is intensively validated using two simulated signals and experimental cases. Results demonstrate its advantages in dynamic change tracking ability and calculation efficiency by comparing it with other state-of-the-art entropy methods. Apart from diversity entropy, the versatility of incorporating the proposed symbol-scale analysis and other entropy methods is also verified using experimental data.

Evaluation of nonlinear response biases of arch dams due to amplitude scaling via three-component record selection based on conditional spectra

The nonlinear response of ultrahigh concrete arch dams under strong or even extreme earthquakes is particularly important for assessing their safety performance, which is highly dependent on the input ground motions (GMs). Based on the current China probabilistic seismic hazard analysis (CPSHA) scheme, a multicomponent hazard consistency conditional spectrum (CS) was first constructed to meet the specific hazard level at the Baihetan site; the existing issues in evaluating the seismic performance of ultrahigh arch dams via CS-based tridirectional records were then investigated. The results show that neglecting the vertical (V) component hazard consistency may result in significant biases in estimating the response dispersion of arch dams under extreme earthquakes as constraints on the V component are removed, but it has little effect on the mean response. In the CS-based tridirectional record, an excessively large scaling factor (SF) can lead to a significant overestimation of the dam's nonlinear responses because the selected record set, which is mainly composed of far-field low-amplitude records with long duration, tends to possess more energy content when scaled up than the true records. By selecting appropriate SF values (e.g., 2–5), the hazard characteristics of the selected records converge to those of the unscaled records, so the response bias of arch dams caused by neglecting the V component hazard consistency can be diminished, which provides a reference for assessing the seismic performance of structures (e.g., high arch dams) that are significantly influenced by the V component.

Multi-image hybrid super-resolution reconstruction via interpolation and multi-scale residual networks

High spatial resolution is necessary for several applications such as visual inspection, and can be achieved using high-resolution (HR) image sensors or through image super-resolution (SR) algorithms. Currently, SR algorithms are applied to either single low-resolution (LR) images or multiple LR image sequences. In this paper, we propose a hybrid super-resolution (HYSR) framework to generate HR images by combining multi-image super-resolution (MISR) and single-image super-resolution (SISR) to obtain high spatial resolution images. This method comprehensively utilizes sub-pixel-level high-frequency detail information between multiple images and co-occurrence prior of a single image to reconstruct SR images with a larger scale factor than the existing methods. Generally, the HYSR reconstruction results have more satisfactory details and visual quality than the SISR or MISR reconstruction results. A large number of qualitative and quantitative evaluation results demonstrate the effectiveness and superiority of the HYSR method over traditional MISR and SISR methods.

Use of two-dimensional refined composite multi-scale time-frequency dispersion entropy for rolling bearing condition monitoring

Rolling bearing vibration signals with local failure usually contain rich fault characteristic in the time and frequency domains. Two-dimensional multi-scale dispersion entropy (MDE2D) is a nonlinear dynamic analysis method widely used in image signal processing. However, the multi-scale coarse-grained process of MDE2D is relatively complex and the computational cost is high. Therefore, this paper develops a two-dimensional multi-scale time-frequency dispersion entropy (MTFDE2D) to solve the problem of high calculation cost of MDE2D. Unfortunately, the length of the coarse-grained sequence used in MTFDE2D will become shorter as the scale factor increases, resulting in a loss of potential useful information. To address this, a two-dimensional refined composite multi-scale time–frequency dispersion entropy (RCMTFDE2D) is proposed. In RCMTFDE2D, the utilization of information contained in the original date is first effectively improved through the coarse-grained process of sliding values of adjacent elements, and then the refined process is used to avoid the fluctuation of entropy on large scale factors. Furthermore, the rolling bearing test data under two different operating conditions are analyzed. The results illustrate that RCMTFDE2D can successfully extract the rolling bearing fault information in time–frequency domain, and the condition monitoring method based on RCMTFDE2D can effectively identify the rolling bearings in different health states and has the highest recognition rate.

Space–time analyses of sediment composition reveals synchronized dynamics at all intertidal flats in the Dutch Wadden Sea

Intertidal mudflat systems are shaped by geological processes and an interplay of hydrodynamics, sediment availability and ecological processes. All around the world these systems are affected by relative sea level rise (RSLR), changing climate and by human activities such as sediment nourishments, dredging, hydrological engineering and bottom trawling. These kinds of perturbations cause changes in morphology and sediment composition which may cause shifts in the composition, spatial distribution and productivity of benthic communities. We analysed the spatial and temporal variability of the sediment grain size of more than 900 km2 intertidal flats in the Dutch Wadden Sea in the period 2009–2019. The large scale coverage was achieved by yearly grid sampling at more than 4000 stations. Spatial panel data models were used to analyse changes in median grain size and mud content between years and to estimate the effects of resuspension due to wind and the accumulation of silt during summer. We show that between years the sediments of the intertidal flats changed synchronously throughout the study area and that the flats became coarser during the period 2011–2015 and muddier again between 2015 and 2019. The system wide changes and the absence of clear local deviations leads to the hypothesis that a large scale factor like changing hydrodynamic regime (e.g. due to RSLR), variability in the composition of suspended sediment in the North Sea or changing microphytobenthic productivity were causally involved in the coarsening of intertidal flats. Our data and analysis provides a base for further scientific enquiry but longer time series on higher temporal resolution of both sediment data and the physical and ecological environment are required. Models simulating the environment may provide further insight into possible development of sediment composition of the intertidal flats of the Wadden Sea.

An improved rescaling algorithm for estimating groundwater depletion rates using the GRACE satellite

ABSTRACT The Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The scaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and the original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are as follows (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we compare it to an analytical solution that restores the relative variability between the rescaled filtered and original GRACE TWSA series. The relative variability approach produces more reliable estimates when comparing to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins in California. Rescaling the complete regional water budget results in a larger scale factor than the scale factor from the large-scale hydrologic model outputs, and the new TWSA results are more consistent with those from GPS. The large scale factor also suggests that regional groundwater depletion is more severe than previously estimated.

Variability and trends of the total cloud cover over Italy (1951–2018)

A new quality-checked and homogenised dataset of total cloud cover (TCC) series over Italy for the period 1951–2018 is presented and the variability and trends of the obtained regional series is discussed. The diurnal cycle highlights the relevant role of convection induced by solar radiation that, as expected, is more relevant at medium and high elevations and in summer. In parallel, the annual cycle presents a strong minimum in July and a maximum during winter for southern Italy, while it exhibits a more complex behaviour with strong differences between low elevations and high elevations in northern Italy. Moreover, the seasonal and annual TCC series are characterized by a significant negative trend over the whole considered period, which is mainly due to the 1951–1990 period. Even if small differences between northern and southern Italy can be observed, the two regions exhibit a coherent behaviour both for long-term trends and decadal time-scale variability suggesting that the causes of variability and trends of the Italian TCC records are more related to large scale factors rather than to local scale changes. Indeed, the comparison with sea level pressure and 500 hPa geopotential height data highlights that large-scale atmospheric circulation explains a relevant fraction of the signal of the Italian TCC records. Finally, the new TCC dataset shows that the long-term evolution of sunshine duration and surface solar radiation in Italy is only partially influenced by changes in TCC.

GPSR: Gradient-Prior-Based Network for Image Super-Resolution

Recent deep learning has shown great potential in super-resolution (SR) tasks. However, most deep learning-based SR networks are optimized via pixel-level loss (i.e., L1, L2, and MSE), which forces the networks to output the average of all possible predictions, leading to blurred details. Especially in SR tasks with large scaling factors (i.e., ×4, ×8), the limitation is further aggravated. To alleviate this limitation, we propose a Gradient-Prior-based Super-Resolution network (GPSR). Specifically, a detail-preserving Gradient Guidance Strategy is proposed to fully exploit the gradient prior to guide the SR process from two aspects. On the one hand, an additional gradient branch is introduced into GPSR to provide the critical structural information. On the other hand, a compact gradient-guided loss is proposed to strengthen the constraints on the spatial structure and to prevent the blind restoration of high-frequency details. Moreover, two residual spatial attention adaptive aggregation modules are proposed and incorporated into the SR branch and the gradient branch, respectively, to fully exploit the crucial intermediate features to enhance the feature representation ability. Comprehensive experimental results demonstrate that the proposed GPSR outperforms state-of-the-art methods regarding both subjective visual quality and objective quantitative metrics in SR tasks with large scaling factors (i.e., ×4 and ×8).

Role of air pollution on COVID-19 in Istanbul

There are many types of research on strong relations between air pollution and the respiration system. This paper is related to the effect of air pollution on pandemic case numbers. By analyses of the number of patients and mean air pollution (µg/m³) data by using data mining, it is concluded that there was evidence of this relationship with a significant level alpha = 0,10. This paper estimates "the number of patients with coronavirus" as a function of daily air pollution and corona case numbers data using single ANN, LSTM, and W-ANN, W-LSTM hybrid methods. This paper explains small, meso, and large scale factors and their role on COVID-19 patients. This finding does not demonstrate a direct cause-effect relationship between air pollution and COVID-19 patients. This study shows the importance of pollution on the number of patients with COVID -19 infection. Although our results have significant uncertainties, we can clearly distinguish the contribution of air pollution to COVID-19 patients. When we used wavelet transformation of air pollution data to estimate COVID-19 patient’s numbers, R2 score is increased in both ANN and LSTM between [0.01- 0.10]. Nevertheless, the actual number of patients is influenced by many additional factors such as the country's health system. After error analysis, sMAPE [3.6-5.9] changed, there is sufficient evidence of model results (M3, M4 Hybrid LSTM) and observation. (0.91

Continuous Mode-Reversal FM Accelerometer With 60-g FSR, 10-μg/K Drift, and VRE Rejection

This work advances the research on continuous mode-reversal frequency modulated accelerometers by characterizing in depth some of their nonidealities, proposing methods for their compensation, and exploiting a renovated sensor design that enables closed-loop compensation of shock and vibrations. The scale factor nonlinearity is first investigated and compensated through a cubic calibration term, yielding a 50% larger range for a 2.5-fold larger scale factor than in former implementations. Additionally, temperature effects on the zero-g offset are compensated by exploiting the value of the low-pass filtered resonance frequency, which yields a valuable information of the silicon sensor temperature. This enables reducing the zero-g-offset temperature dependence by a factor 5 over previous works on similar structures. Finally, a closed-loop compensation of the quality factor, implemented through additional driving electrodes in the structure, guarantees a 25-fold reduction on effects of shocks and vibrations exciting the acceleration-sensitive mode. [2022-0117]

Hierarchical spatio-spectral fusion for hyperspectral image super resolution via sparse representation and pre-trained deep model

Fusing a hyperspectral image (HSI) with a high resolution multispectral image (MSI) has been a highly attractive and effective approach for improving the spatial resolution of HSIs. However, most existing spatio-spectral fusion methods directly accomplish such a fusion process on the desired image scale. This limits the accuracy of the resulting model, especially when large magnification factors are involved. In this paper, a new dual pyramid model is proposed to implement hyperspectral image super resolution, based on a novel hierarchical spatial and spectral fusion method, in an effort to estimate the high resolution image progressively. In particular, an input low resolution HSI is upscaled progressively using a pre-trained deep Laplacian pyramid network, while the corresponding high resolution MSI is down sampled with multiple pyramid layers, forming a dual pyramid model. At each pyramid layer, the HSI and the MSI within the current layer are fused via sparse representation. Systematic qualitative and quantitative evaluations against benchmark datasets demonstrate that this new approach outperforms a number of spatio-spectral fusion based super resolution techniques, achieving outstanding performance over large scale factors.

Toward extreme face super-resolution in the wild: A self-supervised learning approach

Extreme face super-resolution (FSR), that is, improving the resolution of face images by an extreme scaling factor (often greater than ×8) has remained underexplored in the literature of low-level vision. Extreme FSR in the wild must address the challenges of both unpaired training data and unknown degradation factors. Inspired by the latest advances in image super-resolution (SR) and self-supervised learning (SSL), we propose a novel two-step approach to FSR by introducing a mid-resolution (MR) image as the stepping stone. In the first step, we leverage ideas from SSL-based SR reconstruction of medical images (e.g., MRI and ultrasound) to modeling the realistic degradation process of face images in the real world; in the second step, we extract the latent codes from MR images and interpolate them in a self-supervised manner to facilitate artifact-suppressed image reconstruction. Our two-step extreme FSR can be interpreted as the combination of existing self-supervised CycleGAN (step 1) and StyleGAN (step 2) that overcomes the barrier of critical resolution in face recognition. Extensive experimental results have shown that our two-step approach can significantly outperform existing state-of-the-art FSR techniques, including FSRGAN, Bulat's method, and PULSE, especially for large scaling factors such as 64.