Spatial Mechanisms Research Articles

Breaking the rain barrier: A novel approach in image processing with AMGR‐Net

AbstractImage quality is significantly impacted by rain, posing challenges in fields like surveillance, autonomous driving, and outdoor robotics. The field of image deraining, particularly for single image, has attracted considerable attention to improve image clarity in inclement weather. To overcome the inherent complexity of the single‐image rain removal task, we proposed a novel architecture of the attention mechanism and gated recurrent network (AMGR‐Net) that combines spatial and channel attention mechanisms with gated recurrent units. AMGR‐Net contains multiple modules, each of which uses convolution kernels and attention mechanisms to enhance feature extraction. AMGR‐Net demonstrates superior performance over state‐of‐the‐art methods in both synthetic and real‐world image datasets, as evidenced by higher peak signal to noise ratio and structural similarity index measurement scores. The integration of spatial attention significantly enhances feature expression, enabling more effective rain streak removal and detail preservation. Furthermore, this method also shows promising results in the application of stripe noise removal from meteorological satellite cloud images.

Geometric mechanics of kiri-origami-based bifurcated mechanical metamaterials.

We explore a new design strategy of leveraging kinematic bifurcation in creating origami/kirigami-based three-dimensional (3D) hierarchical, reconfigurable, mechanical metamaterials with tunable mechanical responses. We start from constructing three basic, thick, panel-based structural units composed of 4, 6 and 8 rigidly rotatable cubes in close-looped connections. They are modelled, respectively, as 4R, 6R and 8R (R stands for revolute joint) spatial looped kinematic mechanisms, and are used to create a library of reconfigurable hierarchical building blocks that exhibit kinematic bifurcations. We analytically investigate their reconfiguration kinematics and predict the occurrence and locations of kinematic bifurcations through a trial-correction modelling method. These building blocks are tessellated in 3D to create various 3D bifurcated hierarchical mechanical metamaterials that preserve the kinematic bifurcations in their building blocks to reconfigure into different 3D architectures. By combining the kinematics and considering the elastic torsional energy stored in the folds, we develop the geometric mechanics to predict their tunable anisotropic Poisson's ratios and stiffnesses. We find that kinematic bifurcation can significantly effect mechanical responses, including changing the sign of Poisson's ratios from negative to positive beyond bifurcation, tuning the anisotropy, and overcoming the polarity of structural stiffness and enhancing the number of deformation paths with more reconfigured shapes.This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

It's about (taking up) space: Discreteness of individuals and the strength of spatial coexistence mechanisms.

One strand of modern coexistence theory (MCT) partitions invader growth rates (IGR) to quantify how different mechanisms contribute to species coexistence, highlighting fluctuation-dependent mechanisms. A general conclusion from the classical analytic MCT theory is that coexistence mechanisms relying on temporal variation (such as the temporal storage effect) are generally less effective at promoting coexistence than mechanisms relying on spatial or spatiotemporal variation (primarily growth-density covariance). However, the analytic theory assumes continuous population density, and IGRs are calculated for infinitesimally rare invaders that have infinite time to find their preferred habitat and regrow, without ever experiencing intraspecific competition. Here we ask if the disparity between spatial and temporal mechanisms persists when individuals are, instead, discrete and occupy finite amounts of space. We present a simulation-based approach to quantifying IGRs in this situation, building on our previous approach for spatially non-varying habitats. As expected, we found that spatial mechanisms are weakened; unexpectedly, the contribution to IGR from growth-density covariance could even become negative, opposing coexistence. We also found shifts in which demographic parameters had the largest effect on the strength of spatial coexistence mechanisms. Our substantive conclusions are statements about one model, across parameter ranges that we subjectively considered realistic. Using the methods developed here, effects of individual discreteness should be explored theoretically across a broader range of conditions, and in models parameterized from empirical data on real communities.

Weak Edge Target Segmentation Network Based on Dual Attention Mechanism

Segmentation of weak edge targets such as glass and plastic poses a challenge in the field of target segmentation. The detection process is susceptible to background interference and various external factors due to the transparent nature of these materials. To address this issue, this paper introduces a segmentation network for weak edge target objects (WETS-Net). To effectively extract edge information of such objects and eliminate redundant information during feature extraction, a dual-attention mechanism is employed, including the Edge Attention Extraction Module (EAEM) and the Multi-Scale Information Fusion Module (MIFM). Specifically, the EAEM combines improved edge feature extraction kernels to selectively enhance the importance of edge features, aiding in more precise target region extraction. The MIFM utilizes spatial attention mechanisms to fuse multi-scale features, reducing background and external interference. These innovations enhance the performance of WETS-Net, offering a new direction for weak edge target segmentation research. Finally, through ablation experiments, the effectiveness of each module is effectively validated. Moreover, the proposed algorithm achieves an average detection accuracy of 95.83% and 96.13% on the dataset and a self-made dataset, respectively, outperforming similar U-Net-improved networks.

Solar Radiation Prediction Model Based on Spatial Attention Mechanisms and Sun Position Feature Maps

This study introduces a novel method for predicting solar radiation by focusing on the sun's position and cloud conditions in the sky. The proposed approach utilizes sun position feature maps and spatial attention mechanisms to improve the accuracy of solar irradiance prediction. Key contributions include the development of sun position feature maps to precisely locate the sun in all-sky images and the integration of spatial attention mechanisms with convolutional neural networks to enhance predictive modeling. Comparative analysis with three other models demonstrates the effectiveness of the proposed approach, particularly in overcast conditions. These innovative techniques significantly enhance the accuracy of solar radiation prediction and offer valuable insights for the design and operation of renewable energy systems.

Remote sensing scene classification based on contextual attention mechanism of lie group space

ABSTRACT High-resolution remote sensing (HRRS) scene classification is based on the semantic information in the image and its contextual spatial correlation, labelling different semantic categories for different HRRS. Convolutional neural networks (CNN) have been widely studied and applied in remote sensing scene classification in recent years. However, most of the existing CNN models focus on the global and high-level semantic features, ignoring the shallower feature information. In addition, complex background information and variable scales lead to a series of problems with large intra-class differences and high inter-class similarities, which also brings challenges to scene classification. To address the above problems and challenges, a scenario classification model based on the contextual spatial attention and channel attention mechanism of Lie Group manifold space learning is proposed in this study. In this model, we fully explore the multi-scale features of the scene (shallower-level and high-level) and propose a novel contextual spatial attention mechanism and channel attention mechanism. Extensive experimentation was carried out on the Union Remote Sensing Image Data Set (URSIS), which improved by 7.13% compared with the classical model. The experimental results showed that compared with other state-of-the-art remote sensing scene classification models, our proposed method has achieved significant improvement in classification accuracy and performance.

Composite dyadic models for spatio-temporal data.

Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. We develop a dyadic model to account for physical mechanisms commonly found in physical-statistical models and apply our methods to ancient human DNA data to infer the mechanisms that affected human movement in Bronze Age Europe.

A Dual-Dimension Convolutional-Attention Module for Remaining Useful Life Prediction of Aeroengines

Remaining useful life (RUL) prediction of aeroengines not only enhances aviation safety and operational efficiency but also significantly lowers operational costs, offering substantial economic and social benefits to the aviation industry. Aiming at RUL prediction, this paper proposes a novel dual-dimension convolutional-attention (DDCA) mechanism. DDCA consists of two branches: one includes channel attention and spatial attention mechanisms, while the other applies these mechanisms to the inverted dimensions. Pooling and feature-wise pooling operations are employed to extract features from different dimensions of the input data. These branches operate in parallel to capture more complex temporal and spatial feature correlations in multivariate time series data. Subsequently, an end-to-end DDCA-TCN network is constructed by integrating DDCA with a temporal convolutional network (TCN) for RUL prediction. The proposed prediction model is evaluated using the C-MAPSS dataset and compared to several state-of-the-art RUL prediction models. The results show that the RMSE and SCORE metrics of DDCA-TCN decreased by at least 12.8% and 4.6%, respectively, compared to other models on the FD002 subset, and by at least 10.6% and 18.4%, respectively, on the FD004 subset, which demonstrates that the DDCA-TCN model exhibits excellent performance in RUL prediction, particularly under multiple operating conditions.

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network’s attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average.

Multi-attribute balanced dataset generation framework AutoSyn and KinFace Channel-Spatial Feature Extractor for kinship recognition

In the field of kinship verification, facial recognition technology is becoming increasingly vital due to privacy concerns, ethical disputes, and the high costs associated with DNA testing. We have developed a novel method, the AutoSyn framework, to synthesize facial images and enhance kinship image datasets, effectively addressing the challenges of scale and quality in existing datasets. By employing a strategy that mixes ages and genders in the synthesized images, we minimize the impact of these factors on kinship recognition tasks. We have enhanced the original KinFaceW-I dataset by integrating ten distinct styles, including diverse combinations of gender, ethnicity, and age. This enrichment significantly improves both the quality and quantity of the images. Furthermore, this paper introduces an efficient feature extractor for kinship tasks, KinFace-CSFE, within a siamese neural network framework. This model not only utilizes meticulously designed channel feature extraction but also incorporates mixed kernel size spatial attention mechanisms to better focus on local features. We have also integrated YOCO data augmentation techniques to simulate complex imaging conditions, enhancing the model’s robustness and accuracy. The effectiveness of these innovations has been validated through experiments on the KinFaceW-I, KinFaceW-II, and synthesized Syn-KinFaceW-I datasets, achieving accuracy rates of 82.7%, 94.1%, and 83.2% respectively. These results significantly surpass both traditional models and current advanced models.

A 3-DOF spreading precision positional stage for ductile end-face fly-cutting of quartz glass

Ductile removal is widely employed to eliminate subsurface damage in brittle materials. Achieving this requires the cutting depth to be set extremely low, presenting significant challenges for error compensation and precise feeding of the machine tool. In this paper, a novel three-degree-of-freedom spreading precision positioning stage is developed to mitigate the effects of workpiece deflection errors and feed resolution on the depth of cut during the end-face fly-cutting process. First, a bridge and half-bridge composite structure is designed to facilitate the planar spreading of the spatial motion mechanism. A mathematical model of the composite structure is developed based on elastic beam theory. Second, the effects of various structural parameters on the amplification ratio of the structure are investigated. The accuracy of the theoretical model is verified by finite element analysis. Finally, ductile fly-cutting experiments on quartz glass are conducted using a precision 5-axis machine tool.

Research on Image Colorization Based on Deep Learning

Abstract Gray scale picture colorization, a research hotspot in the domain of computer vision, seeks to assign reasonable colours to every single pixel of a grayscale picture, enriching the visual information of the coloured picture. Neural networks take grayscale images as input and output colorized images. From a psychological perspective, colours can provide observers with a more pleasant perceptual experience. Old photos can also be coloured, which are important documentary resources for recording real history and restoring the social landscape of the time. Additionally, due to the limitations of imaging mechanisms, most medical images are grayscale. Therefore, research on grayscale image colorization is a task of significant importance. However, existing automatic image colorization systems have issues such as “blurry boundaries”, “colour overflow”, “inappropriate colour choices” and “incorrect colour regions”. This paper proposes a model combining adversarial neural networks and U-Net networks, optimizing them by incorporating global feature fusion modules, Pathway emphasis mechanisms and spatial focus mechanisms. The idea of Markov discriminator was added to the discriminator, and the loss function was optimized. The ultimate investigative conclusions demonstrate that the PSNR and SSIM of the suggested system are 24.45 and 0.941, accordingly, in relation to the preceding protocol, which are 3% and 5% higher than the previous algorithm, and the example has been ameliorated to some degree. And the loss function is modified, and the final algorithm can improve the effect of picture colouring, which encompasses a broad spectrum of application prospects.

Spatial toughening mechanism of ZrB2-SiC spiral fibers composites up to 1500 °C via SEVNB with femtosecond lasers and 3D-XRM methods

Spatial toughening mechanism of ZrB2-SiC spiral fibers composites up to 1500 °C via SEVNB with femtosecond lasers and 3D-XRM methods

Cloudformer-CycleGAN: an efficient cloud removal network integrating residual learning and channel spatial attention mechanism

Cloudformer-CycleGAN: an efficient cloud removal network integrating residual learning and channel spatial attention mechanism

Factors Affecting Stimulus Duration Threshold for Depth Discrimination of Asynchronous Targets in the Intermediate Distance Range.

Binocular depth discrimination in the near distance range (< 2 m) improves with stimulus duration. However, whether the same response-pattern holds in the intermediate distance range (approximately 2-25 m) remains unknown because the spatial coding mechanisms are thought to be different. We used the two-interval forced choice procedure to measure absolute depth discrimination of paired asynchronous targets (3, 6, or 16 arc min). The paired targets (0.2 degrees) were located over a distance and height range, respectively, of 4.5 to 7.0m and 0.15 to 0.7 m. Experiment 1 estimated duration thresholds for binocular depth discrimination at varying target durations (40-1610 ms), in the presence of a 2 × 6 array of parallel texture-elements spanning 1.5 × 5.83 m on the floor. The texture-elements provided a visible background in the light-tight room (9 × 3 m). Experiment 2 used a similar setup to control for viewing conditions: binocular versus monocular and with versus without texture background. Experiment 3 compared binocular depth discrimination between brief (40, 80, and 125 ms) and continuous texture background presentation. Stimulus duration threshold for depth discrimination decreased with increasing disparity in experiment 1. Experiment 2 revealed depth discrimination performance with texture background was near chance level with monocular viewing. Performance with binocular viewing degraded without texture background. Experiment 3 showed continuous texture background presentation enhances binocular depth discrimination. Absolute depth discrimination improves with target duration, binocular viewing, and texture background. Performance further improved with longer background duration underscoring the role of ground surface representation in spatial coding.

Salinity-Induced Photorespiration in Populus Vascular Tissues Facilitate Nitrogen Reallocation.

Adaptation to abiotic stress is critical for the survival of perennial tree species. Salinity affects plant growth and productivity by interfering with major biosynthetic processes. Detrimental effects of salinity may vary between different plant tissues and cell types. However, spatial molecular mechanisms controlling plant responses to salinity stress are not yet thoroughly understood in perennial trees. We used laser capture microdissection in clones of Populus tremula x alba to isolate palisade and vascular cells of intermediary leaf from plants exposed to 150 mM NaCl for 10 days, followed by a recovery period. Cell-specific changes in proteins and metabolites were determined. Salinity induced a vascular-specific accumulation of proteins associated with photorespiration, and the accumulation of serine, 3-phosphoglycerate and NH4 + suggesting changes in N metabolism. Accumulation of the GLUTAMINE SYNTHETASE 2 protein, and increased GS1.1 gene expression, indicated that NH4 + produced in photorespiration was assimilated to glutamine, the main amino acid translocated in Populus trees. Further analysis of total soluble proteins in stems and roots showed the accumulation of bark storage proteins induced by the salinity treatments. Collectively, our results suggest that the salt-induced photorespiration in vascular cells mediates N-reallocation in Populus, an essential process for the adaptation of trees to adverse conditions.

Electric Field Features and Charge Behavior in Oil-Pressboard Composite Insulation under Impulse Voltage

Oil-pressboard/paper insulation materials are essential in transformers for ensuring their safe and stable operation, primarily due to their roles in spatial electric field distribution and charge migration mechanisms. Current spatial distribution analyses rely on computational methods that lack empirical validation, particularly for oil-pressboard/paper composites. This study leverages the principles of the Kerr electro-optic effect to develop a rapid measurement platform for electric fields within oil-pressboard/paper insulation under impulse voltage conditions, which measures the spatial electric field characteristics using Cu-Cu and Al-Al electrodes under various scenarios: with asymmetric and symmetric pressboard coverage and different numbers of insulating paper layers. Findings indicated: (1) In asymmetric pressboard models, Cu-Cu electrodes exhibit a consistent peak electric field of approximately 16 kV/mm, while Al-Al electrodes show peak values of 18.13 kV/mm and −14.98 kV/mm. Charge density patterns are similar, with Cu-Cu at about 68 μC/m2 and Al-Al at 11.2 μC/m2 and −124.8 μC/m2. (2) Symmetric models present consistent peak electric fields and charge densities for both polarities. (3) Increasing insulating paper layers elevates electric field strengths. Both electrodes show the similar peak field of about 17 kV/mm with differing paper layers due to higher charge injection from the Al electrode. (4) Utilizing the Schottky effect and field emission principles, the study clarifies charge generation and migration mechanisms. These insights could provide a theoretical foundation for designing and verifying oil-pressboard/paper insulation structures in transformers.

DWTN: deep wavelet transform network for lightweight single image deraining

On rainy days the uncertainty of the shape and distribution of rain streaks can cause the images captured by RGB image-based measurement tools to be blurred and distorted. Thanks to the wavelet transform ability to provide spatial and frequency domain information about an image and its multidirectional and multiscale nature, it is widely used in traditional image enhancement methods. In image deraining the distribution of rain streaks is not only related to spatial domain features but is also closely related to frequency domain spatial features. However, deep learning-based rain removal models mainly rely on the spatial features of the image, and RGB data can hardly distinguish rain marks from image details, which leads to the loss of crucial image information during rain removal. We have developed a lightweight single-image rain removal model called the deep wavelet transform network (DWTN) to address this limitation. This method separates image details from rain images and can more effectively remove rain marks. The proposed DWTN has three significant contributions. First, DWTN uses the feature components after the wavelet transform as the input to the model and assigns a separate frequency-aware enhancement block (FAEB) to each element. These blocks focus on specific frequency features that benefit the rain removal task. Second, we introduce a frequency feature fusion block (FFFB) that fuses different wavelet components to reduce noise and enhance the image background through a channel attention mechanism while attenuating rain streaks. Finally, we design a spatial feature enhancement block (SFEB), which uses a spatial attention mechanism to calibrate the spatial position of features to improve the rain removal performance. We evaluate the performance of DWTN using PSNR and SSIM on four synthetic datasets and NIQE and BRISQUE on two real datasets. The results of the evaluation of the six datasets and at least four performance metrics show that the proposed DWTN is superior to existing methods.

Design and parameter optimization of solid-surface antenna parallel mechanism constructed by multi-loop spatial coupling units

The optimization of design and structural parameters of solid-surface antennas is crucial for the development of aerospace satellites. In this paper, the study explores an innovative parallel solid-surface antenna mechanism, inspired by the daisy bloom pattern, which realizes the characteristics of Fewer input-More output (Fi-Mo). A single-DOF deployable/foldable spatial multi-loop coupled mechanism model of the bionic daisy solid-surface antenna is established based on the single-axis tilting hinge, RSSR mechanism, and Bennett mechanism. The motion characteristics of the antenna mechanism are analyzed using the topology, position and orientation characteristic (POC) and inverse screw theory. The error transfer law of multi-loop mechanism is derived by the low-layer-loop sequence. The optimal configuration parameter set of the antenna deployment mechanism is determined using the NSGA-II genetic algorithm. Finally, the optimized antenna mechanism is subjected to physical simulation analysis to verify its stability and the accuracy of its deployment path.

Differential spatial responses and assembly mechanisms of soil microbial communities across region-scale Taiga ecosystems

Different soil microbial communities play distinct key roles in regulating forest ecosystem processes and functions. However, the differences in spatial variability and assembly mechanisms of various taiga forest soil microbial taxa remain poorly understood. Here, we assessed the spatial patterns of bacterial and fungal communities, their assembly processes, and the influencing factors in taiga forest ecosystems in Xinjiang, China. A significant distance decay pattern was observed in the similarity of bacterial and fungal communities, with bacterial communities exhibiting a more pronounced pattern than fungal communities. Stochastic and deterministic processes governed together to drive soil bacterial community assembly, whereas stochastic processes dominated fungal community assembly. The coexistence networks revealed that the interactions of bacterial and fungal networks in the four regions are primarily based on interspecies symbiosis, with fungal coexistence networks demonstrating greater stability than bacterial networks. Additionally, the study identified a positive relationship between the modularity of bacterial networks and dispersal limitation. Analysis of environmental factors revealed that soil pH primarily affects the characteristics and assembly mechanisms of bacterial communities, while vegetation conditions primarily affect fungal diversity and composition, with other unconsidered environmental variables influencing the fungal community assembly process. This study emphasized the distinct ways in which bacteria and fungi respond to environmental factors and interspecies interactions. Our results suggested that distinct restoration measures should be implemented for bacteria and fungi in future conservation efforts for forest soil microorganisms.