Search Results (4,121)

Multimodal change detection (MCD) harnesses multi-source remote sensing data to identify surface changes, thereby presenting prospects for applications within disaster management and environmental surveillance. Nonetheless, disparities in imaging mechanisms across various modalities impede the direct comparison of multimodal images. In response, numerous methodologies employing deep learning features have emerged to derive comparable features from such images. Nevertheless, several of these approaches depend on manually labeled samples, which are resource-intensive, and their accuracy in distinguishing changed and unchanged regions is not satisfactory. In addressing these challenges, a new MCD method based on iterative optimization-enhanced contrastive learning is proposed in this paper. With the participation of positive and negative samples in contrastive learning, the deep feature extraction network focuses on extracting the initial deep features of multimodal images. The common projection layer unifies the deep features of two images into the same feature space. Then, the iterative optimization module expands the differences between changed and unchanged areas, enhancing the quality of the deep features. The final change map is derived from the similarity measurements of these optimized features. Experiments conducted across four real-world multimodal datasets, benchmarked against eight well-established methodologies, incontrovertibly illustrate the superiority of our proposed approach. Full article

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity. Full article

Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator’s ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively. Full article

Backgroud/Objectives: Ferula spp. is an essential crop in Central Asia with pronounced economic benefits governed by its flowering process. However, the mechanisms of the flowering phenotype remain unclear. Methods: In this study, using F. sinkiangensis as a model plant, we integrated transcriptome, proteome, and metabolome analyses to compare the multilayer differences in leaves and roots of plants with flowering and unflowering phenotypes. Results: We found that several variations in the transcriptome, proteome, and metabolome were closely associated with flowering. The Photosynthesis and Phenylpropanoid biosynthesis pathways in plants with the flowering phenotype were more active. Additionally, three flowering genes, named FL2–FL4, were upregulated in the leaves of flowering plants. Notably, six transcription factors were potentially responsible for regulating the expression of FL2–FL4 in the leaves to mediate flowering process of F. sinkiangensis. Moreover, genes relevant to Photosynthesis and Phenylpropanoid biosynthesis were also involved in regulating the expression of FL2–FL4 in flowering plants. Conclusions: The active regulation network together with Photosynthesis and Phenylpropanoid biosynthesis were essential for inducing the expression of flowering-related genes in leaves to promote the flowering process of F. sinkiangensis. Full article

The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified crucial design parameters that directly affect the design and construction of bridge decks. This analysis determines the critical design variables to improve the deck’s energy efficiency, providing practical guidance for engineers and professionals in the field. The methods analyzed in this study are ordinary Kriging and a multilayer perceptron neural network. The methodology involves analyzing the predictive performance of both models through error analysis and assessing their ability to identify local optima on the response surface. The results show that both models generally overestimate the observed values. The Kriging model with second-order polynomials yields a 4% relative error at the local optimum, while the neural network achieves lower root mean square errors (RMSEs). Neither the Kriging model nor the neural network provides precise predictions but point to promising solution regions. Optimizing the response surface to find a local minimum is crucial. High slenderness ratios (around 1/28) and 40 MPa concrete grade are recommended to improve energy efficiency. Full article

Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions. Full article

Time-series forecasting represents of one of the most challenging and widely studied research areas in both academic and industrial communities. Despite the recent advancements in deep learning, the prediction of future time-series values remains a considerable endeavor due to the complexity and dynamic nature of time-series data. In this work, a new prediction model is proposed, named C-KAN, for multi-step forecasting, which is based on integrating convolutional layers with Kolmogorov–Arnold network architecture. The proposed model’s advantages are (i) the utilization of convolutional layers for learning the behavior and internal representation of time-series input data; (ii) activation at the edges of the Kolmogorov–Arnold network for potentially altering training dynamics; and (iii) modular non-linearity for allowing the differentiated treatment of features and potentially more precise control over inputs’ influence on outputs. Furthermore, the proposed model is trained using the DILATE loss function, which ensures that it is able to effectively deal with the dynamics and high volatility of non-stationary time-series data. The numerical experiments and statistical analysis were conducted on five challenging non-stationary time-series datasets, and provide strong evidence that C-KAN constitutes an efficient and accurate model, well suited for time-series forecasting tasks. Full article

The article focusses on detecting the unbalance of a mechanical component in the electric drive system of a two-mass servomechanism with a permanent magnet synchronous motor (PMSM), which is connected to the load via a long, flexible shaft. In the example analysed, the degree of unbalance was determined using the reference current signal from the speed controller of the field-orientated control (FOC) system. The authors presented a two-mass model with an unbalanced mechanical system. The short-time Fourier transform (STFT) transform was used to analyse the symptoms of unbalance, and an artificial neural network multi-layer perceptron (MLP) was used for system state inference. The effectiveness of the presented analysis, based on the reference current signal from the sensor embedded in the control system, was experimentally confirmed. Full article

China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science. Full article

In recent years, new media have exacerbated the complexity of online public opinion scenarios through fragmentation of information, diversification of public opinion, rapid diffusion of public opinion, and concealment of information sources, which have posed several serious challenges to the benign development of online public opinion ecosystems. Therefore, based on diversified public opinion scenarios, we study the interaction between information dissemination and the evolution of group opinions and the dissemination laws to solve the problem of imprecise grasping of the dissemination laws in complex public opinion scenarios. Facing the two-way interaction between online platforms and real society, we constructed a coupled online–offline viewpoint evolution dynamics model, which considers factors such as the user subject level and the network environment level, and combines viewpoint dynamics theory with information dissemination dynamics theory. Based on the real case of dual interaction between online and offline, we carry out the construction of a two-layer coupling network and numerical fitting comparison experiments to study the synergistic and penetration mechanism of public opinion in both online and offline multi-spaces. Based on parametric analysis experiments, the influence of different factors on communication indicators is mined, and the driving effect of the viewpoint environment of offline communication on online public opinion is studied, which reveals the objective role of multi-factors on the law of intralayer communication, cross-network communication, and viewpoint evolution, and provides strategic suggestions for the comprehensive management of public opinion in online–offline large-scale mass incidents. Full article

During energy transition, where sustainability and environmental protection are increasingly prioritized, ensuring safety in coal exploitation remains a critical issue, especially in the context of worker safety. This research focuses on predicting methane concentrations in underground mines, which is vital for both safety and operational efficiency. The article presents a methodology developed to predict methane concentrations at specific points in mine workings using artificial neural networks. The core of this methodology is a forecasting model that allows for the selection and adjustment of the neural network to the phenomenon being studied. This model, based on measurements of ventilation parameters, including methane concentrations in a given area, enables the prediction of gas concentrations at measurement points. The results indicate that with appropriate neural network selection and based on ventilation measurements, it is possible to forecast methane concentrations at acceptable levels in selected excavation points. The effectiveness of these forecasts depends on their timing and the input data to the model. The presented example of applying this methodology in a real mine working demonstrates its high efficiency. The best results were obtained for a 5 min forecast, with slightly less accuracy for longer times (10, 15, 30, and 60 min), though all results remained at an acceptable level. Therefore, it can be concluded that the developed methodology can be successfully applied in underground mining operations to forecast dangerous methane concentrations. Its implementation should improve mining efficiency by reducing instances of exceeding permissible methane concentrations and enhance occupational safety. Full article

Traditional U-shape segmentation models can achieve excellent performance with an elegant structure. However, the single-layer decoder structure of U-Net or SwinUnet is too “thin” to exploit enough information, resulting in large semantic differences between the encoder and decoder parts. Things get worse in the field of medical image processing, where annotated data are more difficult to obtain than other tasks. Based on this observation, we propose a U-like model named MS-UNet with a plug-and-play adaptive denoising module and ELoss for the medical image segmentation task in this study. Instead of the single-layer U-Net decoder structure used in Swin-UNet and TransUNet, we specifically designed a multi-scale nested decoder based on the Swin Transformer for U-Net. The proposed multi-scale nested decoder structure allows for the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features. In addition, ELoss could improve the attention of the model to the segmentation edges, and the plug-and-play adaptive denoising module could prevent the model from learning the wrong features without losing detailed information. The experimental results show that MS-UNet could effectively improve network performance with more efficient feature learning capability and exhibit more advanced performance, especially in the extreme case with a small amount of training data. Furthermore, the proposed ELoss and denoising module not only significantly enhance the segmentation performance of MS-UNet but can also be applied individually to other models. Full article

Object detection, as a crucial aspect of computer vision, plays a vital role in traffic management, emergency response, autonomous vehicles, and smart cities. Despite the significant advancements in object detection, detecting small objects in images captured by high-altitude cameras remains challenging, due to factors such as object size, distance from the camera, varied shapes, and cluttered backgrounds. To address these challenges, we propose small object detection YOLOv8 (SOD-YOLOv8), a novel model specifically designed for scenarios involving numerous small objects. Inspired by efficient generalized feature pyramid networks (GFPNs), we enhance multi-path fusion within YOLOv8 to integrate features across different levels, preserving details from shallower layers and improving small object detection accuracy. Additionally, we introduce a fourth detection layer to effectively utilize high-resolution spatial information. The efficient multi-scale attention module (EMA) in the C2f-EMA module further enhances feature extraction by redistributing weights and prioritizing relevant features. We introduce powerful-IoU (PIoU) as a replacement for CIoU, focusing on moderate quality anchor boxes and adding a penalty based on differences between predicted and ground truth bounding box corners. This approach simplifies calculations, speeds up convergence, and enhances detection accuracy. SOD-YOLOv8 significantly improves small object detection, surpassing widely used models across various metrics, without substantially increasing the computational cost or latency compared to YOLOv8s. Specifically, it increased recall from 40.1% to 43.9%, precision from 51.2% to 53.9%, mAP_0.5 from 40.6% to 45.1%, and mAP_0.5:0.95 from 24% to 26.6%. Furthermore, experiments conducted in dynamic real-world traffic scenes illustrated SOD-YOLOv8’s significant enhancements across diverse environmental conditions, highlighting its reliability and effective object detection capabilities in challenging scenarios. Full article

With the rapid growth in space-imaging demands, the scheduling problem of multiple agile optical satellites has become a crucial problem in the field of on-orbit satellite applications. Because of the considerable solution space and complicated constraints, the existing methods suffer from a huge computation burden and a low solution quality. This paper establishes a mathematical model of this problem, which aims to maximize the observation profit rate and realize the load balance, and proposes a multi-pointer network to solve this problem, which adopts multiple attention layers as the pointers to construct observation action sequences for multiple satellites. In the proposed network, a local feature-enhancement strategy, a remaining time-based decoding sorting strategy, and a feasibility-based task selection strategy are developed to improve the solution quality. Finally, extensive experiments verify that the proposed network outperforms the comparison algorithms in terms of solution quality, computation efficiency, and generalization ability and that the proposed three strategies significantly improve the solving ability of the proposed network. Full article

Black soil is a precious soil resource, yet it is severely affected by gully erosion, which is one of the most serious manifestations of land degradation. The determination of the location and shape of gullies is crucial for the work of gully erosion control. Traditional field measurement methods consume a large amount of human resources, so it is of great significance to use artificial intelligence techniques to automatically extract gullies from satellite remote sensing images. This study obtained the gully distribution map of the southwestern region of the Dahe Bay Farm in Inner Mongolia through field investigation and measurement and created a gully remote sensing dataset. We designed a multi-scale content structure feature extraction network to analyze remote sensing images and achieve automatic gully extraction. The multi-layer information obtained through the resnet34 network is input into the multi-scale structure extraction module and the multi-scale content extraction module designed by us, respectively, obtained richer intrinsic information about the image. We designed a structure content fusion network to further fuse structural features and content features and improve the depth of the model’s understanding of the image. Finally, we designed a muti-scale feature fusion module to further fuse low-level and high-level information, enhance the comprehensive understanding of the model, and improve the ability to extract gullies. The experimental results show that the multi-scale content structure feature extraction network can effectively avoid the interference of complex backgrounds in satellite remote sensing images. Compared with the classic semantic segmentation models, DeepLabV3+, PSPNet, and UNet, our model achieved the best results in several evaluation metrics, the F1 score, recall rate, and intersection over union (IoU), with an F1 score of 0.745, a recall of 0.777, and an IoU of 0.586. These results proved that our method is a highly automated and reliable method for extracting gullies from satellite remote sensing images, which simplifies the process of gully extraction and provides us with an accurate guide to locate the location of gullies, analyze the shape of gullies, and then provide accurate guidance for gully management. Full article