Liqiong Chen¹, Kui Zhao¹, Kai Zhang^1,*, Duo Xv¹, Hongxvan Hu², Guoguang Ma¹, Wenwen Zhan³

Energy Engineering, Vol.121, No.8, pp. 2143-2165, 2024, DOI:10.32604/ee.2024.049362

Abstract The leakage gas from a buried natural gas pipelines has the great potential to cause economic losses and environmental pollution owing to the complexity of the mountainous environment. In this study, computational fluid dynamics (CFD) method was applied to investigate the diffusion law and hazard range of buried natural gas pipeline leakage in mountainous environment. Based on cloud chart, concentration at the monitoring site and hazard range of lower explosion limit (LEL) and upper explosion limit (UEL), the influences of leakage hole direction and shape, soil property, burial depth, obstacle type on the diffusion law… More >

Yaru Song^*, Huahu Xu, Dikai Fang

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 397-416, 2024, DOI:10.32604/iasc.2023.040521

Abstract Visual Question Answering (VQA) has sparked widespread interest as a crucial task in integrating vision and language. VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input questions. The detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more effectively. However, it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details, which is the advantage of grid-based features. In… More >

Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, Rahim Shah Khan⁵, Saifullah Jan², Naeem ur Rahman⁶

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340

Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

Lirong Yin¹, Lei Wang¹, Siyu Lu^2,*, Ruiyang Wang², Haitao Ren², Ahmed AlSanad³, Salman A. AlQahtani³, Zhengtong Yin⁴, Xiaolu Li⁵, Wenfeng Zheng^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2315-2347, 2024, DOI:10.32604/cmes.2024.050853

Abstract At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel More >

Hongchi Liu¹, Xing Deng^1,*, Haijian Shao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737

Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

Nadezhda Zubova^*, Andrey Ivantsov

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1407-1424, 2024, DOI:10.32604/fdmp.2024.051130

Abstract The study deals with the numerical modeling of leachate distribution in the porous medium located under a municipal solid waste disposal landfill (MSWLF). The considered three-layer system is based on geological data obtained from field measurements. For simplicity, the problem is investigated by assuming a two-component approach. Nevertheless, the heat produced by landfills due to biological and chemical processes and the thermal diffusion mechanism contributing to pollution transport are taken into account. The numerical modeling of the propagation of leachate in the considered layered porous medium is implemented for parameters corresponding to natural soil and More >

Yuting Zhou¹, Xuemei Yang¹, Junping Yin^2,3,4,*, Shiqi Liu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5313-5333, 2024, DOI:10.32604/cmc.2024.052060

Abstract Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature… More >

Ragini Thatikonda¹, Prakash Kodali^1,*, Ramalingaswamy Cheruku², Eswaramoorthy K.V³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4683-4702, 2024, DOI:10.32604/cmc.2024.051844

Abstract In the realm of low-level vision tasks, such as image deraining and dehazing, restoring images distorted by adverse weather conditions remains a significant challenge. The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks (CNNs), supplanting traditional methods reliant on prior knowledge. However, the evolution of CNN architectures has tended towards increasing complexity, utilizing intricate structures to enhance performance, often at the expense of computational efficiency. In response, we propose the Selective Kernel Dense Residual M-shaped Network (SKDRMNet), a flexible solution adept at balancing computational efficiency with network accuracy. A… More >

Xuejie Wang¹, Jianxun Zhang^1,*, Ye Tao², Xiaoli Yuan¹, Yifan Guo¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4621-4639, 2024, DOI:10.32604/cmc.2024.051556

Abstract While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity… More >

Xiaofang Jin¹, Yuying Yang^1,*, Yinan Wu¹, Ying Xu²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5225-5242, 2024, DOI:10.32604/cmc.2024.050384

Abstract The emergence of new media in various fields has continuously strengthened the social aspect of social media. Netizens tend to express emotions in social interactions, and many people even use satire, metaphors, and other techniques to express some negative emotions, it is necessary to detect sarcasm in social comment data. For sarcasm, the more reference data modalities used, the better the experimental effect. This paper conducts research on sarcasm detection technology based on image-text fusion data. To effectively utilize the features of each modality, a feature reconstruction output algorithm is proposed. This algorithm is based… More >