Search Results (6)

The production and servicing of cement-based building materials is a source of large amounts of carbon dioxide emissions globally. One of the ways to reduce its negative impact, is to reduce concrete consumption per cubic meter of building structure through the introduction of hollow concrete products. At the same time, to maintain the load-bearing capacity of the building structure, it is necessary to significantly increase the strength of the concrete used. However, an increase in strength should be achieved not by increasing cement consumption, but by increasing the efficiency of its use. This research is focused on the development of technology for the production of thin-walled hollow concrete blocks based on high-strength, self-compacting, dispersed, micro-reinforced, fine-grained concrete. The use of this concrete provides 2–2.5 times higher strength in the amount of Portland cement consumed in comparison with ordinary concrete. The formation of external contours and partitions of thin-walled hollow blocks is ensured through the use of disposable formwork or cores used as void formers obtained by FDM 3D printing. This design solution makes it possible to obtain products based on high-strength concrete with higher structural and thermal insulation properties compared to now existing lightweight concrete-based blocks. Another area of application of this technology could be the production of wall structures of free configuration and cross-section due to their division, at the digital modeling stage, into individual element-blocks, manufactured in a factory environment. Full article

In edge–cloud collaboration scenarios, data sharing is a critical technological tool, yet smart devices encounter significant challenges in ensuring data-sharing security. Attribute-based keyword search (ABKS) is employed in these contexts to facilitate fine-grained access control over shared data, allowing only users with the necessary privileges to retrieve keywords. The implementation of secure data sharing is threatened since most of the current ABKS protocols cannot resist keyword guessing attacks (KGAs), which can be launched by an untrusted cloud server and result in the exposure of sensitive personal information. Using attribute-based encryption (ABE) as the foundation, we build a secure data exchange paradigm that resists KGAs in this work. In our paper, we provide a secure data-sharing framework that resists KGAs and uses ABE as the foundation to achieve fine-grained access control to resources in the ciphertext. To avoid malicious guessing of keywords by the cloud server, the edge layer computes two encryption session keys based on group key agreement (GKA) technology, which are used to re-encrypt the data user’s secret key of the keyword index and keyword trapdoor. The model is implemented using the JPBC library. According to the security analysis, the model can resist KGAs in the random oracle model. The model’s performance examination demonstrates its feasibility and lightweight nature, its large computing advantages, and lower storage consumption. Full article

Magnesium alloys, renowned for their lightweight yet high-strength characteristics, with exceptional mechanical properties, are highly coveted for numerous applications. The emergence of magnesium alloy additive manufacturing (Mg AM) has further propelled their popularity, offering advantages such as unparalleled precision, swift production rates, enhanced design freedom, and optimized material utilization. This technology holds immense potential in fabricating intricate geometries, complex internal structures, and performance-tailored microstructures, enabling groundbreaking applications. In this paper, we delve into the core processes and pivotal influencing factors of the current techniques employed in Mg AM, including selective laser melting (SLM), electron beam melting (EBM), wire arc additive manufacturing (WAAM), binder jetting (BJ), friction stir additive manufacturing (FSAM), and indirect additive manufacturing (I-AM). Laser powder bed fusion (LPBF) excels in precision but is limited by a low deposition rate and chamber size; WAAM offers cost-effectiveness, high efficiency, and scalability for large components; BJ enables precise material deposition for customized parts with environmental benefits; FSAM achieves fine grain sizes, low defect rates, and potential for precision products; and I-AM boasts a high build rate and industrial adaptability but is less studied recently. This paper attempts to explore the possibilities and challenges for future research in AM. Among them, two issues are how to mix different AM applications and how to use the integration of Internet technologies, machine learning, and process modeling with AM, which are innovative breakthroughs in AM. Full article

The need for pedestrian target detection in uncrewed aerial vehicle (UAV) remote sensing images has become increasingly significant as the technology continues to evolve. UAVs equipped with high-resolution cameras can capture detailed imagery of various scenarios, making them ideal for monitoring and surveillance applications. Pedestrian detection is particularly crucial in scenarios such as traffic monitoring, security surveillance, and disaster response, where the safety and well-being of individuals are paramount. However, pedestrian detection in UAV remote sensing images poses several challenges. Firstly, the small size of pedestrians relative to the overall image, especially at higher altitudes, makes them difficult to detect. Secondly, the varying backgrounds and lighting conditions in remote sensing images can further complicate the task of detection. Traditional object detection methods often struggle to handle these complexities, resulting in decreased detection accuracy and increased false positives. Addressing the aforementioned concerns, this paper proposes a lightweight object detection model that integrates GhostNet and YOLOv5s. Building upon this foundation, we further introduce the SPD-Conv module to the model. With this addition, the aim is to preserve fine-grained features of the images during downsampling, thereby enhancing the model’s capability to recognize small-scale objects. Furthermore, the coordinate attention module is introduced to further improve the model’s recognition accuracy. In the proposed model, the number of parameters is successfully reduced to 4.77 M, compared with 7.01 M in YOLOv5s, representing a 32% reduction. The mean average precision (mAP) increased from 0.894 to 0.913, reflecting a 1.9% improvement. We have named the proposed model “GSC-YOLO”. This study holds significant importance in advancing the lightweighting of UAV target detection models and addressing the challenges associated with complex scene object detection. Full article

Existing architecture semantic modeling methods in 3D complex urban scenes continue facing difficulties, such as limited training data, lack of semantic information, and inflexible model processing. Focusing on extracting and adopting accurate semantic information into a modeling process, this work presents a framework for lightweight modeling of buildings that joints point clouds semantic segmentation and 3D feature line detection constrained by geometric and photometric consistency. The main steps are: (1) Extraction of single buildings from point clouds using 2D-3D semi-supervised semantic segmentation under photometric and geometric constraints. (2) Generation of lightweight building models by using 3D plane-constrained multi-view feature line extraction and optimization. (3) Introduction of detailed semantics of building elements into independent 3D building models by using fine-grained segmentation of multi-view images to achieve high-accuracy architecture lightweight modeling with fine-grained semantic information. Experimental results demonstrate that it can perform independent lightweight modeling of each building on point cloud at various scales and scenes, with accurate geometric appearance details and realistic textures. It also enables independent processing and analysis of each building in the scenario, making them more useful in practical applications. Full article

Three-dimensional (3D) point cloud classification methods based on deep learning have good classification performance; however, they adapt poorly to diverse datasets and their classification accuracy must be improved. Therefore, FSDCNet, a neural network model based on the fusion of static and dynamic convolution, is proposed and applied for multiview 3D point cloud classification in this paper. FSDCNet devises a view selection method with fixed and random viewpoints, which effectively avoids the overfitting caused by the traditional fixed viewpoint. A local feature extraction operator of dynamic and static convolution adaptive weight fusion was designed to improve the model’s adaptability to different types of datasets. To address the problems of large parameters and high computational complexity associated with the current methods of dynamic convolution, a lightweight and adaptive dynamic convolution operator was developed. In addition, FSDCNet builds a global attention pooling, integrating the most crucial information on different view features to the greatest extent. Due to these characteristics, FSDCNet is more adaptable, can extract more fine-grained detailed information, and can improve the classification accuracy of point cloud data. The proposed method was applied to the ModelNet40 and Sydney Urban Objects datasets. In these experiments, FSDCNet outperformed its counterparts, achieving state-of-the-art point cloud classification accuracy. For the ModelNet40 dataset, the overall accuracy (OA) and average accuracy (AA) of FSDCNet in a single view reached 93.8% and 91.2%, respectively, which were superior to those values for many other methods using 6 and 12 views. FSDCNet obtained the best results for 6 and 12 views, achieving 94.6%, 93.3%, 95.3%, and 93.6% in OA and AA metrics, respectively. For the Sydney Urban Objects dataset, FSDCNet achieved an OA and F1 score of 81.2% and 80.1% in a single view, respectively, which were higher than most of the compared methods. In 6 and 12 views, FSDCNet reached an OA of 85.3% and 83.6% and an F1 score of 85.5% and 83.7%, respectively. Full article