150 research outputs found
Experimental and Numerical Investigations on Flexural Behaviour of Prestressed Textile Reinforced Concrete Slabs
Nowadays, concrete is mostly prestressed with steel. But the application of prestressing steel is restricted in a highly corrosive environment area due to corrosion of prestressing steel, leading to a reduction in strength and may cause sudden failure. Carbon textile is considered an alternate material due to its corrosive resistance property, high tensile strength, and perfectly elastic. Prestressing is also the only realistic way to utilize fully ultra-high tensile strength in carbon textile material. In this study, experimental and numerical analyses were carried out for the flexural behaviour of prestressed and non-prestressed carbon textile reinforced concrete slabs. This study also focuses on the influences of textile reinforcement ratios, prestressing grades on the flexural behaviour of carbon textile reinforced concrete (TRC). Fifteen precast TRC slabs were tested, of which six were prestressed to various levels with carbon textile. The obtained results show that prestressing textile reinforcement results in a higher load-bearing capacity, stiffness, and crack resistance for TRC slabs. The first-crack load of the prestressed specimens increased by about 85% compared with those of non-prestressed slabs. Three-dimensional finite element models were developed to provide a reliable estimation of global and local response. The modeling techniques accurately reproduced the experimental behaviour. Doi: 10.28991/cej-2021-03091712 Full Text: PD
Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM: Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM
This paper presents an avoid deadlock resource allocation (ADRA) for model V VM-out-of-N PM since cloud computing is a new computing paradigm composed of grid computing, distributed computing and utility concepts. Cloud computing presents a different resource allocation paradigm than either grids or distributed systems. Cloud service providers dynamically scale virtualized computing resources as a service over the internet. Due to variable number of users and limited resources, cloud is prone to deadlock at very large scale. Resource allocation and the associated deadlock avoidance is problem originated in the design and the implementation of the distributed computing, grid computing. In this paper, a new concept of free space cloud is proposed to avoid deadlock by collecting available free resource from all allocated users. New algorithms are developed for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform. An experiment is tested in CloudSim. The performance of resource pool manager is evaluated by using CloudSim and resource utilization and indicating good results
An effective method for clustering-based web service recommendation
Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets
Enhancing Few-shot Image Classification with Cosine Transformer
This paper addresses the few-shot image classification problem, where the
classification task is performed on unlabeled query samples given a small
amount of labeled support samples only. One major challenge of the few-shot
learning problem is the large variety of object visual appearances that
prevents the support samples to represent that object comprehensively. This
might result in a significant difference between support and query samples,
therefore undermining the performance of few-shot algorithms. In this paper, we
tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the
relational map between supports and queries is effectively obtained for the
few-shot tasks. The FS-CT consists of two parts, a learnable prototypical
embedding network to obtain categorical representations from support samples
with hard cases, and a transformer encoder to effectively achieve the
relational map from two different support and query samples. We introduce
Cosine Attention, a more robust and stable attention module that enhances the
transformer module significantly and therefore improves FS-CT performance from
5% to over 20% in accuracy compared to the default scaled dot-product
mechanism. Our method performs competitive results in mini-ImageNet, CUB-200,
and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and
few-shot configurations. We also developed a custom few-shot dataset for Yoga
pose recognition to demonstrate the potential of our algorithm for practical
application. Our FS-CT with cosine attention is a lightweight, simple few-shot
algorithm that can be applied for a wide range of applications, such as
healthcare, medical, and security surveillance. The official implementation
code of our Few-shot Cosine Transformer is available at
https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme
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