Creep evaluation and temperature dependence in self-sensing micro carbon polymer-based composites for further development as an Internet of Things Sensor device

2021 ◽  
pp. 002199832110588
Author(s):  
Miguel Tomás ◽  
Said Jalali ◽  
Alexandre Silva de Vargas
Keyword(s):  
Carbon Fiber ◽  
Internet Of Things ◽  
Electrical Resistance ◽  
Latent Variable ◽  
Fiber Content ◽  
Sensitivity Factor ◽  
Time Data ◽  
Variable Model ◽  
Arrhenius Function ◽  
Further Development

This article investigates the dependency of temperature on electrical resistance (R) change in micro carbon fiber polymer composites (MCFPC), for further development as an Internet of Things sensor from previous research works. Three mixtures were prepared using Dow Corning’s Silastic 145 as base polymer and made vary fiber content weight percentages: fiber diameter to length ratio ∅⁄l 0.13 and carbon fiber content of 13%; ∅⁄l:0.66 and carbon fiber contents of 40% and 50%. Composites tested were submitted to temperature loading, with a constant strain of 0.0%, for assessment of R when a change in the composite’s temperature occurs. The composite response was observed to follow an Arrhenius function, for temperatures ranging from −10°C to 40°C. The apparent activation energy was calculated to evaluate further differences between carbon fiber contents and the sensitivity factor, [Formula: see text] due to temperature is determined. The specimens were also tested with a constant strain of 2.86% to assess creep. It was found that creep and R, over the period of time in the analysis, best fit a discrete latent variable model. The sensitivity factor change is determined in regard to stress relaxation, [Formula: see text]. The properties of MCFPC investigated here can be used to establish relationships between electrical resistance outputs and environmental loading conditions for this type of composites, enabling the possibility of deployment as part of a management system network for structural monitoring with real-time data acquisition.

scholarly journals Graph-based service recommendation in Social Internet of Things

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110090
Author(s):  
Yuanyi Chen ◽  
Yanyun Tao ◽  
Zengwei Zheng ◽  
Dan Chen
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Latent Variable ◽  
Smart Cities ◽  
Knowledge Graph ◽  
Service Recommendation ◽  
Variable Model ◽  
Completion Problem ◽  
Social Internet Of Things ◽  
Object Usage

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

scholarly journals Multifunctional Braided Composite Rods for Civil Engineering Applications

2010 ◽  
Vol 123-125 ◽  
pp. 149-152 ◽  
Author(s):  
Emilijia Zdraveva ◽  
Cristiana Gonilho-Pereira ◽  
Raul Manuel Esteves Sousa Fangueiro ◽  
Senentxu Lanceros-Méndez ◽  
Saíd Jalali ◽  
...  
Keyword(s):  
Carbon Fiber ◽  
Carbon Fibers ◽  
Electrical Resistance ◽  
Fiber Content ◽  
Resistance Change ◽  
Reinforced Composite ◽  
Composite Rod ◽  
Reinforcing Material ◽  
Concrete Elements ◽  
Versus Strain

This paper presents the development of a braided reinforced composite rod (BCR) able to both reinforce and monitor the stress state of concrete elements. Carbon fibers have been used as sensing and reinforcing material along with glass fiber. Various composites rods have been produced using an author patented technique based on a modified conventional braiding machine. The materials investigated were prepared with different carbon fiber content as follows: BCR2 (77% glass/23% carbon fiber), BCR3 (53% glass/47% carbon fiber), BCR4 (100% carbon fiber). BCRs have been tested under bending while the variation of the electrical resistance was simultaneously monitored. The correlations obtained between deformation and electrical resistance show the suitability of the rods to be used as sensors. The fractional resistance change versus strain plots show that the gage factor increases with decreasing carbon fiber content.

A diversified shared latent variable model for efficient image characteristics extraction and modelling

2021 ◽  
Vol 421 ◽  
pp. 244-259
Author(s):  
Hao Xiong ◽  
Yuan Yan Tang ◽  
Fionn Murtagh ◽  
Leszek Rutkowski ◽  
Shlomo Berkovsky
Keyword(s):  
Latent Variable ◽  
Latent Variable Model ◽  
Variable Model ◽  
Image Characteristics

scholarly journals Efficient Dimensionality Reduction Methods in Reservoir History Matching

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3137
Author(s):  
Amine Tadjer ◽  
Reider B. Bratvold ◽  
Remus G. Hanea
Keyword(s):  
Data Assimilation ◽  
Dimensionality Reduction ◽  
Gaussian Process ◽  
Latent Variable ◽  
History Matching ◽  
Production Performance ◽  
Latent Variable Model ◽  
Variable Model ◽  
Multiple Data ◽  
Ensemble Smoother

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

scholarly journals Chinese Character Image Completion Using a Generative Latent Variable Model

2021 ◽  
Vol 11 (2) ◽  
pp. 624
Author(s):  
In-su Jo ◽  
Dong-bin Choi ◽  
Young B. Park
Keyword(s):  
Latent Variable ◽  
Chinese Character ◽  
Image Inpainting ◽  
Latent Variable Model ◽  
Chinese Characters ◽  
Image Completion ◽  
Variable Model ◽  
Variational Autoencoder ◽  
Convolution Model ◽  
Detection Technologies

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.

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