scholarly journals Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7134
Author(s):  
Israr Ilyas ◽  
Adeel Zafar ◽  
Muhammad Faisal Javed ◽  
Furqan Farooq ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Construction Material ◽  
External Validation ◽  
Model Performance ◽  
Experimental Results ◽  
Confined Concrete ◽  
Sustainable Construction ◽  
Critical Parameters ◽  
Proposed Model ◽  
Multi Expression Programming

This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.

scholarly journals Modelagem de Tráfego de Redes Utilizando Cascata Multifractal Generalizada

2008 ◽  
Vol 15 (2) ◽  
pp. 87-104
Author(s):  
Flávio Henrique Teles Vieira ◽  
Lee Luan Ling
Keyword(s):  
Statistical Analysis ◽  
Model Validation ◽  
Model Performance ◽  
Traffic Model ◽  
Real Network ◽  
Behavior Study ◽  
Proposed Model ◽  
Probability Densities ◽  
Traffic Trace ◽  
Multiplicative Cascade

In this paper we propose a multifractal traffic model that is based on a multiplicative cascade presenting specific multiplier distributions in each cascade stage. In the proposed model, the multipliers are obtained through the estimate of their probability densities found in real network traffic by using Kernel and Acceptance/Rejection methods. Statistical analysis and queueing behavior study were carried out for the model validation. Furthermore, we verify the model performance in capturing the traffic trace characteristics in comparison to other multifractal models.

Connected-Tube MPP Model for Unsupervised 3D Fiber Detection

2020 ◽  
Vol 2020 (14) ◽  
pp. 305-1-305-6
Author(s):  
Tianyu Li ◽  
Camilo G. Aguilar ◽  
Ronald F. Agyei ◽  
Imad A. Hanhan ◽  
Michael D. Sangid ◽  
...  
Keyword(s):  
Composite Material ◽  
Point Process ◽  
Marked Point ◽  
Experimental Results ◽  
Marked Point Process ◽  
Fiber Reinforced Composite ◽  
Reinforced Composite ◽  
Proposed Model ◽  
Cylinder Model ◽  
Reinforced Composite Material

In this paper, we extend our previous 2D connected-tube marked point process (MPP) model to a 3D connected-tube MPP model for fiber detection. In the 3D case, a tube is represented by a cylinder model with two spherical areas at its ends. The spherical area is used to define connection priors that encourage connection of tubes that belong to the same fiber. Since each long fiber can be fitted by a series of connected short tubes, the proposed model is capable of detecting curved long tubes. We present experimental results on fiber-reinforced composite material images to show the performance of our method.

scholarly journals Wireless distance measurement by means of ultra-wideband chaotic radio pulses

2019 ◽  
Vol 30 ◽  
pp. 12005
Author(s):  
Elena Efremova ◽  
Alexander Dmitriev ◽  
Lev Kuzmin ◽  
Manvel Petrosyan
Keyword(s):  
Statistical Analysis ◽  
Distance Measurement ◽  
Experimental Results ◽  
Ultra Wideband

A method for wireless distance measurement using ultrawideband chaotic radio pulses based on statistical analysis is proposed. Experimental results are discussed.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Leaching Behaviour of Construction and Demolition Wastes and Recycled Aggregates: Statistical Analysis Applied to the Release of Contaminants

2021 ◽  
Vol 11 (14) ◽  
pp. 6265
Author(s):  
Alessandra Diotti ◽  
Giovanni Plizzari ◽  
Sabrina Sorlini
Keyword(s):  
Particle Size ◽  
Statistical Analysis ◽  
Raw Materials ◽  
Primary Source ◽  
Recycled Aggregates ◽  
Critical Parameters ◽  
Material Particle ◽  
Analysis Methodology ◽  
Construction And Demolition Wastes ◽  
Leaching Behaviour

Construction and demolition wastes represent a primary source of new alternative materials which, if properly recovered, can be used to replace virgin raw materials partially or totally. The distrust of end-users in the use of recycled aggregates is mainly due to the environmental performance of these materials. In particular, the release of pollutants into the surrounding environment appears to be the aspect of greatest concern. This is because these materials are characterized by a strong heterogeneity which can sometimes lead to contaminant releases above the legal limits for recovery. In this context, an analysis of the leaching behaviour of both CDWs and RAs was conducted by applying a statistical analysis methodology. Subsequently, to evaluate the influence of the particle size and the volumetric reduction of the material on the release of contaminants, several experimental leaching tests were carried out according to the UNI EN 12457-2 and UNI EN 12457-4 standards. The results obtained show that chromium, mercury, and COD are the most critical parameters for both CDWs and RAs. Moreover, the material particle size generally affects the release of contaminants (i.e., finer particles showed higher releases), while the crushing process does not always involve higher releases than the sieving process.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
K Lakshmanan ◽  
Sonal Saxena ◽  
Sameer Shrivastava
Keyword(s):  
Differential Privacy ◽  
Model Performance ◽  
Healthcare Services ◽  
Cloud Services ◽  
Efficient Computation ◽  
Sensitive Data ◽  
Classification Framework ◽  
Proposed Model ◽  
Diagnostic Framework ◽  
Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

scholarly journals Postheated Model of Confined High Strength Fibrous Concrete

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Kaleem A. Zaidi ◽  
Umesh K. Sharma ◽  
N. M. Bhandari ◽  
P. Bhargava
Keyword(s):  
High Temperature ◽  
Volume Fraction ◽  
Fibre Volume Fraction ◽  
High Strength ◽  
Fibre Volume ◽  
Experimental Results ◽  
Confined Concrete ◽  
Structural Safety ◽  
Stress Strain ◽  
Fibrous Concrete

HSC normally suffers from low stiffness and poor strain capacity after exposure to high temperature. High strength confined fibrous concrete (HSCFC) is being used in industrial structures and other high rise buildings that may be subjected to high temperature during operation or in case of an accidental fire. The proper understanding of the effect of elevated temperature on the stress-strain relationship of HSCFC is necessary for the assessment of structural safety. Further stress-strain model of HSCFC after exposure to high temperature is scarce in literature. Experimental results are used to generate the complete stress-strain curves of HSCFC after exposure to high temperature in compression. The variation in concrete mixes was achieved by varying the types of fibre, volume fraction of fibres, and temperature of exposure from ambient to 800°C. The degree of confinement was kept constant in all the specimens. A comparative assessment of different models on the high strength confined concrete was also conducted at different temperature for the accuracy of proposed model. The proposed empirical stress-strain equations are suitable for both high strength confined concrete and HSCFC after exposure to high temperature in compression. The predictions were found to be in good agreement and well fit with experimental results.

scholarly journals Multimedia Quality Integration Using Piecewise Function

2011 ◽  
Vol 1 ◽  
pp. 375-380
Author(s):  
Shu Ai Wan ◽  
Kai Fang Yang ◽  
Hai Yong Zhou
Keyword(s):  
Visual Quality ◽  
Experimental Results ◽  
Integration Model ◽  
Quality Performance ◽  
Piecewise Function ◽  
Proposed Model ◽  
Constant Coefficients ◽  
Theoretical Analyses ◽  
Different Levels

In this paper the important issue of multimedia quality evaluation is concerned, given the unimodal quality of audio and video. Firstly, the quality integration model recommended in G.1070 is evaluated using experimental results. Theoretical analyses aide empirical observations suggest that the constant coefficients used in the G.1070 model should actually be piecewise adjusted for different levels of audio and visual quality. Then a piecewise function is proposed to perform multimedia quality integration under different levels of the audio and visual quality. Performance gain observed from experimental results substantiates the effectiveness of the proposed model.

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