Search results for: signal prediction

Authors: Rezvan Ravanfar Haghighi, S. Chatterjee, Pratik Kumar, V. C. Vani, Priya Jagia, Sanjiv Sharma, Susama Rani Mandal, R. Lakshmy

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The dual energy computed tomography (DECT) aims at noting the HU(V) values for the sample at two different voltages V=V1, V2 and thus obtain the electron densities (ρe) and effective atomic number (Zeff) of the substance. In the present paper, we aim to obtain a numerical algorithm by which (ρe, Zeff) can be obtained from the HU(100) and HU(140) data, where V=100, 140 kVp. The idea is to use this inversion method to characterize and distinguish between the lipid and fibrous coronary artery plaques.With the idea to develop the inversion algorithm for low Zeff materials, as is the case with non calcified coronary artery plaque, we prepare aqueous samples whose calculated values of (ρe, Zeff) lie in the range (2.65×1023≤ ρe≤ 3.64×1023 per cc ) and (6.80≤ Zeff ≤ 8.90). We fill the phantom with these known samples and experimentally determine HU(100) and HU(140) for the same pixels. Knowing that the HU(V) values are related to the attenuation coefficient of the system, we present an algorithm by which the (ρe, Zeff) is calibrated with respect to (HU(100), HU(140)). The calibration is done with a known set of 20 samples; its accuracy is checked with a different set of 23 known samples. We find that the calibration gives the ρe with an accuracy of ± 4% while Zeff is found within ±1% of the actual value, the confidence being 95%.In this inversion method (ρe, Zeff) of the scanned sample can be found by eliminating the effects of the CT machine and also by ensuring that the determination of the two unknowns (ρe, Zeff) does not interfere with each other. It is found that this algorithm can be used for prediction of chemical characteristic (ρe, Zeff) of unknown scanned materials with 95% confidence level, by inversion of the DECT data.

Keywords: chemical composition, dual-energy computed tomography, inversion algorithm

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Authors: Nader Khalafian, Mohsen Ghaderi

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Field observations have revealed many examples of structures which were damaged due to ground surface deformation caused by the faulting phenomena. In this paper some efforts were made in order to estimate the length and location of the ground surface where large displacements were created due to the reverse faulting. This research has conducted in two steps; (1) in the first step, a 2D explicit finite element model were developed using ABAQUS software. A subroutine for Mohr-Coulomb failure criterion with strain softening model was developed by the authors in order to properly model the stress strain behavior of the soil in the fault rapture zone. The results of the numerical analysis were verified with the results of available centrifuge experiments. Reasonable coincidence was found between the numerical and experimental data. (2) In the second step, the effects of the fault dip angle (δ), depth of soil layer (H), dilation and friction angle of sand (ψ and φ) and the amount of fault offset (d) on the soil surface displacement and fault rupture path were investigated. An artificial neural network-based model (ANN), as a powerful prediction tool, was developed to generate a general model for predicting faulting characteristics. A properly sized database was created to train and test network. It was found that the length and location of the zone of displaced ground surface can be accurately estimated using the proposed model.

Keywords: reverse faulting, surface deformation, numerical, neural network

Procedia PDF Downloads 420

Authors: Shakir H. Mohammed Al-Alwany, Saad Hasan M. Ali, Ibrahim Mohammed S. Shnawa

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The study was aimed to define the percentage of detection of high-oncogenic risk types of HPV and their genotyping in archival tissue specimens that ranged from apparently healthy tissue to invasive breast cancer by using one of the recent versions of In Situ Hybridization(ISH) 0.2. To find out rational significance of such genotypes as well as over expressed products of mutants P53 and RB genes on the severity of underlying breast cancers. The DNA of HPV was detected in 46.5 % of tissues from breast cancers while HPV DNA in the tissues from benign breast tumours was detected in 12.5%. No HPV positive–ISH reaction was detected in healthy breast tissues of the control group. HPV DNA of genotypes (16, 18, 31 and 33) was detected in malignant group in frequency of 25.6%, 27.1%, 30.2% and 12.4%, respectively. Over expression of p53 was detected by IHC in 51.2% breast cancer cases and in 50% benign breast tumour group, while none of control group showed P53- over expression. Retinoblastoma protein was detected by IHC test in 49.7% of malignant breast tumours, 54.2% of benign breast tumours but no signal was reported in the tissues of control group. The significance prevalence of expression of mutated p53 & Rb genes as well as detection of high-oncogenic HPV genotypes in patients with breast cancer supports the hypothesis of an etiologic role for the virus in breast cancer development.

Keywords: human papilloma virus, P53, RB, breast cancer

Procedia PDF Downloads 478

Authors: Pattra Pukdeekiat, Siriluk Ruangrungrote

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This work was originated to precisely determine direct solar radiation by using atmospheric physics models since the accurate prediction of solar radiation is necessary and useful for solar energy applications including atmospheric research. The possible models and techniques for a calculation of regional direct solar radiation were challenging and compulsory for the case of unavailable instrumental measurement. The investigation was mathematically governed by six astronomical parameters i.e. declination (δ), hour angle (ω), solar time, solar zenith angle (θz), extraterrestrial radiation (Iso) and eccentricity (E0) along with two atmospheric parameters i.e. air mass (mr) and dew point temperature at Bangna meteorological station (13.67° N, 100.61° E) in Bangkok, Thailand. Analyses of five models of solar radiation determination with the assumption of clear sky were applied accompanied by three statistical tests: Mean Bias Difference (MBD), Root Mean Square Difference (RMSD) and Coefficient of determination (R2) in order to validate the accuracy of obtainable results. The calculated direct solar radiation was in a range of 491-505 Watt/m2 with relative percentage error 8.41% for winter and 532-540 Watt/m2 with relative percentage error 4.89% for summer 2014. Additionally, dataset of seven continuous days, representing both seasons were considered with the MBD, RMSD and R2 of -0.08, 0.25, 0.86 and -0.14, 0.35, 3.29, respectively, which belong to Kumar model for winter and CSR model for summer. In summary, the determination of direct solar radiation based on atmospheric models and empirical equations could advantageously provide immediate and reliable values of the solar components for any site in the region without a constraint of actual measurement.

Keywords: atmospheric physics models, astronomical parameters, atmospheric parameters, clear sky condition

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Authors: João Filipe Papel, Tatsuji Munaka

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With society's aging and the number of elderly with dementia rising, researchers have been actively studying how to support the elderly in the early stages of dementia with the objective of allowing them to have a better life quality and as much as possible independence. To make this possible, most researchers in this field are using the Internet Of Things to monitor the elderly activities and assist them in performing them. The most common sensor used to monitor the elderly activities is the Camera sensor due to its easy installation and configuration. The other commonly used sensor is the sound sensor. However, we need to consider privacy when using these sensors. This research aims to develop a system capable of predicting the early stages of dementia based on monitoring and controlling the elderly activities of daily living. To make this system possible, some issues need to be addressed. First, the issue related to elderly privacy when trying to detect their Activities of Daily Living. Privacy when performing detection and monitoring Activities of Daily Living it's a serious concern. One of the purposes of this research is to achieve this detection and monitoring without putting the privacy of the elderly at risk. To make this possible, the study focuses on using an approach based on using Magnet Sensors to collect binary data. The second is to use the data collected by monitoring Activities of Daily Living to predict the early stages of Dementia. To make this possible, the research team suggests developing a proprietary ontology combined with both data-driven and knowledge-driven.

Keywords: dementia, activity recognition, magnet sensors, ontology, data driven and knowledge driven, IoT, activities of daily living

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Benkouiten Imene, Chabli Ouerdia, Boutoutaou Hamid, Kadri Nesrine, Bouledroua Omar

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Algeria is actively enhancing gas productivity by augmenting the supply flow. However, this effort has led to increased internal pressure, posing a potential risk to the pipeline's integrity, particularly in the presence of corrosion defects. Sonatrach relies on a vast network of pipelines spanning 24,000 kilometers for the transportation of gas and oil. The aging of these pipelines raises the likelihood of corrosion both internally and externally, heightening the risk of ruptures. To address this issue, a comprehensive inspection is imperative, utilizing specialized scraping tools. These advanced tools furnish a detailed assessment of all pipeline defects. It is essential to recalculate the pressure parameters to safeguard the corroded pipeline's integrity while ensuring the continuity of production. In this context, Sonatrach employs symbolic pressure limit calculations, such as ASME B31G (2009) and the modified ASME B31G (2012). The aim of this study is to perform a comparative analysis of various limit pressure calculation methods documented in the literature, namely DNV RP F-101, SHELL, P-CORRC, NETTO, and CSA Z662. This comparative assessment will be based on a dataset comprising 329 burst tests published in the literature. Ultimately, we intend to introduce a novel approach grounded in the finite element method, employing ANSYS software.

Keywords: pipeline burst pressure, burst test, corrosion defect, corroded pipeline, finite element method

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Authors: Salem Alsanusi, Loubna Bentaher

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Response Surface Methods (RSM) provide statistically validated predictive models that can then be manipulated for finding optimal process configurations. Variation transmitted to responses from poorly controlled process factors can be accounted for by the mathematical technique of propagation of error (POE), which facilitates ‘finding the flats’ on the surfaces generated by RSM. The dual response approach to RSM captures the standard deviation of the output as well as the average. It accounts for unknown sources of variation. Dual response plus propagation of error (POE) provides a more useful model of overall response variation. In our case, we implemented this technique in predicting compressive strength of concrete of 28 days in age. Since 28 days is quite time consuming, while it is important to ensure the quality control process. This paper investigates the potential of using design of experiments (DOE-RSM) to predict the compressive strength of concrete at 28th day. Data used for this study was carried out from experiment schemes at university of Benghazi, civil engineering department. A total of 114 sets of data were implemented. ACI mix design method was utilized for the mix design. No admixtures were used, only the main concrete mix constituents such as cement, coarse-aggregate, fine aggregate and water were utilized in all mixes. Different mix proportions of the ingredients and different water cement ratio were used. The proposed mathematical models are capable of predicting the required concrete compressive strength of concrete from early ages.

Keywords: mix proportioning, response surface methodology, compressive strength, optimal design

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Authors: Alireza Monzavi Chaleshtori, Mahnaz Aliakbari Dehkordi, Maryam Aliakbari, Solmaz Seyed Mostafaii

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Objective: Considering the growing obesity among children and the role of mother's psychological factors as well as the need to prevent childhood obesity, this study aimed to investigate the mediating role of mother's self-efficacy in the relationship between mother's stress and depression and child obesity. Method: For this purpose, in a descriptive-correlation study, 222 mothers and children aged 1 to 5 years in Tehran, who had the opportunity to answer an online questionnaire, were selected by random sampling and to the depression scales of the Kroenke and Spitzer Patient Health Questionnaire, Cohen's stress and Self-efficacy of Berkeley mothers answered. Pearson correlation test and path analysis were used for data analysis. Findings: The findings showed that maternal depression had an indirect and significant effect on child obesity, and the effect of stress and depression on child obesity was indirect and non-significant. Therefore, the model has a good fit with the research data, and stress and depression indirectly predicted child obesity with the mediating role of self-efficacy. Conclusion: The hypothesized model tested based on mother's stress and depression with the mediating role of mother's self-efficacy was a good model in explaining the prediction of child obesity. Based on the findings of this research, a practical framework can be provided to explain the psychological factors of the mother in relation to child obesity and its treatment.

Keywords: stress, self-efficacy, child obesity, depression

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Authors: Robson C. Santos, Julio C. P. Ribeiro, Iorran M. de Castro, Luan C. F. Rodrigues, Sandro R. L. Silva, Diego M. Quesada

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The work aims to develop a robot in the form of autonomous vehicle to detect, inspection and mapping of underground pipelines through the ATmega328 Arduino platform. Hardware prototyping very similar to C / C ++ language that facilitates its use in robotics open source, resembles PLC used in large industrial processes. The robot will traverse the surface independently of direct human action, in order to automate the process of detecting buried pipes, guided by electromagnetic induction. The induction comes from coils that sends the signal to the Arduino microcontroller contained in that will make the difference in intensity and the treatment of the information, then this determines actions to electrical components such as relays and motors, allowing the prototype to move on the surface and getting the necessary information. The robot was developed by electrical and electronic assemblies that allowed test your application. The assembly is made up of metal detector coils, circuit boards and microprocessor, which interconnected circuits previously developed can determine, process control and mechanical actions for a robot (autonomous car) that will make the detection and mapping of buried pipelines plates.

Keywords: robotic, metal detector, embedded system, pipeline inspection

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Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

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The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

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Authors: S. Saju, G. Thirugnanam

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In this paper, a medical image watermarking algorithm based on contourlet is proposed. Medical image watermarking is a special subcategory of image watermarking in the sense that images have special requirements. Watermarked medical images should not differ perceptually from their original counterparts because clinical reading of images must not be affected. Watermarking techniques based on wavelet transform are reported in many literatures but robustness and security using contourlet are better when compared to wavelet transform. The main challenge in exploring geometry in images comes from the discrete nature of the data. In this paper, original image is decomposed to two level using contourlet and the watermark is embedded in the resultant sub-bands. Sub-band selection is based on the value of Peak Signal to Noise Ratio (PSNR) that is calculated between watermarked and original image. To extract the watermark, Kernel ICA is used and it has a novel characteristic is that it does not require the transformation process to extract the watermark. Simulation results show that proposed scheme is robust against attacks such as Salt and Pepper noise, Median filtering and rotation. The performance measures like PSNR and Similarity measure are evaluated and compared with Discrete Wavelet Transform (DWT) to prove the robustness of the scheme. Simulations are carried out using Matlab Software.

Keywords: digital watermarking, independent component analysis, wavelet transform, contourlet

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Authors: Krzysztof Malczewski

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PET is widely applied scanning procedure in medical imaging based research. It delivers measurements of functioning in distinct areas of the human brain while the patient is comfortable, conscious and alert. This article presents the new compression sensing based super-resolution algorithm for improving the image resolution in clinical Positron Emission Tomography (PET) scanners. The issue of motion artifacts is well known in Positron Emission Tomography (PET) studies as its side effect. The PET images are being acquired over a limited period of time. As the patients cannot hold breath during the PET data gathering, spatial blurring and motion artefacts are the usual result. These may lead to wrong diagnosis. It is shown that the presented approach improves PET spatial resolution in cases when Compressed Sensing (CS) sequences are used. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. The application of CS to PET has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the goal is to combine super-resolution image enhancement algorithm with CS framework to achieve high resolution PET output. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity.

Keywords: PET, super-resolution, image reconstruction, pattern recognition

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Authors: Kolade Olawande Owoeye, George Weir

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Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime.

Keywords: sentiposit, classification, extremism, terrorism

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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Authors: Diana Pombo, Maria Loaiza, Mauricio Quijano, Alberto Cadena, Juan Pablo Tello

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In this paper, we present a semi-automatic tool for calculating ejection fraction from an echocardiographic video signal which is derived from a database in DICOM format, of Clinica de la Costa - Barranquilla. Described in this paper are each of the steps and methods used to find the respective calculation that includes acquisition and formation of the test samples, processing and finally the calculation of the parameters to obtain the ejection fraction. Two imaging segmentation methods were compared following a methodological framework that is similar only in the initial stages of processing (process of filtering and image enhancement) and differ in the end when algorithms are implemented (Active Contour and Region Growing Algorithms). The results were compared with the measurements obtained by two different medical specialists in cardiology who calculated the ejection fraction of the study samples using the traditional method, which consists of drawing the region of interest directly from the computer using echocardiography equipment and a simple equation to calculate the desired value. The results showed that if the quality of video samples are good (i.e., after the pre-processing there is evidence of an improvement in the contrast), the values provided by the tool are substantially close to those reported by physicians; also the correlation between physicians does not vary significantly.

Keywords: echocardiography, DICOM, processing, segmentation, EDV, ESV, ejection fraction

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Authors: Jianghao Hu, Hongyu Wang

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The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

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Authors: Shirui Huang, Wei Chen, Chuanshu Huang

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Basal muscle-invasive bladder cancer (BMIBC) is considered one of the subtypes of BC with the highest metastatic rate and the poorest prognosis. Therefore, elucidating the mechanisms underlying BMIBC metastasis and identifying novel precision therapeutic targets are current research hotspots and challenges to cancer researchers. Through a series of in vitro and in vivo functional experiments, we have identified the crucial role of KRT14 in the high invasiveness and adverse prognosis of BMIBC. We found that the K294 site within the IGF2BP1-KH2 domain is responsible for reading the conserved genetic information carried by D226/E227 in the KRT14 nuclear export signal (NES). Activation of the KRT14-IGF2BP1 signaling axis is essential for IGF2BP1-mediated stabilization of SNAI1 mRNA through FTO modification. Additionally, IGF2BP1 forms a positive feedback loop by stabilizing its own mRNA, thereby accelerating the invasion and metastasis of BMIBC. Collectively, our study identifies the KRT14/IGF2BP1/FTO/Snail signaling axis as an essential regulatory mechanism associated with poor prognosis in BMIBC, providing a theoretical basis for KRT14 and its downstream regulated molecules as therapeutic targets for BMIBC and the development of corresponding targeted therapies.

Keywords: BMIBC, KRT4, IFGF2BP1, DNA methylation

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Authors: Aaditya U. Jhamb

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Due to the current energy crisis that many countries are battling, energy-efficient buildings are the subject of extensive research in the modern technological era because of growing worries about energy consumption and its effects on the environment. The paper explores 8 factors that help determine energy efficiency for a building: (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution), with Tsanas and Xifara providing a dataset. The data set employed 768 different residential building models to anticipate heating and cooling loads with a low mean squared error. By optimizing these characteristics, machine learning algorithms may assess and properly forecast a building's heating and cooling loads, lowering energy usage while increasing the quality of people's lives. As a result, the paper studied the magnitude of the correlation between these input factors and the two output variables using various statistical methods of analysis after determining which input variable was most closely associated with the output loads. The most conclusive model was the Decision Tree Regressor, which had a mean squared error of 0.258, whilst the least definitive model was the Isotonic Regressor, which had a mean squared error of 21.68. This paper also investigated the KNN Regressor and the Linear Regression, which had to mean squared errors of 3.349 and 18.141, respectively. In conclusion, the model, given the 8 input variables, was able to predict the heating and cooling loads of a residential building accurately and precisely.

Keywords: energy efficient buildings, heating load, cooling load, machine learning models

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Authors: Q. Toufeeq, R. Kauser, U. R. Jamil, N. Sohaib

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Foundation design of a structure needs soil investigation to avoid failures due to settlements. This soil investigation is expensive and time-consuming. Developments of new residential societies involve huge leveling of large sites that is accompanied by heavy land filling. Poor practices of land fill for deep depths cause differential settlements and consolidations of underneath soil that sometimes result in the collapse of structures. The extent of filling remains unknown to the individual developer unless soil investigation is carried out. Soil investigation cannot be performed on each available site due to involved costs. However, fair estimate of bearing capacity can be made if such tests are already done in the surrounding areas. The geotechnical guidance maps can provide a fair assessment of soil properties. Previously, GIS-based approaches have been used to develop maps using extrapolation and interpolations techniques for bearing capacities, underground recharge, soil classification, geological hazards, landslide hazards, socio-economic, and soil liquefaction mapping. Standard penetration test (SPT) data of surrounding sites were already available. Google Earth is used for digitization of collected data. Few points were considered for data calibration and validation. Resultant Geographic information system (GIS)-based guidance maps are helpful to anticipate the bearing capacity in the real estate industry.

Keywords: bearing capacity, soil classification, geographical information system, inverse distance weighted, radial basis function

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Authors: B. Siva Kumar Reddy, B. Lakshmi

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WiMAX is a high speed broadband wireless access technology that adopted OFDM/OFDMA techniques to supply higher data rates with high spectral efficiency. However, OFDM suffers in view of high Peak to Average Power Ratio (PAPR) and high affect to synchronization errors. In this paper, the high PAPR problem is solved by using phase modulation to get Constant Envelop Orthogonal Frequency Division Multiplexing (CE-OFDM). The synchronization failures are brought down by employing a frequency lock loop, Poly phase clock synchronizer, Costas loop and blind equalizers such as Constant Modulus Algorithm (CMA) equalizer and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA) equalizers. The WiMAX physical layer is executed on Software Defined Radio (SDR) prototype by utilizing USRP N210 as hardware and GNU Radio as software plat-forms. A SNR estimation is performed on the signal received through USRP N210. To empathize wireless propagation in specific environments, a sliding correlator wireless channel sounding system is designed by using SDR testbed.

Keywords: BER, CMA equalizer, Kurtosis equalizer, GNU Radio, OFDM/OFDMA, USRP N210

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Authors: Esam I. Jassim

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The main aim of this paper is to demonstrate the prediction of the model capability of predicting the nucleation process, the growth rate, and the deposition potential of second phase particles in gas flowlines. The primary objective of the research is to predict the risk hazards involved in the marine transportation of compressed natural gas. However, the proposed model can be equally used for other applications including production and transportation of natural gas in any high-pressure flow-line. The proposed model employs the following three main components to approach the problem: computational fluid dynamics (CFD) technique is used to configure the flow field; the nucleation model is developed and incorporated in the simulation to predict the incipient hydrate particles size and growth rate; and the deposition of the gas/particle flow is proposed using the concept of the particle deposition velocity. These components are integrated in a comprehended model to locate the hydrate deposition in natural gas flowlines. The present research is prepared to foresee the deposition location of solid particles that could occur in a real application in Compressed Natural Gas loading and offloading. A pipeline with 120 m length and different sizes carried a natural gas is taken in the study. The location of particle deposition formed as a result of restriction is determined based on the procedure mentioned earlier and the effect of water content and downstream pressure is studied. The critical flow speed that prevents such particle to accumulate in the certain pipe length is also addressed.

Keywords: hydrate deposition, compressed natural gas, marine transportation, oceanography

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Alireza Gholipour, Mergen H. Ghayesh, Anthony Zander, Stephen J. Nicholls, Peter J. Psaltis

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A three-dimensional numerical model of an atherosclerotic coronary ‎artery is developed for the determination of high-risk situation and ‎hence heart attack prediction. Employing the finite element method ‎‎(FEM) using ANSYS, fluid-structure interaction (FSI) model of the ‎artery is constructed to determine the shear stress distribution as well ‎as the von Mises stress field. A flexible model for an atherosclerotic ‎coronary artery conveying pulsatile blood is developed incorporating ‎three-dimensionality, artery’s tapered shape via a linear function for ‎artery wall distribution, motion of the artery, blood viscosity via the ‎non-Newtonian flow theory, blood pulsation via use of one-period ‎heartbeat, hyperelasticity via the Mooney-Rivlin model, viscoelasticity ‎via the Prony series shear relaxation scheme, and micro-calcification ‎inside the plaque. The material properties used to relate the stress field ‎to the strain field have been extracted from clinical data from previous ‎in-vitro studies. The determined stress fields has potential to be used as ‎a predictive tool for plaque rupture and dissection.‎ The results show that stress concentration due to micro-calcification ‎increases the von Mises stress significantly; chance of developing a ‎crack inside the plaque increases. Moreover, the blood pulsation varies ‎the stress distribution substantially for some cases.‎

Keywords: atherosclerosis, fluid-structure interaction‎, coronary arteries‎, pulsatile flow

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Authors: Thamer Kubat, Riadh Al-Mahaidi, Ahmad Shayan

Abstract:

The alkali-aggregate reaction (AAR) in concrete has a negative influence on the mechanical properties and durability of concrete. Confinement by carbon fibre-reinforced polymer (CFRP) is an effective method of treatment for some AAR-affected elements. Eighteen reinforced columns affected by different levels of expansion due to AAR were confined using CFRP to evaluate the effect of expansion level on confinement efficiency. Strength and strain capacities (axial and circumferential) were measured using photogrammetry under uniaxial compressive loading to evaluate the efficiency of CFRP wrapping for the rehabilitation of affected columns. In relation to uniaxial compression capacity, the results indicated that the confinement of AAR-affected columns by one layer of CFRP is sufficient to reach and exceed the load capacity of unaffected sound columns. Parallel to the experimental study, finite element (FE) modeling using ATENA software was employed to predict the behavior of CFRP-confined damaged concrete and determine the possibility of using the model in a parametric study by simulating the number of CFRP layers. A comparison of the experimental results with the results of the theoretical models showed that FE modeling could be used for the prediction of the behavior of confined AAR-damaged concrete.

Keywords: carbon fiber reinforced polymer (CFRP), finite element (FE), ATENA, confinement efficiency

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Authors: Aman Kaur, Colin Ribton, Wamadeva Balachandaran

Abstract:

Electron beam welding due to its inherent advantages is being extensively used for material processing where high precision is required. Especially in aerospace or nuclear industries, there are high quality requirements and the cost of materials and processes is very high which makes it very important to ensure the beam quality is maintained and checked prior to carrying out the welds. Although the processes in these industries are highly controlled, however, even the minor changes in the operating parameters of the electron gun can make large enough variations in the beam quality that can result in poor welding. To measure the beam quality a simple device has been designed that can be used at high powers. The device consists of two slits in x and y axis which collects a small portion of the beam current when the beam is deflected over the slits. The signals received from the device are processed in data acquisition hardware and the dedicated software developed for the device. The device has been used in controlled laboratory environments to analyse the signals and the weld quality relationships by varying the focus current. The results showed matching trends in the weld dimensions and the beam characteristics. Further experimental work is being carried out to determine the ability of the device and signal processing software to detect subtle changes in the beam quality and to relate these to the physical weld quality indicators.

Keywords: electron beam welding, beam quality, high power, weld quality indicators

Procedia PDF Downloads 323

Authors: Babar Sultan, Kiran Sultan, Waseem Khan, Ijaz Mansoor Qureshi

Abstract:

In this paper, a novel idea for the performance enhancement of secondary network is proposed for Underlay Cognitive Radio Networks (CRNs). In Underlay CRNs, primary users (PUs) impose strict interference constraints on the secondary users (SUs). The proposed scheme is based on Artificial Bee Colony (ABC) optimization for relay selection and power allocation to handle the highlighted primary challenge of Underlay CRNs. ABC is a simple, population-based optimization algorithm which attains global optimum solution by combining local search methods (Employed and Onlooker Bees) and global search methods (Scout Bees). The proposed two-phase relay selection and power allocation algorithm aims to maximize the signal-to-noise ratio (SNR) at the destination while operating in an underlying mode. The proposed algorithm has less computational complexity and its performance is verified through simulation results for a different number of potential relays, different interference threshold levels and different transmit power thresholds for the selected relays.

Keywords: artificial bee colony, underlay spectrum sharing, cognitive radio networks, amplify-and-forward

Procedia PDF Downloads 581

Authors: Zeineb Ben Salem, Wissem Frikha, Mounir Bouassida

Abstract:

Liquefaction has been responsible for tremendous amounts of damage in historical earthquakes around the world. The installation of stone columns is widely adopted to prevent liquefaction. Stone columns provide a drainage path, and due to their high permeability, allow for the quick dissipation of earthquake generated excess pore water pressure. Several excess pore pressure generation models in silty sand have been developed and calibrated based on the results of shaking table and centrifuge tests focusing on the effect of silt content on liquefaction resistance. In this paper, the generation and dissipation of excess pore pressure variation of reinforced silty sand by stone columns during liquefaction are analyzedwith different silt content based on test results. In addition, the installation effect of stone columns is investigated. This effect is described by a decrease in horizontal permeability within a disturbed zone around the column. Obtained results show that reduced soil permeability and a larger disturbed zone around the stone column increases the generation of excess pore pressure during the cyclic loading and decreases the dissipation rate after cyclic loading. On the other hand, beneficial effects of silt content were observed in the form of a decrease in excess pore water pressure.

Keywords: stone column, liquefaction, excess pore pressure, silt content, disturbed zone, reduced permeability

Procedia PDF Downloads 152

Authors: N. Abbas, S. Lagomarsino, S. Cattari

Abstract:

Examining existing experimental results for shallow rigid foundations subjected to vertical centric load (N), accompanied or not with a bending moment (M), two main non-linear mechanisms governing the cyclic ‎response of the soil-foundation system can be distinguished: foundation uplift and soil yielding. A soil-foundation failure limit, is defined as a domain of resistance in the two dimensional (2D) load space (N, M) inside of which lie all the admissible combinations of loads; these latter correspond to a pure elastic, non-linear elastic or plastic behavior of the soil-foundation system, while the points lying on the failure limit correspond to a combination of loads leading to a failure of the soil-foundation system. In this study, the proposed resistance domain is constructed analytically based on mechanics. Original elastic limit, uplift initiation ‎limit and iso-uplift limits are constructed inside this domain. These limits give a prediction ‎of the mechanisms activated for each combination of loads applied to the ‎foundation. A comparison of the proposed failure limit with experimental tests existing in the literature shows interesting results. Also, the developed uplift initiation limit and iso-uplift curves are confronted with others already proposed in the literature and widely used due to the absence of other alternatives, and remarkable differences are noted, showing evident errors in the past proposals and relevant accuracy for those given in the present work.

Keywords: foundation uplift, iso-uplift curves, resistance domain, soil yield

Procedia PDF Downloads 382

Authors: Safa Gharbi, Hayfa Zgaya, Nesrine Zoghlami, Slim Hammadi, Cyril De Barbarin, Laurent Vinatier, Christiane Coupier

Abstract:

Increasing productivity and quality of service, improving the working comfort and ensuring the efficiency of all processes are important challenges for every warehouse. The order picking is recognized to be the most important and costly activity of all the process in warehouses. This paper presents a new approach using Augmented Reality (AR) in the field of logistics. It aims to create a Head-Up Display (HUD) interface with a Warehouse Management System (WMS), using AR glasses. Integrating AR technology allows the optimization of order picking by reducing time of picking process, increasing the efficiency and delivering quickly. The picker will be able to access immediately to all the information needed for his tasks. All the information is displayed when needed in the field of vision (FOV) of the operator, without any action requested from him. These research works are part of the industrial project RASL (Réalité Augmentée au Service de la Logistique) which gathers two major partners: the LAGIS (Laboratory of Automatics, Computer Engineering and Signal Processing in Lille-France) and Genrix Group, European leader in warehouses logistics, who provided his software for implementation, and his logistics expertise.

Keywords: Augmented Reality (AR), logistics and optimization, Warehouse Management System (WMS), Head-Up Display (HUD)

Procedia PDF Downloads 481