145 research outputs found
A low-cost closed-loop solar tracking system based on the sun position algorithm
Sun position and the optimum inclination of a solar panel to the sun vary over time throughout the day. A simple but accurate solar position measurement system is essential for maximizing the output power from a solar panel in order to increase the panel efficiency while minimizing the system cost. Solar position can be measured either by a sensor (active/passive) or through the sun position monitoring algorithm. Sensor-based sun position measuring systems fail to measure the solar position in a cloudy or intermittent day, and they require precise installation and periodic calibrations. In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. A standalone low-cost but high-precision dual-axis closed-loop sun-tracking system using the sun position algorithm was implemented in an 8-bit microcontroller platform. The Astronomical Almanac's (AA) algorithm was used for its simplicity, reliability, and fast computation capability of the solar position. Results revealed that incorporation of the sun position algorithm into a solar tracking system helps in outperforming the fixed system and optical tracking system by 13.9% and 2.1%, respectively. In summary, even for a small-scale solar tracking system, the algorithm-based closed-loop dual-axis tracking system can increase overall system efficiency. - 2019 Muhammad E. H. Chowdhury et al.The publication of this article was funded by the Qatar National Library. The authors would like to thank Qatar University for granting the student grant (QUST--CENG-SPR\2017-23) which made this work possible. We would like to thank the Mechanical Engineering Department, Qatar University, for their assistance in designing the mechanical system.Scopu
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
Pneumonia is a life-threatening disease, which occurs in the lungs caused by
either bacterial or viral infection. It can be life-endangering if not acted
upon in the right time and thus an early diagnosis of pneumonia is vital. The
aim of this paper is to automatically detect bacterial and viral pneumonia
using digital x-ray images. It provides a detailed report on advances made in
making accurate detection of pneumonia and then presents the methodology
adopted by the authors. Four different pre-trained deep Convolutional Neural
Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for
transfer learning. 5247 Bacterial, viral and normal chest x-rays images
underwent preprocessing techniques and the modified images were trained for the
transfer learning based classification task. In this work, the authors have
reported three schemes of classifications: normal vs pneumonia, bacterial vs
viral pneumonia and normal, bacterial and viral pneumonia. The classification
accuracy of normal and pneumonia images, bacterial and viral pneumonia images,
and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3%
respectively. This is the highest accuracy in any scheme than the accuracies
reported in the literature. Therefore, the proposed study can be useful in
faster-diagnosing pneumonia by the radiologist and can help in the fast airport
screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with
arXiv:2003.1314
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated
directly from the Blood pressure (BP). Hypertension always leads to other
health complications. Continuous monitoring of BP is very important; however,
cuff-based BP measurements are discrete and uncomfortable to the user. To
address this need, a cuff-less, continuous and a non-invasive BP measurement
system is proposed using Photoplethysmogram (PPG) signal and demographic
features using machine learning (ML) algorithms. PPG signals were acquired from
219 subjects, which undergo pre-processing and feature extraction steps. Time,
frequency and time-frequency domain features were extracted from the PPG and
their derivative signals. Feature selection techniques were used to reduce the
computational complexity and to decrease the chance of over-fitting the ML
algorithms. The features were then used to train and evaluate ML algorithms.
The best regression models were selected for Systolic BP (SBP) and Diastolic BP
(DBP) estimation individually. Gaussian Process Regression (GPR) along with
ReliefF feature selection algorithm outperforms other algorithms in estimating
SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively.
This ML model can be implemented in hardware systems to continuously monitor BP
and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table
RamanNet: A generalized neural network architecture for Raman Spectrum Analysis
Raman spectroscopy provides a vibrational profile of the molecules and thus
can be used to uniquely identify different kind of materials. This sort of
fingerprinting molecules has thus led to widespread application of Raman
spectrum in various fields like medical dignostics, forensics, mineralogy,
bacteriology and virology etc. Despite the recent rise in Raman spectra data
volume, there has not been any significant effort in developing generalized
machine learning methods for Raman spectra analysis. We examine, experiment and
evaluate existing methods and conjecture that neither current sequential models
nor traditional machine learning models are satisfactorily sufficient to
analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt
to mix the best of both worlds and propose a novel network architecture
RamanNet. RamanNet is immune to invariance property in CNN and at the same time
better than traditional machine learning models for the inclusion of sparse
connectivity. Our experiments on 4 public datasets demonstrate superior
performance over the much complex state-of-the-art methods and thus RamanNet
has the potential to become the defacto standard in Raman spectra data analysi
Application of Green Polymeric Nanocomposites for Enhanced Oil Recovery by Spontaneous Imbibition from Carbonate Reservoirs
This study experimentally investigates the effect of green polymeric nanoparticles on the interfacial tension (IFT) and wettability of carbonate reservoirs to effectively change the enhanced oil recovery (EOR) parameters. This experimental study compares the performance of xanthan/magnetite/SiO2 nanocomposites (NC) and several green materials, i.e., eucalyptus plant nanocomposites (ENC) and walnut shell ones (WNC) on the oil recovery with performing series of spontaneous imbibition tests. Scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDAX), and BET (Brunauer, Emmett, and Teller) surface analysis tests are also applied to monitor the morphology and crystalline structure of NC, ENC, and WNC. Then, the IFT and contact angle (CA) were measured in the presence of these materials under various reservoir conditions and solvent salinities. It was found that both ENC and WNC nanocomposites decreased CA and IFT, but ENC performed better than WNC under different salinities, namely, seawater (SW), double diluted salted (2 SW), ten times diluted seawater (10 SW), formation water (FW), and distilled water (DIW), which were applied at 70 °C, 2000 psi, and 0.05 wt.% nanocomposites concentration. Based on better results, ENC nanofluid at salinity concentrations of 10 SW and 2 SW ENC were selected for the EOR of carbonate rocks under reservoir conditions. The contact angles of ENC nanocomposites at the salinities of 2 SW and 10 SW were 49 and 43.4°, respectively. Zeta potential values were −44.39 and −46.58 for 2 SW and 10 SW ENC nanofluids, which is evidence of the high stability of ENC nanocomposites. The imbibition results at 70 °C and 2000 psi with 0.05 wt.% ENC at 10 SW and 2 SW led to incremental oil recoveries of 64.13% and 60.12%, respectively, compared to NC, which was 46.16%.The publication of this article was funded by the Qatar National Library
An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control
In the 34 developed and 156 developing countries, there are about 132 million
disabled people who need a wheelchair constituting 1.86% of the world
population. Moreover, there are millions of people suffering from diseases
related to motor disabilities, which cause inability to produce controlled
movement in any of the limbs or even head.The paper proposes a system to aid
people with motor disabilities by restoring their ability to move effectively
and effortlessly without having to rely on others utilizing an eye-controlled
electric wheelchair. The system input was images of the users eye that were
processed to estimate the gaze direction and the wheelchair was moved
accordingly. To accomplish such a feat, four user-specific methods were
developed, implemented and tested; all of which were based on a benchmark
database created by the authors.The first three techniques were automatic,
employ correlation and were variants of template matching, while the last one
uses convolutional neural networks (CNNs). Different metrics to quantitatively
evaluate the performance of each algorithm in terms of accuracy and latency
were computed and overall comparison is presented. CNN exhibited the best
performance (i.e. 99.3% classification accuracy), and thus it was the model of
choice for the gaze estimator, which commands the wheelchair motion. The system
was evaluated carefully on 8 subjects achieving 99% accuracy in changing
illumination conditions outdoor and indoor. This required modifying a motorized
wheelchair to adapt it to the predictions output by the gaze estimation
algorithm. The wheelchair control can bypass any decision made by the gaze
estimator and immediately halt its motion with the help of an array of
proximity sensors, if the measured distance goes below a well-defined safety
margin.Comment: Accepted for publication in Sensor, 19 Figure, 3 Table
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
Although numerous R-peak detectors have been proposed in the literature,
their robustness and performance levels may significantly deteriorate in
low-quality and noisy signals acquired from mobile electrocardiogram (ECG)
sensors, such as Holter monitors. Recently, this issue has been addressed by
deep 1-D convolutional neural networks (CNNs) that have achieved
state-of-the-art performance levels in Holter monitors; however, they pose a
high complexity level that requires special parallelized hardware setup for
real-time processing. On the other hand, their performance deteriorates when a
compact network configuration is used instead. This is an expected outcome as
recent studies have demonstrated that the learning performance of CNNs is
limited due to their strictly homogenous configuration with the sole linear
neuron model. In this study, to further boost the peak detection performance
along with an elegant computational efficiency, we propose 1-D Self-Organized
ONNs (Self-ONNs) with generative neurons. The most crucial advantage of 1-D
Self-ONNs over the ONNs is their self-organization capability that voids the
need to search for the best operator set per neuron since each generative
neuron has the ability to create the optimal operator during training. The
experimental results over the China Physiological Signal Challenge-2020 (CPSC)
dataset with more than one million ECG beats show that the proposed 1-D
Self-ONNs can significantly surpass the state-of-the-art deep CNN with less
computational complexity. Results demonstrate that the proposed solution
achieves a 99.10% F1-score, 99.79% sensitivity, and 98.42% positive
predictivity in the CPSC dataset, which is the best R-peak detection
performance ever achieved.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0221
Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media
This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future
Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection
Phishing emails continue to pose a significant threat, causing financial
losses and security breaches. This study addresses limitations in existing
research, such as reliance on proprietary datasets and lack of real-world
application, by proposing a high-performance machine learning model for email
classification. Utilizing a comprehensive and largest available public dataset,
the model achieves a f1 score of 0.99 and is designed for deployment within
relevant applications. Additionally, Explainable AI (XAI) is integrated to
enhance user trust. This research offers a practical and highly accurate
solution, contributing to the fight against phishing by empowering users with a
real-time web-based application for phishing email detection.Comment: 19 pages, 7 figures, dataset link:
https://www.kaggle.com/datasets/naserabdullahalam/phishing-email-dataset
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