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

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

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

Wind Power Integration with Smart Grid and Storage System: Prospects and Limitations

Wind power generation is playing a pivotal role in adopting renewable energy sources in many countries. Over the past decades, we have seen steady growth in wind power generation throughout the world. This article aims to summarize the operation, conversion and integration of the wind power with conventional grid and local microgrids so that it can be a one-stop reference for early career researchers. The study is carried out primarily based on the horizontal axis wind turbine and the vertical axis wind turbine. Afterward, the types and methods of storing this electric power generated are discussed elaborately. On top of that, this paper summarizes the ways of connecting the wind farms with conventional grid and microgrid to portray a clear picture of existing technologies. Section-wise, the prospects and limitations are discussed and opportunities for future technologies are highlighted. It is envisaged that, this paper will help researchers and engineering professionals to grasp the fundamental concepts related to wind power generation concisely and effectively