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Flexural Performance of Concrete Beams Reinforced with Basalt Fiber Reinforced Polymers (BFRP) Bars
No full textA Master of Science thesis in Civil Engineering by Mustafa Adel Muhsin Al-Mimar entitled, “Flexural Performance of Concrete Beams Reinforced with Basalt Fiber Reinforced Polymers (BFRP) Bars”, submitted in May 2019. Thesis advisor is Dr. Farid Abed. Soft and hard copy available.This research studies the flexural behavior of concrete beams that are reinforced longitudinally with the newly discovered Basalt Fiber Reinforced Polymers (BFRP) bars. The aim is to examine the suitability of using BFRP bars in flexure as compared to Carbon FRP bars and to validate its compatibility with the recommendations and guidelines of the current ACI440 Code. The effect of different design parameters such as BFRP reinforcement ratio, reinforcement axial stiffness and concrete compressive strength are studied. In addition, this research sheds the light on evaluating the bond-dependent coefficient (kb) value for this new type of FRP composites. Four-point loading tests were performed on a total of 14 beams (180 mm × 230 mm × 2200 mm) to achieve the objectives of this research. The loads vs mid-span deflections, cracks behavior and widths, ultimate capacities and failure modes of all 14 beams are recorded and discussed. Results confirmed that BFRP bars could be a valuable alternative to steel and CFRP bars and can be used in similar applications in the construction industry. The flexural behavior of BFRP-reinforced beams compared well with the ACI440.1R guidelines. The experimental moment capacities well-matched their theoretical counterparts using the code equations. For all the beams, the calculated bond dependent coefficients (kb) values were found to be lower than the values recommended by the ACI440.1R code which clearly indicates a strong bond between the FRP bars and concrete. The test results can be utilized as a control for a long-term durability study on the performance of BFRP bars under the harsh environment.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Expatriate time to proficiency: individual antecedents and the moderating effect of home country
No full textThe purpose of this paper is to examine the direct impact of individual variables (cultural openness, social orientation, willingness to communicate, confidence in own technical abilities, active stress resistance, prior international experience) on expatriate time to proficiency (TTP); and the moderating effects of the home country on the relationships between these individual variables and expatriate TTP
Cargo Delivery Box System for modeling the Last mile of Business to Consumer Logistics
No full textA Master of Science thesis in Engineering Systems Management by Osama Saqib Qazi entitled, “Cargo Delivery Box System for the modelling of Last mile of B2C Logistics”, submitted in February 2019. Thesis advisor is Dr. Malick Ndiaye and thesis co-advisor is Dr. Mojahid Faroug Saeed Osman. Soft and hard copy available.The current business trends suggest that the e-commerce business in the UAE is increasing rapidly, which means that the supply chain facilities must be improved to sustain efficiency and profitability. The last mile is highlighted to be the most expensive part of the supply chain. Therefore, optimizing the last mile can increase cost savings for the logistics companies and may even translate into reduced charges for the end consumer. The traditional approach of house to house delivery not only poses high costs but it also contributes to road congestions, delays in deliveries, fragmentation of deliveries and higher carbon emissions. The research showed a real gap in providing a solution that considers all aspects of delivery; the parcel routing, delivery mechanism but also the reception method for the convenience of the customer and the company alike. In this research, we propose a new solution, which calls for consolidating the deliveries from e-retailers at the urban consolidation centers. The urban consolidation center then groups the shipments according to their destinations, loads them on to the cargo delivery boxes. The cargo delivery boxes are then shipped to the corresponding locations using commercially economical vehicles. These potential delivery locations can be either restaurants, grocery stores or retail shops that are spread across the city and are easily in reach to the customers of that area. The customers would be able to collect their deliveries at their conveniences, and the delivery box is collected by the delivery vehicle at night. We formulated a two-stage approach solution and a consolidated model. We validated the models on many scenarios before finally testing the final model (Two-stage Solution) on a data set of five hundred customers, spread across two hundred and fifty-kilometer squares. Finally, the sensitivity analysis on the model showed that the customer footprint across a given area and the size range options of the cargo delivery boxes were the most sensitive parameters to the total last-mile cost.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
Using Linear Regression and Back Propagation Neural Networks to Predict Performance of Soiled PV Modules
No full textThis paper presents a study on neural network-based modeling techniques and sensor data to estimate the power output of photovoltaic systems under soiling conditions. Predicting maximum power output under soiling conditions is considered an important and difficult problem and a variety of models using a host of factors including temperature and weather profiles have been proposed. This study used linear regression models and artificial neural networks and used only solar irradiation and ambient temperature, as well and the maximum power point (MPP) characteristic variables of photovoltaic (PV) modules obtained from online current-voltage (IV) tracers in the site of a PV installation. The two models were trained and validated using actual monitoring data of two 100-Watt PV modules installed in the UAE. One reference panel was cleaned on a weekly basis and the second panel was left to accumulate dust over the entire period between July 1, 2018 and 17 September, 2018. The results show that it is possible to predict maximum power output of soiled PV modules at about 97% accuracy. The proposed models perform at an accuracy comparable to more complex models in literature
FPGA-Based Network Traffic Classification Using Machine Learning
No full textA Master of Science thesis in Computer Engineering by Mohammed Elnawawy entitled, “FPGA-Based Network Traffic Classification Using Machine Learning”, submitted in November 2019. Thesis advisor is Dr. Tamer Shanableh and thesis co-advisor is Dr. Assim Sagahyroon. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).Traffic classification is the process of associating network traffic with the application or group of applications that generated it. It is an essential part of network management at datacentres and network operators due to its importance in traffic shaping, bandwidth allocation, and cybersecurity. Several techniques were investigated by researchers to classify traffic accurately with methods based on machine learning achieving encouraging results. In this work, we conduct several experiments using naïve Bayes, support vector machine, k-nearest neighbour, and random forest trees on two traffic datasets which are both publicly available. While the first dataset was collected in an uncontrolled environment that resembles real network behavior, the second was captured using a highly controlled environment. In the experiments conducted in this work, we look at the classifiers’ performance and their effect on the classification accuracy and F-score. We also assess the suitability of extracted features using feature selection techniques. Moreover, we determine the optimal percentage of packets within a flow that need to be considered while extracting flow-level features. It is observed that when a larger number of packets is considered, the classification performance improves, but the required processing delay increases. Thus, we argue that 60% of packets in a flow would be a good compromise that ensures high performance in the least possible time. Several graphs are generated during each experiment to investigate the effect of varying each parameter on the classification performance. The results of our experiments indicate that random forest outperforms all other algorithms achieving a maximum accuracy of 98.5% and an F-score of 0.932. Finally, since software-based classifiers are usually slow and hence incapable of coping with the increasing amount of traffic within congested networks, we implement a highly pipelined random forest classifier on a Field-Programmable Gate Array (FPGA). The implementation makes use of the parallel architecture of the FPGA in accelerating such a time-consuming task. The implemented design is capable of achieving an average throughput of 163.24 Gbps which is more than twice the maximum throughput compared to reported work. This enables datacentres to achieve efficient online traffic classification given the dynamic nature of modern networks.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE
Discrimination between genuine and acted expressions using EEG signals and machine learning
No full textA Master of Science thesis in Biomedical Engineering by Meera Alex entitled, “Discrimination between genuine and acted expressions using EEG signals and machine learning”, submitted in April 2019. Thesis advisor is Dr. Hasan Al Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Hasan Mir.The main purpose of this thesis work was to quantify happiness in an objective manner. This is in line with the objectives of the National Program for Happiness and Positivity in the UAE. The major contribution to this thesis work included designing and conducting experiments to study the emotion-related cognitive process using EEG signals. The focus is to develop a novel method for classifying EEG signals related to genuine and acted expressions. A framework for quantifying three different affective states: actual/true positive, acted/fake and neutral positive emotions were developed. The major stages involved the development of an emotion related EEG database comprising of 28 subjects, feature extraction, and finally the application of machine learning algorithms. Two main approaches were used for feature extraction: the first method included discrete wavelet transform while, the second method involved a combination of discrete wavelet transform (DWT) and empirical mode decomposition (EMD). Average power features extracted from both the techniques were used for classification of the three affective states. Highest accuracy of 69.2 % using the DWT method and 94.2 % using DWT-EMD method was achieved.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
B − L violating interactions in supersymmetric SO(10) models
No full textAn analysis is given of B − L violating interactions in SO(10) arising from the integration of of SU(5) of heavy Higgs. The analysis is done within the well defined missing partner model where the doublet-triplet splitting arises naturally. The Higgs representations of the model consist of two 10-plets, one 120, one 210 plet and a representations of SO(10). The SO(10) symmetry is spontaneously broken by the singlet in the and 210. In this work we focus on the B−L interactions arising from the elimination of plets of SU(5). We compute five field and six field B−L violating operators. The analysis extends previous analyses where B−L violating interactions arising from the elimination of and were computed. Specifically we compute dimension 6, 7 and 9 operators. These interactions can generate GUT scale baryogengesis, proton decay, and -\bar{} oscillations
Honesty, ability, norm, and socioeconomic status: experimental evidence from Bangladesh
No full textIn a real effort task experiment, we study the (dis)honesty of undergraduate students in Bangladesh. Consistent with earlier studies, when they self-report their performance, a significant fraction of students cheats to varying degrees. We find that an individual's own ability, as well as social norms in terms of beliefs about peers' behavior, are the two most important factors influencing (dis)honesty in our experiment. In particular, a higher actual performance in the real effort task reduces both the likelihood and extent of cheating, while the belief that peers are cheating increases both the likelihood and extent of cheating. Additionally, a lower perceived fear of detection increases the extent of cheating, but does not increase the likelihood of cheating. Among the two most important indicators of socioeconomic status that we considered, such as parents' education and income, only mother's level of education shows a significant negative effect on the likelihood of cheating
Adaptive High Gain PI Controller for Speed Regulation of Induction Motor Drive System
No full textA Master of Science thesis in Electrical Engineering by Sarah Adnan AlBarri entitled, “Adaptive High Gain PI Controller for Speed Regulation of Induction Motor Drive System”, submitted in December 2019. Thesis advisor is Dr. Habibur Rehman and thesis co-advisor is Dr Shayok Mukhopadhyay. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).The performance of a speed regulator of an indirect field oriented (IFO) induction motor with a fixed gain proportional-integral (PI) controller degrades specifically under external disturbances and parameter variations. To date, most of the manufacturing industry prefers the use of PI controllers due to their implementation simplicity and predictable PI tuning behavior. One of the proposed solutions for tuning PI gains is by using high gain adaptation, which automatically adjusts the gains based on the motor speed tracking. Despite various proposed techniques in which high gain adaptive PI tuning is utilized, instability occurs when this controller is tested on an induction motor. The high gain adaptive PI gains keep increasing boundlessly under external disturbances and encoder noise, thus making the system unstable. To overcome this instability, sigma, deadzone, and epsilon modifications are applied to the high gain adaptive law. In this work, the performance of the high gain adaptively tuned PI controller with sigma, deadzone, and epsilon modification is evaluated. The epsilon high gain controller is found to perform better than sigma and deadzone modifications. Therefore, detailed experimental validation of the epsilon modified high gain controller is performed. The tests applied are step and square wave reference tracking, disturbance rejection, detuning and field weakening. Moreover, the effect of the initial conditions of the adaptive PI gains is investigated. The results are compared with the conventional fixed gains PI controller. The epsilon modification high gain adaptively tuned PI controller is also investigated on the electric vehicle traction system. The effects of the adaptively tuned PI gains on the battery state of charge is noted using Coulomb’s counting method. The evaluation results show that epsilon modified high-gain adaptive PI controller provides a better speed tracking performance than the fixed gain PI controller, while requiring almost the same torque commanded current.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
