Computer Science and Engineering

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    Resilient Suppliers Selection System Using Machine Learning Algorithms And Risk Assessment Methodology
    (2022-07-02) Albadrani, Abdullah Meteb; Zohdy, Mohamed A.; Olawoyin, Richard; Edwards, William; Ruegg, Erica
    Demand, supply, pricing, and lead time are all unpredictable in the manufacturingindustry, and the manufacturer must function in this environment. Because of the enormous amount of data available and the introduction of new technologies, such as theinternet of things (IoT), machine learning (ML), and Blockchain, administrators and government officials are better able to deal with uncertainty by applying intelligent decision-making principles to their situations. All supply chains must make use of new technology and analyze previous data to forecast and improve the success of future operations. At the moment, we rely on the supply chain and its facilities for healthcare when we receive our vaccine, for our food when we go grocery shopping, and for transportation when we drive our cars. Supplier selection is exposed to the three most significant factors: quality, delivery, and performance history, which are all evaluated separately. The use of data analytic capabilities in the selection of robust supplier portfolios has not been thoroughly investigated. Manufacturers typically have three to four resilient suppliers for the same item, but occasionally one or two of them will fail, causing a ripple effect throughout the entire supply chain. This is a frequent problem that the supply chain must deal with on a daily basis. Supply chain resilience, on the other hand, ignores or is incompatible with the risk profiles of suppliers' performance.
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    (2022-03-25) Khasawneh, Lubna S.; Das, Manohar; Ka, Cheok C; Shilor, Meir; Guangzhi, Qu
    This thesis addresses the problem of controlling the lateral motion of an autonomous vehicle in the presence of parametric uncertainties, disturbances, and hard nonlinearities in the steering system, such as backlash in gears, stiction, hysteresis, and dead zones. The lateral motion of an autonomous vehicle is controlled by two cascaded controllers, the trajectory tracking controller and the steering angle controller. This thesis focuses on the development of both controllers using robust and adaptive control techniques. Two control strategies are developed to control the electric power steering angle, sliding mode control and adaptive backstepping control. The limitation of sliding mode control is first addressed, which is the chattering phenomena, and then a proposed methodology is presented to solve it using variable gain sliding mode control. Self-aligning moment acts as disturbance on the steering system that the controller has to compensate for. A model-based approach to estimate it is first developed and its limitations are addressed, which is tire parameters dependemce. Two other approaches are then developed to overcome these limitations, the first one is a sliding mode observer, and the second one is part of a backstepping controller. Two approaches are developed to control the vehicle lateral trajectory, non-adaptive backstepping and adaptive backstepping. The extended matching design procedure is used in the adaptive backstepping controller to avoid the overestimation problem. Road curvature must be accurately known by the controller to follow the planned trajectory. It is usually measured by a camera, but the quality of the measurement is affected by environmental factors. An adaptive law is developed to estimate the road curvature online as part of an adaptive backstepping controller. Two feedforward approaches are presented to compensate for road curvature, one is derived from steady state vehicle lateral dynamics, and another is based on estimating the transfer function dynamics from road curvature to steering angle. Road bank angle is a significant disturbance in vehicle lateral control systems. A vehicle lateral state and disturbance observer is developed to estimate the road bank angle and the vehicle side slip angle, which are expensive to measure in current road vehicles, using extended Kalman filter. The observer combines a dynamical vehicle model with two measurements from inexpensive sensors.
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    (2022-03-15) Salih, Saif Yoseif; Olawoyin, Richard O; Cooley, Christopher; Debnath, Debatosh; ElSayed, Suzan
    The increasing complexities and functionalities of the electrical and/or electronic (E/E) systems in present day automobiles, make it challenging for original equipment manufacturers (OEMs) and suppliers to ensure a high level of safety in the automotive critical safety systems. The steering systems represent a standard functionality on every vehicle to control the direction of the vehicle literally and provide more stability for the vehicle motion. High automated vehicles require intelligent steering systems in which more Advanced Driver Assistance Systems (ADAS) applications are linked together such as cameras, radars, Lidars, and global positioning system (GPS). These integrated systems and applications are required for environmental perception, communications, data fusion, planning, prediction, decision making, and actuation processes all in real-time. Therefore, hardware (HW) and software (SW) solutions are developed and implemented in compliance with ISO 26262 standard, Road Vehicles – Functional Safety. Due to the lack of the steering systems published information and the crucial role of the steering associated with complex functionalities challenges, this dissertation provides a case study of how the steering systems of different automated driving levels can be complied with ISO 26262 given the emerging challenges imposed by the electric vehicle curb weight, increasing trend for the near future. The analysis focused on the safety lifecycle of the E/E components of the steering systems to ensure high availability of the steering systems and avoid any sudden loss of assistance (SLOA). Various safety mechanisms were evaluated and analyzed to improve the functional safety of the steering systems architecture and logic control paths. Based on the proposed controllability metrics performed in this dissertation, it was found that the hazard or malfunction of the steering systems shifted from the Automotive Safety Integrity Level (ASIL) B to ASIL C, the second most critical safety level. To comply with the ISO 26262 and to mitigate the residual risks of E/E systems failure, several solutions proposed in the concept for compliance with the standard such as redundant HW or SW in the controller path. The controllability classes or categories of the high automated vehicles based on the vehicle global position related to the lane marker lines were investigated and redefined to accommodate for the machine or system in the loop controlling the dynamic driving task (DDT) in autonomous vehicle maneuvering. A new wheel offset marker concept was introduced when the vehicle is approaching the lane marker lines. Also, it was found that the are human factors challenges in SAE level 4 and 5 and the interaction between the driver and the automated control systems of the vehicle that require human machine interface (HMI) modalities. The driver – automated control system engagement in the steering system of the vehicles is one of the crucial control complex scenarios that add uncertainties and potential risks when handing over the steering control between the driver and-or the automated control system with the allotted time. This study highlights the need to define the driver intervention in high-automated vehicle of SAE level 4 and 5 in order to sustain the traffic safety and keep the vehicle in the intended trajectory or path. This can be addressed by deploying HMI and the human factor implementation in ISO 26262 to standardize the driver-machine relation with the DDT in real time and interactive environment. Both manual and automated driving modes demand the functional safety implementation of the steering system to mitigate any system malfunction or failure.An artificial neural network (ANN) model was developed to predict the steering torque commands and steering wheel angle (SWA) based on the steering system dataset and vehicle’s parameters. ANN model was developed using Neural Network Training (nntraintool) toolbox of MATLAB to evaluate the intelligent steering system performance. The trained ANN model delivered a regression value of ~ 98.5 % versus the measured SWA. The results showed that the ANN was effective in predicting the steering wheel angle patterns based on the input dataset, considering the non-linearity and complexity of the steering system control. This finding helps to improve the functional safety of autonomous vehicles and introduce the concept of intelligent steering systems for path and trajectory planning. Therefore, ANN should be implemented as an abstraction layer in the control module and deployed in the control and actuation processes to support sensor data fusion and support the prediction and pattern recognition.
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    (2022-03-15) Altherwi, Abdulhadi; Zohdy, Mohamed; Malik, Ali; Edwards, William; Cho, Seong-Yeon; Alwerfalli, Daw
    Nowadays, many challenges affect global supply chain networks including disruptions, delays, and failures during shipment of products. These challenges also incur penalty costs due to customers’ unmet demands and failures in supply. In this dissertation, the model was developed as a multi-objective supply chain network under two risk factors including failure in supply and unmet demand based on three different scenarios. The objective of scenario I was to minimize the total expected transportation costs between stages for each supply chain and penalty costs associated with shortage of products. Supply chain with no failure in supply will communicate with supply chain with failure to deliver its product to the final customer. For scenario II, the objective was to maximize the profits of the supply chain that face extra inventory. This supply chain with surplus products will collaborate with supply chains with shortage of products to prevent any undesirable costs associated with extra inventory. The objective of scenario III was to develop a multi-objective function, which maximizes the profit and minimizes the total costs associated with production, holding, and penalties due to supplier failure of raw materials. Once a supply chain faces failure in supply of raw materials, other supply chains with no supply failure will collaborate to prevent any associated costs. This research investigates the applicability of the Modified Firefly Algorithm for a multi-stage supply chain network consisting of suppliers, manufacturers, storages, and markets under risks of failure. Commercial software cannot obtain the optimal results for these problems considered in this research. To achieve better findings, we applied a Modified Firefly Algorithm to solve the problem. Two case studies for a pipe and a steel manufacturing integrated supply chain demonstrated the efficiency of the model and the solutions obtained by the Firefly Algorithm. We used four optimization algorithms in ModeFRONTIER and MATLAB software to test the efficiency of the proposed algorithm. The results revealed that when compared with other four optimization algorithms, Firefly Algorithm can help achieve maximum profits and minimizing the total expected costs of supply chain networks.