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Case Study: Spark GPU-Enabled Framework to Control COVID-19 Spread Using Cell-Phone Spatio-Temporal Data

Computers, Materials & Continua 2020年第11期65卷 1303-1320页

作者： hussein Shahata Abdallah Mohamed H.Khafagy Fatma A.Omara Department of Computer Science Fayoum UniversityFayoum63514Egypt Department of Computer Science Cairo UniversityGiza1261Egypt

Nowadays,the world is fighting a dangerous form of Coronavirus that represents an emerging *** its early appearance in China Wuhan city,many countries undertook several strict regulations including lockdowns and social distancing ***,these procedures have badly impacted the world *** and isolating positive/probable virus infected cases using a tree tracking mechanism constitutes a backbone for containing and resisting such fast spreading *** helping this hard effort,this research presents an innovative case study based on big data processing techniques to build a complete tracking system able to identify the central areas of infected/suspected people,and the new suspected cases using health records integration with mobile stations spatio-temporal data *** main idea is to identify the positive cases historical movements by tracking their phone location for the last 14 days(i.e.,the virus incubation period).Then,by acquiring the citizen’s mobile phone locations for the same period,the system will be able to measure the Euclidean distances between positive case locations and other nearby people to identify the in-contact suspected-cases using parallel clustering and classification ***,the daily change of the clusters size and its centroids will be used to predict new regions of infection,as well as,new ***,this approach will support infection avoidance by alerting people approaching areas of high probability of infection using their mobile GPS *** case study has been developed as a simulation system consisting of three components;positive cases/citizens movement’s data generation subsystem,big data processing platform including CPU/GPU tasks,and data visualization/map geotagging *** processing of such a big data system requires intensive computing ***,GPU tasks carried out to achieve high performance and accelerate the data *** to the sim

关键词： Big data Coronavirus(COVID-19) parallel K-Means GPU Spark

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An Expanded Optimal Control Policy for a Coupled Tanks System with Random Disturbance

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Advances in Pure Mathematics 2019年第4期9卷 317-329页

作者： Sie Long Kek Sy Yi sim Chuei Yee Chen Department of Mathematics and Statistics Universiti Tun Hussein Onn Malaysia Pagoh Malaysia Department of Electrical Engineering Technology Universiti Tun Hussein Onn Malaysia Pagoh Malaysia Department of Mathematics Universiti Putra Malaysia Serdang Malaysia

In this paper, an expanded optimal control policy is proposed to study the coupled tanks system, where the random disturbance is added into the system. Since the dynamics of the coupled tanks system can be formulated as a nonlinear system, determination of the optimal water level in the tanks is useful for the operation decision. On this point of view, the coupled tanks system dynamics is usually linearized to give the steady state operating height. In our approach, a model-based optimal control problem, which is adding with adjusted parameters, is considered to obtain the true operating height of the real coupled tanks system. During the computation procedure, the differences between the real plant and the model used are measured repeatedly, where the optimal solution of the model used is updated. On this basis, system optimization and parameter estimation are integrated. At the end of the iteration, the iterative solution approximates to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. In conclusion, the efficiency of the approach proposed is shown.

关键词： Expanded Optimal Control Coupled Tanks System Model-Reality Differences Iterative Solution Adjusted Parameters

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Discrete-Time Nonlinear Stochastic Optimal Control Problem Based on Stochastic Approximation Approach

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Advances in Pure Mathematics 2018年第3期8卷 232-244页

作者： Sie Long Kek Sy Yi sim Wah June Leong Kok Lay Teo Department of Mathematics and Statistics Universiti Tun Hussein Onn Malaysia Pagoh Malaysia Department of Electrical Engineering Technology Universiti Tun Hussein Onn Malaysia Pagoh Malaysia Department of Mathematics Universiti Putra Malaysia Serdang Malaysia Department of Mathematics and Statistics Curtin University of Technology Perth Australia

In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimal control problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended.

关键词： Nonlinear Optimal Control Output Error Model-Reality Differences Iterative Solution Stochastic Approximation

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Application of Conjugate Gradient Approach for Nonlinear Optimal Control Problem with Model-Reality Differences

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Applied Mathematics 2018年第8期9卷 940-953页

作者： Sie Long Kek Wah June Leong Sy Yi sim Kok Lay Teo Department of Mathematics and Statistics Universiti Tun Hussein Onn Malaysia Campus Pagoh Panchor Malaysia Department of Mathematics Universiti Putra Malaysia Serdang Malaysia Department of Electrical Engineering Technology Universiti Tun Hussein Onn Malaysia Campus Pagoh Panchor Malaysia Department of Mathematics and Statistics Curtin University of Technology Perth Australia

In this paper, an efficient computational algorithm is proposed to solve the nonlinear optimal control problem. In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. The aim of applying this model is to take into account the differences between the real plant and the model used during the calculation procedure. In doing so, an expanded optimal control problem is introduced such that system optimization and parameter estimation are mutually interactive. Accordingly, the optimality conditions are derived after the Hamiltonian function is defined. Specifically, the modified model-based optimal control problem is resulted. Here, the conjugate gradient approach is used to solve the modified model-based optimal control problem, where the optimal solution of the model used is calculated repeatedly, in turn, to update the adjusted parameters on each iteration step. When the convergence is achieved, the iterative solution approaches to the correct solution of the original optimal control problem, in spite of model-reality differences. For illustration, an economic growth problem is solved by using the algorithm proposed. The results obtained demonstrate the efficiency of the algorithm proposed. In conclusion, the applicability of the algorithm proposed is highly recommended.

关键词： Nonlinear Optimal Control Conjugate Gradient Approach Iterative Solution Adjusted Parameters Model-Reality Differences

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