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MONOTONE ITERATION SCHEME FOR A FORCED DUFFING EQUATION WITH NONLOCAL THREE-POINT CONDITIONS
Alsaedi, Ahmed Korean Mathematical Society 2007 대한수학회논문집 Vol.22 No.1
In this paper, we apply the generalized quasilinearization technique to a forced Duffing equation with three-point mixed nonlinear nonlocal boundary conditions and obtain sequences of upper and lower solutions converging monotonically and quadratically to the unique solution of the problem.
A Review on Preserving Data Confidentiality in Blockchain-based IoT-Supply Chain Systems
Omimah Alsaedi,Omar Batarfi,Mohammed Dahab International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.11
Data confidentiality refers to the characteristic that information kept undisclosed or hidden from unauthorized parties. It considered a key security requirement in current supply chain management (SCM) systems. Currently, academia and industry tend to adopt blockchain and IoT technologies in order to develop efficient and secure SCM systems. However, providing confidential data sharing among these technologies is quite challenging due to the limitations associated with blockchain and IoT devices. This review paper illustrates the importance of preserving data confidentiality in SCM systems by highlighting the state of the art on confidentiality-preserving methodologies in the context of blockchain based IoT-SCM systems and the challenges associated with it.
Secure and Scalable Blockchain-Based Framework for IoT-Supply Chain Management Systems
Omimah, Alsaedi,Omar, Batarfi,Mohammed, Dahab International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.12
Modern supply chains include multiple activities from collecting raw materials to transferring final products. These activities involve many parties who share a huge amount of valuable data, which makes managing supply chain systems a challenging task. Current supply chain management (SCM) systems adopt digital technologies such as the Internet of Things (IoT) and blockchain for optimization purposes. Although these technologies can significantly enhance SCM systems, they have their own limitations that directly affect SCM systems. Security, performance, and scalability are essential components of SCM systems. Yet, confidentiality and scalability are one of blockchain's main limitations. Moreover, IoT devices are lightweight and have limited power and storage. These limitations should be considered when developing blockchain-based IoT-SCM systems. In this paper, the requirements of efficient supply chain systems are analyzed and the role of both IoT and blockchain technologies in providing each requirement are discussed. The limitations of blockchain and the challenges of IoT integration are investigated. The limitations of current literature in the same field are identified, and a secure and scalable blockchain-based IoT-SCM system is proposed. The proposed solution employs a Hyperledger fabric blockchain platform and tackles confidentiality by implementing private data collection to achieve confidentiality without decreasing performance. Moreover, the proposed framework integrates IoT data to stream live data without consuming its limited resources and implements a dualstorge model to support supply chain scalability. The proposed framework is evaluated in terms of security, throughput, and latency. The results demonstrate that the proposed framework maintains confidentiality, integrity, and availability of on-chain and off-chain supply chain data. It achieved better performance through 31.2% and 18% increases in read operation throughput and write operation throughput, respectively. Furthermore, it decreased the write operation latency by 83.3%.
Ahmad, Bashir,Alsaedi, Ahmed,Garout, Doa'a Korean Mathematical Society 2008 대한수학회지 Vol.45 No.5
In this paper, we consider an impulsive nonlinear second order ordinary differential equation with nonlinear three-point boundary conditions and develop a monotone iteration scheme by relaxing the convexity assumption on the function involved in the differential equation and the concavity assumption on nonlinearities in the boundary conditions. In fact, we obtain monotone sequences of iterates (approximate solutions) converging quadratically to the unique solution of the impulsive three-point boundary value problem.
Bashir Ahmad,Ahmed Alsaedi,Doa'A Garout 대한수학회 2008 대한수학회지 Vol.45 No.5
In this paper, we consider an impulsive nonlinear second order ordinary differential equation with nonlinear three-point boundary conditions and develop a monotone iteration scheme by relaxing the convexity assumption on the function involved in the differential equation and the concavity assumption on nonlinearities in the boundary conditions. In fact, we obtain monotone sequences of iterates (approximate solutions) converging quadratically to the unique solution of the impulsive three-point boundary value problem. In this paper, we consider an impulsive nonlinear second order ordinary differential equation with nonlinear three-point boundary conditions and develop a monotone iteration scheme by relaxing the convexity assumption on the function involved in the differential equation and the concavity assumption on nonlinearities in the boundary conditions. In fact, we obtain monotone sequences of iterates (approximate solutions) converging quadratically to the unique solution of the impulsive three-point boundary value problem.
Alsulami, Fairouz,Alseleahbi, Hind,Alsaedi, Rawan,Almaghdawi, Rasha,Alafif, Tarik,Ikram, Mohammad,Zong, Weiwei,Alzahrani, Yahya,Bawazeer, Ahmed International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.9
Glaucoma is a chronic neuropathy that affects the optic nerve which can lead to blindness. The detection and prediction of glaucoma become possible using deep neural networks. However, the detection performance relies on the availability of a large number of data. Therefore, we propose different frameworks, including a hybrid of a generative adversarial network and a convolutional neural network to automate and increase the performance of glaucoma detection. The proposed frameworks are evaluated using five public glaucoma datasets. The framework which uses a Deconvolutional Generative Adversarial Network (DCGAN) and a DenseNet pre-trained model achieves 99.6%, 99.08%, 99.4%, 98.69%, and 92.95% of classification accuracy on RIMONE, Drishti-GS, ACRIMA, ORIGA-light, and HRF datasets respectively. Based on the experimental results and evaluation, the proposed framework closely competes with the state-of-the-art methods using the five public glaucoma datasets without requiring any manually preprocessing step.
Decomposition-based Gradient Estimation Algorithms for Multivariable Equation-error Systems
Xian Lu,Feng Ding,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.8
This paper concerns the parameter identification methods of multivariable equation-error systems. By means of the decomposition technique, the multivariable identification model is transformed into two subidentification models and a decomposition-based stochastic gradient (D-SG) algorithm is presented for estimating the parameters of these two submodels. In order to further improve the convergence rate and the parameter estimation accuracy, we expand the innovation vectors to the innovation matrices and develop a decomposition-based multi-innovation stochastic gradient (D-MISG) algorithm. The simulation results confirm that the D-MISG algorithm can provide more accurate parameter estimates than the D-SG algorithm.
Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data
Ling Xu,Weili Xiong,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.4
This paper studies the problem of parameter estimation for frequency response signals. For a linear system, the frequency response is a sine signal with the same frequency as the input sine signal. When a multifrequency sine signal is applied to a system, the system response also is a multi-frequency sine signal. The signal modeling for multi-frequency sine signals is very difficult due to the highly nonlinear relations between the characteristic parameters and the model output. In order to obtain the parameter estimates of the multi-frequency sine signal, the signal modeling methods based on statistical identification are proposed by means of the dynamical window discrete measured data. By constructing a criterion function with respect to the model parameters to be estimated, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition. Moreover, the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm. The simulation results show the effectiveness of the proposed methods.
Recursive Identification Algorithms for a Class of Linear Closed-loop Systems
Huan Xu,Feng Ding,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.12
This paper focuses on the identification problems for a class of linear closed-loop systems. On one hand, the identifiability condition is investigated for the case where the controller is in series with the plant on the forward channel. On the other hand, the identification model is derived after parametrization, in which the parameter vector only contains the parameters of the controlled plant instead of the whole closed-loop system, and a recursive least squares algorithm and a stochastic gradient algorithm are proposed for closed-loop systems. In order to improve the parameter estimation accuracy, a forgetting factor and a convergence index are introduced into the proposed stochastic gradient algorithm. The simulation results demonstrate the effectiveness of the proposed algorithms.