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Structural optimization of elliptical-tip star propellant grain using sub-problem approximation

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航空动力学报 2012年第2期27卷 457-464页

作者： Muhammad adnan SUN Bing ZHANG Jian-wei Ali Sarosh School of Astronautics Beijing University of Aeronautics and AstronauticsBeijing 100191China

A method is presented here for structural optimization of elliptical-tip star *** grain structural integrity was improved by minimizing the most critical area of inner bore hoop strain during cool *** was done by sub-problem approximation method in conjunction with finite element *** radii of the ellipse were varied during optimization to find the optimal *** optimization resulted in grain geometry having minimum level of Inner bore hoop strain without violating the preset limits of burning *** von mises strain at grain inner bore was also reduced in resultant grain.

关键词： solid propellant grain star grain viscoelastic analysis structure optimization sub-problem approximation

Solution of Algebraic Lyapunov Equation on Positive-Definite Hermitian Matrices by Using Extended Hamiltonian Algorithm

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Computers, Materials & Continua 2018年第2期54卷 181-195页

作者： Muhammad Shoaib Arif Mairaj Bibi adnan Jhangir Department of Mathematics Air UniversityPAF ComplexE-9IslamabadPakistan Department of Mathematics Comsats Institute of Information TechnologyPark RoadChak-ShahzadIslamabadPakistan Department of Mathematics Comsats Institute of Information TechnologyWah CanttPakistan

This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian *** choose the geodesic distance between􀀀AHX􀀀XA and P as the cost function,and put forward the Extended Hamiltonian algorithm(EHA)and Natural gradient algorithm(NGA)for the ***,several numerical experiments give you an idea about the effectiveness of the proposed *** also show the comparison between these two algorithms EHA and *** results are provided and analyzed *** also conclude that the extended Hamiltonian algorithm has better convergence speed than the natural gradient algorithm,whereas the trajectory of the solution matrix is optimal in case of Natural gradient algorithm(NGA)as compared to Extended Hamiltonian Algorithm(EHA).The aim of this paper is to show that the Extended Hamiltonian algorithm(EHA)has superior convergence properties as compared to Natural gradient algorithm(NGA).Upto the best of author’s knowledge,no approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices is found so far in the literature.

关键词： Information geometry algebraic lyapunov equation positive-definite hermitianmatrix manifold natural gradient algorithm extended hamiltonian algorithm

Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

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Computer Systems Science & Engineering 2022年第1期40卷 65-74页

作者： Nagham A.Al-Madi adnan A.Hnaif Al-Zaytoonah University of Jordan Faculty of Science and Information TechnologyAmman11733Jordan

Current traffic signals in Jordan suffer from severe congestion due to many factors,such as the considerable increase in the number of vehicles and the use of fixed timers,which still control existing traffic *** condition affects travel demand on the streets of *** study aims to improve an intelligent road traffic management system(IRTMS)derived from the human community-based genetic algorithm(HCBGA)to mitigate traffic signal congestion in Amman,Jordan’s capital *** parameters considered for IRTMS are total time and waiting time,and fixed timers are still used for *** contrast,the enhanced system,called enhanced-IRTMS(E-IRTMS),considers additional important parameters,namely,the speed performance index(SPI),speed reduction index(SRI),road congestion index(R i),and congestion period,to enhance IRTMS decision.A significant reduction in congestion period was measured using E-IRTMS,improving by 13% compared with that measured using ***,the IRTMS result surpasses that of the current traffic signal system by approximately 83%.This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI,SRI,and R_(i) compared with IRTMS.

关键词： Genetic algorithm traffic signal congestion smart cities

Stump appendicitis is a rare delayed complication of appendectomy:A case report

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World Journal of Gastroenterology 2006年第33期12卷 5401-5403页

作者： Mehmet Uludag adnan Isgor Muzaffer Basak Second Department of General Surgery Sisli Etfal Training and Research Hospital Sisli Istanbul Turkey Golden Horn University Aksaray Istanbul Turkey Department of Radiology Sisli Etfal Training and Research Hospital Sisli Istanbul Turkey

Stump appendicitis is an acute inflammation of the residual appendix and one of the rare complications after appendectomy. Paying attention to the possibility of stump appendicitis in patients with right lower abdominal pain after appendectomy can prevent the delay of diagnosis and treatment. In patients with stump appendicitis, CT scan not only assists in making an accurate preoperative diagnosis but also excludes other etiologies. We report a 47-year old man with preoperatively diagnosed stump appendicitis by CT, who underwent an open appendectomy 20 years ago.

关键词： Stump appendicitis Delayed complication Preoperative diagnosis Computed tomography Incomplete appendectomy

Data-driven Approach for State Prediction and Detection of False Data Injection Attacks in Smart Grid

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Journal of Modern Power Systems and Clean Energy 2023年第2期11卷 455-467页

作者： Haftu Tasew Reda adnan Anwar Abdun Mahmood Naveen Chilamkurti Department of Computer Science and IT La Trobe UniversityBundooraVIC 3086Australia School of IT Deakin UniversityWaurn PondsVIC 3216Australia

In a smart grid,state estimation(SE)is a very important component of energy management *** main functions include system SE and detection of cyber ***,it has been shown that conventional SE techniques are vulnerable to false data injection(FDI)attack,which is a sophisticated new class of attacks on data integrity in smart *** main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model,which is different from the traditional weighted least square based SE *** SE model has a number of unique advantages compared with traditional SE ***,the prediction technique can better maintain the inherent temporal correlations among consecutive measurement ***,the proposed SE model can learn the actual power system ***,this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data *** proposed FDI attack detection technique is evaluated on a number of standard bus *** performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-ofthe-art *** results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.

关键词： Data-driven false data injection machine learning power system security state estimation smart grid

Detection of Lung Nodules on X-ray Using Transfer Learning and Manual Features

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Computers, Materials & Continua 2022年第7期72卷 1445-1463页

作者： Imran Arshad Choudhry adnan N.Qureshi Faculty of Information Technology University of Central PunjabLahorePakistan

The well-established mortality rates due to lung cancers,scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second *** this end,we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health(NIH)chest X-Ray dataset comprised of 30,805 unique *** performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom *** classification with both approaches,Support VectorsMachines(SVM)are *** results from the 5-fold cross validation report Area Under Curve(AUC)of 0.92 and accuracy of 96.87%in detecting lung nodules with the proposed *** is a plausible improvement against the observed accuracy of transfer learning using Inception(79.87%).The specificity of allmethods is>99%,however,the sensitivity of the proposed method(97.24%)surpasses that of transfer learning approaches(<67%).Furthermore,it is observed that the true positive rate with SVM is highest at the same false-positive rate in experiments amongst Random Forests,Decision Trees,and K-Nearest Neighbor ***,the proposed approach can be used in clinical and research environments to provide second opinions very close to the experts’intuition.

关键词： Lungs cancer convolutional neural network hand-crafted feature extraction deep learning classification

An iterative approach for modal analysis of solid rocket motor incorporating frequency dependent modulus

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航空动力学报 2013年第2期28卷 452-458页

作者： Muhammad adnan SUN Bing ZHANG Jian-wei Sarosh Ali School of Astronautics Beijing University of Aeronautics and Astronautics

The modulus of viscoelastic materials varies with excitation ***,during modal analysis of frequency dependent materials,a material evaluation frequency is necessary because stiffness cannot be modified during eigenfrequency *** a result,only those vibration modes are accurate,of which eigenfrequency is close to the material evaluation *** order to obtain vibration modes of solid rocket motor(SRM) using material modulus based on frequency which is the same as the eigenfrequency,an iterative approach was *** of the iterative technique show that frequency modes obtained from the method are in complete agreement with the eigenfrequency and material evaluation frequency.

关键词： solid rocket motor viscoelastic materials modal analysis eigenfrequency frequency dependent modulus

Automatic Unusual Activities Recognition Using Deep Learning in Academia

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Computers, Materials & Continua 2022年第1期70卷 1829-1844页

作者： Muhammad Ramzan adnan Abid Shahid Mahmood Awan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Electronics Computing and MathematicsUniversity of DerbyDerbyUnited Kingdom

In the current era,automatic surveillance has become an active research problem due to its vast real-world applications,particularly for maintaining law and order.A continuous manual monitoring of human activities is a tedious *** use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few *** computer vision techniques have been applied for observation and surveillance of real-world *** research study focuses on detecting and recognizing unusual activities in an academic situation such as examinationhalls,which may help the invigilators observe and restrict the students from cheating or using *** the best of our knowledge,this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual *** proposed model has been named Automatic Unusual Activity Recognition(AUAR),which employsmotion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse *** evaluation using standard performancemeasures confirmthat theAUARmodel outperforms the already proposed approaches for unusual activity *** from evaluating the proposed model on the examination dataset,we also apply AUAR on Violent and Movies datasets,widely used in the relevant literature to detect suspicious *** results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

关键词： Deep learning unusual activities examination CNN surveillance human activity recognition

Geometrically Nonlinear Deformation Reconstruction Based on iQS4 Elements Using a Linearized Iterative iFEM Algorithm

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Acta Mechanica Solida Sinica 2023年第1期36卷 166-180页

作者： Mengying Li Dawei Jia He Huang Ziyan Wu adnan Kefal School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an 710129China Faculty of Engineering and Natural Sciences Sabanci UniversityTuzlaIstanbul 34956Turkey Integrated Manufacturing Technologies Research and Application Center Sabanci UniversityTuzlaIstanbul 34956Turkey

Structural shape monitoring plays a vital role in the structural health monitoring *** inverse finite element method(iFEM)has been demonstrated to be a practical method of deformation reconstruction owing to its unique *** iFEM formulations have been applied to small deformation of structures based on the small-displacement assumption of linear ***,this assumption may be inapplicable to some structures with large displacements in practical ***,geometric nonlinearity needs to be *** this study,to expand the practical utility of iFEM for large displacement monitoring,we propose a nonlinear iFEM algorithm based on a four-node inverse quadrilateral shell element *** the advantage of an iterative iFEM algorithm,a nonlinear response is linearized to compute the geometrically nonlinear deformation reconstruction,like the basic concept of nonlinear FE *** examples are solved to verify the proposed *** is demonstrated that large displacements can be accurately estimated even if the in-situ sensor data includes different levels of randomly generated *** is proven that the nonlinear iFEM algorithm provides a more accurate displacement response as compared to the linear iFEM methodology for structures undergoing large ***,the proposed approach can be utilized as a viable tool to effectively characterize geometrically nonlinear deformations of structures in real-time applications.

关键词： Deformation reconstruction iQS4 element Iterative algorithm Nonlinear iFEM Geometric nonlinearity