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RISS 인기검색어
- 검색키워드
- 저자 : Ali Sadaqat (검색결과 3 건)
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Ali Sadaqat,Haider Shafiq,Huda Aamina Bintul,Hadi Hussain,Ammar Khawaja 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2
In this manuscript, various balanced-truncation methods for limited frequency interval model order reduction applications of unstable second order systems (SOSs) are considered. Limited interval gramians are computed by solving the proposed algebraic Lyapunov equations (CALEs). Reduced order model (ROM) performance is optimized in required interval of frequency. It is observed that CALEs for unstable-SOSs become unsolvable and thus halt the reduction-procedure. To bridle this restraint, in first proposed method, fragmentation of continuous time unstable SOS in to stable and unstable segments is performed and ROM for stable part is computed. ROM thus acquired for stable segment is appended with unstable segment to obtain the reduced model in total. However, SOS-structure in ROM obtained from first proposed method gets lost and appended unstable part degrade ROM integrity. To avert these restraints, in later part, structure preserving approaches for unstable SOSs are presented. In structure preserving technique, system is made stable using Bernoulli feedback and gramians are calculated by solving frequency-limited CALEs. The resultant gramians are partitioned in to position and velocity portions in order to achieve structure-preservation so that interpretation of original-system is retained. The partitioned gramians are balanced with different combinations to achieve various second order balancing procedures that provide ROM with optimized performance in required frequency-interval. The comparison of proposed frequency-limited and infinite interval procedures is performed to prove the superiority of proposed procedures for multiple systems. The proposed development can be utilized for ROM performance optimization applications in limited-frequency interval for unstable-SOSs.
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Preparation of graphene-coated anodic alumina substrates for selective molecular transport
Akhtar Sultan,Ali Sadaqat,Kafiah Feras M.,Ibrahim Ahmed,Matin Asif,Laoui Tahar 한국탄소학회 2020 Carbon Letters Vol.30 No.1
In this paper, we report graphene composite membranes prepared by transfer of a layer of chemical vapor deposition graphene onto porous anodic alumina (AA) substrates with nominal pore size 20 and 30 nm, referred as 20AA and 30AA. The coated and uncoated substrates were characterized using optical and electron microscopy techniques. The bare substrates exhibited a smooth surfaces with a well-organized array of hexagonal pores, displaying an average pore size of 17±3 (20AA) and 23±3 nm (30AA). The scanning electron microscopy and atomic force microscopy analyses confrmed the successful transfer of graphene layer onto the target substrates. The molecular transport study was performed by introducing 0.5 M potassium chloride (KCl) and deionized water in a Side-bi-Side Franz difusion cell. The graphene/20AA specimen blocked 66% ions transport, and graphene/30AA membrane about 64%. The ions blockage exceeded 90%, near the characteristics of defect�free graphene when the defects of the transferred graphene were sealed with Nylon 6,6. The results of this study suggest the potential use of graphene on AA substrates for water desalination and gas purifcation applications.
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Comparative Analysis for Chronic Disease Prediction via Deep Machine Learning Approaches
Rabia Javed,Tahir Abbas,Jamshaid Iqbal Janjua,Sadaqat Ali Ramay,M. Kashan Basit,Muhammad Irfan 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
Globally, chronic diseases have a significant impact on health. The diagnosis of chronic diseases has seen extensive usage of machine learning techniques. Early disease detection and treatment lower the risk of increasing disease severity and, consequently, related mortality. The major goal of this research is to provide a technique that increases classification accuracy while also shortening computing time. This comparative research shows the impact of distinct model architectures and features on disease prediction accuracy in addition to assessing the advantages and disadvantages of each technique. These discoveries have implications for personalized healthcare, allowing medical professionals to select the best models for various chronic conditions. Additionally, this research can direct the creation of better forecasting technologies, as well as influence healthcare legislation and budget allocation. In our study comparative analysis of the state-of-the-art approaches has been presented. Using a hybrid model combination of CNN and RNN could be more beneficial. In conclusion, our comparison research improves our comprehension of the potential of deep machine learning for chronic disease prediction, highlighting the significance of adjusting model selection to certain disease types. To progress the field of chronic disease prediction, future research should concentrate on improving these models, and further explore their applicability across various and larger datasets.
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