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- Department of Mechanical and Materials Engineering: Faculty Publications (4)
- Department of Chemistry: Dissertations, Theses, and Student Research (1)
- Durham School of Architectural Engineering and Construction: Dissertations, Thesis, and Student Research (1)
- Library Philosophy and Practice (e-journal) (1)
Articles 1 - 7 of 7
Full-Text Articles in Engineering
Finite Element-Based Machine Learning Model For Predicting The Mechanical Properties Of Composite Hydrogels, Yasin Shokrollahi, Pengfei Dong, Peshala T. Gamage, Nashaita Patrawalla, Vipuil Kishore, Hozhabr Mozafari, Linxia Gu
Finite Element-Based Machine Learning Model For Predicting The Mechanical Properties Of Composite Hydrogels, Yasin Shokrollahi, Pengfei Dong, Peshala T. Gamage, Nashaita Patrawalla, Vipuil Kishore, Hozhabr Mozafari, Linxia Gu
Department of Mechanical and Materials Engineering: Faculty Publications
In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 μm to 1.5 μm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used …
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem, Sara A. Myers
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem, Sara A. Myers
Department of Mechanical and Materials Engineering: Faculty Publications
Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi M. Alsaleem, Sara A. Myers
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi M. Alsaleem, Sara A. Myers
Department of Mechanical and Materials Engineering: Faculty Publications
Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …
Ultra-Broadband And Polarization-Insensitive Metasurface Absorber With Behavior Prediction Using Machine Learning, Shobhit K. Patel, Juveriya Parmar, Vijay Katkar, Fahad Ahmed Al-Zahrani, Kawsar Ahmed
Ultra-Broadband And Polarization-Insensitive Metasurface Absorber With Behavior Prediction Using Machine Learning, Shobhit K. Patel, Juveriya Parmar, Vijay Katkar, Fahad Ahmed Al-Zahrani, Kawsar Ahmed
Department of Mechanical and Materials Engineering: Faculty Publications
The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurface solar absorber based on Ge2Sb2Te5 (GST) substrate which increases the absorption in visible, infrared and ultraviolet regions. GST is a phase-changing material having two different phases amorphous (aGST) and crystalline (cGST). The absorber is also analyzed using machine learning algorithm to predict the absorption values for different wavelengths. The solar absorber is showing an ultra-broadband response covering a 0.2–1.5 …
Investigation Of The Prevalence Of Faults In The Heating, Ventilation, And Air-Conditioning Systems Of Commercial Buildings, Amir Ebrahimifakhar
Investigation Of The Prevalence Of Faults In The Heating, Ventilation, And Air-Conditioning Systems Of Commercial Buildings, Amir Ebrahimifakhar
Durham School of Architectural Engineering and Construction: Dissertations, Thesis, and Student Research
This dissertation describes a large-scale investigation of heating, ventilation, and air-conditioning (HVAC) fault prevalence in commercial buildings in the United States. A multi-year dataset with 36,556 pieces of HVAC equipment including air handling units (AHUs), air terminal units (ATUs), and packaged rooftop units (RTUs) was analyzed to determine values for several HVAC fault prevalence metrics. The primary source of data for this study comes from three commercial fault detection and diagnostics (FDD) providers. Since each FDD provider uses different terms to refer to the same fault in an HVAC system, a mapping function was created for each FDD provider’s dataset, …
Artificial Intelligence In Plasma Electrolytic Micro-Oxidation For Surface Hardening - Insights From Scholarly Citation Networks And Patents., Priya Jadhav, Dr.Arun Bongale, Dr.Satish Kumar, Dr.Amit Kumar Tiwari
Artificial Intelligence In Plasma Electrolytic Micro-Oxidation For Surface Hardening - Insights From Scholarly Citation Networks And Patents., Priya Jadhav, Dr.Arun Bongale, Dr.Satish Kumar, Dr.Amit Kumar Tiwari
Library Philosophy and Practice (e-journal)
Objective - The purpose of this article is to analyze the top work areas and patent domains in the field of surface hardening by micro-arc oxidation. Also, it is directed on the opportunities of data analysis by different machine learning tools. Material and methods - The www.lens.org database is used to collect articles from Elsevier, Trans tech publications, Springer New York, MDPI, etc. to review the relevant articles as well as patents related to the topic. The result - A total of 1057 articles were published in 60 different journals and 756 patents in the area of research under various …
Computational Studies Of Thermal Properties And Desalination Performance Of Low-Dimensional Materials, Yang Hong
Department of Chemistry: Dissertations, Theses, and Student Research
During the last 30 years, microelectronic devices have been continuously designed and developed with smaller size and yet more functionalities. Today, hundreds of millions of transistors and complementary metal-oxide-semiconductor cells can be designed and integrated on a single microchip through 3D packaging and chip stacking technology. A large amount of heat will be generated in a limited space during the operation of microchips. Moreover, there is a high possibility of hot spots due to non-uniform integrated circuit design patterns as some core parts of a microchip work harder than other memory parts. This issue becomes acute as stacked microchips get …