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Full-Text Articles in Engineering
Performance Enhancement Of A Solar-Driven Dcmd System Using An Air-Cooled Condenser And Oil: Experimental And Machine Learning Investigations, Pooria Behnam, Abdellah Shafieian, Masoumeh Zargar, Mehdi Khiadani
Performance Enhancement Of A Solar-Driven Dcmd System Using An Air-Cooled Condenser And Oil: Experimental And Machine Learning Investigations, Pooria Behnam, Abdellah Shafieian, Masoumeh Zargar, Mehdi Khiadani
Research outputs 2022 to 2026
Solar-driven direct contact membrane distillation systems (DCMD) are disadvantaged by low freshwater productivity and low gain-output-ratio (GOR). Consequently, this study aims to achieve two primary objectives: i) improving the solar DCMD performance, and ii) harnessing machine learning models for precise and straightforward modeling of the solar DCMD system. To achieve these goals, a novel solar DCMD system powered with oil-filled heat pipe evacuated tube collectors (HP-ETCs) and equipped with an air-cooled condenser was used for the first time. The system was evaluated under eight different scenarios covering both its energy and economic performances. The performance prediction of three different machine …
Enhancing Wettability Prediction In The Presence Of Organics For Hydrogen Geo-Storage Through Data-Driven Machine Learning Modeling Of Rock/H2/Brine Systems, Zeeshan Tariq, Muhammad Ali, Nurudeen Yekeen, Auby Baban, Bicheng Yan, Shuyu Sun, Hussein Hoteit
Enhancing Wettability Prediction In The Presence Of Organics For Hydrogen Geo-Storage Through Data-Driven Machine Learning Modeling Of Rock/H2/Brine Systems, Zeeshan Tariq, Muhammad Ali, Nurudeen Yekeen, Auby Baban, Bicheng Yan, Shuyu Sun, Hussein Hoteit
Research outputs 2022 to 2026
The success of geological H2 storage relies significantly on rock–H2–brine interactions and wettability. Experimentally assessing the H2 wettability of storage/caprocks as a function of thermos-physical conditions is arduous because of high H2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling predictions of rock–H2–brine wettability are less strenuous and more precise. They can be conducted at geo-storage conditions that are impossible or hazardous to attain in the laboratory. Thus, ML models were utilized in this research to accurately model the wettability behavior of a ternary system consisting of H2, rock minerals (quartz and mica), and brine at different operating geological …
Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani
Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani
Research outputs 2022 to 2026
Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system …
Machine Learning Methods For Inferring The Number Of Uav Emitters Via Massive Mimo Receive Array, Yifan Li, Feng Shu, Jinsong Hu, Shihao Yan, Haiwei Song, Weiqiang Zhu, Da Tian, Yaoliang Song, Jiangzhou Wang
Machine Learning Methods For Inferring The Number Of Uav Emitters Via Massive Mimo Receive Array, Yifan Li, Feng Shu, Jinsong Hu, Shihao Yan, Haiwei Song, Weiqiang Zhu, Da Tian, Yaoliang Song, Jiangzhou Wang
Research outputs 2022 to 2026
To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by …
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
Research outputs 2022 to 2026
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we …
An Intelligent Approach For Predicting The Strength Of Geosynthetic-Reinforced Subgrade Soil, Muhammad Nouman Amjad Raja, Sanjay K. Shukla, Muhammad Umer Arif Khan
An Intelligent Approach For Predicting The Strength Of Geosynthetic-Reinforced Subgrade Soil, Muhammad Nouman Amjad Raja, Sanjay K. Shukla, Muhammad Umer Arif Khan
Research outputs 2014 to 2021
In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model …
Artificial Intelligence-Based Material Discovery For Clean Energy Future, Reza Maleki, Mohsen Asadnia, Amir Razmjou
Artificial Intelligence-Based Material Discovery For Clean Energy Future, Reza Maleki, Mohsen Asadnia, Amir Razmjou
Research outputs 2022 to 2026
Artificial intelligence (AI)-assisted materials design and discovery methods can come to the aid of global concerns for introducing new efficient materials in different applications. Also, a sustainable clean future requires a transition to a low-carbon economy that is material-intensive. AI-assisted methods advent as inexpensive and accelerated methods in the design of new materials for clean energies. Herein, the emerging research area of AI-assisted material discovery with a focus on developing clean energies is discussed. The applications, advantages, and challenges of using AI in material discovery are discussed and the future perspective of using AI in clean energy is studied. This …
Physical Layer Authentication Using Ensemble Learning Technique In Wireless Communications, Muhammad Waqas, Shehr Bano, Fatima Hassan, Shanshan Tu, Ghulam Abbas, Ziaul Haq Abbas
Physical Layer Authentication Using Ensemble Learning Technique In Wireless Communications, Muhammad Waqas, Shehr Bano, Fatima Hassan, Shanshan Tu, Ghulam Abbas, Ziaul Haq Abbas
Research outputs 2022 to 2026
Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the most promising solution to detect …
Analysis Of Gps And Uwb Positioning System For Athlete Tracking, Adnan Waqar, Iftekhar Ahmad, Daryoush Habibi, Quoc Viet Phung
Analysis Of Gps And Uwb Positioning System For Athlete Tracking, Adnan Waqar, Iftekhar Ahmad, Daryoush Habibi, Quoc Viet Phung
Research outputs 2014 to 2021
In recent years, wearable performance monitoring systems have become increasingly popular in competitive sports. Wearable devices can provide vital information including distance covered, velocity, change of direction, and acceleration, which can be used to improve athlete performance and prevent injuries. Tracking technology that monitors the movement of an athlete is an important element of sport wearable devices. For tracking, the cheapest option is to use global positioning system (GPS) data however, their large margins of error are a major concern in many sports. Consequently, indoor positioning systems (IPS) have become popular in sports in recent years where the ultra-wideband (UWB) …
Quantitative Prediction Of Fractures In Shale Using The Lithology Combination Index, Zhengchen Zhang, Pingping Li, Yujie Yuan, Kouqi Liu, Jingyu Hao, Huayao Zou
Quantitative Prediction Of Fractures In Shale Using The Lithology Combination Index, Zhengchen Zhang, Pingping Li, Yujie Yuan, Kouqi Liu, Jingyu Hao, Huayao Zou
Research outputs 2014 to 2021
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Fractures, which are related to tectonic activity and lithology, have a significant impact on the storage and production of oil and gas in shales. To analyze the impact of lithological factors on fracture development in shales, we selected the shale formation from the Da’anzhai member of the lower Jurassic shales in a weak tectonic deformation zone in the Sichuan Basin. We defined a lithology combination index (LCI), that is, an attribute quantity value of some length artificially defined by exploring the lithology combination. LCI contains information on shale content at a …
A Comprehensive Review Of Fruit And Vegetable Classification Techniques, Khurram Hameed, Douglas Chai, Alexander Rassau
A Comprehensive Review Of Fruit And Vegetable Classification Techniques, Khurram Hameed, Douglas Chai, Alexander Rassau
Research outputs 2014 to 2021
Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for …