College of Engineering and Applied Sciences

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Now showing 1 - 5 of 236
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    Segmentation of thermographies from electronic systems by using the global-best brain storm optimization algorithm
    (Springer Netherlands, 2023-12-01) El-Abd, Mohammed; Mousavirad, Seyed Jalaleddin; Nadimi-Shahraki, Mohammad H.; Navarro, Mario A.; Oliva, Diego; Ortega-Sanchez, Noé; Ramos-Michel, Alfonso
    Segmentation is an important and basic task in image processing. Although no unique method is applicable to all types of images (as thermographies), multilevel thresholding is one of the most widely used techniques for this purpose. Multilevel thresholding segmentation has a major drawback that is to properly find the best configuration of thresholds. For that reason some metaheuristic algorithms are used to optimize the searching for the best thresholds. This paper proposes a combination of the minimum cross-entropy method and the Global-best brain storm optimization algorithm (GBSO), which improves the standard BSO to find the optimal solutions in complex search spaces. The GBSO uses a population of agents based on a global best and a re-initialization scheme that is triggered by the current state of its population. Here, the GBSO is used to find the best configuration of thresholds by optimizing the minimum cross entropy that is commonly using in image segmentation. Once the best thresholds are obtained they are applied over the images to extract only the regions of interest. For example, in the case of thermographies the parts with higher temperatures. To verify the performance of the proposed method it is firstly applied to classical reference images and after that over thermal images from electronic devices. The idea is to provide an alternative to segment thermographies that permits separating regions with higher temperatures. This could be used as a preprocessing step in a complex image processing system. The experimental result in terms of segmentation of electronic devices in thermographies provides evidence of the good performance of the GBSO. Different comparison with recent methods from the state-of-the-art were conducted where the GBSO obtains 1st place with the best values for the MCET. To validate the quality of segmentation they were used metrics as the peak signal-to-noise ratio (PSNR) where the GBSO is in the 4th rank of comparison, the structural similarity index (SSIM) and the feature similarity index (FSIM). For the FSIM and SSIM the GBSO in the 4th and 3rd rank, respectively.
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    Green Certificate-Driven Photovoltaic Promotion in Distribution Networks Hosting Hydrogen Fueling Stations for Future Sustainable Transportation
    (Elsevier BV, 2023-12-01) Alasedi, Kasim Kadhim; Alenizi, Farhan A.; Alsaalamy, Ali; Bostani, Ali; El-Shafai, Walid; GUO, Xiaoqiang; LI, Xiao; Mehbodniya, Abolfazl; Mohammed, F. Adil Hussein; Mohsen, Karrar Shareef; Uktamov, Khusniddin Fakhriddinovich
    The growing number of hydrogen vehicles (HVs) has necessitated the development of hydrogen fueling stations (HFSs) to meet the hydrogen demand. This development will target environmental concerns related to electricity generation as HFSs consume power to convert electricity into hydrogen. This study focuses on the optimal risk-aware scheduling problem of a distributed network highly penetrated with photovoltaic (PV) resources. The model addresses the optimal operation of HFs under time-of-use, demand response, and multi-market mechanisms with an expanded role for PV generation under the green certificate (GCT) approach. This brings further environmental and economic benefits, as there is a growing global emphasis on the shift to a low-carbon economy. However, the uncertainties arising from PV operation, HVs’ demand, electricity load, and market prices, potentially affect the decision-maker's ability under the risky conditions. Though second-order stochastic dominance (STD) is implemented for risk management. Results show that applying the GCT method increases 5% (from 0.52 to 0.61 MW) of renewable generation and reduces 23% (300 kg) of CO2 emissions. As the conservativity of decision-makers enhances, 10% of further operation costs are imposed on the system. Results indicate that next to curbing CO2 emissions, the flexibility and robustness of the system can be improved.
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    Recent Machine-Learning-Driven Developments in E-Commerce: Current Challenges and Future Perspectives
    (Engineered Science Publisher, 2023-12-07) Al-Ebrahim, Meshari A.; Bunian, Sara; Nour, Amro A.
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    Thermal-hydraulic characteristics of nitric acid: An experimental and numerical analysis
    (Elsevier BV, 2024-01-15) Alali, Sabah A.; Alanezi, Khaled M; Al-Ebrahim, Meshari A.; Alhasan, Meshal F; Bunian, Sara; Nour, Amro
    Nitric acid is one of the most important products in the chemical industry, ranking third globally in terms of acid production. Although nitric acid has many industrial applications, its primary function is the production of ammonium nitrate, which is used in the fertilizer industry. In this report, we propose a plan for an Ostwald process plant that will produce 1000 metric tons of nitric acid per day. Based on an effective energy analysis, we have concluded that using a single pressure method provides optimal results. First, ammonia is vaporized using process heat at 1000 kPa and 35 °C before being superheated using steam to a temperature of 80 °C. Filtered air is compressed by an axial compressor to a discharge pressure of approximately 740 kPa and a temperature of 155 °C. After conducting a site evaluation, three existing manufacturing sites are being considered for the acid production plant: Ogun State (Nigeria), Gwadar Seaport (Pakistan), and Ras-Alkhair Seaport (Saudi Arabia). Based on the results of the site assessment, Ras-Alkhair has been selected as the most suitable location for the nitric acid plant. About 65 % of all nitric acid produced worldwide is used in the production of ammonium nitrate, which is in turn used in the fertilizer and explosives industries. The synthetic nitric acid that will be produced from this plant will be used in the production of fertilizers.
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    Fuzzy particle swarm for the right-first-time of fused deposition
    (IOS Press BV, 2023-12-02) Abuhamour, Saif O.; Abukaraky, Ashraf E.; AlAlaween, Wafa'; Alalawin, Abdallah H.; Alsoussi, Ahmad; Gharaibeh, Belal M.Y.; Mahfouf, Mahdi
    Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient.