Sidi Mohamed Ahmed Ghaly, Mohammad Obaidullah Khan, Mohamed Shalaby, Khalid A. Alsnaie, Majdi Oraiqat
Accurate and real-time measurement of fluid flow velocity is crucial in various industrial processes, especially when dealing with multiple phase fluids. Traditional flow measurement methods often struggle to accurately quantify the velocity of complex multiphase flows within pipes. This challenge necessitates the exploration of innovative techniques capable of providing reliable measurements. This paper proposes the utilization of Electrical Capacitance Tomography (ECT) as a promising approach for measuring the velocity of multiple phase fluids in pipes. The ECT technique involves the non-intrusive imaging of the electrical capacitance distribution within the pipe. By utilizing an array of electrodes placed around the pipe circumference, the capacitance distribution can be reconstructed, offering insight into the fluid flow patterns. By analyzing the temporal changes in the capacitance distribution, the velocity of different phases within the pipe can be estimated. To achieve accurate velocity measurements, an ECT system needs to account for the complexities introduced by multiphase flows. Various image reconstruction algorithms, such as linear back-projection and iterative algorithms like Gauss-Newton and Levenberg-Marquardt, are employed to reconstruct the capacitance distribution. Additionally, advanced signal processing techniques, such as cross-correlation analysis and time-difference methods, are used to extract velocity information from the reconstructed images. This paper presents an experimental investigation of measuring the velocity of multiple-phase fluids in pipes using the ECT technique. The study aims to address the challenges associated with different flow regimes, fluid properties, and pipe geometries by exploring advancements in electrode design, system calibration, and data processing techniques to enhance the accuracy and robustness of ECT-based velocity measurements.
{"title":"Real Time Measurement of Multiphase Flow Velocity using Electrical Capacitance Tomography","authors":"Sidi Mohamed Ahmed Ghaly, Mohammad Obaidullah Khan, Mohamed Shalaby, Khalid A. Alsnaie, Majdi Oraiqat","doi":"10.48084/etasr.6130","DOIUrl":"https://doi.org/10.48084/etasr.6130","url":null,"abstract":"Accurate and real-time measurement of fluid flow velocity is crucial in various industrial processes, especially when dealing with multiple phase fluids. Traditional flow measurement methods often struggle to accurately quantify the velocity of complex multiphase flows within pipes. This challenge necessitates the exploration of innovative techniques capable of providing reliable measurements. This paper proposes the utilization of Electrical Capacitance Tomography (ECT) as a promising approach for measuring the velocity of multiple phase fluids in pipes. The ECT technique involves the non-intrusive imaging of the electrical capacitance distribution within the pipe. By utilizing an array of electrodes placed around the pipe circumference, the capacitance distribution can be reconstructed, offering insight into the fluid flow patterns. By analyzing the temporal changes in the capacitance distribution, the velocity of different phases within the pipe can be estimated. To achieve accurate velocity measurements, an ECT system needs to account for the complexities introduced by multiphase flows. Various image reconstruction algorithms, such as linear back-projection and iterative algorithms like Gauss-Newton and Levenberg-Marquardt, are employed to reconstruct the capacitance distribution. Additionally, advanced signal processing techniques, such as cross-correlation analysis and time-difference methods, are used to extract velocity information from the reconstructed images. This paper presents an experimental investigation of measuring the velocity of multiple-phase fluids in pipes using the ECT technique. The study aims to address the challenges associated with different flow regimes, fluid properties, and pipe geometries by exploring advancements in electrode design, system calibration, and data processing techniques to enhance the accuracy and robustness of ECT-based velocity measurements.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Trong Nghia Le, Hoang Minh Vu Nguyen, Thi Trang Hoang, Ngoc Au Nguyen
Optimizing the operational parameters and control of the power system in steady-state conditions is a crucial issue in reducing the costs of power generation and operation. In the case of long-term operation of a power system, besides aiming to minimize power generation costs, the cost of damage caused by load shedding also needs to be considered. This paper presents the optimization of the total cost of a power system including minimizing the generation cost function of power plants or power companies and minimizing the damage cost function caused to customers due to load shedding or power outages. At the same time, the objective function must also ensure the constraints on the operating conditions of the power system. This contributes to maintaining the continuity of the power supply to critical loads and minimizing damage. Base loads, priority loads, or loads that are not allowed to be shed are considered as constraints. The optimization problem is addressed by using the Particle Swarm Optimization (PSO) algorithm and the Cuckoo Search Algorithm (CSA). The IEEE 30-bus test system is applied to validate the reduction in total cost. The result comparison shows that when applying the CSA, the total cost is significantly reduced by 3.75% in comparison with the PSO algorithm. The algorithms are implemented in Matlab to demonstrate the efficiency and accuracy of the proposed method.
{"title":"Optimizing the Power System Operation Problem towards minimizing Generation and Damage Costs due to Load Shedding","authors":"Trong Nghia Le, Hoang Minh Vu Nguyen, Thi Trang Hoang, Ngoc Au Nguyen","doi":"10.48084/etasr.6221","DOIUrl":"https://doi.org/10.48084/etasr.6221","url":null,"abstract":"Optimizing the operational parameters and control of the power system in steady-state conditions is a crucial issue in reducing the costs of power generation and operation. In the case of long-term operation of a power system, besides aiming to minimize power generation costs, the cost of damage caused by load shedding also needs to be considered. This paper presents the optimization of the total cost of a power system including minimizing the generation cost function of power plants or power companies and minimizing the damage cost function caused to customers due to load shedding or power outages. At the same time, the objective function must also ensure the constraints on the operating conditions of the power system. This contributes to maintaining the continuity of the power supply to critical loads and minimizing damage. Base loads, priority loads, or loads that are not allowed to be shed are considered as constraints. The optimization problem is addressed by using the Particle Swarm Optimization (PSO) algorithm and the Cuckoo Search Algorithm (CSA). The IEEE 30-bus test system is applied to validate the reduction in total cost. The result comparison shows that when applying the CSA, the total cost is significantly reduced by 3.75% in comparison with the PSO algorithm. The algorithms are implemented in Matlab to demonstrate the efficiency and accuracy of the proposed method.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Syed Ibrahim Syed Mahamood Shazuli, Arunachalam Saravanan
Several Deep Learning (DL) and medical image Machine Learning (ML) methods have been investigated for efficient data representations of medical images, such as image classification, Content-Based Image Retrieval (CBIR), and image segmentation. CBIR helps medical professionals make decisions by retrieving similar cases and images from electronic medical image databases. CBIR needs expressive data representations for similar image identification and knowledge discovery in massive medical image databases explored by distinct algorithmic methods. In this study, an Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval (IWOADL-RFIGR) approach was developed. The presented IWOADL-RFIGR method mainly focused on retrieving and classifying retinal fundus images. The proposed IWOADL-RFIGR method used the Bilateral Filtering (BF) method to preprocess the retinal images, a lightweight Convolutional Neural Network (CNN) based on scratch learning with Euclidean distance-based similarity measurement for image retrieval, and the Least Square Support Vector Machine (LS-SVM) model for image classification. Finally, the IWOA was used as a hyperparameter optimization technique to improve overall performance. The experimental validation of the IWOADL-RFIGR model on a benchmark dataset exhibited better performance than other models.
{"title":"Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval","authors":"Syed Ibrahim Syed Mahamood Shazuli, Arunachalam Saravanan","doi":"10.48084/etasr.6111","DOIUrl":"https://doi.org/10.48084/etasr.6111","url":null,"abstract":"Several Deep Learning (DL) and medical image Machine Learning (ML) methods have been investigated for efficient data representations of medical images, such as image classification, Content-Based Image Retrieval (CBIR), and image segmentation. CBIR helps medical professionals make decisions by retrieving similar cases and images from electronic medical image databases. CBIR needs expressive data representations for similar image identification and knowledge discovery in massive medical image databases explored by distinct algorithmic methods. In this study, an Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval (IWOADL-RFIGR) approach was developed. The presented IWOADL-RFIGR method mainly focused on retrieving and classifying retinal fundus images. The proposed IWOADL-RFIGR method used the Bilateral Filtering (BF) method to preprocess the retinal images, a lightweight Convolutional Neural Network (CNN) based on scratch learning with Euclidean distance-based similarity measurement for image retrieval, and the Least Square Support Vector Machine (LS-SVM) model for image classification. Finally, the IWOA was used as a hyperparameter optimization technique to improve overall performance. The experimental validation of the IWOADL-RFIGR model on a benchmark dataset exhibited better performance than other models.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such approach would be the cutting-edge matrix factorization technique. Facebook is one of the most widely used social networking platforms. It has more than one billion monthly active users who engage with each other on the platform by sharing status updates, images, events, and other types of content. Facebook's mission includes fostering stronger connections between individuals, and to that end, the platform employs techniques from recommender systems in an effort to better comprehend the actions and patterns of its users, after which it suggests forming new connections with other users. However, relatively little study has been done in this area to investigate the low-dimensional spaces included within the black box system by employing methods such as matrix factorization. Using a probabilistic matrix factorization approach, the interactions that users have with the posts of other users, such as liking, commenting, and other similar activities, were utilized in an effort to generate a list of potential friends that the user who is the focus of this work may not yet be familiar with. The proposed model performed better in terms of suggestion accuracy in comparison to the original matrix factorization, which resulted in the creation of a recommendation list that contained more correct information.
{"title":"A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach","authors":"Aadil Alshammari, Mohammed Alshammari","doi":"10.48084/etasr.6325","DOIUrl":"https://doi.org/10.48084/etasr.6325","url":null,"abstract":"Existing recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such approach would be the cutting-edge matrix factorization technique. Facebook is one of the most widely used social networking platforms. It has more than one billion monthly active users who engage with each other on the platform by sharing status updates, images, events, and other types of content. Facebook's mission includes fostering stronger connections between individuals, and to that end, the platform employs techniques from recommender systems in an effort to better comprehend the actions and patterns of its users, after which it suggests forming new connections with other users. However, relatively little study has been done in this area to investigate the low-dimensional spaces included within the black box system by employing methods such as matrix factorization. Using a probabilistic matrix factorization approach, the interactions that users have with the posts of other users, such as liking, commenting, and other similar activities, were utilized in an effort to generate a list of potential friends that the user who is the focus of this work may not yet be familiar with. The proposed model performed better in terms of suggestion accuracy in comparison to the original matrix factorization, which resulted in the creation of a recommendation list that contained more correct information.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dam Xuan Dong, Phap Vu Minh, Nguyen Quang Ninh, Dam Xuan Dinh
Agriculture plays an important role in the economy of many countries, including Vietnam. Traditional agricultural manufacturing processes are inefficient in energy and material consumption and generate substantial carbon emissions. In recent decades, environmentalists and policymakers have been actively involved in the transition from conventional fossil fuels to renewables. This study investigated the potential Strengths, Weaknesses, Opportunities, and Threats (SWOT) associated with developing Renewable Energy sources to serve agriculture in Vietnam. The results of the analysis revealed that renewable energy sources have numerous strengths, including reducing greenhouse gas (GHG) emissions and the cost of electricity, accessing new technologies, and providing economic benefits to farmers. However, the system also faces several weaknesses and threats, such as policy mechanisms, infrastructure, investment capital, foreign-dependent technologies, and potential environmental impacts. This study provides strategic recommendations to maximize the potential of agrivoltaic systems while mitigating their weaknesses and threats. The findings can help stakeholders make informed decisions and take appropriate actions in the development of renewable energy sources in agriculture.
{"title":"Development of Renewable Energy Sources to Serve Agriculture in Vietnam: A Strategic Assessment using the SWOT Analysis","authors":"Dam Xuan Dong, Phap Vu Minh, Nguyen Quang Ninh, Dam Xuan Dinh","doi":"10.48084/etasr.6211","DOIUrl":"https://doi.org/10.48084/etasr.6211","url":null,"abstract":"Agriculture plays an important role in the economy of many countries, including Vietnam. Traditional agricultural manufacturing processes are inefficient in energy and material consumption and generate substantial carbon emissions. In recent decades, environmentalists and policymakers have been actively involved in the transition from conventional fossil fuels to renewables. This study investigated the potential Strengths, Weaknesses, Opportunities, and Threats (SWOT) associated with developing Renewable Energy sources to serve agriculture in Vietnam. The results of the analysis revealed that renewable energy sources have numerous strengths, including reducing greenhouse gas (GHG) emissions and the cost of electricity, accessing new technologies, and providing economic benefits to farmers. However, the system also faces several weaknesses and threats, such as policy mechanisms, infrastructure, investment capital, foreign-dependent technologies, and potential environmental impacts. This study provides strategic recommendations to maximize the potential of agrivoltaic systems while mitigating their weaknesses and threats. The findings can help stakeholders make informed decisions and take appropriate actions in the development of renewable energy sources in agriculture.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"288 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yarshini Thamilarasan, Raja Rina Raja Ikram, Mashanum Osman, Lizawati Salahuddin, Wan Yaakob Wan Bujeri, Kasturi Kanchymalay
The objective of this paper is to improve the current System Usability Scale (SUS) and assess its applicability in the context of Learning Management Systems (LMS). The need to evaluate the usability of systems has become increasingly important in today's market, as it can have a significant impact on the user experience. In light of the COVID-19 pandemic, e-learning has become an essential tool for students, making LMS an appropriate research case study. Through a comprehensive literature review, it was discovered that SUS is the most widely used tool for evaluating system usability. However, SUS fails to satisfy some of the usability criteria outlined in ISO 9126 and ISO 9241-11. Therefore, this paper proposes an enhanced SUS model and its conceptual framework to address these limitations. The proposed model was validated using a case study approach, involving subject matter experts and software testing students, who evaluated the reliability of the enhanced SUS. Additionally, the existing and enhanced SUS models were evaluated based on an LMS case study and the results were used to calculate the enhanced SUS's reliability coefficient using Cronbach's alpha. The validation results show that the enhanced SUS has higher reliability with improved quality coverage compared to the original SUS. The proposed model has the potential to enhance the evaluation of system usability and, consequently, improve user experience.
{"title":"Enhanced System Usability Scale using the Software Quality Standard Approach","authors":"Yarshini Thamilarasan, Raja Rina Raja Ikram, Mashanum Osman, Lizawati Salahuddin, Wan Yaakob Wan Bujeri, Kasturi Kanchymalay","doi":"10.48084/etasr.5971","DOIUrl":"https://doi.org/10.48084/etasr.5971","url":null,"abstract":"The objective of this paper is to improve the current System Usability Scale (SUS) and assess its applicability in the context of Learning Management Systems (LMS). The need to evaluate the usability of systems has become increasingly important in today's market, as it can have a significant impact on the user experience. In light of the COVID-19 pandemic, e-learning has become an essential tool for students, making LMS an appropriate research case study. Through a comprehensive literature review, it was discovered that SUS is the most widely used tool for evaluating system usability. However, SUS fails to satisfy some of the usability criteria outlined in ISO 9126 and ISO 9241-11. Therefore, this paper proposes an enhanced SUS model and its conceptual framework to address these limitations. The proposed model was validated using a case study approach, involving subject matter experts and software testing students, who evaluated the reliability of the enhanced SUS. Additionally, the existing and enhanced SUS models were evaluated based on an LMS case study and the results were used to calculate the enhanced SUS's reliability coefficient using Cronbach's alpha. The validation results show that the enhanced SUS has higher reliability with improved quality coverage compared to the original SUS. The proposed model has the potential to enhance the evaluation of system usability and, consequently, improve user experience.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research article presents a comprehensive study on the performance modeling of 3D printed parts using Artificial Neural Networks (ANNs). The aim of this study is to optimize the mechanical properties of 3D printed components through accurate prediction and analysis. The study focuses on the widely employed Fused Deposition Modeling (FDM) technique. The ANN model is trained and validated using experimental data, incorporating input parameters such as temperature, speed, infill direction, and layer thickness to predict mechanical properties including yield stress, Young's modulus, ultimate tensile strength, flexural strength, and elongation at fracture. The results demonstrate the effectiveness of the ANN model with an average error below 10%. The study also reveals the significant impact of process parameters on the mechanical properties of 3D printed parts and highlights the potential for optimizing these parameters to enhance the performance of printed components. The findings of this research contribute to the field of additive manufacturing by providing valuable insights into the optimization of 3D printing processes and facilitating the development of high-performance 3D printed components.
{"title":"Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts","authors":"Sivarao Subramonian, Kumaran Kadirgama, Abdulkareem Sh. Mahdi Al-Obaidi, Mohd Shukor Mohd Salleh, Umesh Kumar Vatesh, Satish Pujari, Dharsyanth Rao, Devarajan Ramasamy","doi":"10.48084/etasr.6185","DOIUrl":"https://doi.org/10.48084/etasr.6185","url":null,"abstract":"This research article presents a comprehensive study on the performance modeling of 3D printed parts using Artificial Neural Networks (ANNs). The aim of this study is to optimize the mechanical properties of 3D printed components through accurate prediction and analysis. The study focuses on the widely employed Fused Deposition Modeling (FDM) technique. The ANN model is trained and validated using experimental data, incorporating input parameters such as temperature, speed, infill direction, and layer thickness to predict mechanical properties including yield stress, Young's modulus, ultimate tensile strength, flexural strength, and elongation at fracture. The results demonstrate the effectiveness of the ANN model with an average error below 10%. The study also reveals the significant impact of process parameters on the mechanical properties of 3D printed parts and highlights the potential for optimizing these parameters to enhance the performance of printed components. The findings of this research contribute to the field of additive manufacturing by providing valuable insights into the optimization of 3D printing processes and facilitating the development of high-performance 3D printed components.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niranjan C. Kundur, Bellary Chiterki Anil, Praveen M. Dhulavvagol, Renuka Ganiger, Balakrishnan Ramadoss
Pneumonia is a severe respiratory disease with potentially life-threatening consequences if not promptly diagnosed and treated. Chest X-rays are commonly employed for pneumonia detection, but interpreting the images can pose challenges. This study explores the efficacy of four popular transfer learning models, namely VGG16, ResNet, InceptionNet, and DenseNet, alongside a custom CNN model for this task. The model performance is evaluated using Mean Absolute Error (MAE) as the performance metric. The findings reveal that VGG16 outperforms the other transfer learning models, achieving the lowest MAE (66.19). To optimize the model training process, a distributed training strategy utilizing TensorFlow's TPU (Tensor Processing Unit) strategy is implemented. The custom CNN model is parallelized using TPU's multiple instances available over the cloud, enabling efficient computation parallelization and significantly reducing model training times. The experimental results demonstrate a remarkable decrease of 68.36% and 54.74% in model training times for the CNN model when trained using TPU compared to training on a CPU and GPU, respectively.
{"title":"Pneumonia Detection in Chest X-Rays using Transfer Learning and TPUs","authors":"Niranjan C. Kundur, Bellary Chiterki Anil, Praveen M. Dhulavvagol, Renuka Ganiger, Balakrishnan Ramadoss","doi":"10.48084/etasr.6335","DOIUrl":"https://doi.org/10.48084/etasr.6335","url":null,"abstract":"Pneumonia is a severe respiratory disease with potentially life-threatening consequences if not promptly diagnosed and treated. Chest X-rays are commonly employed for pneumonia detection, but interpreting the images can pose challenges. This study explores the efficacy of four popular transfer learning models, namely VGG16, ResNet, InceptionNet, and DenseNet, alongside a custom CNN model for this task. The model performance is evaluated using Mean Absolute Error (MAE) as the performance metric. The findings reveal that VGG16 outperforms the other transfer learning models, achieving the lowest MAE (66.19). To optimize the model training process, a distributed training strategy utilizing TensorFlow's TPU (Tensor Processing Unit) strategy is implemented. The custom CNN model is parallelized using TPU's multiple instances available over the cloud, enabling efficient computation parallelization and significantly reducing model training times. The experimental results demonstrate a remarkable decrease of 68.36% and 54.74% in model training times for the CNN model when trained using TPU compared to training on a CPU and GPU, respectively.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria Tanase, Dragos Gabriel Zisopol, Alexandra Ileana Portoaca
This paper presents a technical-economical optimization by maximizing the ratio between the critical buckling pressure (technical characteristic) and the production cost (economic characteristic) of stiffened cylindrical shells, a basic concept of value analysis. Critical buckling load values were determined using both the Finite Element Method (FEM) and analytical calculations to validate the accuracy of the results obtained. The maximum difference between the analytical and numerical results was 10%. Technical-economic optimization was carried out using the design of experiments method with MINITAB 19 and allowed to select the optimal input parameters, stiffener dimensional ratio 0.10, shell wall thickness 2.50 mm, and distance between circumferential stiffeners 400 mm, and identify the main factors that impact the output response. For the optimal constructive configuration, the ratio between the critical buckling load and the production cost of the stiffened cylindrical shells was maximized by 199%.
{"title":"A Study regarding the Technical-Economical Optimization of Structural Components for enhancing the Buckling Resistance in Stiffened Cylindrical Shells","authors":"Maria Tanase, Dragos Gabriel Zisopol, Alexandra Ileana Portoaca","doi":"10.48084/etasr.6135","DOIUrl":"https://doi.org/10.48084/etasr.6135","url":null,"abstract":"This paper presents a technical-economical optimization by maximizing the ratio between the critical buckling pressure (technical characteristic) and the production cost (economic characteristic) of stiffened cylindrical shells, a basic concept of value analysis. Critical buckling load values were determined using both the Finite Element Method (FEM) and analytical calculations to validate the accuracy of the results obtained. The maximum difference between the analytical and numerical results was 10%. Technical-economic optimization was carried out using the design of experiments method with MINITAB 19 and allowed to select the optimal input parameters, stiffener dimensional ratio 0.10, shell wall thickness 2.50 mm, and distance between circumferential stiffeners 400 mm, and identify the main factors that impact the output response. For the optimal constructive configuration, the ratio between the critical buckling load and the production cost of the stiffened cylindrical shells was maximized by 199%.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geopolymer Concrete (GPC) has emerged as an alternative to cement concrete due to its reduced carbon footprint and excellent mechanical properties. However, not much emphasis is made on the development of mix designs using industrial waste. The current study focuses on the mix-design considerations for GPC using fly ash and Ground Granulated Blast Furnace Slag (GGBS). The mix design of GPC involves in selecting materials to produce the desired strength. In this investigation, Water Glass (WG) is used as an activator for the activation of the polymerization reaction. The mix design of GPC is the optimization of a group of various parameters, such as the activator to binder ratio, aggregate to binder ratio, coarse aggregate to fine aggregate ratio, activator concentration, and amount of binder content. The activator to binder ratio affects workability and strength, while the activator concentration influences the polymerization reaction and final strength development. The selection of suitable aggregates plays a vital role in achieving a dense and durable GPC matrix. The mix design for GPC requires a holistic approach that considers the selection of appropriate binders, activators, and aggregates. Proper optimization of these factors can result in excellent strength and durability of the GPC and a reduced carbon footprint. Further research is needed to explore alternative binders, evaluate long-term performance, and establish standardized mix design guidelines for the widespread adoption of GPC in construction.
{"title":"Mix Design of Fly Ash and GGBS based Geopolymer Concrete activated with Water Glass","authors":"Rajashekar Sangi, Bollapragada Shesha Sreenivas, Kandukuri Shanker","doi":"10.48084/etasr.6216","DOIUrl":"https://doi.org/10.48084/etasr.6216","url":null,"abstract":"Geopolymer Concrete (GPC) has emerged as an alternative to cement concrete due to its reduced carbon footprint and excellent mechanical properties. However, not much emphasis is made on the development of mix designs using industrial waste. The current study focuses on the mix-design considerations for GPC using fly ash and Ground Granulated Blast Furnace Slag (GGBS). The mix design of GPC involves in selecting materials to produce the desired strength. In this investigation, Water Glass (WG) is used as an activator for the activation of the polymerization reaction. The mix design of GPC is the optimization of a group of various parameters, such as the activator to binder ratio, aggregate to binder ratio, coarse aggregate to fine aggregate ratio, activator concentration, and amount of binder content. The activator to binder ratio affects workability and strength, while the activator concentration influences the polymerization reaction and final strength development. The selection of suitable aggregates plays a vital role in achieving a dense and durable GPC matrix. The mix design for GPC requires a holistic approach that considers the selection of appropriate binders, activators, and aggregates. Proper optimization of these factors can result in excellent strength and durability of the GPC and a reduced carbon footprint. Further research is needed to explore alternative binders, evaluate long-term performance, and establish standardized mix design guidelines for the widespread adoption of GPC in construction.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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