MalScanner is a tool that aims to provide a simple, effective, and user-friendly method of scanning files for malicious behavior. Furthermore, MalScanner scans a file and extracts features to be used in machine learning assisted static malware analysis and inspects the file’s behavior dynamically. This tool also implements a blockchain database to store analysis results. The solution will be presented to the user in a straightforward manner via web application.
{"title":"Malscanner – File Behavior Analysis using Machine Learning","authors":"Basil Abdulrahman, Abdulrahman Qanadeely, Abdulaziz Al-Hassan, Omar Al-Ghamdi, Nawaf Al-Sukaibi, N. Saqib","doi":"10.1109/LT58159.2023.10092346","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092346","url":null,"abstract":"MalScanner is a tool that aims to provide a simple, effective, and user-friendly method of scanning files for malicious behavior. Furthermore, MalScanner scans a file and extracts features to be used in machine learning assisted static malware analysis and inspects the file’s behavior dynamically. This tool also implements a blockchain database to store analysis results. The solution will be presented to the user in a straightforward manner via web application.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125049078","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092297
Norh Alshebil, Kulud Alkadi, Nagarajkumar Yenugadhati, M. Dubayee
Introduction: Diabetes therapeutic education assists patients in taking responsibility for self-management of their condition, and technology support systems promote this education. In this study, we introduce augmented reality (AR) as an instructional tool to supplement therapeutic education for diabetic patients. Objectives: The purpose of this study is to investigate two educational approaches used within the clinic: Augmented reality (AR) and traditional educational methods. The objective is to determine which one has a better impact on nutrition knowledge improvement by using the Nutritional Diabetes Knowledge Survey (NKS) score. Method: A total of 65 children and adolescent patients with Type 1 Diabetes, aged 10-16 years old, currently receiving healthcare services at the KAMC-R nutrition diabetes clinic, were invited to choose type of education for Carbohydrate counting (AR or traditional education method) and complete a diabetes nutrition knowledge survey before the education and after. The difference between scores with a higher percentage change indicate better diabetes nutrition knowledge. Results: We discovered that the children who participated in the study had an average level of knowledge regarding nutrition (a mean of 8.98 out of a score of 23). This suggests that diabetic patients require therapeutic education. When the findings of the pre-knowledge questionnaire and the post-knowledge questionnaire were compared, it was discovered that the children learned more about carbohydrate dietary choices by watching AR videos with a p-value <.0001 than by using traditional educational methods. Furthermore, the gained information was independent of gender or age. This AR instructional tool has the potential to be a significant therapeutic education tool for diabetic patients. Conclusion: This study revealed a positive impact of using a digitalized educational method (AR) on the patient’s diabetes nutrition knowledge, specifically in relation to carbohydrate counting.
{"title":"Using Augmented Reality to improve Nutritional Educational for Type 1 Diabetic Children and Adolescents: Quantitative study of Patient Knowledge Retention","authors":"Norh Alshebil, Kulud Alkadi, Nagarajkumar Yenugadhati, M. Dubayee","doi":"10.1109/LT58159.2023.10092297","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092297","url":null,"abstract":"Introduction: Diabetes therapeutic education assists patients in taking responsibility for self-management of their condition, and technology support systems promote this education. In this study, we introduce augmented reality (AR) as an instructional tool to supplement therapeutic education for diabetic patients. Objectives: The purpose of this study is to investigate two educational approaches used within the clinic: Augmented reality (AR) and traditional educational methods. The objective is to determine which one has a better impact on nutrition knowledge improvement by using the Nutritional Diabetes Knowledge Survey (NKS) score. Method: A total of 65 children and adolescent patients with Type 1 Diabetes, aged 10-16 years old, currently receiving healthcare services at the KAMC-R nutrition diabetes clinic, were invited to choose type of education for Carbohydrate counting (AR or traditional education method) and complete a diabetes nutrition knowledge survey before the education and after. The difference between scores with a higher percentage change indicate better diabetes nutrition knowledge. Results: We discovered that the children who participated in the study had an average level of knowledge regarding nutrition (a mean of 8.98 out of a score of 23). This suggests that diabetic patients require therapeutic education. When the findings of the pre-knowledge questionnaire and the post-knowledge questionnaire were compared, it was discovered that the children learned more about carbohydrate dietary choices by watching AR videos with a p-value <.0001 than by using traditional educational methods. Furthermore, the gained information was independent of gender or age. This AR instructional tool has the potential to be a significant therapeutic education tool for diabetic patients. Conclusion: This study revealed a positive impact of using a digitalized educational method (AR) on the patient’s diabetes nutrition knowledge, specifically in relation to carbohydrate counting.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130194930","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092331
C. Kumar. J, M. Arunsi. B, M. A. Majid
Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology.
{"title":"A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations","authors":"C. Kumar. J, M. Arunsi. B, M. A. Majid","doi":"10.1109/LT58159.2023.10092331","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092331","url":null,"abstract":"Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128840720","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092350
Dahlia Omran, S. Fawzi, A. Kandil
correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%.
{"title":"Automatic Detection of Some Tajweed Rules","authors":"Dahlia Omran, S. Fawzi, A. Kandil","doi":"10.1109/LT58159.2023.10092350","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092350","url":null,"abstract":"correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116930170","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092303
M. El-Amin
When employing pure hydrogen, its leakage poses a serious safety risk since it can cause fire or explode if it comes into contact with the air. In this study, hydrogen leakage in a form of a buoyant jet is investigated using machine learning approaches. As the experiments used to explore hydrogen leaks are extremely dangerous, and there is a limitation of data, we instead construct an artificial dataset using a traditional numerical model. The dataset was produced using a combined empirical-analytical-numerical model. Investigations into dataset preparation, feature significance, correlation, and hyperparameter adjustment are conducted. Artificial neural networks, random forests, gradient boosting regression, and decision trees are the machine-learning approaches that have been used to forecast the distribution of hydrogen leaks in the atmosphere. Different error metrics and R2 correlation have been used to assess the prediction accuracy. The RF method was found to be the most effective approach for forecasting the dispersion of hydrogen leaking into the air.
{"title":"Detection of Hydrogen Leakage Using Different Machine Learning Techniques","authors":"M. El-Amin","doi":"10.1109/LT58159.2023.10092303","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092303","url":null,"abstract":"When employing pure hydrogen, its leakage poses a serious safety risk since it can cause fire or explode if it comes into contact with the air. In this study, hydrogen leakage in a form of a buoyant jet is investigated using machine learning approaches. As the experiments used to explore hydrogen leaks are extremely dangerous, and there is a limitation of data, we instead construct an artificial dataset using a traditional numerical model. The dataset was produced using a combined empirical-analytical-numerical model. Investigations into dataset preparation, feature significance, correlation, and hyperparameter adjustment are conducted. Artificial neural networks, random forests, gradient boosting regression, and decision trees are the machine-learning approaches that have been used to forecast the distribution of hydrogen leaks in the atmosphere. Different error metrics and R2 correlation have been used to assess the prediction accuracy. The RF method was found to be the most effective approach for forecasting the dispersion of hydrogen leaking into the air.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114836068","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092356
Passent El-Kafrawy, Hajer Abbas, Joud AlFarra, Leena Alam, Mehreen Junaid
The term "mobile health" (sometimes spelled "mHealth" or "m-Health") refers to the delivery of medical services via smartphones, tablets, PDAs, and PCs. The metaverse combines the real world and the virtual world, allowing people to interact with their avatars in a setting supported by cutting-edge technologies like high-speed internet, virtual reality, augmented reality, mixed reality, extended reality, blockchain, digital twins, artificial intelligence (AI), all of which are enhanced by practically limitless data. This paper discusses how these technologies might be used in digital medicine in the future, as well as the potential of the medical metaverse. This qualitative study examines and evaluates past articles and websites. The healthcare business depends heavily on physical connection, eye contact, facial expressions, and gestures, which the metaverse can simulate virtually. However, the metaverse may be viewed as a tool to improve the effectiveness of the healthcare system in terms of intervention and treatment, worldwide education, assuring consistent training, and assisting in the development of global databases for research. Finally, the metaverse may be a location where young people can start practicing and acquiring new skills considering how much time they spend in front of screens.
{"title":"The Redefinition of mHealth Applications in the Metaverse","authors":"Passent El-Kafrawy, Hajer Abbas, Joud AlFarra, Leena Alam, Mehreen Junaid","doi":"10.1109/LT58159.2023.10092356","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092356","url":null,"abstract":"The term \"mobile health\" (sometimes spelled \"mHealth\" or \"m-Health\") refers to the delivery of medical services via smartphones, tablets, PDAs, and PCs. The metaverse combines the real world and the virtual world, allowing people to interact with their avatars in a setting supported by cutting-edge technologies like high-speed internet, virtual reality, augmented reality, mixed reality, extended reality, blockchain, digital twins, artificial intelligence (AI), all of which are enhanced by practically limitless data. This paper discusses how these technologies might be used in digital medicine in the future, as well as the potential of the medical metaverse. This qualitative study examines and evaluates past articles and websites. The healthcare business depends heavily on physical connection, eye contact, facial expressions, and gestures, which the metaverse can simulate virtually. However, the metaverse may be viewed as a tool to improve the effectiveness of the healthcare system in terms of intervention and treatment, worldwide education, assuring consistent training, and assisting in the development of global databases for research. Finally, the metaverse may be a location where young people can start practicing and acquiring new skills considering how much time they spend in front of screens.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115913972","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}
Pub Date : 2023-01-26DOI: 10.1109/lt58159.2023.10092367
Biliana Popova
The paper discusses the possibilities for developing open access Islamic education Metaverse platforms. Teaching Islam through multi-participant immersive platforms would enhance pedagogical practices and allow for globalized and de-centralized interactions within and among different Muslim communities around the world. The study contemplates the practical, educational, and theological aspects of using Lifelogging, Immersive Virtual Reality and Mirror Worlds in order to create Metaverse spaces that would enable students of Islam, as well as devotees in general, to learn and practice the main teachings of Islam. The paper also discusses questions regarding the possibility of accurately translating Islamic orthopraxies from a physical to a virtual reality and some of the key challenges that such a translation/transition may pose.
{"title":"Embracing the Metaverse: The Future of Islamic Teaching and Learning","authors":"Biliana Popova","doi":"10.1109/lt58159.2023.10092367","DOIUrl":"https://doi.org/10.1109/lt58159.2023.10092367","url":null,"abstract":"The paper discusses the possibilities for developing open access Islamic education Metaverse platforms. Teaching Islam through multi-participant immersive platforms would enhance pedagogical practices and allow for globalized and de-centralized interactions within and among different Muslim communities around the world. The study contemplates the practical, educational, and theological aspects of using Lifelogging, Immersive Virtual Reality and Mirror Worlds in order to create Metaverse spaces that would enable students of Islam, as well as devotees in general, to learn and practice the main teachings of Islam. The paper also discusses questions regarding the possibility of accurately translating Islamic orthopraxies from a physical to a virtual reality and some of the key challenges that such a translation/transition may pose.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124450646","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092287
S. Kunhibava, Aishath Muneeza, Zakariya Mustapha, M. Khalid
The use of blockchain technology in financing has been based on its high benefits of efficiency and transparency. However, not many in-depth discussions have been done on such blockchain utilization for Islamic social finance or the metaverse. This paper further explores these three areas of blockchain, the metaverse and Islamic social finance by proposing the use of blockchain and the metaverse for social projects of providing education to the underprivileged with the use of Islamic social finance instruments. In fulfilling this objective this research will explain how blockchain technology was applied in Islamic social finance through the case study of Blossom Smart sukuk, thereafter the opportunities for education under Islamic financial principles through blockchain in metaverse will be explored. The authors believe that further innovation in this area would see renaissance in Islamic social finance both in the real world and in metaverse.
{"title":"Understanding Blockchain technology in Islamic social finance and its opportunities in metaverse","authors":"S. Kunhibava, Aishath Muneeza, Zakariya Mustapha, M. Khalid","doi":"10.1109/LT58159.2023.10092287","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092287","url":null,"abstract":"The use of blockchain technology in financing has been based on its high benefits of efficiency and transparency. However, not many in-depth discussions have been done on such blockchain utilization for Islamic social finance or the metaverse. This paper further explores these three areas of blockchain, the metaverse and Islamic social finance by proposing the use of blockchain and the metaverse for social projects of providing education to the underprivileged with the use of Islamic social finance instruments. In fulfilling this objective this research will explain how blockchain technology was applied in Islamic social finance through the case study of Blossom Smart sukuk, thereafter the opportunities for education under Islamic financial principles through blockchain in metaverse will be explored. The authors believe that further innovation in this area would see renaissance in Islamic social finance both in the real world and in metaverse.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121716462","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092321
Lina Almatrafi, Salma Badaam, S. Qaisar
The conventional vehicles use fuels to provide excellent performance. However, conventional vehicles have some cons such as environmental pollution due to exhaust gas emissions that is the reason behind the interest in electric vehicles (EVs) is increasing. It is mainly to reduce the pollution and greenhouse gas (GHG) emissions. In this research, the EV model is implemented via SIMULINK platform with the use of Simscape and Simulink blocks. The implemented model consists of the driving cycles which are connected to the longitudinal driver block. The Driver block is attached to the power controller system. A DC motor is used to move the vehicle body subsystem. Also a battery pack is used to power the EV. The performance of this model is evaluated, based on the speed, state of charge (SoC) of the battery, and power consumption, using three known drive cycles which are Urban Dynamometer Driving Schedule (UDDS), New York City Cycle (NYCC), and the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). A performance comparison for the three considered drive cycles is also made. The findings of this paper shown that the WLTP drive cycle causes the lowest power consumption of 11.113 kW and the SoC value of 87.62%.
{"title":"Electric Vehicle Performance Evaluation Using UDDS, NYCC and WLTP Drive Cycles","authors":"Lina Almatrafi, Salma Badaam, S. Qaisar","doi":"10.1109/LT58159.2023.10092321","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092321","url":null,"abstract":"The conventional vehicles use fuels to provide excellent performance. However, conventional vehicles have some cons such as environmental pollution due to exhaust gas emissions that is the reason behind the interest in electric vehicles (EVs) is increasing. It is mainly to reduce the pollution and greenhouse gas (GHG) emissions. In this research, the EV model is implemented via SIMULINK platform with the use of Simscape and Simulink blocks. The implemented model consists of the driving cycles which are connected to the longitudinal driver block. The Driver block is attached to the power controller system. A DC motor is used to move the vehicle body subsystem. Also a battery pack is used to power the EV. The performance of this model is evaluated, based on the speed, state of charge (SoC) of the battery, and power consumption, using three known drive cycles which are Urban Dynamometer Driving Schedule (UDDS), New York City Cycle (NYCC), and the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). A performance comparison for the three considered drive cycles is also made. The findings of this paper shown that the WLTP drive cycle causes the lowest power consumption of 11.113 kW and the SoC value of 87.62%.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127395312","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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092369
Ammar Alzaydi
This paper presents the utilization of polymer and reflective polyester and other flexible films to fabricate deformable rotational mirrors, where the rotation is utilized to help and filter a reflected image impacted by mirror surface impurities. A self-supporting membrane is fabricated using Mylar polyester film, with a diameter of 120mm and a thickness of 1mm. Electromagnetic actuation is used in combination with a magnetic electrode array beneath the mirror membrane to deform the reflective surface and change the location of the focal point. The mechanical properties of the Mylar polyester film are such that the deflection required for focal point position is minimal, thus increasing mirror flexibility and sensitivity when controlled electromagnetically, whilst the fabrication process is simple and potentially low cost. The developed mirror in this paper consists of two similar membranes lying on top of each other, both rotating at equal speeds around the center to ensure electro-magnetic forces used for deflection are spread evenly between each reflective film sector. The developed mirror prototype functionality was successfully demonstrated when used to filter an image using rotational motion and focus an unfocused picture taken by a regular camera. Therefore, the two main features of the developed mirror are: The rotational behaviour of the mirror and its divided surface that give it the ability to filter the impact of surface impurities on the reflected image and the flexibility to concave and change focal point location while in rotation. This design can be implemented on MEMS devices that look to reduce manufacturing costs by dealing with less than perfect reflective surfaces or other example applications that aim to digitize objects (take images) to be used in virtual reality related projects.
{"title":"Adaptive Optics Rotational Design and Electro-Magnetic Actuation","authors":"Ammar Alzaydi","doi":"10.1109/LT58159.2023.10092369","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092369","url":null,"abstract":"This paper presents the utilization of polymer and reflective polyester and other flexible films to fabricate deformable rotational mirrors, where the rotation is utilized to help and filter a reflected image impacted by mirror surface impurities. A self-supporting membrane is fabricated using Mylar polyester film, with a diameter of 120mm and a thickness of 1mm. Electromagnetic actuation is used in combination with a magnetic electrode array beneath the mirror membrane to deform the reflective surface and change the location of the focal point. The mechanical properties of the Mylar polyester film are such that the deflection required for focal point position is minimal, thus increasing mirror flexibility and sensitivity when controlled electromagnetically, whilst the fabrication process is simple and potentially low cost. The developed mirror in this paper consists of two similar membranes lying on top of each other, both rotating at equal speeds around the center to ensure electro-magnetic forces used for deflection are spread evenly between each reflective film sector. The developed mirror prototype functionality was successfully demonstrated when used to filter an image using rotational motion and focus an unfocused picture taken by a regular camera. Therefore, the two main features of the developed mirror are: The rotational behaviour of the mirror and its divided surface that give it the ability to filter the impact of surface impurities on the reflected image and the flexibility to concave and change focal point location while in rotation. This design can be implemented on MEMS devices that look to reduce manufacturing costs by dealing with less than perfect reflective surfaces or other example applications that aim to digitize objects (take images) to be used in virtual reality related projects.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125752843","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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