Pub Date : 2024-02-28DOI: 10.35940/ijeat.c4361.13030224
Nur Fadhilah Abdul Jalil, U. A. Hasran, Siti Fadzilah Mat Noor, Muhammad Helmi Norman
Fuel cell technology is currently being widely promoted to the general public as one of the most promising sustainable energy sources that can contribute to reducing carbon emissions. Considering this, digital game-based learning (DGBL) was created to educate the general public about fuel cells, with a focus on the younger generation such as secondary school students. This paper discusses the design and development phases, during which instructional design and game elements are integrated into producing a fuel cell DGBL prototype. Five-panel experts examine the learning content to make sure it is valid in the design phase. Next, two testing cycles were conducted on the developed fuel cell DGBL prototype: one internal group test and one focused group test including five 14-year-old students from a chosen secondary school. During the testing, three different approaches to collecting data were used: written surveys, in-person interviews, and observation. The outcome presents useful information that may be applied to enhance the game's efficacy and playability. Therefore, any novice designer or practitioner can benefit from these findings' helpful advice while developing an effective DGBL.
{"title":"Design and Development of Fuel Cell Learning through Digital Game-Based Learning to Raise Awareness of Low Carbon Emissions","authors":"Nur Fadhilah Abdul Jalil, U. A. Hasran, Siti Fadzilah Mat Noor, Muhammad Helmi Norman","doi":"10.35940/ijeat.c4361.13030224","DOIUrl":"https://doi.org/10.35940/ijeat.c4361.13030224","url":null,"abstract":"Fuel cell technology is currently being widely promoted to the general public as one of the most promising sustainable energy sources that can contribute to reducing carbon emissions. Considering this, digital game-based learning (DGBL) was created to educate the general public about fuel cells, with a focus on the younger generation such as secondary school students. This paper discusses the design and development phases, during which instructional design and game elements are integrated into producing a fuel cell DGBL prototype. Five-panel experts examine the learning content to make sure it is valid in the design phase. Next, two testing cycles were conducted on the developed fuel cell DGBL prototype: one internal group test and one focused group test including five 14-year-old students from a chosen secondary school. During the testing, three different approaches to collecting data were used: written surveys, in-person interviews, and observation. The outcome presents useful information that may be applied to enhance the game's efficacy and playability. Therefore, any novice designer or practitioner can benefit from these findings' helpful advice while developing an effective DGBL.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421481","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 : 2024-02-28DOI: 10.35940/ijeat.c4356.13030224
John Nyamekye Ansah, Loretta Owusu-Ansah, Selikem Asare-Brown
The issue of vehicular traffic congestion is faced by most road users all over the world, including Ghana. The complications intensify day in and day out, especially in most urban areas, due to development and urbanization. The exponential increase in road users awakens concern for an effective road transportation system to convey people and goods from one place to another. In an attempt to mitigate the effect of the problem, a system based on a statistically programmed lighting sequence was introduced. This technique served its purpose for some time and was realized to be inefficient because it controlled traffic flow by assigning a fixed amount of green light time to each phase of traffic, which meant that green light time was sometimes given to lanes even when there was no conflicting traffic. The persistent nature of the problem requires the need for an intelligent traffic management system to effectively coordinate the flow of vehicles through the available road network. The proposed system works based on priority queuing, where green and red phases are dynamically assigned to lanes depending on the present traffic volume. The proposed system uses two methods of counting to determine the highest lane count. They are the Digital Vehicle Counting (DVC) and the Manuel Vehicle Counting (MVC) methods. An effective detection zone of sixty meters is declared away from the traffic intersection. The values produced by both counting methods are fed to the Traffic Phase Router (TPR) for comparison. The lane with the highest vehicle counts from both counters is given the chance to leave the intersection. The proposed system was designed using Simulation of Urban Mobility (SUMO) software. Results obtained after the simulation showed that the proposed system performed better than the existing system based on the Key Performance Indicators (KPIs) used.
{"title":"Intelligent Traffic Management System to Improve Mobility at Ayigya, a Commuter City in Ghana","authors":"John Nyamekye Ansah, Loretta Owusu-Ansah, Selikem Asare-Brown","doi":"10.35940/ijeat.c4356.13030224","DOIUrl":"https://doi.org/10.35940/ijeat.c4356.13030224","url":null,"abstract":"The issue of vehicular traffic congestion is faced by most road users all over the world, including Ghana. The complications intensify day in and day out, especially in most urban areas, due to development and urbanization. The exponential increase in road users awakens concern for an effective road transportation system to convey people and goods from one place to another. In an attempt to mitigate the effect of the problem, a system based on a statistically programmed lighting sequence was introduced. This technique served its purpose for some time and was realized to be inefficient because it controlled traffic flow by assigning a fixed amount of green light time to each phase of traffic, which meant that green light time was sometimes given to lanes even when there was no conflicting traffic. The persistent nature of the problem requires the need for an intelligent traffic management system to effectively coordinate the flow of vehicles through the available road network. The proposed system works based on priority queuing, where green and red phases are dynamically assigned to lanes depending on the present traffic volume. The proposed system uses two methods of counting to determine the highest lane count. They are the Digital Vehicle Counting (DVC) and the Manuel Vehicle Counting (MVC) methods. An effective detection zone of sixty meters is declared away from the traffic intersection. The values produced by both counting methods are fed to the Traffic Phase Router (TPR) for comparison. The lane with the highest vehicle counts from both counters is given the chance to leave the intersection. The proposed system was designed using Simulation of Urban Mobility (SUMO) software. Results obtained after the simulation showed that the proposed system performed better than the existing system based on the Key Performance Indicators (KPIs) used.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422439","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 : 2024-02-28DOI: 10.35940/ijeat.c4360.13030224
Umar S. Alqasemi, Sultan A. Almutawa, Shadi M. Obaid
Detection and classification of brain tumors in manual or traditional way is an area which could be improved by having such automated detection and clarification system for brain tumors. In this paper, enhanced Computer-Aided Diagnosis CAD software system is introduced for brain tumor detection and classification. Total of 229 brain MRI images was taken as dataset for the purpose of this research; those dataset images include 105 normal brain MRI images, and 124 abnormal brain MRI images. Proposed CAD system is specialized for Meningioma brain tumor detection and classification, and the technique could be generalized and implemented for Glioma, and Pituitary brain tumors as well, and the whole system was implemented using MATLAB software. We started by cropping the region of interest (ROI) of dataset images. Then, feature extraction was implemented using first order statistical features, as well as using of some wavelets filters in combination with the former. T-test is used to exclude features of no statistical significance (p-value < 0.05). After that, different types of classifiers were used to separate the normal set from the abnormal one. Note that, we used an iterative approach to by changing features with many runs until we got best performance, where, best accuracy results were gotten with SVM-Kernel Function (Linear), KNN-1, KNN-3, and KNN-5 classifiers. Note also that, we used convolutional neural networks (CNN) from Deep Learning toolbox of MATLAB as a control method to compare, where the images were fed directly to the CNN. The results were evaluated using performance assessment techniques which are Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Error Rate, and Area Under the Curve (AUC) of Reciever Operator Characteristic (ROC). With SVM classifier, the best gotten accuracy results were 91 % with CNN classifier, 82% with SVM classifier, and 77 % with KNN classifier. Furthermore, it was very beneficial to find such feature extraction techniques which gave acceptable accuracy results with three different classifiers; this was the case two times as mentioned the study. All proposed CAD system areas was developed and implemented using MATLAB software.
{"title":"Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors","authors":"Umar S. Alqasemi, Sultan A. Almutawa, Shadi M. Obaid","doi":"10.35940/ijeat.c4360.13030224","DOIUrl":"https://doi.org/10.35940/ijeat.c4360.13030224","url":null,"abstract":"Detection and classification of brain tumors in manual or traditional way is an area which could be improved by having such automated detection and clarification system for brain tumors. In this paper, enhanced Computer-Aided Diagnosis CAD software system is introduced for brain tumor detection and classification. Total of 229 brain MRI images was taken as dataset for the purpose of this research; those dataset images include 105 normal brain MRI images, and 124 abnormal brain MRI images. Proposed CAD system is specialized for Meningioma brain tumor detection and classification, and the technique could be generalized and implemented for Glioma, and Pituitary brain tumors as well, and the whole system was implemented using MATLAB software. We started by cropping the region of interest (ROI) of dataset images. Then, feature extraction was implemented using first order statistical features, as well as using of some wavelets filters in combination with the former. T-test is used to exclude features of no statistical significance (p-value < 0.05). After that, different types of classifiers were used to separate the normal set from the abnormal one. Note that, we used an iterative approach to by changing features with many runs until we got best performance, where, best accuracy results were gotten with SVM-Kernel Function (Linear), KNN-1, KNN-3, and KNN-5 classifiers. Note also that, we used convolutional neural networks (CNN) from Deep Learning toolbox of MATLAB as a control method to compare, where the images were fed directly to the CNN. The results were evaluated using performance assessment techniques which are Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Error Rate, and Area Under the Curve (AUC) of Reciever Operator Characteristic (ROC). With SVM classifier, the best gotten accuracy results were 91 % with CNN classifier, 82% with SVM classifier, and 77 % with KNN classifier. Furthermore, it was very beneficial to find such feature extraction techniques which gave acceptable accuracy results with three different classifiers; this was the case two times as mentioned the study. All proposed CAD system areas was developed and implemented using MATLAB software.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"38 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418409","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 : 2024-02-28DOI: 10.35940/ijeat.c4346.13030224
Dr. K Nagaiah
Heart disease playing a vital role in human life, Early detection of heart-disease we can save humans lives and it remains a leading cause of mortality worldwide, making early and accurate prediction of heart disease a critical task for improving patient outcomes. Machine learning has shown great promise in this area, with various models being developed to predict heart disease based on a range of clinical and demographic features. However, there is a growing need for more efficient machine learning models that can accurately predict heart disease while minimizing computational costs, particularly in resource-constrained settings. This research paper proposes an efficient machine learning model for heart disease prediction that combines feature selection, model optimization, and interpretability techniques to achieve accurate predictions with reduced computational complexity. The proposed model utilizes a dataset of clinical and demographic features, such as age, sex, blood pressure, cholesterol levels, and other relevant risk factors, to train a machine learning model using a large real-world dataset. The proposed efficient machine learning model is evaluated on benchmark datasets and compared with other state-of-the-art models in terms of precision, Accuracy, Recall and F1- Score. The results demonstrate the model achieved by superior prediction performance to existing models. Proposed method accuracy increased by 4.8%
心脏病在人类生活中扮演着至关重要的角色，及早发现心脏病可以挽救人类的生命，而心脏病仍然是导致全球死亡的主要原因，因此及早准确地预测心脏病是改善患者预后的关键任务。机器学习在这一领域大有可为，目前已开发出各种模型，可根据一系列临床和人口特征预测心脏病。然而，人们越来越需要更高效的机器学习模型，既能准确预测心脏病，又能最大限度地降低计算成本，尤其是在资源有限的情况下。本研究论文提出了一种高效的心脏病预测机器学习模型，该模型结合了特征选择、模型优化和可解释性技术，可在降低计算复杂度的同时实现准确预测。提出的模型利用临床和人口特征数据集，如年龄、性别、血压、胆固醇水平和其他相关风险因素，使用大型真实世界数据集训练机器学习模型。在基准数据集上对所提出的高效机器学习模型进行了评估，并在精确度、准确度、召回率和 F1 分数方面与其他最先进的模型进行了比较。结果表明，该模型的预测性能优于现有模型。建议方法的精确度提高了 4.8%
{"title":"Smart Artificial Intelligence System for Heart Disease Prediction","authors":"Dr. K Nagaiah","doi":"10.35940/ijeat.c4346.13030224","DOIUrl":"https://doi.org/10.35940/ijeat.c4346.13030224","url":null,"abstract":"Heart disease playing a vital role in human life, Early detection of heart-disease we can save humans lives and it remains a leading cause of mortality worldwide, making early and accurate prediction of heart disease a critical task for improving patient outcomes. Machine learning has shown great promise in this area, with various models being developed to predict heart disease based on a range of clinical and demographic features. However, there is a growing need for more efficient machine learning models that can accurately predict heart disease while minimizing computational costs, particularly in resource-constrained settings. This research paper proposes an efficient machine learning model for heart disease prediction that combines feature selection, model optimization, and interpretability techniques to achieve accurate predictions with reduced computational complexity. The proposed model utilizes a dataset of clinical and demographic features, such as age, sex, blood pressure, cholesterol levels, and other relevant risk factors, to train a machine learning model using a large real-world dataset. The proposed efficient machine learning model is evaluated on benchmark datasets and compared with other state-of-the-art models in terms of precision, Accuracy, Recall and F1- Score. The results demonstrate the model achieved by superior prediction performance to existing models. Proposed method accuracy increased by 4.8%","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"67 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419844","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}
One of the more controversial uses of artificial intelligence (AI) in the petroleum industry has been in technological advancement. The gas business generates data on a constant basis from several operational procedures. The gas sector is now very concerned about recording these data and using them appropriately. Making decisions based on inferential and predictive data analytics facilitates timely and accurate decision-making. The gas business is seeing a significant increase in the use of data analytics for decision-making despite numerous obstacles. Considerable progress has been made in the aforementioned field of study. With the use of artificial intelligence (AI) and machine learning (ML) techniques, many complicated issues may now be resolved with ease. This study, which looks at artificial intelligence applications in the natural gas sector, collected its data from numerous sources between 2005 and 2023. The current work might offer a technical framework for selecting pertinent technologies that will enable efficient information extraction from the massive amount of data produced by the gas industry.
{"title":"Artificial Intelligence Applications in Natural Gas Industry: A Literature Review","authors":"Siddhartha Nuthakki, Chinmay Shripad Kulkarni, Satish Kathiriya, Yudhisthir Nuthakki","doi":"10.35940/ijeat.c4383.13030224","DOIUrl":"https://doi.org/10.35940/ijeat.c4383.13030224","url":null,"abstract":"One of the more controversial uses of artificial intelligence (AI) in the petroleum industry has been in technological advancement. The gas business generates data on a constant basis from several operational procedures. The gas sector is now very concerned about recording these data and using them appropriately. Making decisions based on inferential and predictive data analytics facilitates timely and accurate decision-making. The gas business is seeing a significant increase in the use of data analytics for decision-making despite numerous obstacles. Considerable progress has been made in the aforementioned field of study. With the use of artificial intelligence (AI) and machine learning (ML) techniques, many complicated issues may now be resolved with ease. This study, which looks at artificial intelligence applications in the natural gas sector, collected its data from numerous sources between 2005 and 2023. The current work might offer a technical framework for selecting pertinent technologies that will enable efficient information extraction from the massive amount of data produced by the gas industry.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"250 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422024","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 : 2024-02-28DOI: 10.35940/ijeat.c4369.13030224
Abdullah Alzahrani
Cancer is one of the most and frequent causes of death around the world. Brain tumor is a critical and dangerous type and has a few difficulties of the techniques used for its detection; it is hard to determine its location when it is small at an early stage. The purpose of this work is to design a patch antenna sensor that is a low-cost microstrip which is suitable to detect a brain cancer tumor. The computer simulation technology CST Studio Suite 3D EM simulation and analysis was used to design a patch antenna with different frequencies of 2.8 GHz, 3.9 GHz, 5GHz and 5.6GHz to diagnose brain tumors. A comparison study between these resonance frequencies (lower-band (L-B) 2 GHz, middle-band (M-B) 3.9-5 GHz and upper-band (U-B) > 5 GHz) has been performed with six layers of brain phantom of fat, dura, brain, skin, CSF (Cerebrospinal Fluid) and skull. The designed patch sensor was assessed on both scenarios without and with a tumor cell on a brain phantom. Three parameters have been observed, the frequency phase shift, the deep amount of reflection return loss and power absorption were used to indicate the presence of the tumor cell. This study concludes that the middle-band (M-B) results in good penetration and better return loss depth around - 20dB. Meanwhile, the higher band provides high resolution of 21 MHz phase-shift but with only depth value of difference return loss of -0.1dB. The proposed work could provide a pathway on the design of patch sensors for biomedical applications.
癌症是全世界最常见的死亡原因之一。脑肿瘤是一种严重而危险的类型，其检测技术存在一些困难；当肿瘤较小时，很难在早期确定其位置。这项工作的目的是设计一种适用于检测脑癌肿瘤的低成本微带贴片天线传感器。利用计算机仿真技术 CST Studio Suite 三维电磁仿真和分析，设计了一个频率分别为 2.8GHz、3.9GHz、5GHz 和 5.6GHz 的贴片天线，用于诊断脑肿瘤。这些共振频率（低频段（L-B）2 GHz、中频段（M-B）3.9-5 GHz 和高频段（U-B）> 5 GHz）之间的比较研究是通过脂肪、硬脑膜、大脑、皮肤、CSF（脑脊液）和头骨六层大脑模型进行的。设计的贴片传感器在脑模型上没有肿瘤细胞和有肿瘤细胞的两种情况下都进行了评估。观察到了三个参数，即频率相移、深度反射回波损耗和功率吸收，用于指示肿瘤细胞的存在。这项研究得出结论，中波段（M-B）具有良好的穿透性和较好的回波损耗深度，约为 -20dB。同时，高频段具有 21 MHz 相移的高分辨率，但回波损耗差深度值仅为-0.1dB。所提出的工作可为生物医学应用中的贴片传感器设计提供一条途径。
{"title":"A Low-Cost Patch-Antenna for Non-Invasive Brain Cell Detection","authors":"Abdullah Alzahrani","doi":"10.35940/ijeat.c4369.13030224","DOIUrl":"https://doi.org/10.35940/ijeat.c4369.13030224","url":null,"abstract":"Cancer is one of the most and frequent causes of death around the world. Brain tumor is a critical and dangerous type and has a few difficulties of the techniques used for its detection; it is hard to determine its location when it is small at an early stage. The purpose of this work is to design a patch antenna sensor that is a low-cost microstrip which is suitable to detect a brain cancer tumor. The computer simulation technology CST Studio Suite 3D EM simulation and analysis was used to design a patch antenna with different frequencies of 2.8 GHz, 3.9 GHz, 5GHz and 5.6GHz to diagnose brain tumors. A comparison study between these resonance frequencies (lower-band (L-B) 2 GHz, middle-band (M-B) 3.9-5 GHz and upper-band (U-B) > 5 GHz) has been performed with six layers of brain phantom of fat, dura, brain, skin, CSF (Cerebrospinal Fluid) and skull. The designed patch sensor was assessed on both scenarios without and with a tumor cell on a brain phantom. Three parameters have been observed, the frequency phase shift, the deep amount of reflection return loss and power absorption were used to indicate the presence of the tumor cell. This study concludes that the middle-band (M-B) results in good penetration and better return loss depth around - 20dB. Meanwhile, the higher band provides high resolution of 21 MHz phase-shift but with only depth value of difference return loss of -0.1dB. The proposed work could provide a pathway on the design of patch sensors for biomedical applications.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"12 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423365","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 : 2024-02-28DOI: 10.35940/ijeat.a4273.13030224
Dr. Abhilasha Sharma, Aryan Bansal
With the extensive development in infrastructures, many airports are built in order to satisfy travelling needs of people. The frequent arrival and departure of numerous plans lead to substantial runway damage and related safety concerns. So, the regular maintenance of runway has become an essential task specially for detection and classification of cracks in terms of owing to the intensity heterogeneity of cracks such as low real-time performance and the long time-consuming manual inspection. This paper introduces a new dataset named as ARID with 8 different crack classes. A runway crack detection model based on YOLOv5 and Faster RCNN has been proposed which is annotated on 8,228 collected datasets. Then the model is trained with different parameters for training to obtain the optimal result. Finally, based on experimental result, the crack detection precision has improved from 83% to 92%, while the recall has increased from 62.8% to 76%.
{"title":"Airport Runway Crack Detection to Classify and Densify Surface Crack Type","authors":"Dr. Abhilasha Sharma, Aryan Bansal","doi":"10.35940/ijeat.a4273.13030224","DOIUrl":"https://doi.org/10.35940/ijeat.a4273.13030224","url":null,"abstract":"With the extensive development in infrastructures, many airports are built in order to satisfy travelling needs of people. The frequent arrival and departure of numerous plans lead to substantial runway damage and related safety concerns. So, the regular maintenance of runway has become an essential task specially for detection and classification of cracks in terms of owing to the intensity heterogeneity of cracks such as low real-time performance and the long time-consuming manual inspection. This paper introduces a new dataset named as ARID with 8 different crack classes. A runway crack detection model based on YOLOv5 and Faster RCNN has been proposed which is annotated on 8,228 collected datasets. Then the model is trained with different parameters for training to obtain the optimal result. Finally, based on experimental result, the crack detection precision has improved from 83% to 92%, while the recall has increased from 62.8% to 76%.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418403","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 : 2024-02-28DOI: 10.35940/ijeat.c4357.13030224
Mr. Pandurang Maruti Jadhav, Dr. K. B. Waghulde, Dr. Rupesh V. Bhortake,
Customer comfort in terms of NVH is a tangible and in-tangible effect. NVH is directly and indirectly connected to the psychoacoustics of human beings and lives. As a part of the advanced NVH analysis, the effects of noise have been studied in terms of psychoacoustic parameters such as loudness, sharpness, roughness, fluctuation strength, tonality, etc. Car door or door assembly is an integral part of the car or vehicle. The door is softly and flexibly connected to the main body of the vehicle; it protects passengers from weather effects and accidental impacts. Because of the inherent flexibility of the door, its flexible connections, sharp - transient closing, and vehicle operational excitations, the door assembly is one of the main sources of noise and vibration in vehicles. It is a prime requirement to understand the NVH effect of doors on vehicles, its analysis and ways of improvement. To understand the current status of the basic and advanced NVH analysis of the door, an extensive survey and in detail study was conducted. The main focus is given on technical papers published related to noise/ sound quality (SQ) during the last two decades, i.e., between 1999 – 2022. Total 31 technical papers were scrutinized and summarized in different categories. Broadly divided into: the number of papers published each year, Number of papers on types of SQ assessment, and the number of papers discussed SQ parameters. This study of these 31 papers published between 1999 – 2022 has given a ready reference for the work done on sound quality, mainly related to the vehicle and its door NVH. The total number of parameters considered by different researchers and approaches used by them to assess the psychoacoustic parameters of noise/ sound. Finally, these parameters and their level help to determine the quality of the sound produced or generated by any source.
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Pub Date : 2024-02-28DOI: 10.35940/ijeat.c4355.13020224
Dr. Sarat Kumar Dash, Sandhya V. Kamat
Usually, resistors and capacitors populate majority portion of a common electrical circuit, hence miniaturization drive of any package or subsystem starts with miniaturized resistor and capacitors. In this context, thin film chip resistors are the most sought-after components for any electronic/electrical circuit due to their small size, wide range of values, military temperature range, stringent tolerance, low TCR value (recognised with PPM). Increased use of thin-film surface mount chip resistors in military and space application has led to an increased awareness of its potential failure modes in harsh environments. Thin film resistor with lower TCR (5PPM, 10PPM and 25PPM) are most preferable and widely used because of very low temperature coefficient of resistance (TCR) and high resistivity, which suits for high precision measurement application. Low temperature coefficients characteristics of thin film resistors also makes them stable and reliable. Because of their high-volume usage in recent times, failure in thin film resistor with lower TCR/PPM (Parts per Million) are also being seen more predominantly. In general, there are two types of thin film chip resistors, one is discrete type and the other is die type or wire bondable type. Discrete type chip resistor are used directly on cards, whereas, die type/wire bondable type chip resistors used in hermetically sealed HMC packages. Standard failure mode of a resistor is open mode or high resistance mode, whereas short mode failure has a very low probability. Hence in this paper, failure modes and mechanisms of both types of thin film chip resistors, with respect to common failure causes such as EOS, ESD are discussed, which is in continuation to Fabrication/Workmanship related failures discussed in our earlier technical paper. With this, all possible failure modes and mechanism related thin film chip resistors are explained. Discussion in totality always provide in depth analysis on a subject of concern, which in turn facilitate reliability assessment of the component and corrective action, if any.
{"title":"A Comprehensive Study on Failure Modes and Mechanisms of Thin Film Chip Resistors","authors":"Dr. Sarat Kumar Dash, Sandhya V. Kamat","doi":"10.35940/ijeat.c4355.13020224","DOIUrl":"https://doi.org/10.35940/ijeat.c4355.13020224","url":null,"abstract":"Usually, resistors and capacitors populate majority portion of a common electrical circuit, hence miniaturization drive of any package or subsystem starts with miniaturized resistor and capacitors. In this context, thin film chip resistors are the most sought-after components for any electronic/electrical circuit due to their small size, wide range of values, military temperature range, stringent tolerance, low TCR value (recognised with PPM). Increased use of thin-film surface mount chip resistors in military and space application has led to an increased awareness of its potential failure modes in harsh environments. Thin film resistor with lower TCR (5PPM, 10PPM and 25PPM) are most preferable and widely used because of very low temperature coefficient of resistance (TCR) and high resistivity, which suits for high precision measurement application. Low temperature coefficients characteristics of thin film resistors also makes them stable and reliable. Because of their high-volume usage in recent times, failure in thin film resistor with lower TCR/PPM (Parts per Million) are also being seen more predominantly. In general, there are two types of thin film chip resistors, one is discrete type and the other is die type or wire bondable type. Discrete type chip resistor are used directly on cards, whereas, die type/wire bondable type chip resistors used in hermetically sealed HMC packages. Standard failure mode of a resistor is open mode or high resistance mode, whereas short mode failure has a very low probability. Hence in this paper, failure modes and mechanisms of both types of thin film chip resistors, with respect to common failure causes such as EOS, ESD are discussed, which is in continuation to Fabrication/Workmanship related failures discussed in our earlier technical paper. With this, all possible failure modes and mechanism related thin film chip resistors are explained. Discussion in totality always provide in depth analysis on a subject of concern, which in turn facilitate reliability assessment of the component and corrective action, if any.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"286 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140420933","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 : 2023-12-30DOI: 10.35940/ijeat.b4338.1213223
Dr. Monisha Pathak, Dr. Mrinal Buragohain
In this paper, an adaptive sliding mode control utilizing a fuzzy system approximation is introduced. The fuzzy system is used to approximate the unknown function of an uncertain nonlinear system. The robustness of the system is ensured by the sliding mode control, while the adaptive fuzzy system improves real-time performance. To approximate unknown nonlinearities, a set of fuzzy rules is formulated whose parameters are adjusted in real-time by an adaptive algorithm. The chattering problem of sliding mode control is satisfactorily resolved, and stable operation is assured.
{"title":"Fuzzy System Approximation based Adaptive Sliding Mode Control for Nonlinear System","authors":"Dr. Monisha Pathak, Dr. Mrinal Buragohain","doi":"10.35940/ijeat.b4338.1213223","DOIUrl":"https://doi.org/10.35940/ijeat.b4338.1213223","url":null,"abstract":"In this paper, an adaptive sliding mode control utilizing a fuzzy system approximation is introduced. The fuzzy system is used to approximate the unknown function of an uncertain nonlinear system. The robustness of the system is ensured by the sliding mode control, while the adaptive fuzzy system improves real-time performance. To approximate unknown nonlinearities, a set of fuzzy rules is formulated whose parameters are adjusted in real-time by an adaptive algorithm. The chattering problem of sliding mode control is satisfactorily resolved, and stable operation is assured.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139140391","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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