Physical education is an integral component of academic curricula focused on promoting overall health and well-being through physical activity and exercise. It encompasses a range of activities designed to enhance students' physical fitness, motor skills, and knowledge of healthy lifestyle habits. In addition to fostering physical development, physical education contributes to the development of social skills, teamwork, and discipline. Students engage in various sports, fitness routines, and educational modules that encourage a lifelong commitment to an active and healthy lifestyle. This demand for improvement in the teaching quality assessment of physical education among the students. Hence, this paper proposed a novel Gaussian Hidden Chain Probabilistic Machine Learning (GHCP-ML). The proposed GHCP-ML model estimates the features for the teaching quality assessment using the Gaussian Hidden Chain model. With the proposed GHCP-ML model features related to the teaching assessment of the physical education are computed. The proposed GHCP-ML model uses the machine learning model for the assessment and computation of the factors related to the teaching quality of students in physical education. With the Gaussian Chain model, the factors related to physical education are evaluated for the classification of the relationship between physical education and teaching quality assessment. Simulation analysis demonstrated that with the proposed GHCP-ML model physical education is improved significantly with teaching quality by ~12% than the conventional techniques. The student physical education performance is improved by more than 80% with the proposed GHCP-ML model compared with the conventional techniques.
{"title":"Physical Education Teaching Quality Assessment Model Based on Gaussian Process Machine Learning Algorithm","authors":"ZA Wang","doi":"10.5750/ijme.v1i1.1399","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1399","url":null,"abstract":"Physical education is an integral component of academic curricula focused on promoting overall health and well-being through physical activity and exercise. It encompasses a range of activities designed to enhance students' physical fitness, motor skills, and knowledge of healthy lifestyle habits. In addition to fostering physical development, physical education contributes to the development of social skills, teamwork, and discipline. Students engage in various sports, fitness routines, and educational modules that encourage a lifelong commitment to an active and healthy lifestyle. This demand for improvement in the teaching quality assessment of physical education among the students. Hence, this paper proposed a novel Gaussian Hidden Chain Probabilistic Machine Learning (GHCP-ML). The proposed GHCP-ML model estimates the features for the teaching quality assessment using the Gaussian Hidden Chain model. With the proposed GHCP-ML model features related to the teaching assessment of the physical education are computed. The proposed GHCP-ML model uses the machine learning model for the assessment and computation of the factors related to the teaching quality of students in physical education. With the Gaussian Chain model, the factors related to physical education are evaluated for the classification of the relationship between physical education and teaching quality assessment. Simulation analysis demonstrated that with the proposed GHCP-ML model physical education is improved significantly with teaching quality by ~12% than the conventional techniques. The student physical education performance is improved by more than 80% with the proposed GHCP-ML model compared with the conventional techniques.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For compression ignition engines, butanol is the most promising alternative fuel, in comparison with other alcoholic fuels. Butanol is superior to other alcoholic fuels because it has excellent physical and chemical properties that make it appropriate for diesel fuel blends. When butanol and diesel are blended, butanol is fully miscible in all proportions. Because butanol is hygroscopic, it does not absorb moisture from the environment. Because acetone-butanol-ethanol (ABE) fermentation may create butanol, it is commonly touted as a possible biofuel. This research is a significant step in gaining a thorough understanding of the effects of butanol on the fuel based on hydrocarbon. The fuel's molecular interactions mixes are studied using infrared (IR) spectroscopy. Binary mixes of butanol and n-decane, are investigated initially. After that, the mixture of butanol and diesel is investigated. When butanol is mixed with diesel, it forms strong bonds including the components of biodiesel that contain groups of esters. Furthermore, the possibility of employing Infrared spectroscopy for numerical mix analysis is assessed. The spectra are provided to enable a highly precise determination of the butanol concentration.
{"title":"Butanol Used as a Potential Alternative Fuel Blend with N-Decane and Diesel in CI Engines for Marine Application","authors":"Vijay Kumar, Bharat Singh, Manish Saraswat, Rishu Chabra","doi":"10.5750/ijme.v1i1.1334","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1334","url":null,"abstract":"For compression ignition engines, butanol is the most promising alternative fuel, in comparison with other alcoholic fuels. Butanol is superior to other alcoholic fuels because it has excellent physical and chemical properties that make it appropriate for diesel fuel blends. When butanol and diesel are blended, butanol is fully miscible in all proportions. Because butanol is hygroscopic, it does not absorb moisture from the environment. Because acetone-butanol-ethanol (ABE) fermentation may create butanol, it is commonly touted as a possible biofuel. This research is a significant step in gaining a thorough understanding of the effects of butanol on the fuel based on hydrocarbon. The fuel's molecular interactions mixes are studied using infrared (IR) spectroscopy. Binary mixes of butanol and n-decane, are investigated initially. After that, the mixture of butanol and diesel is investigated. When butanol is mixed with diesel, it forms strong bonds including the components of biodiesel that contain groups of esters. Furthermore, the possibility of employing Infrared spectroscopy for numerical mix analysis is assessed. The spectra are provided to enable a highly precise determination of the butanol concentration.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pitch synthesis with violin sound involves the generation of musical pitches using technology to mimic the distinctive tonal characteristics of a violin. This process typically employs digital signal processing techniques to recreate the timbre, articulation, and nuances of a real violin. Advanced algorithms analyze and model the acoustic properties of a violin sound, allowing for the synthesis of realistic pitch variations and expressive qualities. Whether utilized in electronic music production, virtual instruments, or sound design, pitch synthesis with violin sound aims to emulate the rich and complex sonic palette of the violin, offering musicians and composers versatile tools for creative expression and sonic exploration. In this paper proposed Fuzzy Pitch Clustering Machine Learning (FPC-ML) for the violin Music Pitch Synthesis using Machine Learning. The proposed FPC-ML model uses the Fuzzy Clustering model for the estimation of pitches in the violin music signal. Based on the Fuzzy clustering model membership degree is computed for the proposed FPC-ML for the estimation of the pitch in the violin music. With the estimation of linguistic variables, clustering is performed in the Music signal for the computation of pitches. With the estimated pitches in the violin music, the features are trained in the machine learning model for the classification and estimation of features in the Violin Music. Simulation analysis demonstrated that the proposed FPC-ML model computes the features of the Violin Music Pitch values based on the estimated clustering values synthesis performed for the classification of the Violin Music signal. The proposed FPC-ML technique achieves an accuracy value of 0.98 for the violin signal with an iteration of 20. With the increase in several iterations and epoch, the accuracy of the FPC-ML model is further increased for the synthesis of the Violin Music.
{"title":"Fuzzy Cluster Pitch Synthesis System for the Violin Sound with Machine Learning","authors":"Yu Han","doi":"10.5750/ijme.v1i1.1396","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1396","url":null,"abstract":"Pitch synthesis with violin sound involves the generation of musical pitches using technology to mimic the distinctive tonal characteristics of a violin. This process typically employs digital signal processing techniques to recreate the timbre, articulation, and nuances of a real violin. Advanced algorithms analyze and model the acoustic properties of a violin sound, allowing for the synthesis of realistic pitch variations and expressive qualities. Whether utilized in electronic music production, virtual instruments, or sound design, pitch synthesis with violin sound aims to emulate the rich and complex sonic palette of the violin, offering musicians and composers versatile tools for creative expression and sonic exploration. In this paper proposed Fuzzy Pitch Clustering Machine Learning (FPC-ML) for the violin Music Pitch Synthesis using Machine Learning. The proposed FPC-ML model uses the Fuzzy Clustering model for the estimation of pitches in the violin music signal. Based on the Fuzzy clustering model membership degree is computed for the proposed FPC-ML for the estimation of the pitch in the violin music. With the estimation of linguistic variables, clustering is performed in the Music signal for the computation of pitches. With the estimated pitches in the violin music, the features are trained in the machine learning model for the classification and estimation of features in the Violin Music. Simulation analysis demonstrated that the proposed FPC-ML model computes the features of the Violin Music Pitch values based on the estimated clustering values synthesis performed for the classification of the Violin Music signal. The proposed FPC-ML technique achieves an accuracy value of 0.98 for the violin signal with an iteration of 20. With the increase in several iterations and epoch, the accuracy of the FPC-ML model is further increased for the synthesis of the Violin Music.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y H Li, Y J Wang, L J Xu, J Li, Di Zhang, Y P Wang, C W Li, Y C Chen
Exercise training plays a pivotal role in enhancing limb function and overall physical performance. Through targeted and progressive exercise regimes, individuals can improve strength, flexibility, coordination, and endurance in their limbs. This paper presents a novel Neural Network-Based Exercise Training and Limb Function Evaluation System tailored for Traditional Chinese Medicine (TCM) guiding techniques. This paper constructed a novel Multi-Layer Fuzzy Pattern Neural Network (MLFPNN) for the estimation of limbs for exercise training. The proposed MLFPNN model acquires information about the limb muscles through the acquired information features are normalized. With the normalized features, TCM is evaluated for the computation of the feature for the exercise training in MLFPNN. The proposed model uses the multilayer fuzzy for the estimation of the limb features associated with the limb function. The estimated features of the limb are applied over the pattern network for the classification of limb function based on TCM with MLFPNN. The proposed MLFPNN model evaluates the 10 features in the limb muscle estimation for TCM-based exercise training. Experimental analysis is conducted for the proposed MLFPNN to achieve a higher prediction based on the actual values. The comparative analysis demonstrated that the proposed MLFPNN model achieves an accuracy of 92.5% while conventional SVM, RF, and k-NN achieve a classification accuracy of 88.3%, 90.7%, and 87.6% respectively. The findings stated that the proposed MLFPNN model is significant for the limb function estimation for the TCM-based training.
{"title":"Neural Network-Based Exercise Training and Limb Function Evaluation System for Traditional Chinese Medicine Guiding Technique","authors":"Y H Li, Y J Wang, L J Xu, J Li, Di Zhang, Y P Wang, C W Li, Y C Chen","doi":"10.5750/ijme.v1i1.1389","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1389","url":null,"abstract":"Exercise training plays a pivotal role in enhancing limb function and overall physical performance. Through targeted and progressive exercise regimes, individuals can improve strength, flexibility, coordination, and endurance in their limbs. This paper presents a novel Neural Network-Based Exercise Training and Limb Function Evaluation System tailored for Traditional Chinese Medicine (TCM) guiding techniques. This paper constructed a novel Multi-Layer Fuzzy Pattern Neural Network (MLFPNN) for the estimation of limbs for exercise training. The proposed MLFPNN model acquires information about the limb muscles through the acquired information features are normalized. With the normalized features, TCM is evaluated for the computation of the feature for the exercise training in MLFPNN. The proposed model uses the multilayer fuzzy for the estimation of the limb features associated with the limb function. The estimated features of the limb are applied over the pattern network for the classification of limb function based on TCM with MLFPNN. The proposed MLFPNN model evaluates the 10 features in the limb muscle estimation for TCM-based exercise training. Experimental analysis is conducted for the proposed MLFPNN to achieve a higher prediction based on the actual values. The comparative analysis demonstrated that the proposed MLFPNN model achieves an accuracy of 92.5% while conventional SVM, RF, and k-NN achieve a classification accuracy of 88.3%, 90.7%, and 87.6% respectively. The findings stated that the proposed MLFPNN model is significant for the limb function estimation for the TCM-based training.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A smart campus signifies the profound integration of machine vision technology with physical education, creating an innovative and dynamic learning environment. By incorporating machine vision into physical education settings, the campus becomes an intelligent ecosystem where advanced image recognition and analysis enhance various aspects of student engagement and well-being. From automated fitness assessments to real-time monitoring of physical activities, machine vision contributes to personalized and data-driven physical education experiences. This integration not only revolutionizes the way students interact with fitness routines but also facilitates efficient tracking of progress and overall health. The study proposes a novel IoT-enabled routing scheme based on Middle-Order Chain Deep Learning (MOCDL) to enhance the synergy between machine vision and physical education initiatives. By integrating IoT capabilities, the smart campus establishes a network that seamlessly connects various physical education resources and facilities, fostering a more interconnected and intelligent learning environment. The MOCDL algorithm, acting as the backbone of this integration, optimizes the routing of information, enabling efficient data exchange between machine vision systems and physical education programs. This deep integration facilitates real-time monitoring of student activities, personalized fitness assessments, and data-driven insights into overall well-being. The proposed framework not only elevates the quality of physical education experiences but also contributes to the establishment of a technologically advanced and holistic smart campus paradigm.
{"title":"Smart Campus: The Deep Integration of Machine Vision and Physical Education","authors":"Yukun Lu, Xingli Hu, Jiangtao Li","doi":"10.5750/ijme.v1i1.1348","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1348","url":null,"abstract":"A smart campus signifies the profound integration of machine vision technology with physical education, creating an innovative and dynamic learning environment. By incorporating machine vision into physical education settings, the campus becomes an intelligent ecosystem where advanced image recognition and analysis enhance various aspects of student engagement and well-being. From automated fitness assessments to real-time monitoring of physical activities, machine vision contributes to personalized and data-driven physical education experiences. This integration not only revolutionizes the way students interact with fitness routines but also facilitates efficient tracking of progress and overall health. The study proposes a novel IoT-enabled routing scheme based on Middle-Order Chain Deep Learning (MOCDL) to enhance the synergy between machine vision and physical education initiatives. By integrating IoT capabilities, the smart campus establishes a network that seamlessly connects various physical education resources and facilities, fostering a more interconnected and intelligent learning environment. The MOCDL algorithm, acting as the backbone of this integration, optimizes the routing of information, enabling efficient data exchange between machine vision systems and physical education programs. This deep integration facilitates real-time monitoring of student activities, personalized fitness assessments, and data-driven insights into overall well-being. The proposed framework not only elevates the quality of physical education experiences but also contributes to the establishment of a technologically advanced and holistic smart campus paradigm.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bhavya Agarwal, Varsha Pathak, Bhavay Nagar, B. Chauhan
This research summarizes research on electric vehicles (EVs) and sustainable transit solutions, highlighting advancements in battery technologies, motor efficiency, and vehicle-to-grid (V2G) integration. It explores the conversion of a Rover Mini into the electric E-MINI and discusses the potential of proton exchange membrane (PEM) fuel cells and hybrid electric vehicle (HEV) architectures. The study emphasizes the importance of effective power management and infrastructure upgrades to support EV adoption. Despite inherent complexities, innovative designs offer promising solutions for future transportation sustainability. advanced strategies for optimizing electric powertrains, transit networks, and fast charging systems to enhance sustainability in electromobility. Additionally, analysis is done on how quick charging affects Li-ion battery deterioration, revealing insights into mitigating effects and identifying cost-effective solutions for various battery technologies. Overall, this comprehensive investigation contributes crucial insights for advancing sustainable electric mobility.
{"title":"Driving Towards Sustainability: A Comprehensive Review of Electric Vehicles","authors":"Bhavya Agarwal, Varsha Pathak, Bhavay Nagar, B. Chauhan","doi":"10.5750/ijme.v1i1.1333","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1333","url":null,"abstract":"This research summarizes research on electric vehicles (EVs) and sustainable transit solutions, highlighting advancements in battery technologies, motor efficiency, and vehicle-to-grid (V2G) integration. It explores the conversion of a Rover Mini into the electric E-MINI and discusses the potential of proton exchange membrane (PEM) fuel cells and hybrid electric vehicle (HEV) architectures. The study emphasizes the importance of effective power management and infrastructure upgrades to support EV adoption. Despite inherent complexities, innovative designs offer promising solutions for future transportation sustainability. advanced strategies for optimizing electric powertrains, transit networks, and fast charging systems to enhance sustainability in electromobility. Additionally, analysis is done on how quick charging affects Li-ion battery deterioration, revealing insights into mitigating effects and identifying cost-effective solutions for various battery technologies. Overall, this comprehensive investigation contributes crucial insights for advancing sustainable electric mobility.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid advancement of nanotechnology has boosted the applications of TiO2 nanoparticles (TiO2 NPs) in various industries, resulting in their release into marine environments. This review article provides a comprehensive overview of recent findings on the adverse effects of TiO2 NPs in marine algae and zooplankton. Special attention is given to the underlying mechanisms of toxicity, including oxidative stress, genotoxicity, and disruptions in cellular processes. This review consolidates recent scientific evidence to underscore the emerging concerns surrounding the adverse effects of TiO2 NPs in marine aquatics, emphasizing the urgency of further research and the implementation of precautionary measures to protect marine ecosystems from potential harm.
{"title":"Recent Findings in Adverse Effects of Tio2 NPs in Marine Algae and Zooplanktons: A Threat to Marine Ecosystems","authors":"Ranjay Shaw, Himanshu Kumar, Monit Kapoor","doi":"10.5750/ijme.v1i1.1355","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1355","url":null,"abstract":"The rapid advancement of nanotechnology has boosted the applications of TiO2 nanoparticles (TiO2 NPs) in various industries, resulting in their release into marine environments. This review article provides a comprehensive overview of recent findings on the adverse effects of TiO2 NPs in marine algae and zooplankton. Special attention is given to the underlying mechanisms of toxicity, including oxidative stress, genotoxicity, and disruptions in cellular processes. This review consolidates recent scientific evidence to underscore the emerging concerns surrounding the adverse effects of TiO2 NPs in marine aquatics, emphasizing the urgency of further research and the implementation of precautionary measures to protect marine ecosystems from potential harm.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dance ecology, a burgeoning field at the intersection of dance, technology, and environmental studies, relies on real-time data analysis for understanding and optimizing dance performances. This paper proposed a novel Parallel Edge Big Data Analytics (PEBDA) framework, designed to efficiently process and analyze dance movement data in real time. The proposed PEBDA model uses parallel processing in the edge computing model for the analysis of the live dance ecology. Through the parallel processing of the edge model in the network big data analytics is implemented for the estimation of the multiple nodes in the network. The PEBDA model estimates the nodes across multiple environments for the examination of the ecology in the live dance. Finally, through parallel processing classification is performed with the deep learning model for the classification of live dance ecology in the computing platform. The proposed PEBDA framework, assesses classification accuracy, precision, recall, and F1-score. The simulation analysis expressed that Node 8 consistently outperforms others, achieving exceptional accuracy and precision levels above 0.97. These findings highlight the potential of edge computing in revolutionizing dance ecology analysis, enabling enhanced real-time monitoring, decision-making, and optimization of dance performances.
{"title":"Big Data Analytics Model with Deep Learning Architecture to Evaluate Live Dance Ecology Through the Internet","authors":"Lixiong Gao","doi":"10.5750/ijme.v1i1.1357","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1357","url":null,"abstract":"Dance ecology, a burgeoning field at the intersection of dance, technology, and environmental studies, relies on real-time data analysis for understanding and optimizing dance performances. This paper proposed a novel Parallel Edge Big Data Analytics (PEBDA) framework, designed to efficiently process and analyze dance movement data in real time. The proposed PEBDA model uses parallel processing in the edge computing model for the analysis of the live dance ecology. Through the parallel processing of the edge model in the network big data analytics is implemented for the estimation of the multiple nodes in the network. The PEBDA model estimates the nodes across multiple environments for the examination of the ecology in the live dance. Finally, through parallel processing classification is performed with the deep learning model for the classification of live dance ecology in the computing platform. The proposed PEBDA framework, assesses classification accuracy, precision, recall, and F1-score. The simulation analysis expressed that Node 8 consistently outperforms others, achieving exceptional accuracy and precision levels above 0.97. These findings highlight the potential of edge computing in revolutionizing dance ecology analysis, enabling enhanced real-time monitoring, decision-making, and optimization of dance performances.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With technological advances happening in almost every industry, the manufacturing industry has seen quite a growth in terms of scientific advancements due to incorporation of hi-tech instruments and processes. To cope with the fluctuating demands of manufactured products, companies have adopted 3D printing technology for small-quantity batch production. 3D printing is an additive manufacturing (AM) based techniques that is capable of producing complex shapes, reducing material wastage, and reducing production time. In the present work, different researches which predict the Ra of parts produced through fusion deposition modeling are discussed. The present work consists of all the latest research work that has been conducted to identify the factors impacting the surface finish of products developed through Fusion Deposition Modeling (FDM).
随着几乎所有行业的技术进步，制造业也因采用了高科技仪器和工艺而在科学进步方面取得了长足的发展。为了应对不断变化的制成品需求，企业已采用 3D 打印技术进行小批量生产。三维打印是一种基于增材制造（AM）的技术，能够制造复杂的形状，减少材料浪费，缩短生产时间。在本作品中，讨论了预测通过熔融沉积建模生产的零件的 Ra 的不同研究。本作品包含了所有最新的研究工作，这些工作旨在确定影响通过熔融沉积建模（FDM）开发的产品表面光洁度的因素。
{"title":"Surface Roughness Prediction of Parts Produced Through Fusion Deposition Modelling","authors":"Nathi Ram Chauhan, Srishti Singh, Dhriti Sood, Soumya Tyagi, Shubhi Jadaun, Rajan Verma","doi":"10.5750/ijme.v1i1.1378","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1378","url":null,"abstract":"With technological advances happening in almost every industry, the manufacturing industry has seen quite a growth in terms of scientific advancements due to incorporation of hi-tech instruments and processes. To cope with the fluctuating demands of manufactured products, companies have adopted 3D printing technology for small-quantity batch production. 3D printing is an additive manufacturing (AM) based techniques that is capable of producing complex shapes, reducing material wastage, and reducing production time. In the present work, different researches which predict the Ra of parts produced through fusion deposition modeling are discussed. The present work consists of all the latest research work that has been conducted to identify the factors impacting the surface finish of products developed through Fusion Deposition Modeling (FDM).","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.
利用计算机视觉进行图像处理，特别是在投影几何领域，为各种应用提供了巨大的潜力。通过投影几何的视角，可以对图像进行精确的转换、增强和重建，从而为图像校正、三维重建和物体跟踪等任务提供便利。计算机视觉中的地标估算是一项重要任务，可广泛应用于各个领域。这一过程包括识别图像中的关键点或地标，从而完成面部识别、物体跟踪和手势识别等任务。本文提出了一种利用投影几何地标估算（PGLM）进行计算机视觉地标估算的新方法。该模型旨在通过投影几何模型估算地标特征。通过对几何特征的估计，可以计算出与面部、物体和医学图像相关的地标。PGLM 模型使用点特征来确定地标特征的位置。为了将 PGLM 的性能与随机森林、K-近邻（KNN）和支持向量机（SVM）等传统分类方法进行比较，我们进行了模拟分析。我们可以看到，在比较准确度、精确度、召回率和 F1 分数时，PGLM 完全胜过这些方法。研究结果表明，PGLM 是一种在图像处理任务中进行地标估计的有效方法，为这一领域的进一步发展铺平了道路。
{"title":"Computer Vision Algorithm Design in Image Processing Based on Projective Geometry","authors":"YG Kang, Di Zhao","doi":"10.5750/ijme.v1i1.1385","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1385","url":null,"abstract":"Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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