International Journal of Maritime Engineering最新文献_第6页

Physical Education Teaching Quality Assessment Model Based on Gaussian Process Machine Learning Algorithm 基于高斯过程机器学习算法的体育教学质量评估模型

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1399

ZA Wang

{"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}

引用次数: 0

Butanol Used as a Potential Alternative Fuel Blend with N-Decane and Diesel in CI Engines for Marine Application 丁醇与 N-癸烷和柴油在船用 CI 发动机中用作混合替代燃料的潜力

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1334

Vijay Kumar, Bharat Singh, Manish Saraswat, Rishu Chabra

{"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}

引用次数: 0

Fuzzy Cluster Pitch Synthesis System for the Violin Sound with Machine Learning 利用机器学习的小提琴音高模糊簇合成系统

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1396

Yu Han

{"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}

引用次数: 0

Neural Network-Based Exercise Training and Limb Function Evaluation System for Traditional Chinese Medicine Guiding Technique 基于神经网络的中医导引术运动训练和肢体功能评估系统

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1389

Y H Li, Y J Wang, L J Xu, J Li, Di Zhang, Y P Wang, C W Li, Y C Chen

{"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}

引用次数: 0

Smart Campus: The Deep Integration of Machine Vision and Physical Education 智慧校园：机器视觉与体育教育的深度融合

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1348

Yukun Lu, Xingli Hu, Jiangtao Li

{"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}

引用次数: 0

Driving Towards Sustainability: A Comprehensive Review of Electric Vehicles 迈向可持续发展：电动汽车综合评述

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1333

Bhavya Agarwal, Varsha Pathak, Bhavay Nagar, B. Chauhan

引用次数: 0

Recent Findings in Adverse Effects of Tio2 NPs in Marine Algae and Zooplanktons: A Threat to Marine Ecosystems 海洋藻类和浮游动物中 Tio2 NPs 负面影响的最新发现：对海洋生态系统的威胁

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1355

Ranjay Shaw, Himanshu Kumar, Monit Kapoor

引用次数: 0

Big Data Analytics Model with Deep Learning Architecture to Evaluate Live Dance Ecology Through the Internet 利用深度学习架构的大数据分析模型，通过互联网评估现场舞蹈生态

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1357

Lixiong Gao

{"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}

引用次数: 0

Surface Roughness Prediction of Parts Produced Through Fusion Deposition Modelling 通过熔融沉积建模生产零件的表面粗糙度预测

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1378

Nathi Ram Chauhan, Srishti Singh, Dhriti Sood, Soumya Tyagi, Shubhi Jadaun, Rajan Verma

引用次数: 0

Computer Vision Algorithm Design in Image Processing Based on Projective Geometry 基于投影几何的图像处理中的计算机视觉算法设计

IF 0.7 4区工程技术

International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI: 10.5750/ijme.v1i1.1385

YG Kang, Di Zhao

{"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}

引用次数: 0