Sumit Nagar, S Sreenivasa, Gaurav Kumar, Gopal Krishan Gard
This work comprised of Paraffin wax as phase change material mixed/doped with functionalized graphene (hydroxyl group) for countering the poor thermal conductivity of the pristine phase change material. Firstly, Experiments were conducted on Thermal energy storage system during charging (melting) of the doped PCM. The main outcomes of these experiments were that as the volume concentrations of doped PCM and flow rate of the heat transfer fluid (HTF) were increased the charging time decreases along with increments in PCM temperature and accumulated energies. In case of OH functionalized graphene mixed with paraffin wax the charging time decreases by 38.4% to 84.61% for the HTF flow rate 25 ml/s over pure paraffin wax. Secondly, the advanced functional materials i.e., Paraffin wax mixed with functionalized hydroxyl group graphene was characterized by FTIR, XRD, TGA, DSC and FESEM, . The latent heat of melting obtained was 179.36 J/g for 1% graphene doping by DSC.
{"title":"Characterizations and Energy Analysis of Hydroxyl Group Functionalized Graphene (Nanomaterial) Mixed with Paraffin Wax (Polymer Pcm) as Thermal Energy Storage Materials And Applications","authors":"Sumit Nagar, S Sreenivasa, Gaurav Kumar, Gopal Krishan Gard","doi":"10.5750/ijme.v1i1.1376","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1376","url":null,"abstract":"This work comprised of Paraffin wax as phase change material mixed/doped with functionalized graphene (hydroxyl group) for countering the poor thermal conductivity of the pristine phase change material. Firstly, Experiments were conducted on Thermal energy storage system during charging (melting) of the doped PCM. The main outcomes of these experiments were that as the volume concentrations of doped PCM and flow rate of the heat transfer fluid (HTF) were increased the charging time decreases along with increments in PCM temperature and accumulated energies. In case of OH functionalized graphene mixed with paraffin wax the charging time decreases by 38.4% to 84.61% for the HTF flow rate 25 ml/s over pure paraffin wax. Secondly, the advanced functional materials i.e., Paraffin wax mixed with functionalized hydroxyl group graphene was characterized by FTIR, XRD, TGA, DSC and FESEM, . The latent heat of melting obtained was 179.36 J/g for 1% graphene doping by DSC.","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":"141797842","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 proportional integral derivative (PID) controller method is a famous and influential approach used in a variety of engineering domain systems. This paper presents Bond Graph modelling and implementation of PID controller in the active quarter-vehicle suspension system for the purpose of achieving better ride comfort. Bond graph is a better tool for modelling multi-domain energy systems with control hardware associated with it. SYMBOL SHAKTI software is used to simulate the active vehicle suspension system, which is theoretically represented as a 4-DOF human Bio-dynamic model combined with a 3-DOF quarter automobile system. The suggested PID Controller's performance is contrasted with that of the passive model. To measure the effectiveness of the suggested PID controller suspension system, time domain evaluations of systems performance criteria are conducted. The findings show that a significant improvement in ride comfort is offered by the suggested Bond graph PID controller model of the active vehicle suspension.
{"title":"Modeling and Implementation of PID Controller in Vehicle Ride Comfort Improvement Using Bond Graph","authors":"Lalit Batra, Sanjay Kumar, Saurabh Kumar Agrawal","doi":"10.5750/ijme.v1i1.1361","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1361","url":null,"abstract":"The proportional integral derivative (PID) controller method is a famous and influential approach used in a variety of engineering domain systems. This paper presents Bond Graph modelling and implementation of PID controller in the active quarter-vehicle suspension system for the purpose of achieving better ride comfort. Bond graph is a better tool for modelling multi-domain energy systems with control hardware associated with it. SYMBOL SHAKTI software is used to simulate the active vehicle suspension system, which is theoretically represented as a 4-DOF human Bio-dynamic model combined with a 3-DOF quarter automobile system. The suggested PID Controller's performance is contrasted with that of the passive model. To measure the effectiveness of the suggested PID controller suspension system, time domain evaluations of systems performance criteria are conducted. The findings show that a significant improvement in ride comfort is offered by the suggested Bond graph PID controller model of the active vehicle suspension.","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":"141797878","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}
Amit Kumar, JMA Sulaiman, Pranav Kumar, Sohini Chowdhury, S. Sreenivasa
Energy generation and preservation are significant difficulties that can be met using fuel cells, solar cells, and batteries. Micro- and nanomaterials with exciting chemical and physical characteristics offer a new avenue for addressing these issues. Nanostructured materials (NSMs) have attracted more attention due to their excellent electrical, optical, and thermal properties. In this paper, the innovative research on the synthesis, development, and classification of NSMs for various applications has been reviewed comprehensively. Furthermore, highlights the most recent research status on the structure and properties of NSMs and concludes the prospects and future difficulties in this area.
{"title":"Novel Advancements in the Field of Nanostructured Materials: Zero-, One-, Two- and Three-Dimensional Materials","authors":"Amit Kumar, JMA Sulaiman, Pranav Kumar, Sohini Chowdhury, S. Sreenivasa","doi":"10.5750/ijme.v1i1.1352","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1352","url":null,"abstract":"Energy generation and preservation are significant difficulties that can be met using fuel cells, solar cells, and batteries. Micro- and nanomaterials with exciting chemical and physical characteristics offer a new avenue for addressing these issues. Nanostructured materials (NSMs) have attracted more attention due to their excellent electrical, optical, and thermal properties. In this paper, the innovative research on the synthesis, development, and classification of NSMs for various applications has been reviewed comprehensively. Furthermore, highlights the most recent research status on the structure and properties of NSMs and concludes the prospects and future difficulties in this area.","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":"141798029","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}
Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.
{"title":"Assessment and Enhancement of Chinese College Students’ Cross- Cultural Learning Competence Based on BP Neural Network Algorithm","authors":"Yang Jiao","doi":"10.5750/ijme.v1i1.1370","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1370","url":null,"abstract":"Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.","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":"141798333","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 complexity of Systems-on-Chip (SoC) designs necessitates robust linear regulator architectures to ensure stable operations and efficient power management in modern devices. In response to this demand, low-dropout (LDO) voltage regulators have emerged as a focal point for their scalability and superior performance across diverse application domains. This paper proposes an LDO linear regulator characterized by high power supply rejection ratio (PSR) and rapid transient response. The design of this LDO emphasizes low power consumption and minimal inrush current while incorporating advanced techniques to enhance PSR and stability across varying frequencies. Despite its compact footprint and low-power profile, this LDO achieves remarkable PSR across a broad spectrum of frequencies. To achieve swift transient response, the proposed design integrates variable biasing and transient boost capacitance. The variable bias structure enhances the slew rate and PSR of the LDO, contributing to its overall performance optimization. Additionally, the strategic placement and utilization of transient-boost capacitance leverage its voltage characteristics to bolster transient response without introducing additional quiescent current, thereby further enhancing circuit stability.
{"title":"Noise Aware Fully Integrated Low Power and Low Inrush Current Fast Transient Response LDO","authors":"Y Avanija, K. Suresh Reddy","doi":"10.5750/ijme.v1i1.1336","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1336","url":null,"abstract":"The complexity of Systems-on-Chip (SoC) designs necessitates robust linear regulator architectures to ensure stable operations and efficient power management in modern devices. In response to this demand, low-dropout (LDO) voltage regulators have emerged as a focal point for their scalability and superior performance across diverse application domains. This paper proposes an LDO linear regulator characterized by high power supply rejection ratio (PSR) and rapid transient response. The design of this LDO emphasizes low power consumption and minimal inrush current while incorporating advanced techniques to enhance PSR and stability across varying frequencies. Despite its compact footprint and low-power profile, this LDO achieves remarkable PSR across a broad spectrum of frequencies. To achieve swift transient response, the proposed design integrates variable biasing and transient boost capacitance. The variable bias structure enhances the slew rate and PSR of the LDO, contributing to its overall performance optimization. Additionally, the strategic placement and utilization of transient-boost capacitance leverage its voltage characteristics to bolster transient response without introducing additional quiescent current, thereby further enhancing circuit stability.","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":"141798441","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}
Soccer teaching involves imparting fundamental skills, tactics, and strategies related to the sport of soccer. Coaches and instructors focus on teaching players proper techniques for dribbling, passing, shooting, and defending, while also emphasizing teamwork, sportsmanship, and game awareness. Soccer teaching sessions typically include a combination of drills, scrimmages, and tactical discussions tailored to the age and skill level of the players. This paper proposed an effective soccer assessment technique for teaching ability with the Automated Probabilistic Deep Learning (APDL) model. The proposed APDL model comprises an automated model for the assessment of student performance. The proposed APDL model processes the input soccer images with the pre-processing of the features. With APDL model uses the probabilistic computation features for the computation of the variables in the soccer data. With the extraction of the features, the maximum likelihood is computed for the classification with the deep learning model features. The APDL model implements the classification-based deep learning model features with the examination of soccer teaching, instruction, development of skill, and players. Simulation results demonstrated that prediction with the APDL model estimates the probability of prediction as 0.91 with an estimated uncertainty value of 0.08. In the case of teaching and coaching assessment, the uncertainty is stated as 0.08 for both with the prediction assessment score of 0.92. The classification accuracy of the proposed APDL model is achieved as 0.95 with the precision value of 0.97. The findings of this research contribute to the advancement of coaching methodologies in soccer, providing coaches and educators with valuable insights into their teaching effectiveness and areas for improvement. Additionally, the automated nature of the APDL model offers scalability and efficiency in assessing coaching performance, paving the way for enhanced coaching practices and player development in soccer.
{"title":"Assessment of Soccer Teaching Ability Based on Deep Learning Algorithm","authors":"FL Yu","doi":"10.5750/ijme.v1i1.1401","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1401","url":null,"abstract":"Soccer teaching involves imparting fundamental skills, tactics, and strategies related to the sport of soccer. Coaches and instructors focus on teaching players proper techniques for dribbling, passing, shooting, and defending, while also emphasizing teamwork, sportsmanship, and game awareness. Soccer teaching sessions typically include a combination of drills, scrimmages, and tactical discussions tailored to the age and skill level of the players. This paper proposed an effective soccer assessment technique for teaching ability with the Automated Probabilistic Deep Learning (APDL) model. The proposed APDL model comprises an automated model for the assessment of student performance. The proposed APDL model processes the input soccer images with the pre-processing of the features. With APDL model uses the probabilistic computation features for the computation of the variables in the soccer data. With the extraction of the features, the maximum likelihood is computed for the classification with the deep learning model features. The APDL model implements the classification-based deep learning model features with the examination of soccer teaching, instruction, development of skill, and players. Simulation results demonstrated that prediction with the APDL model estimates the probability of prediction as 0.91 with an estimated uncertainty value of 0.08. In the case of teaching and coaching assessment, the uncertainty is stated as 0.08 for both with the prediction assessment score of 0.92. The classification accuracy of the proposed APDL model is achieved as 0.95 with the precision value of 0.97. The findings of this research contribute to the advancement of coaching methodologies in soccer, providing coaches and educators with valuable insights into their teaching effectiveness and areas for improvement. Additionally, the automated nature of the APDL model offers scalability and efficiency in assessing coaching performance, paving the way for enhanced coaching practices and player development in soccer.","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":"141797634","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}
Physical fitness is the state of being physically healthy and capable of performing daily tasks with vigor and resilience. Physical fitness and machine learning intersect in various ways, primarily through the use of wearable devices, fitness apps, and data analysis. Wearable fitness trackers equipped with sensors, such as heart rate monitors, accelerometers, and GPS trackers, collect vast amounts of data on individuals' physical activity, sleep patterns, and vital signs. The paper presents an innovative approach to physical fitness assessment and training program recommendation using the Gradient Probabilistic Automated Recommender System with Machine Learning (GPA-RS-ML). This system utilizes machine learning techniques to assess fitness data from individuals and then suggests training programs that are customized to their specific goals and needs. By incorporating gradient values and probabilistic predictions, the GPA-RS-ML algorithm offers a comprehensive and individualized approach to fitness training, enhancing the efficiency and effectiveness of training interventions. The study demonstrates the efficacy of the GPA-RS-ML system in accurately predicting suitable training programs for participants, considering their unique fitness profiles and preferences. This research contributes to the advancement of automated fitness assessment and recommendation systems, providing a valuable tool for fitness professionals and enthusiasts to optimize fitness outcomes and improve adherence to training regimens.
{"title":"Physical Fitness Test Data Analysis and Training Program Recommendation Based on Machine Learning","authors":"Tingting Zhao","doi":"10.5750/ijme.v1i1.1347","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1347","url":null,"abstract":"Physical fitness is the state of being physically healthy and capable of performing daily tasks with vigor and resilience. Physical fitness and machine learning intersect in various ways, primarily through the use of wearable devices, fitness apps, and data analysis. Wearable fitness trackers equipped with sensors, such as heart rate monitors, accelerometers, and GPS trackers, collect vast amounts of data on individuals' physical activity, sleep patterns, and vital signs. The paper presents an innovative approach to physical fitness assessment and training program recommendation using the Gradient Probabilistic Automated Recommender System with Machine Learning (GPA-RS-ML). This system utilizes machine learning techniques to assess fitness data from individuals and then suggests training programs that are customized to their specific goals and needs. By incorporating gradient values and probabilistic predictions, the GPA-RS-ML algorithm offers a comprehensive and individualized approach to fitness training, enhancing the efficiency and effectiveness of training interventions. The study demonstrates the efficacy of the GPA-RS-ML system in accurately predicting suitable training programs for participants, considering their unique fitness profiles and preferences. This research contributes to the advancement of automated fitness assessment and recommendation systems, providing a valuable tool for fitness professionals and enthusiasts to optimize fitness outcomes and improve adherence to training regimens.","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":"141798013","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 study focuses on creating and analyzing paraffin wax composites enriched with heated walnut shell powder and reduced graphene oxide nanoparticles. Experimental procedures include careful blending of materials to form homogeneous mixtures, followed by analysis using techniques such as thermal gravimetric analysis (TGA), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC). The composites show improved thermal properties compared to pure paraffin wax, with increased melting temperatures, heat of fusion, and thermal conductivity. The study also examines density and thermal diffusivity, providing insights into thermal behavior. Additionally, it ensures the corrosion resistance of nano-enhanced bio-based phase change materials (PCMs) for marine engineering through thorough material selection, corrosion testing, and long-term exposure studies simulating seawater conditions. The study evaluates environmental impacts, toxicity, and compliance with standards and proposes mitigation strategies like protective coatings and encapsulation techniques. Overall, the research contributes to developing environmentally sustainable PCM materials for marine applications and offers insights into their thermal behavior for thermal energy storage and management.
{"title":"Synergistic Effects of Heated Walnut Shell Powder and Reduced Graphene Oxide in Paraffin Wax Composites for Marine Thermal Applications","authors":"Aman Sharma, Pradeep Kumar Singh, Kamal Sharma","doi":"10.5750/ijme.v1i1.1359","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1359","url":null,"abstract":"The study focuses on creating and analyzing paraffin wax composites enriched with heated walnut shell powder and reduced graphene oxide nanoparticles. Experimental procedures include careful blending of materials to form homogeneous mixtures, followed by analysis using techniques such as thermal gravimetric analysis (TGA), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC). The composites show improved thermal properties compared to pure paraffin wax, with increased melting temperatures, heat of fusion, and thermal conductivity. The study also examines density and thermal diffusivity, providing insights into thermal behavior. Additionally, it ensures the corrosion resistance of nano-enhanced bio-based phase change materials (PCMs) for marine engineering through thorough material selection, corrosion testing, and long-term exposure studies simulating seawater conditions. The study evaluates environmental impacts, toxicity, and compliance with standards and proposes mitigation strategies like protective coatings and encapsulation techniques. Overall, the research contributes to developing environmentally sustainable PCM materials for marine applications and offers insights into their thermal behavior for thermal energy storage and management.","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":"141798058","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}
Immersive virtual reality (VR) is a technology that transports users into fully immersive digital environments, often through the use of specialized headsets and sensory equipment. A three-dimensional environment, immersive VR offers users an unparalleled sense of presence and interaction, enabling them to explore and interact with virtual worlds as if they were physically present. This paper develops an effective immersive Virtual Reality (VR) model for tourism. The proposed model uses the fuzzy-based logic rules for tourism in VR for the classification with the Backpropagation Feedforward Neural Network (BFNN). Through the developed model efficacy of BFNN-based algorithms in accurately classifying diverse virtual environments and detecting edges with precision. The analysis of the results stated that the through BFNN model MSE and PSNR value is achieved with the value of 0.007 and 28.9 respectively. With the developed model significant classification is achieved with the BFNN model value for the exploration, cultural heritage, adventure, Urban exploration, and Relaxation. Additionally, comparative analyses demonstrate the superiority of BFNNs over alternative classification models, underscoring their effectiveness in accurately categorizing immersive tourism experiences. These findings stated that the advancement of immersive VR technology also offers practical insights for optimizing computational algorithms in immersive tourism applications. The potential of BFNNs redefine the landscape of immersive VR tourism, delivering captivating and personalized virtual experiences that elevate user engagement and satisfaction to unprecedented levels.
{"title":"Design of Immersive VR Tourism Analysis Model Based on Fuzzy Logic Algorithm","authors":"Hongru He","doi":"10.5750/ijme.v1i1.1379","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1379","url":null,"abstract":"Immersive virtual reality (VR) is a technology that transports users into fully immersive digital environments, often through the use of specialized headsets and sensory equipment. A three-dimensional environment, immersive VR offers users an unparalleled sense of presence and interaction, enabling them to explore and interact with virtual worlds as if they were physically present. This paper develops an effective immersive Virtual Reality (VR) model for tourism. The proposed model uses the fuzzy-based logic rules for tourism in VR for the classification with the Backpropagation Feedforward Neural Network (BFNN). Through the developed model efficacy of BFNN-based algorithms in accurately classifying diverse virtual environments and detecting edges with precision. The analysis of the results stated that the through BFNN model MSE and PSNR value is achieved with the value of 0.007 and 28.9 respectively. With the developed model significant classification is achieved with the BFNN model value for the exploration, cultural heritage, adventure, Urban exploration, and Relaxation. Additionally, comparative analyses demonstrate the superiority of BFNNs over alternative classification models, underscoring their effectiveness in accurately categorizing immersive tourism experiences. These findings stated that the advancement of immersive VR technology also offers practical insights for optimizing computational algorithms in immersive tourism applications. The potential of BFNNs redefine the landscape of immersive VR tourism, delivering captivating and personalized virtual experiences that elevate user engagement and satisfaction to unprecedented levels.","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":"141798167","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 composite was created by adding a lubricating agent (tin, graphite, or both) and 10% of ilmenite (particle size range of 20–120 µm). To manufacture this composite material, stir casting—a low-cost method was used. Furthermore, three weight percent of solid lubricants (tin, graphite, or both) were added in order to study their effects on solid lubrication. Ilmenite particles were uniformly dispersed throughout the Al-matrix during optical microscope examination. The refinement of primary silicon when ilmenite reinforcement was added. Comparing the composite samples to the base alloy, LM30, wear analysis showed that the addition of ilmenite increased the samples resistance to wear. The results of the investigation showed that the use of solid lubricants in aluminum metal composites reduced material losses by 21% to 41%. According to wear testing, the use of two solid lubricants demonstrated even greater wear resistance, ranging from 46% to 56%.
{"title":"Examine the Optical and Dry Sliding Wear Characteristics of Aluminum Metal Matrix Composites Reinforced With Ilmenite/GR/SN","authors":"Varun Singhal, Subhash Mishra, Komal Sharma, Sohini Chowdhury","doi":"10.5750/ijme.v1i1.1360","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1360","url":null,"abstract":"The composite was created by adding a lubricating agent (tin, graphite, or both) and 10% of ilmenite (particle size range of 20–120 µm). To manufacture this composite material, stir casting—a low-cost method was used. Furthermore, three weight percent of solid lubricants (tin, graphite, or both) were added in order to study their effects on solid lubrication. Ilmenite particles were uniformly dispersed throughout the Al-matrix during optical microscope examination. The refinement of primary silicon when ilmenite reinforcement was added. Comparing the composite samples to the base alloy, LM30, wear analysis showed that the addition of ilmenite increased the samples resistance to wear. The results of the investigation showed that the use of solid lubricants in aluminum metal composites reduced material losses by 21% to 41%. According to wear testing, the use of two solid lubricants demonstrated even greater wear resistance, ranging from 46% to 56%. ","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":"141798513","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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