Art design is a form of creative expression that encompasses the visual aesthetics and conceptual elements of various mediums. Art design has undergone a transformative evolution with the integration of digital media, reshaping the landscape of creative expression. In contemporary art, artists leverage digital tools and technologies to explore innovative ways of crafting visual narratives. Hence, to improve the quality of the art design this paper constructed a framework of Weighted Genetic Optimization (WGO). The proposed WGO model incorporates the statistical modeling of digital media technology. The statistical technique comprises the estimation of the features in the art design model. Through the integration of WGO with the statistical model features related to the art design with the incorporation of digital media are evaluated. The statistical features in the art design are observed as the digital information such as geometric, GLCM and HUE are the essential features in the integrated WGO with statistical techniques. The estimated features are applied over the deep learning model with the LSTM network for the automated classification of art design that uses digital media for improvement. Simulation results demonstrated that the proposed WGO integrated statistical model achieves the HUE value ranges from 0 -360 which is effective for art design modeling. Also, the proposed model achieves a significant classification rate of 0.98 accuracy with a loss value of 0.2 which is ~9% less loss than the conventional techniques.
{"title":"Optimized Art Design Model With Statistical Model with Digital Media","authors":"Jian Su, Honglin Li","doi":"10.5750/ijme.v1i1.1369","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1369","url":null,"abstract":"Art design is a form of creative expression that encompasses the visual aesthetics and conceptual elements of various mediums. Art design has undergone a transformative evolution with the integration of digital media, reshaping the landscape of creative expression. In contemporary art, artists leverage digital tools and technologies to explore innovative ways of crafting visual narratives. Hence, to improve the quality of the art design this paper constructed a framework of Weighted Genetic Optimization (WGO). The proposed WGO model incorporates the statistical modeling of digital media technology. The statistical technique comprises the estimation of the features in the art design model. Through the integration of WGO with the statistical model features related to the art design with the incorporation of digital media are evaluated. The statistical features in the art design are observed as the digital information such as geometric, GLCM and HUE are the essential features in the integrated WGO with statistical techniques. The estimated features are applied over the deep learning model with the LSTM network for the automated classification of art design that uses digital media for improvement. Simulation results demonstrated that the proposed WGO integrated statistical model achieves the HUE value ranges from 0 -360 which is effective for art design modeling. Also, the proposed model achieves a significant classification rate of 0.98 accuracy with a loss value of 0.2 which is ~9% less loss than 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":"141797386","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}
In contemporary society, management of physical documents such as educational certificates, identity proofs, vehicle registrations, and marriage certificates is an integral part of daily life. However, in present online world, there is a pressing need for digital transformation and the management of these documents. One significant challenge associated with this transformation is the susceptibility of original documents to replication or duplication. This vulner- ability is particularly concerning in the case of educational certificates, where fraud is prevalent. Fraudulent activities in the education sector can influence the proliferation of counterfeit educational and skill certificates, posing serious risks to society. For instance, individuals holding fraudulent degrees in professions such as engineering, medicine, law, and pharmacy may lack genuine competence, thereby posing substantial societal harm. To address the issue of certificate oversight and deter forgery, various approaches have been employed. Traditional methods typically involve the use of centralized databases or web servers for certificate storage, which introduces vulnerabilities because they represent single points of failure that leads to forgery and information loss. An optimal solution lies in the adoption of a Blockchain system that leverages a decentralized database structure to enhance data storage capacity and security. Blockchain technology has demonstrated disruptive potential and in- novative capabilities across multiple sectors because of its decentralized, transparent, and secure attributes. Its impact spans various domains, including banking, supply chain management, healthcare, education, and finance. Notably, in the education sector, Blockchain technology holds promise in enhancing security, transparency, and efficiency across different educational processes. In this study, we explore existing Blockchain oriented certificate management systems, critically analyze their limitations, and propose a novel hybrid educational certificate management model. The proposed model integrates Hyperledger Fabric, IoT, and 2D Barcode to develop a robust and secure framework for managing educational certificates.
{"title":"Hybrid Digital Certificate Management System with QR Code and IoT Integrated on Hyperledger Fabric Blockchain","authors":"Dumpeti Naveen Kumar, Radhika Kavuri","doi":"10.5750/ijme.v1i1.1392","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1392","url":null,"abstract":"In contemporary society, management of physical documents such as educational certificates, identity proofs, vehicle registrations, and marriage certificates is an integral part of daily life. However, in present online world, there is a pressing need for digital transformation and the management of these documents. One significant challenge associated with this transformation is the susceptibility of original documents to replication or duplication. This vulner- ability is particularly concerning in the case of educational certificates, where fraud is prevalent. Fraudulent activities in the education sector can influence the proliferation of counterfeit educational and skill certificates, posing serious risks to society. For instance, individuals holding fraudulent degrees in professions such as engineering, medicine, law, and pharmacy may lack genuine competence, thereby posing substantial societal harm. To address the issue of certificate oversight and deter forgery, various approaches have been employed. Traditional methods typically involve the use of centralized databases or web servers for certificate storage, which introduces vulnerabilities because they represent single points of failure that leads to forgery and information loss. An optimal solution lies in the adoption of a Blockchain system that leverages a decentralized database structure to enhance data storage capacity and security. Blockchain technology has demonstrated disruptive potential and in- novative capabilities across multiple sectors because of its decentralized, transparent, and secure attributes. Its impact spans various domains, including banking, supply chain management, healthcare, education, and finance. Notably, in the education sector, Blockchain technology holds promise in enhancing security, transparency, and efficiency across different educational processes. In this study, we explore existing Blockchain oriented certificate management systems, critically analyze their limitations, and propose a novel hybrid educational certificate management model. The proposed model integrates Hyperledger Fabric, IoT, and 2D Barcode to develop a robust and secure framework for managing educational certificates.","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":"141797402","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}
Pedestrian identification and tracking is a crucial duty in smart building monitoring. The development of sensors has led to architects' focus on smart building design. The image distortions caused by numerous external environmental factors present a significant problem for pedestrian recognition in smart buildings. It is difficult for machine learning algorithms and other conventional filter-based image classification methods, such as histograms of oriented gradient filters, to function efficiently when dealing with many input photos of pedestrians. Deep learning algorithms are now performing substantially better when processing an enormous amount of image data. This article evaluates a novel multimodal classifier-based pedestrian identification method. The proposed method is Multimodal Faster RCNN Inception and ResNet V2 (MM Fast RCNN ResNet). The collected attributes address a tracking problem and establish the foundation for several object recognition tasks (novelty). Our method's neural network is regularized, and the feature representation is automatically adjusted to the detection assignment, resulting in high accuracy (superior to the proposed method). The proposed method is assessed using the PenFudan dataset and contemporary techniques regarding several factors. It is discovered that the recommended MM Fast RCNN ResNet obtains precision, recall, FPPI, FPPW, and average precision of 0.9057, 0.8629, 0.0898, and 0.0943.
行人识别和跟踪是智能楼宇监控的一项重要职责。传感器的发展使建筑师开始关注智能建筑的设计。众多外部环境因素造成的图像失真是智能建筑中行人识别的一大难题。机器学习算法和其他传统的基于滤波器的图像分类方法（如定向梯度直方图滤波器）在处理大量输入的行人照片时很难有效发挥作用。现在，深度学习算法在处理海量图像数据时的性能大大提高。本文评估了一种基于多模态分类器的新型行人识别方法。提出的方法是多模态快速 RCNN Inception 和 ResNet V2（MM Fast RCNN ResNet）。收集到的属性可解决跟踪问题，并为多项物体识别任务奠定基础（新颖性）。我们的方法对神经网络进行了正则化处理，并根据检测任务自动调整特征表示，从而实现了高准确度（优于建议的方法）。我们使用彭福旦数据集和当代技术对提出的方法进行了评估。结果发现，推荐的 MM Fast RCNN ResNet 可获得 0.9057、0.8629、0.0898 和 0.0943 的精度、召回率、FPPI、FPPW 和平均精度。
{"title":"MM_Fast_RCNN_ResNet: Construction of Multimodal Faster RCNN Inception and ResNet V2 for Pedestrian Tracking and detection","authors":"Johnson Kolluri, Sandeep Kumar Dash, Ranjita Das","doi":"10.5750/ijme.v1i1.1381","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1381","url":null,"abstract":"Pedestrian identification and tracking is a crucial duty in smart building monitoring. The development of sensors has led to architects' focus on smart building design. The image distortions caused by numerous external environmental factors present a significant problem for pedestrian recognition in smart buildings. It is difficult for machine learning algorithms and other conventional filter-based image classification methods, such as histograms of oriented gradient filters, to function efficiently when dealing with many input photos of pedestrians. Deep learning algorithms are now performing substantially better when processing an enormous amount of image data. This article evaluates a novel multimodal classifier-based pedestrian identification method. The proposed method is Multimodal Faster RCNN Inception and ResNet V2 (MM Fast RCNN ResNet). The collected attributes address a tracking problem and establish the foundation for several object recognition tasks (novelty). Our method's neural network is regularized, and the feature representation is automatically adjusted to the detection assignment, resulting in high accuracy (superior to the proposed method). The proposed method is assessed using the PenFudan dataset and contemporary techniques regarding several factors. It is discovered that the recommended MM Fast RCNN ResNet obtains precision, recall, FPPI, FPPW, and average precision of 0.9057, 0.8629, 0.0898, and 0.0943.","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":"141797516","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}
Ranjeet Rai, R.R. Sahoo, Rajan Verma, Kuldeep Sharma
The present investigation examines the exergy, energy, and pollutants of a diesel-powered engine fuelled with a blend of Shorea Robusta Biodiesel (SRB) and normal diesel. The impact of engine power, speed, and fuel mix on emissions and operating characteristics are investigated. The use of blended fuels including SRB reduces a number of performance metrics, including BTE, exergy efficiency, and the sustainability index. Furthermore, CO (carbon monoxide), smoke, and HC (hydrocarbon) emissions are reduced when compared to utilising pure diesel fuel. SRB20 has the lowest HC emissions among the mixes SRB. At an engine speed of 1500 rpm and a power output of 5.5kW, SRB10, SRB20, and SRB30 blends lower HC emissions by 8.92%, 10.71%, and 7.14%, respectively. Similarly, when compared to conventional diesel fuel, SRB10, SRB20, and SRB30 blends reduce CO emissions by 1.25%, 5%, and 6.25%, respectively.
本研究考察了以娑罗罗布斯塔生物柴油（SRB）和普通柴油混合燃料为燃料的柴油发动机的放能、能量和污染物。研究了发动机功率、转速和混合燃料对排放和运行特性的影响。包括 SRB 在内的混合燃料的使用降低了一系列性能指标，包括 BTE、放能效率和可持续性指数。此外，与使用纯柴油相比，CO（一氧化碳）、烟雾和 HC（碳氢化合物）排放量也有所减少。在各种混合 SRB 中，SRB20 的 HC 排放量最低。在发动机转速为 1500 rpm、输出功率为 5.5kW 时，SRB10、SRB20 和 SRB30 混合燃料的 HC 排放量分别降低了 8.92%、10.71% 和 7.14%。同样，与传统柴油相比，SRB10、SRB20 和 SRB30 混合燃料的 CO 排放量分别减少了 1.25%、5% 和 6.25%。
{"title":"An Energy, and Emission Assessment of Diesel Engines Powered with Shorea Robusta Biodiesel and Blends","authors":"Ranjeet Rai, R.R. Sahoo, Rajan Verma, Kuldeep Sharma","doi":"10.5750/ijme.v1i1.1351","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1351","url":null,"abstract":"The present investigation examines the exergy, energy, and pollutants of a diesel-powered engine fuelled with a blend of Shorea Robusta Biodiesel (SRB) and normal diesel. The impact of engine power, speed, and fuel mix on emissions and operating characteristics are investigated. The use of blended fuels including SRB reduces a number of performance metrics, including BTE, exergy efficiency, and the sustainability index. Furthermore, CO (carbon monoxide), smoke, and HC (hydrocarbon) emissions are reduced when compared to utilising pure diesel fuel. SRB20 has the lowest HC emissions among the mixes SRB. At an engine speed of 1500 rpm and a power output of 5.5kW, SRB10, SRB20, and SRB30 blends lower HC emissions by 8.92%, 10.71%, and 7.14%, respectively. Similarly, when compared to conventional diesel fuel, SRB10, SRB20, and SRB30 blends reduce CO emissions by 1.25%, 5%, and 6.25%, respectively.","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":"141798137","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}
Dry powders mixed with air transportation had bend erosion issues. CFD analysis by Shear-Stress Transport (SST) k-ɯ Model within Ansys was used to conduct a comprehensive numerical analysis of particle erosion within a 90° mild steel pipe bend in Pneumatic Conveying System. Part of the conveying pipe rotation achieved with the external motor swirling device was used to create swirling of particle before bend. Different-sized sand particles at different rpm were tested with and without a swirling device. Reduction in erosion rate of pipe bend carries significant implications, notably an extension of the system's operational lifespan. Reduction in erosion rate reports an impressive increase of 50% bend lifespan at higher RPM levels of the swirling device. The results underscore the effectiveness of this device in mitigating erosion, with significant implications for enhancing the longevity and reliability of bend life in piping systems of Pneumatic Conveying applications.
{"title":"CFD Modelling of Swirling Mechanism to Reduce Erosion of Pipe Bend in Pneumatic Conveying System","authors":"Bharat Singh Yadav, Rajiv Chaudhary, R.C. Singh","doi":"10.5750/ijme.v1i1.1364","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1364","url":null,"abstract":"Dry powders mixed with air transportation had bend erosion issues. CFD analysis by Shear-Stress Transport (SST) k-ɯ Model within Ansys was used to conduct a comprehensive numerical analysis of particle erosion within a 90° mild steel pipe bend in Pneumatic Conveying System. Part of the conveying pipe rotation achieved with the external motor swirling device was used to create swirling of particle before bend. Different-sized sand particles at different rpm were tested with and without a swirling device. Reduction in erosion rate of pipe bend carries significant implications, notably an extension of the system's operational lifespan. Reduction in erosion rate reports an impressive increase of 50% bend lifespan at higher RPM levels of the swirling device. The results underscore the effectiveness of this device in mitigating erosion, with significant implications for enhancing the longevity and reliability of bend life in piping systems of Pneumatic Conveying applications.","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":"141798169","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}
This paper explores the application of novel, form-stable eutectic mixtures in thermal energy storage systems for maritime environments. These advanced materials present a significant leap forward, addressing critical challenges faced by conventional phase change materials (PCMs) in marine environments. The stability, eliminating leakage and fluidity issues encountered with liquid PCMs are ensured by incorporating 2-hydroxypropyl ether cellulose (HPEC) as a gelling agent. Additionally, the thermal properties and heat transfer capacities were significantly enhanced, and eventually, overall system efficiency improved by including nano-graphene platelets (NGPs). Notably, NGPs effectively suppress supercooling, minimizing energy losses and guaranteeing consistent performance at elevated temperatures. Further, the eutectic mixture demonstrates exceptional durability through an accelerated thermal reliability test, guaranteeing optimal performance over a projected seventy-year lifespan. This enhanced thermal performance, and enduring stability combination establishes the form-stable eutectic mixture with NGPs as an up-and-coming solution for diverse maritime applications requiring efficient and reliable thermal energy storage. This research provides a compelling case for implementing form-stable eutectic mixtures with NGPs in maritime thermal energy storage systems. Its superior performance and sustainability offer significant advantages for diverse shipboard and offshore applications, contributing to improved energy efficiency, environmental sustainability, and operational resilience within the maritime sector.
{"title":"Enhancement of Thermo-Physical Properties of Form-Stable Nano-Enhanced Phase Change Materials: Advancing Maritime Sustainability","authors":"Rishabh Chaturvedi, Kamal Sharma","doi":"10.5750/ijme.v1i1.1344","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1344","url":null,"abstract":"This paper explores the application of novel, form-stable eutectic mixtures in thermal energy storage systems for maritime environments. These advanced materials present a significant leap forward, addressing critical challenges faced by conventional phase change materials (PCMs) in marine environments. The stability, eliminating leakage and fluidity issues encountered with liquid PCMs are ensured by incorporating 2-hydroxypropyl ether cellulose (HPEC) as a gelling agent. Additionally, the thermal properties and heat transfer capacities were significantly enhanced, and eventually, overall system efficiency improved by including nano-graphene platelets (NGPs). Notably, NGPs effectively suppress supercooling, minimizing energy losses and guaranteeing consistent performance at elevated temperatures. \u0000Further, the eutectic mixture demonstrates exceptional durability through an accelerated thermal reliability test, guaranteeing optimal performance over a projected seventy-year lifespan. This enhanced thermal performance, and enduring stability combination establishes the form-stable eutectic mixture with NGPs as an up-and-coming solution for diverse maritime applications requiring efficient and reliable thermal energy storage. This research provides a compelling case for implementing form-stable eutectic mixtures with NGPs in maritime thermal energy storage systems. Its superior performance and sustainability offer significant advantages for diverse shipboard and offshore applications, contributing to improved energy efficiency, environmental sustainability, and operational resilience within the maritime sector. ","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":"141798179","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}
Present study investigates the enhancement of pump impeller materials using aluminium matrix composites (AMCs), specifically focusing on Al 7075 Alloy strengthened with rice husk ash (RHA) for enhanced durability and resistance to corrosion. It employs Thermal spraying using high-velocity oxygen-fuel (HVOF) to apply WC-Co-Cr powder, emphasizing low porosity, high adherence, and superior wear and corrosion resistance. By optimizing HVOF spray parameters using Taguchi L25 Orthogonal array, the research aims to minimize porosity and maximize hardness, addressing challenges in material dispersion, sustainability, and process control. The objective is to develop predictive models for the microhardness and porosity characteristics of WC–10Co–4Cr coating powder utilized through HVOF on AMMC substrates. AMMC substrates. Spray distance, carrier gas flow rate, powder feed rate, oxygen flow rate, and LPG flow rate are examined, with spray distance exerting the most significant influence on porosity, followed by powder feed rate and other parameters. This study contributes to improving coating characteristics crucial for industrial applications.
{"title":"Optimization of HVOF Spray Parameters for WC-Co-Cr Coatings on AMMC (Al-RHA) for Pump Impeller Protection","authors":"Varsha Pathak, R S Mishra, Ranganath MS","doi":"10.5750/ijme.v1i1.1353","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1353","url":null,"abstract":"Present study investigates the enhancement of pump impeller materials using aluminium matrix composites (AMCs), specifically focusing on Al 7075 Alloy strengthened with rice husk ash (RHA) for enhanced durability and resistance to corrosion. It employs Thermal spraying using high-velocity oxygen-fuel (HVOF) to apply WC-Co-Cr powder, emphasizing low porosity, high adherence, and superior wear and corrosion resistance. By optimizing HVOF spray parameters using Taguchi L25 Orthogonal array, the research aims to minimize porosity and maximize hardness, addressing challenges in material dispersion, sustainability, and process control. The objective is to develop predictive models for the microhardness and porosity characteristics of WC–10Co–4Cr coating powder utilized through HVOF on AMMC substrates. AMMC substrates. Spray distance, carrier gas flow rate, powder feed rate, oxygen flow rate, and LPG flow rate are examined, with spray distance exerting the most significant influence on porosity, followed by powder feed rate and other parameters. This study contributes to improving coating characteristics crucial for industrial applications.","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":"141798312","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}
Cloud technology revolutionizes the way businesses and individuals store, access, and manage data and applications. It involves the use of remote servers hosted on the internet to store and process information, providing on-demand computing resources. With the cloud, users can access their data and applications from anywhere with an internet connection, fostering increased collaboration, flexibility, and scalability. This research explores the convergence of smart media video cloud technology and news communication, introducing a novel framework named Big Data Analytics Parallel Edge Computing with ECC (PEC-ECC). With the proliferation of video content in news dissemination, there is a growing need for innovative technologies to enhance efficiency, security, and accessibility. PEC-ECC integrates the power of big data analytics, parallel edge computing, and error-correcting code mechanisms to optimize video processing and delivery. This framework not only addresses the challenges of data volume and computational speed but also ensures the integrity and security of transmitted content. By leveraging edge computing capabilities, PEC-ECC minimizes latency, making real-time news communication more responsive and reliable. This research contributes to the advancement of smart media technology, offering a robust solution to elevate the quality and effectiveness of video-based news communication in the era of digital journalism.
{"title":"Smart Media Video Cloud Technology in News Communication","authors":"YY Zhang","doi":"10.5750/ijme.v1i1.1350","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1350","url":null,"abstract":"Cloud technology revolutionizes the way businesses and individuals store, access, and manage data and applications. It involves the use of remote servers hosted on the internet to store and process information, providing on-demand computing resources. With the cloud, users can access their data and applications from anywhere with an internet connection, fostering increased collaboration, flexibility, and scalability. This research explores the convergence of smart media video cloud technology and news communication, introducing a novel framework named Big Data Analytics Parallel Edge Computing with ECC (PEC-ECC). With the proliferation of video content in news dissemination, there is a growing need for innovative technologies to enhance efficiency, security, and accessibility. PEC-ECC integrates the power of big data analytics, parallel edge computing, and error-correcting code mechanisms to optimize video processing and delivery. This framework not only addresses the challenges of data volume and computational speed but also ensures the integrity and security of transmitted content. By leveraging edge computing capabilities, PEC-ECC minimizes latency, making real-time news communication more responsive and reliable. This research contributes to the advancement of smart media technology, offering a robust solution to elevate the quality and effectiveness of video-based news communication in the era of digital journalism.","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":"141797326","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}
Adaptive target tracking with visual attention represents a sophisticated approach to object detection and localization in dynamic environments. With principles inspired by human visual perception, this methodology employs mechanisms of selective attention to prioritize relevant visual information for tracking moving targets. By dynamically adjusting attentional focus based on salient visual cues and target motion characteristics, adaptive target tracking enhances the efficiency and accuracy of object localization in cluttered scenes. This research presents a novel adaptive target tracking algorithm designed for robotic systems, integrating a visual attention mechanism with the Fuzzy Clustering Multi-Point Tracking utilizing the Green Channel (FC-MPT-GC) approach. The proposed FC-MPT-GC model comprises of Fuzzy Clustering for the extraction of features in the robots-based environment. The FC-MPT-GC model uses the estimation of green channels in the classification environment. With the estimation of features in the environment with Fuzzy C-means clustering green channels are deployed in the deep learning, The proposed algorithm aims to enhance the adaptability and precision of target tracking in dynamic environments. By incorporating a visual attention mechanism, the algorithm dynamically allocates attentional focus to salient regions of the visual input, optimizing the tracking process for moving targets. The FC-MPT-GC methodology further refines target localization by utilizing fuzzy clustering and multi-point tracking strategies, particularly leveraging information from the Green Channel to improve robustness in various lighting conditions. Simulation analysis demonstrated that the proposed FC-MPT-GC model tracking accuracy is achieved at 95.1% with the minimal computation time of 15.2 ms.
{"title":"Design of Adaptive Target Tracking Algorithm for Robots Based on Visual Attention Mechanism","authors":"Chao Zhang, Wei Chen, Zebin Zhou","doi":"10.5750/ijme.v1i1.1400","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1400","url":null,"abstract":"Adaptive target tracking with visual attention represents a sophisticated approach to object detection and localization in dynamic environments. With principles inspired by human visual perception, this methodology employs mechanisms of selective attention to prioritize relevant visual information for tracking moving targets. By dynamically adjusting attentional focus based on salient visual cues and target motion characteristics, adaptive target tracking enhances the efficiency and accuracy of object localization in cluttered scenes. This research presents a novel adaptive target tracking algorithm designed for robotic systems, integrating a visual attention mechanism with the Fuzzy Clustering Multi-Point Tracking utilizing the Green Channel (FC-MPT-GC) approach. The proposed FC-MPT-GC model comprises of Fuzzy Clustering for the extraction of features in the robots-based environment. The FC-MPT-GC model uses the estimation of green channels in the classification environment. With the estimation of features in the environment with Fuzzy C-means clustering green channels are deployed in the deep learning, The proposed algorithm aims to enhance the adaptability and precision of target tracking in dynamic environments. By incorporating a visual attention mechanism, the algorithm dynamically allocates attentional focus to salient regions of the visual input, optimizing the tracking process for moving targets. The FC-MPT-GC methodology further refines target localization by utilizing fuzzy clustering and multi-point tracking strategies, particularly leveraging information from the Green Channel to improve robustness in various lighting conditions. Simulation analysis demonstrated that the proposed FC-MPT-GC model tracking accuracy is achieved at 95.1% with the minimal computation time of 15.2 ms.","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":"141797546","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 Talent Cultivation Management Model for Universities represents a strategic framework designed to optimize the development and oversight of academic programs. This model focuses on identifying, nurturing, and assessing talents within the university ecosystem. It incorporates innovative methodologies to align educational offerings with individual needs and career goals. This study presents an innovative approach to developing a talent cultivation management model for universities, leveraging the integration of the Fuzzy Neural Network Algorithm with the proposed Optimized Spider Monkey Fuzzy Neural Network (OSMF-NN). Recognizing the critical importance of talent development in higher education, this research seeks to enhance the efficacy and adaptability of existing management models. The OSMF-NN algorithm, inspired by the optimization capabilities of spider monkey behavior, enhances the traditional fuzzy neural network algorithm, enabling more precise and efficient talent management. By harnessing the synergies between fuzzy logic and neural networks, the proposed model offers a robust framework for identifying, nurturing, and evaluating talents within the university ecosystem. Through comprehensive experimentation and validation, this study demonstrates the effectiveness of the OSMF-NN algorithm in optimizing talent cultivation strategies, promoting personalized learning experiences, and fostering student success in higher education institutions.
{"title":"Study on Talent Cultivation Management Model of Universities Based on Fuzzy Neural Network Algorithm","authors":"H Cheng","doi":"10.5750/ijme.v1i1.1382","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1382","url":null,"abstract":"The Talent Cultivation Management Model for Universities represents a strategic framework designed to optimize the development and oversight of academic programs. This model focuses on identifying, nurturing, and assessing talents within the university ecosystem. It incorporates innovative methodologies to align educational offerings with individual needs and career goals. This study presents an innovative approach to developing a talent cultivation management model for universities, leveraging the integration of the Fuzzy Neural Network Algorithm with the proposed Optimized Spider Monkey Fuzzy Neural Network (OSMF-NN). Recognizing the critical importance of talent development in higher education, this research seeks to enhance the efficacy and adaptability of existing management models. The OSMF-NN algorithm, inspired by the optimization capabilities of spider monkey behavior, enhances the traditional fuzzy neural network algorithm, enabling more precise and efficient talent management. By harnessing the synergies between fuzzy logic and neural networks, the proposed model offers a robust framework for identifying, nurturing, and evaluating talents within the university ecosystem. Through comprehensive experimentation and validation, this study demonstrates the effectiveness of the OSMF-NN algorithm in optimizing talent cultivation strategies, promoting personalized learning experiences, and fostering student success in higher education institutions.","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":"141797771","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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