In the presented research work, LN2 supplied directly at rake face and in another localised machining condition, LN2 supplied at rake and flank face simultaneously. The DoE of performance of experiments was in accordance with Taguchi S/N ratio L18. It was found that flank wear length and crater wear width at LN2 supply at rake & flank face simultaneously declined by 23-38% and 20-30% respectively as compared to LN2 supply at rake face only. ANOVA gave the highest effect of contribution in the percentage to LN2 supply at rake & flank face simultaneously as 76.06% and 77.67%, next in decreasing order followed by speed, feed and depth of cut. SEM images depicted that flank wear length and crater wear width in both machining conditions. Tool wear was low during turning LN2 supply at rake and flank face. Optimized values of each response was confirmed by repeating the experiments.
{"title":"Tool Wear Analysis During Turning with Single and Dual Supply of LN2","authors":"A Sharma","doi":"10.5750/ijme.v1i1.1332","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1332","url":null,"abstract":"In the presented research work, LN2 supplied directly at rake face and in another localised machining condition, LN2 supplied at rake and flank face simultaneously. The DoE of performance of experiments was in accordance with Taguchi S/N ratio L18. It was found that flank wear length and crater wear width at LN2 supply at rake & flank face simultaneously declined by 23-38% and 20-30% respectively as compared to LN2 supply at rake face only. ANOVA gave the highest effect of contribution in the percentage to LN2 supply at rake & flank face simultaneously as 76.06% and 77.67%, next in decreasing order followed by speed, feed and depth of cut. SEM images depicted that flank wear length and crater wear width in both machining conditions. Tool wear was low during turning LN2 supply at rake and flank face. Optimized values of each response was confirmed by repeating the experiments.","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":"141797285","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}
Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.
{"title":"Optimized Resource Management and Dynamic Routing Protocol for Wireless Sensor Networks Through Load Balancing, Packet Scheduling, and Intelligent Clustering","authors":"B. Komuraiah, MS. Anuradha","doi":"10.5750/ijme.v1i1.1388","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1388","url":null,"abstract":"Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.","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":"141797319","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 order to get an efficient comprehensive analysis on Doppler estimation in RADAR; need an enhanced arithmetic formulation procedure for density, power and latency optimisations. Modular adders and multipliers are very crucial components in the performance of residue number system-based applications. The Residue Number System (RNS) is a non-positional number system that allows parallel computations without transfers between digits. However, some operations in RNS require knowledge of the positional characteristic of a number. Among these operations is the conversion from RNS to the positional number system. The methods of reverse conversion for general form moduli based on the Chinese remainder theorem and the mixed-radix conversion are considered, as well as the optimized methods for special form moduli. A modified New CRT-I & New CRT-II with conjugate moduli set is considered to implement adder, multipliers and subtractions with optimised algorithms. This paper mainly deals with the conversion of numbers from binary to RNS as well RNS to binary with the specific modulo {2^n±k} which proves this new method. Modified Radix16 booth encoding algorithm and square carry bypass adder are used in implementation of RNS system to reduce parameter constraints.
{"title":"Optimised Implementation of Adaptive Rns Using Power-Aware CRT","authors":"Bentipalli Sekhar, G Appala Naidu, K. Babulu","doi":"10.5750/ijme.v1i1.1380","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1380","url":null,"abstract":"In order to get an efficient comprehensive analysis on Doppler estimation in RADAR; need an enhanced arithmetic formulation procedure for density, power and latency optimisations. Modular adders and multipliers are very crucial components in the performance of residue number system-based applications. The Residue Number System (RNS) is a non-positional number system that allows parallel computations without transfers between digits. However, some operations in RNS require knowledge of the positional characteristic of a number. Among these operations is the conversion from RNS to the positional number system. The methods of reverse conversion for general form moduli based on the Chinese remainder theorem and the mixed-radix conversion are considered, as well as the optimized methods for special form moduli. A modified New CRT-I & New CRT-II with conjugate moduli set is considered to implement adder, multipliers and subtractions with optimised algorithms. This paper mainly deals with the conversion of numbers from binary to RNS as well RNS to binary with the specific modulo {2^n±k} which proves this new method. Modified Radix16 booth encoding algorithm and square carry bypass adder are used in implementation of RNS system to reduce parameter constraints.","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":"141797357","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}
Intelligent learning represents a dynamic approach to education that provides innovative technologies and personalized methodologies to enhance learning outcomes. Intelligent teaching adapts instruction to the individual needs, preferences, and progress of each student. This approach enables educators to tailor curriculum delivery, identify areas for improvement, and provide timely feedback, fostering a more engaging and effective learning environment. Moreover, intelligent teaching promotes collaborative learning experiences and encourages critical thinking skills, preparing students for success in an increasingly digital and interconnected world. This paper proposed a framework of Generative Platform-Oriented Intelligent Deep Neural Network (GPoIDNN) for Korean language teaching in Universities. The proposed GPoIDNN network comprises a social media platform for the promotion of Korean language teaching among students. With the GPoIDNN platform, a Generative network is implemented for the analysis of the factors involved in Language teaching in universities. The platform considered for the proposed model is Weibo for acquiring in-depth information about the language learning process. Upon the estimated features GPoIDNN uses the Generative Deep Neural Network platform for the classification and examination of the student performance. With the Weibo platform in social media, the Generative network constructs the intelligent teaching system for the Korean language teaching process in University students. The examination of student performance demonstrated that the proposed GPoIDNN model improves the student learning of Korean language with improved by 73% through the intelligent model. Further, the keywords and opinions classified with the GPoIDNN model exhibits a higher classification rate of 0.98 based on the opinion of the students in the universities.
{"title":"Intelligent Learning Platform with Deep Neural Network for Korean Language Teaching in Universities","authors":"Yuwen Zhang","doi":"10.5750/ijme.v1i1.1411","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1411","url":null,"abstract":"Intelligent learning represents a dynamic approach to education that provides innovative technologies and personalized methodologies to enhance learning outcomes. Intelligent teaching adapts instruction to the individual needs, preferences, and progress of each student. This approach enables educators to tailor curriculum delivery, identify areas for improvement, and provide timely feedback, fostering a more engaging and effective learning environment. Moreover, intelligent teaching promotes collaborative learning experiences and encourages critical thinking skills, preparing students for success in an increasingly digital and interconnected world. This paper proposed a framework of Generative Platform-Oriented Intelligent Deep Neural Network (GPoIDNN) for Korean language teaching in Universities. The proposed GPoIDNN network comprises a social media platform for the promotion of Korean language teaching among students. With the GPoIDNN platform, a Generative network is implemented for the analysis of the factors involved in Language teaching in universities. The platform considered for the proposed model is Weibo for acquiring in-depth information about the language learning process. Upon the estimated features GPoIDNN uses the Generative Deep Neural Network platform for the classification and examination of the student performance. With the Weibo platform in social media, the Generative network constructs the intelligent teaching system for the Korean language teaching process in University students. The examination of student performance demonstrated that the proposed GPoIDNN model improves the student learning of Korean language with improved by 73% through the intelligent model. Further, the keywords and opinions classified with the GPoIDNN model exhibits a higher classification rate of 0.98 based on the opinion of the students in the universities.","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":"141797683","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}
An IoT-enabled innovative environment with efficient routing is proposed for digital library services to analyze user behavior. This conceptual framework leverages the Internet of Things (IoT) to create a smart library ecosystem where connected devices collect and share data in real-time. The emphasis on efficient routing ensures seamless access to digital resources, optimizing the user experience. This study introduces an IoT-enabled innovative environment aimed at optimizing digital library services by examining user behavior, incorporating the Clustered Centered Routing Cryptography Scheme (CCRCS). The proposed framework leverages the Internet of Things (IoT) to create a dynamic ecosystem within digital libraries, facilitating efficient data collection and analysis. By implementing the CCRCS, data transmission is secured through clustered-centered routing, ensuring the integrity and confidentiality of user interactions and resource access. Through the IoT infrastructure, libraries can monitor user behavior in real-time, capturing valuable insights into preferences, browsing patterns, and resource utilization. This holistic approach enables libraries to adapt their services to better meet user needs, optimize resource allocation, and enhance the overall user experience. The integration of IoT technologies with robust cryptographic protocols represents a significant advancement in digital library management, offering unparalleled opportunities for data-driven decision-making and personalized service delivery.
{"title":"IoT-Enabled Innovative Environment with Efficient Routing for the Digital Library Services to Examine the Behavior of Users","authors":"W Zhou","doi":"10.5750/ijme.v1i1.1394","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1394","url":null,"abstract":"An IoT-enabled innovative environment with efficient routing is proposed for digital library services to analyze user behavior. This conceptual framework leverages the Internet of Things (IoT) to create a smart library ecosystem where connected devices collect and share data in real-time. The emphasis on efficient routing ensures seamless access to digital resources, optimizing the user experience. This study introduces an IoT-enabled innovative environment aimed at optimizing digital library services by examining user behavior, incorporating the Clustered Centered Routing Cryptography Scheme (CCRCS). The proposed framework leverages the Internet of Things (IoT) to create a dynamic ecosystem within digital libraries, facilitating efficient data collection and analysis. By implementing the CCRCS, data transmission is secured through clustered-centered routing, ensuring the integrity and confidentiality of user interactions and resource access. Through the IoT infrastructure, libraries can monitor user behavior in real-time, capturing valuable insights into preferences, browsing patterns, and resource utilization. This holistic approach enables libraries to adapt their services to better meet user needs, optimize resource allocation, and enhance the overall user experience. The integration of IoT technologies with robust cryptographic protocols represents a significant advancement in digital library management, offering unparalleled opportunities for data-driven decision-making and personalized service delivery.","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":"141797799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A movie or TV script is a meticulously crafted document that serves as the blueprint for a visual storytelling project. It outlines the dialogue, actions, and scene descriptions that will unfold on screen, guiding actors, directors, and crew members in bringing the story to life. Each script is divided into scenes and acts, with clear instructions on character entrances and exits, camera angles, and pacing. This paper introduces a novel framework for enhancing the quality of movie and TV scripts through the integration of the Combinational Multi-Stage Genetic Optimization (CMSGO) model with ChatGPT, a state-of-the-art language generation model. The CMSGO model utilizes iterative optimization techniques to systematically refine and enhance script elements such as coherence, dialogue flow, character development, and overall narrative structure. The proposed CMSGO model comprises the Combinational model with the genetic optimization function. The function CMSGO model examines the fitness function with the Multi-Stage Optimization process. The proposed CMSGO model uses the estimation of features in the Multi-stage optimization model with the computation of features related to the scripts. Through 20 generations of optimization, the CMSGO model demonstrates its effectiveness in improving script quality, as evidenced by a steady increase in average script quality scores. Additionally, the multi-stage optimization approach targets specific aspects of script quality, allowing for targeted adjustments to parameters related to character motivations, plot coherence, and tone. Viewer opinions further validate the efficacy of the generated scripts, with positive evaluations across various aspects such as audience engagement, coherence, emotional impact, and originality. The proposed framework offers a robust and data-driven approach to scriptwriting, enabling the creation of high-quality movie and TV scripts that captivate and resonate with audiences, thus enriching the overall viewing experience.
{"title":"Automatic Generation Algorithm of Movie and TV Scripts Based on ChatGPT","authors":"Y Wang","doi":"10.5750/ijme.v1i1.1383","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1383","url":null,"abstract":"A movie or TV script is a meticulously crafted document that serves as the blueprint for a visual storytelling project. It outlines the dialogue, actions, and scene descriptions that will unfold on screen, guiding actors, directors, and crew members in bringing the story to life. Each script is divided into scenes and acts, with clear instructions on character entrances and exits, camera angles, and pacing. This paper introduces a novel framework for enhancing the quality of movie and TV scripts through the integration of the Combinational Multi-Stage Genetic Optimization (CMSGO) model with ChatGPT, a state-of-the-art language generation model. The CMSGO model utilizes iterative optimization techniques to systematically refine and enhance script elements such as coherence, dialogue flow, character development, and overall narrative structure. The proposed CMSGO model comprises the Combinational model with the genetic optimization function. The function CMSGO model examines the fitness function with the Multi-Stage Optimization process. The proposed CMSGO model uses the estimation of features in the Multi-stage optimization model with the computation of features related to the scripts. Through 20 generations of optimization, the CMSGO model demonstrates its effectiveness in improving script quality, as evidenced by a steady increase in average script quality scores. Additionally, the multi-stage optimization approach targets specific aspects of script quality, allowing for targeted adjustments to parameters related to character motivations, plot coherence, and tone. Viewer opinions further validate the efficacy of the generated scripts, with positive evaluations across various aspects such as audience engagement, coherence, emotional impact, and originality. The proposed framework offers a robust and data-driven approach to scriptwriting, enabling the creation of high-quality movie and TV scripts that captivate and resonate with audiences, thus enriching the overall viewing experience. \u0000 ","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":"141797847","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}
Virtualization is a critical technology that enables users to leverage the vast resources available within datacenters. Despite its numerous benefits, such as on-demand scalability, continuous availability, and cost efficiency, virtualization is susceptible to various security challenges, including intrusion, data compromise, and session hijacking. To address these threats, this study presents an innovative approach based on deep learning for detecting attacks and proactively isolating virtual machines (VMs) to mitigate their impact. The event sequences of VMs are transformed into event images using advanced techniques Integrated Gramian Markov Plot (IGMP). The proposed IGMP model comprises of the Gramian model with Markov estimate. The model uses the recurrence plot for the estimation of the IGMP in the virtualization process with the computation of data centers. Additionally, to improve the security IGMP model uses the aggregation signature generation model for the security features in the Virtual Machines. The proposed IGMP model uses the Deep learning models are then employed to extract meaningful features from these event images, which are subsequently classified into specific attack classes. Once an attack is predicted within the physical machine, the suspected VMs are immediately isolated to prevent further damage. Experimental results demonstrated that the high efficacy of the IGMP method, achieving an impressive attack prediction accuracy of 96%, surpassing existing approaches by at least 2%.
{"title":"Deep Learning-Based Vulnerability Detection and Mitigation in Virtualization Data Center","authors":"J Manikandan, U. Srilakshmi","doi":"10.5750/ijme.v1i1.1393","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1393","url":null,"abstract":"Virtualization is a critical technology that enables users to leverage the vast resources available within datacenters. Despite its numerous benefits, such as on-demand scalability, continuous availability, and cost efficiency, virtualization is susceptible to various security challenges, including intrusion, data compromise, and session hijacking. To address these threats, this study presents an innovative approach based on deep learning for detecting attacks and proactively isolating virtual machines (VMs) to mitigate their impact. The event sequences of VMs are transformed into event images using advanced techniques Integrated Gramian Markov Plot (IGMP). The proposed IGMP model comprises of the Gramian model with Markov estimate. The model uses the recurrence plot for the estimation of the IGMP in the virtualization process with the computation of data centers. Additionally, to improve the security IGMP model uses the aggregation signature generation model for the security features in the Virtual Machines. The proposed IGMP model uses the Deep learning models are then employed to extract meaningful features from these event images, which are subsequently classified into specific attack classes. Once an attack is predicted within the physical machine, the suspected VMs are immediately isolated to prevent further damage. Experimental results demonstrated that the high efficacy of the IGMP method, achieving an impressive attack prediction accuracy of 96%, surpassing existing approaches by at least 2%.","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":"141797867","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}
Packaging design is a critical component of product marketing and branding, encompassing the visual and structural elements that encase and present goods to consumers. The hand-drawn illustration is a timeless art form that embodies the unique style, skill, and creativity of the artist's hand. This paper presents a novel approach to deep learning techniques for enhancing packaging design through the classification of hand-drawn illustrations. The proposed model is stated as a Weighted Augmented Deep Generative Network (WADGN). The proposed WADGN model uses the augmentation network for the generation of the augmented images for the creative products. With the augmented images features are extracted in the hand-drawn illustration of the products. The extracted features are implemented with the weighted augmented feature vector for the application of the generative deep learning network. The proposed WADGN model uses the feature vector of the deep learning model for the design of creative product design. With the deep learning the creative features of the hand-drawn illustration are classified for the creative package design. Simulation results demonstrated that proposed WADGN model higher performance than the conventional technique such as CNN, LSTM and SVM classifier. The proposed WADGN model achieves the ~21% higher performance than the SVM, ~16% than the LSTM and ~9% improvement than the CNN model.
{"title":"Deep Learning-Based Hand-Drawn Illustration in Packaging Design of Cultural and Creative Products","authors":"Jianfei Wang","doi":"10.5750/ijme.v1i1.1368","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1368","url":null,"abstract":"Packaging design is a critical component of product marketing and branding, encompassing the visual and structural elements that encase and present goods to consumers. The hand-drawn illustration is a timeless art form that embodies the unique style, skill, and creativity of the artist's hand. This paper presents a novel approach to deep learning techniques for enhancing packaging design through the classification of hand-drawn illustrations. The proposed model is stated as a Weighted Augmented Deep Generative Network (WADGN). The proposed WADGN model uses the augmentation network for the generation of the augmented images for the creative products. With the augmented images features are extracted in the hand-drawn illustration of the products. The extracted features are implemented with the weighted augmented feature vector for the application of the generative deep learning network. The proposed WADGN model uses the feature vector of the deep learning model for the design of creative product design. With the deep learning the creative features of the hand-drawn illustration are classified for the creative package design. Simulation results demonstrated that proposed WADGN model higher performance than the conventional technique such as CNN, LSTM and SVM classifier. The proposed WADGN model achieves the ~21% higher performance than the SVM, ~16% than the LSTM and ~9% improvement than the CNN model.","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":"141798011","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}
Exchange Market Pressure (EMP) indices are used as comprehensive indicators of pressure on a currency. This paper examines the relationship of India government debt, India’s GDP, world money supply and world GDP with exchange market pressure in India. We use quarterly data from 1992: II to 2018: III. The results suggest a significant positive relationship between EMP and the India government debt and GDP and a negative relationship between EMP and world money supply. The relationship between EMP and world GDP is found to be insignificant. This study sheds light on the complex dynamics of EMP and its determinants in India, highlighting the impact of key economic factors and historical events on currency stability. These findings have important implications for policymakers and stakeholders in maritime trade, providing insights into the economic factors influencing currency stability in maritime trade.
外汇市场压力（EMP）指数被用作衡量货币压力的综合指标。本文研究了印度政府债务、印度国内生产总值、世界货币供应量和世界国内生产总值与印度汇率市场压力的关系。我们使用的是1992：II至2018年的季度数据：III 的季度数据。结果表明，EMP 与印度政府债务和 GDP 之间存在显著的正相关关系，EMP 与世界货币供应量之间存在负相关关系。本研究揭示了印度 EMP 及其决定因素的复杂动态，突出了关键经济因素和历史事件对货币稳定性的影响。这些发现对海上贸易的政策制定者和利益相关者具有重要意义，为影响海上贸易货币稳定性的经济因素提供了深刻见解。
{"title":"Exchange Market Pressure, Government Debt, US Money Supply, GDP Growth and Maritime Trade: An Empirical Evidence from India","authors":"Sanjay Kumar, Nand Kumar","doi":"10.5750/ijme.v1i1.1335","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1335","url":null,"abstract":"Exchange Market Pressure (EMP) indices are used as comprehensive indicators of pressure on a currency. This paper examines the relationship of India government debt, India’s GDP, world money supply and world GDP with exchange market pressure in India. We use quarterly data from 1992: II to 2018: III. The results suggest a significant positive relationship between EMP and the India government debt and GDP and a negative relationship between EMP and world money supply. The relationship between EMP and world GDP is found to be insignificant.\u0000This study sheds light on the complex dynamics of EMP and its determinants in India, highlighting the impact of key economic factors and historical events on currency stability. These findings have important implications for policymakers and stakeholders in maritime trade, providing insights into the economic factors influencing currency stability in maritime trade.","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":"141798132","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, Deepak Kumar, Harpreet S. Bhatia
In present study, the ideal engine functioning condition for pollutants as well as functionality was determined using RSM. The L16 Orthogonal Array experiment table was designed using Minitab 16 software with Taguchi's design of experiments methodology with Three variables—fuel type, engine speed, and engine load, each of which was varied across four distinct levels. After a comprehensive model examination, the R2 and modified R2 values are close, indicating a low risk of including unimportant components. The model found that an engine load of 6.85 kgf, an engine speed of 2000 rpm, and the 20% OPB blended fuel (OPB20) would optimise BTE, EE, BSFC, and NO, HC, and CO emissions. The model's maximum desirability was 86.79%, indicating that the predicted optimum answers and experimental responses were similar. The utilisation of RSM optimisation in conjunction with OPB fuel has the potential to enhance engine performance and mitigate emissions.
{"title":"Diesel Engine Performance and Emission Parameters Optimization Using Taguchi and Response Surface Methodology","authors":"Ranjeet Rai, R.R. Sahoo, Deepak Kumar, Harpreet S. Bhatia","doi":"10.5750/ijme.v1i1.1362","DOIUrl":"https://doi.org/10.5750/ijme.v1i1.1362","url":null,"abstract":"In present study, the ideal engine functioning condition for pollutants as well as functionality was determined using RSM. The L16 Orthogonal Array experiment table was designed using Minitab 16 software with Taguchi's design of experiments methodology with Three variables—fuel type, engine speed, and engine load, each of which was varied across four distinct levels. After a comprehensive model examination, the R2 and modified R2 values are close, indicating a low risk of including unimportant components. The model found that an engine load of 6.85 kgf, an engine speed of 2000 rpm, and the 20% OPB blended fuel (OPB20) would optimise BTE, EE, BSFC, and NO, HC, and CO emissions. The model's maximum desirability was 86.79%, indicating that the predicted optimum answers and experimental responses were similar. The utilisation of RSM optimisation in conjunction with OPB fuel has the potential to enhance engine performance and mitigate emissions.","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":"141798187","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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