Pub Date : 2023-10-30DOI: 10.2174/0118722121262457230927092639
Yuan Zhang, Xiaolong Wang, Yuanxin Qu
Background:: Bearings are a kind of precision mechanical basic parts that are widely used in aerospace, automobile, home appliances and industrial fields. At the same time, bearings are also one of the most important wear parts in mechanical equipment. The research and analysis of bearing life are of great significance as they directly affect the efficiency and safety of the equipment. Objective:: By analyzing and discussing the patents on bearing life testing devices, some valuable conclusions have been drawn, and the development direction of bearing life research and bearing testing devices is speculated. Methods:: Various bearing life testing devices, such as life testing machines, performance testing machines and simulation testing machines, are observed, and the influence of bearing life testing devices on bearing quality assessment and product development is analyzed. The patents about the bearing life testing device are investigated in conjunction with the bearing life data signal acquisition method. Results:: The analysis of bearing life detection devices in bearing fatigue life and performance failure in the bearing failure detection collection method primarily uses vibration detection method, while bearing life detection devices primarily focus on structural innovation and detection method innovation to improve the accuracy of bearing detection. Conclusion:: The development of the bearing detection device and the advancement of test software, multi-species, small batch, high precision, multiple control, group control, simulation of working conditions, and special development work in tandem to address technical issues and improve bearing detection device functionality.
{"title":"Development and Perspectives of Bearing Life Testers","authors":"Yuan Zhang, Xiaolong Wang, Yuanxin Qu","doi":"10.2174/0118722121262457230927092639","DOIUrl":"https://doi.org/10.2174/0118722121262457230927092639","url":null,"abstract":"Background:: Bearings are a kind of precision mechanical basic parts that are widely used in aerospace, automobile, home appliances and industrial fields. At the same time, bearings are also one of the most important wear parts in mechanical equipment. The research and analysis of bearing life are of great significance as they directly affect the efficiency and safety of the equipment. Objective:: By analyzing and discussing the patents on bearing life testing devices, some valuable conclusions have been drawn, and the development direction of bearing life research and bearing testing devices is speculated. Methods:: Various bearing life testing devices, such as life testing machines, performance testing machines and simulation testing machines, are observed, and the influence of bearing life testing devices on bearing quality assessment and product development is analyzed. The patents about the bearing life testing device are investigated in conjunction with the bearing life data signal acquisition method. Results:: The analysis of bearing life detection devices in bearing fatigue life and performance failure in the bearing failure detection collection method primarily uses vibration detection method, while bearing life detection devices primarily focus on structural innovation and detection method innovation to improve the accuracy of bearing detection. Conclusion:: The development of the bearing detection device and the advancement of test software, multi-species, small batch, high precision, multiple control, group control, simulation of working conditions, and special development work in tandem to address technical issues and improve bearing detection device functionality.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:: Selective laser melting technology has the advantages of rapid manufacture, high precision, and the ability to produce fine structures. Medical implants made using selective laser melting technology have high precision and excellent mechanical properties that meet the needs of patients and make medical implants more promising in the medical field. This paper reviews the progress of research on selective laser melting of medical implants. This paper draws on numerous journals and patents. This paper firstly reviews the classification of medical implants, mainly including hip implants, knee implants, maxillo-craniofacial implants and spinal implants. Secondly, the common porous structure design methods, the effect of porous structure on the mechanical properties of the implant, and the effect of pore structure on the growth properties of porous titanium alloy bone are summarised. Finally, the process of manufacturing titanium alloy implants by selective laser melting technology is described. Medical implants made by selective laser melting have excellent properties and are widely used in the medical field. Compared to traditional mechanical processing methods, selective laser melting technology can better preserve the properties of the raw material, while providing higher precision and faster preparation. However, selective laser melting has a number of drawbacks, including differences in material microstructure, reduced strength and plasticity, inadequate surface treatment, and enhanced safety and reliability. Further scientific research and technological innovation are needed to solve these problems. In the future, as technology continues to innovate and develop, SLM technology will become more mature, resulting in implants that are more natural, suitable for the body and long-lasting. At the same time, as implants are personalised, there will be a huge market demand and development opportunities. In addition, the continuous improvement of regulatory policies is expected to further promote the market development and application of medical implants.
{"title":"Research Progress of Selective Laser Melting Forming Medical Implants","authors":"Bingwei Gao, Zhixin Sun, Hongtao Yu, Liqing Peng, Hongjian Zhao","doi":"10.2174/0118722121268952231020111446","DOIUrl":"https://doi.org/10.2174/0118722121268952231020111446","url":null,"abstract":"Abstract:: Selective laser melting technology has the advantages of rapid manufacture, high precision, and the ability to produce fine structures. Medical implants made using selective laser melting technology have high precision and excellent mechanical properties that meet the needs of patients and make medical implants more promising in the medical field. This paper reviews the progress of research on selective laser melting of medical implants. This paper draws on numerous journals and patents. This paper firstly reviews the classification of medical implants, mainly including hip implants, knee implants, maxillo-craniofacial implants and spinal implants. Secondly, the common porous structure design methods, the effect of porous structure on the mechanical properties of the implant, and the effect of pore structure on the growth properties of porous titanium alloy bone are summarised. Finally, the process of manufacturing titanium alloy implants by selective laser melting technology is described. Medical implants made by selective laser melting have excellent properties and are widely used in the medical field. Compared to traditional mechanical processing methods, selective laser melting technology can better preserve the properties of the raw material, while providing higher precision and faster preparation. However, selective laser melting has a number of drawbacks, including differences in material microstructure, reduced strength and plasticity, inadequate surface treatment, and enhanced safety and reliability. Further scientific research and technological innovation are needed to solve these problems. In the future, as technology continues to innovate and develop, SLM technology will become more mature, resulting in implants that are more natural, suitable for the body and long-lasting. At the same time, as implants are personalised, there will be a huge market demand and development opportunities. In addition, the continuous improvement of regulatory policies is expected to further promote the market development and application of medical implants.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background:: Due to the difficulty of obtaining the real dataset of paired underwater images, it is urgent to build an unsupervised underwater image enhancement network. Objective:: To address the problem, a novel underwater image enhancement based on Retinex decomposition and Unsupervised Generative Adversarial Network (RUGAN) is proposed. Method:: A color correction module is proposed considering the different color distortions of underwater images. Further, considering the human visual perception mechanism, the RUGAN network, which is similar to U-Net, is constructed using the characteristics of underwater imaging and Retinex decomposition. Based on Retinex decomposition and the characteristics of underwater imaging, the RUGAN network similar to U-Net is constructed. The reflectance image and illumination image are obtained. The reflectance image with a better effect is taken as the enhancement result. Unlike the previous supervised methods, RUGAN adopts clear air images and distorted underwater images as training. RUGAN adopts the underwater image of the color correction module as pseudo-ground truth to achieve an unsupervised effect. Results:: The superiority of RUGAN network is further supported by extensive experiments that compared it with more methods. conclusion: The proposed RUGAN achieves better results both subjectively and objectively.
{"title":"Underwater Image Enhancement based on Retinex Decomposition and Unsupervised Generative Adversarial Networks","authors":"Yong Lai, Xuebo Zhang, Zhouyan He, Yang Song, Ting Luo, Haiyong Xu","doi":"10.2174/0118722121231723231005112802","DOIUrl":"https://doi.org/10.2174/0118722121231723231005112802","url":null,"abstract":"Background:: Due to the difficulty of obtaining the real dataset of paired underwater images, it is urgent to build an unsupervised underwater image enhancement network. Objective:: To address the problem, a novel underwater image enhancement based on Retinex decomposition and Unsupervised Generative Adversarial Network (RUGAN) is proposed. Method:: A color correction module is proposed considering the different color distortions of underwater images. Further, considering the human visual perception mechanism, the RUGAN network, which is similar to U-Net, is constructed using the characteristics of underwater imaging and Retinex decomposition. Based on Retinex decomposition and the characteristics of underwater imaging, the RUGAN network similar to U-Net is constructed. The reflectance image and illumination image are obtained. The reflectance image with a better effect is taken as the enhancement result. Unlike the previous supervised methods, RUGAN adopts clear air images and distorted underwater images as training. RUGAN adopts the underwater image of the color correction module as pseudo-ground truth to achieve an unsupervised effect. Results:: The superiority of RUGAN network is further supported by extensive experiments that compared it with more methods. conclusion: The proposed RUGAN achieves better results both subjectively and objectively.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Deep learning object detection algorithm is widely used in the field of image classification and has become an indispensable part. With the improvement of image classification accuracy, sonar image target detection algorithm based on deep learning has gradually become the focus of more and more people's research. Objective: This article aims to provide a summary and analysis of deep learning-based sonar image object detection algorithms, with the hope of offering insights for future research in the field of sonar target detection technology. Method: This paper systematically summarizes sonar image target detection algorithms based on deep learning. According to the method principle, the existing deep learning target detection algorithms are divided into four categories: target detection algorithm based on candidate region, deep target detection method based on regression, Anchor Free deep learning target detection algorithm, and search-based target detection and recognition algorithm. Then, the performance of algorithms based on COCO data sets is compared, and the standard sonar data sets and formats are introduced. Results: The sonar image object detection algorithm based on deep learning has made significant progress. The combination of deep learning and object detection methods has been applied to sonar images, resulting in the emergence of excellent performing algorithms. However, most algorithms are still in the developmental stage and face challenges in practical applications. Subsequently, several invention patents have been developed based on the aforementioned algorithms, including a feature extraction method for side-scan sonar images based on fully convolutional neural networks, an underwater sonar image target detection method based on improved YOLOv3-tiny, and more. Conclusion: Sonar image object detection technology based on deep learning has a wide range of application needs but also faces many difficulties and challenges, we still need to continue to learn and explore in future research, and we believe that we can make greater breakthroughs in the future.
{"title":"Review of Object Detection Algorithms for Sonar Images based on Deep Learning","authors":"Xu Liu, Hanhao Zhu, Weihua Song, Jiahui Wang, Zhigang Chai, Shaohua Hong","doi":"10.2174/0118722121257145230927041949","DOIUrl":"https://doi.org/10.2174/0118722121257145230927041949","url":null,"abstract":"Background: Deep learning object detection algorithm is widely used in the field of image classification and has become an indispensable part. With the improvement of image classification accuracy, sonar image target detection algorithm based on deep learning has gradually become the focus of more and more people's research. Objective: This article aims to provide a summary and analysis of deep learning-based sonar image object detection algorithms, with the hope of offering insights for future research in the field of sonar target detection technology. Method: This paper systematically summarizes sonar image target detection algorithms based on deep learning. According to the method principle, the existing deep learning target detection algorithms are divided into four categories: target detection algorithm based on candidate region, deep target detection method based on regression, Anchor Free deep learning target detection algorithm, and search-based target detection and recognition algorithm. Then, the performance of algorithms based on COCO data sets is compared, and the standard sonar data sets and formats are introduced. Results: The sonar image object detection algorithm based on deep learning has made significant progress. The combination of deep learning and object detection methods has been applied to sonar images, resulting in the emergence of excellent performing algorithms. However, most algorithms are still in the developmental stage and face challenges in practical applications. Subsequently, several invention patents have been developed based on the aforementioned algorithms, including a feature extraction method for side-scan sonar images based on fully convolutional neural networks, an underwater sonar image target detection method based on improved YOLOv3-tiny, and more. Conclusion: Sonar image object detection technology based on deep learning has a wide range of application needs but also faces many difficulties and challenges, we still need to continue to learn and explore in future research, and we believe that we can make greater breakthroughs in the future.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.2174/0118722121248139231023111754
V. Hemamalini, D. Jayasutha, V. R. Vinothini, R. Manjula Devi, Arun Kumar, E. Anitha
Background: The method includes: receiving a set of video data and labeling it into categories, segmenting the received videos into N segments, randomly selecting M frames for each video segment in the training phase, concatenating the video images into multi-channel images, and rolling. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible. 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent- based outcomes.
{"title":"Innovative Video Classification Method Based on Deep Learning Approach","authors":"V. Hemamalini, D. Jayasutha, V. R. Vinothini, R. Manjula Devi, Arun Kumar, E. Anitha","doi":"10.2174/0118722121248139231023111754","DOIUrl":"https://doi.org/10.2174/0118722121248139231023111754","url":null,"abstract":"Background: The method includes: receiving a set of video data and labeling it into categories, segmenting the received videos into N segments, randomly selecting M frames for each video segment in the training phase, concatenating the video images into multi-channel images, and rolling. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible. 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent- based outcomes.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.2174/0118722121248802231004053522
Ying Zhou, Zhichao Zheng, Quansen Sun
Background:: Collaborative Representation (CR) has been widely used in Single Image Super Resolution (SISR) with the assumption that Low-resolution (LR) and high-resolution (HR) features can be linearly represented by neighborhoods and share consistent CR coefficients. Numerous patents and journal papers have been published. However, this CR consistency does not hold in the reconstruction phase, which leads to degraded performance. Methods:: To fulfill this gap, we propose a novel bi-directional projection model (BDPM) to establish a bi-directional mapping between LR and HR features without any consistency constraint. The multiple projection matrices are offline computed to reduce reconstruction time greatly. We further develop several strategies to extract features and group neighborhoods such that local structures can be preserved better. Results:: Compared to the learning-based methods, BDPM is about 2 to 10 times faster and compared to the reconstruction-based methods, it is about 500 to 2,000 times faster. Conclusion:: The empirical studies verify the effectiveness of BDPM and extensive experimental results demonstrate that BDPM achieves better SISR performance than many state-of-the-arts.
{"title":"Bi-directional Projection Framework for Fast Single Image Super Resolution","authors":"Ying Zhou, Zhichao Zheng, Quansen Sun","doi":"10.2174/0118722121248802231004053522","DOIUrl":"https://doi.org/10.2174/0118722121248802231004053522","url":null,"abstract":"Background:: Collaborative Representation (CR) has been widely used in Single Image Super Resolution (SISR) with the assumption that Low-resolution (LR) and high-resolution (HR) features can be linearly represented by neighborhoods and share consistent CR coefficients. Numerous patents and journal papers have been published. However, this CR consistency does not hold in the reconstruction phase, which leads to degraded performance. Methods:: To fulfill this gap, we propose a novel bi-directional projection model (BDPM) to establish a bi-directional mapping between LR and HR features without any consistency constraint. The multiple projection matrices are offline computed to reduce reconstruction time greatly. We further develop several strategies to extract features and group neighborhoods such that local structures can be preserved better. Results:: Compared to the learning-based methods, BDPM is about 2 to 10 times faster and compared to the reconstruction-based methods, it is about 500 to 2,000 times faster. Conclusion:: The empirical studies verify the effectiveness of BDPM and extensive experimental results demonstrate that BDPM achieves better SISR performance than many state-of-the-arts.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.2174/1872212118666230915103755
Chengyi Pan, Jingren Zhang, Shuhao Li
Background: As the lightest metal structural material in engineering, magnesium alloy has excellent mechanical properties, such as high specific strength, high specific stiffness, good damping performance, and good machinability. It is widely used in the fields of precision parts, automobiles, aerospace, and military. However, poor friction and wear performance are significant magnesium defects of the alloys, which make its use limited in some areas with high working conditions, so it is essential to improve the wear resistance of the magnesium alloy surface. Objective: The aim of this study was to summarize the technology of improving the wear resistance of magnesium alloy in recent year. The influence of different surface treatment technology for enhancing friction and wear properties was also analyzed, which could provide a reference for related scholars and researchers. Method: In this paper, the literature related to friction and wear properties of magnesium alloys in recent years were reviewed, the principles of various surface treatment technology of magnesium alloys were explained, and the advantages and disadvantages of each technology were analyzed. Results: Based on the literature analyses related to the wear resistance of magnesium alloys, the problems existing in the surface treatment technology for improving the wear resistance of magnesium alloys are summarized, and future development directions are put forward. Conclusion: Among the technologies to improve the wear resistance of magnesium alloys, the combination of various techniques can better meet the working demands. The environmentally friendly and efficient manner has a good prospect for development.
{"title":"Review of Surface Treatment Technology for Improving Wear Resistance of Magnesium Alloys","authors":"Chengyi Pan, Jingren Zhang, Shuhao Li","doi":"10.2174/1872212118666230915103755","DOIUrl":"https://doi.org/10.2174/1872212118666230915103755","url":null,"abstract":"Background: As the lightest metal structural material in engineering, magnesium alloy has excellent mechanical properties, such as high specific strength, high specific stiffness, good damping performance, and good machinability. It is widely used in the fields of precision parts, automobiles, aerospace, and military. However, poor friction and wear performance are significant magnesium defects of the alloys, which make its use limited in some areas with high working conditions, so it is essential to improve the wear resistance of the magnesium alloy surface. Objective: The aim of this study was to summarize the technology of improving the wear resistance of magnesium alloy in recent year. The influence of different surface treatment technology for enhancing friction and wear properties was also analyzed, which could provide a reference for related scholars and researchers. Method: In this paper, the literature related to friction and wear properties of magnesium alloys in recent years were reviewed, the principles of various surface treatment technology of magnesium alloys were explained, and the advantages and disadvantages of each technology were analyzed. Results: Based on the literature analyses related to the wear resistance of magnesium alloys, the problems existing in the surface treatment technology for improving the wear resistance of magnesium alloys are summarized, and future development directions are put forward. Conclusion: Among the technologies to improve the wear resistance of magnesium alloys, the combination of various techniques can better meet the working demands. The environmentally friendly and efficient manner has a good prospect for development.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background:: Sustainability concerns are rapidly being acknowledged as a key concern for hospitality sectors worldwide. Sustainable initiatives immediately contribute to improved organizational performance in terms of utility consumption, waste management, and regulatory compliance, resulting in cost-effectiveness and competitive advantage through distinctiveness. Objective:: The purpose of the study is to analyze and summarize the motivations, indicators, and barriers towards applications of sustainable initiatives and modern technologies in the hospitality industry using the existing literature to develop a current understanding of the subject and know the way the current industry is thinking about it. Method:: This study is a combination of systematic and bibliometric review, where the systematic review was based on selected articles from reputed journal databases, and the bibliometric review was conducted using VOS viewer and web of science database for a period of 20 years (2002- 2022) Seven research questions were framed and answered for the systematic review. Result:: By describing the motivations, barriers, and impacts of implementing sustainability initiatives and cutting-edge technologies like AI and machine learning in the hospitality sector, the study helps practitioners and academics understand its present state for robust research. The current condition of such implantations in the hospitality sector is also discussed. Conclusion:: This study adds value by shedding light on the perspective of sustainability in the hospitality industry by considering the recommendations and practical advice for hotel management suggested in the existing literature about the application of current sustainability innovations and effective sustainability initiatives in hotel management.
背景:可持续性问题正迅速被认为是世界各地酒店业关注的一个关键问题。可持续的计划立即有助于在公用事业消耗、废物管理和法规遵从方面改善组织绩效，从而通过独特性产生成本效益和竞争优势。目的:本研究的目的是利用现有文献，分析和总结在酒店业应用可持续举措和现代技术的动机、指标和障碍，以形成对该主题的当前理解，并了解当前行业对该主题的思考方式。方法:本研究采用系统综述与文献计量学综述相结合的方法，从知名期刊数据库中选取文章进行系统综述，利用VOS viewer和web of science数据库进行文献计量学综述，历时20年(2002- 2022)。结果:通过描述在酒店业实施可持续发展举措和人工智能和机器学习等尖端技术的动机、障碍和影响，该研究帮助从业者和学者了解其现状，以便进行强有力的研究。本文还讨论了酒店部门这种植入的现状。结论:通过考虑现有文献中关于当前可持续性创新和有效的可持续性举措在酒店管理中的应用的建议和实际建议，本研究通过阐明酒店行业可持续性的观点而增加了价值。
{"title":"Current Understanding, Motivations, and Barriers Towards Implementing Sustainable Initiatives in the Hospitality Industry in the Age of Automation and Artificial Intelligence","authors":"Alok Bihari Singh, Gaurav Gaurav, Prabir Sarkar, Govind Sharan Dangayach, Makkhan Lal Meena","doi":"10.2174/0118722121239293230926034213","DOIUrl":"https://doi.org/10.2174/0118722121239293230926034213","url":null,"abstract":"Background:: Sustainability concerns are rapidly being acknowledged as a key concern for hospitality sectors worldwide. Sustainable initiatives immediately contribute to improved organizational performance in terms of utility consumption, waste management, and regulatory compliance, resulting in cost-effectiveness and competitive advantage through distinctiveness. Objective:: The purpose of the study is to analyze and summarize the motivations, indicators, and barriers towards applications of sustainable initiatives and modern technologies in the hospitality industry using the existing literature to develop a current understanding of the subject and know the way the current industry is thinking about it. Method:: This study is a combination of systematic and bibliometric review, where the systematic review was based on selected articles from reputed journal databases, and the bibliometric review was conducted using VOS viewer and web of science database for a period of 20 years (2002- 2022) Seven research questions were framed and answered for the systematic review. Result:: By describing the motivations, barriers, and impacts of implementing sustainability initiatives and cutting-edge technologies like AI and machine learning in the hospitality sector, the study helps practitioners and academics understand its present state for robust research. The current condition of such implantations in the hospitality sector is also discussed. Conclusion:: This study adds value by shedding light on the perspective of sustainability in the hospitality industry by considering the recommendations and practical advice for hotel management suggested in the existing literature about the application of current sustainability innovations and effective sustainability initiatives in hotel management.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.2174/0118722121267661231013062252
Megha Bansal, Vaibhav Vyas
Background: Life nowadays is full of stress due to lifestyle changes and the modernera race. Almost everyone around us is suffering from stress and anxiety. Mostly, stress identification is done by medical practitioners in a very late stage in which suitable help measures cannot be provided and hence result in suicides or early age deaths due to cardiac arrest, etc. One major reason behind the delay is the time required in stress identification by traditional approaches, and above that, the amount of time and financial support expected is always not feasible to be available. Hence, in this paper, we proposed an evolutionary research framework for stress identification by the usage of both machine learning and IoT. Here, we also conducted a pilot study on 83 records available over the decade since 2014 using PRISMA guidelines, and a bibliographic network visualization was also performed using VOS viewer. Objectives: This study aimed to develop a stress detection framework using Machine Learning and the Internet of Things (IoT) as technology advanced over a decade. Methods: More than 80 research papers from honorable repositories like Scopus and Web of Science were gathered according to the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020, and the VOSviewer tool was further applied to construct the bibliographic depictions. Various datasets and methods used over ten years with their performance were also discussed. Results: This research was conducted to gather various types of stressors, the impact of various Machine Learning and IoT algorithms and concepts on various datasets and their respective results. Conclusion: Various available datasets and results with multiple algorithms were discussed in a crisp tabular form for better understanding. A methodology based on an amalgamation of Machine Learning and IoT was also proposed due to various research gaps available so that stress detection could be done in a cost-effective way.
背景:由于生活方式的改变和现代种族，现在的生活充满了压力。几乎我们周围的每个人都承受着压力和焦虑。大多数情况下，压力识别是由医生在非常晚的阶段进行的，在这个阶段无法提供适当的帮助措施，因此导致自杀或因心脏骤停而过早死亡等。延迟背后的一个主要原因是传统方法的应力识别需要时间，除此之外，预期的时间和资金支持总是不可行的。因此，在本文中，我们提出了一个通过使用机器学习和物联网来识别压力的进化研究框架。本文还利用PRISMA指南对2014年以来10年间的83份文献进行了初步研究，并利用VOS查看器进行了文献网络可视化。目的:本研究旨在利用机器学习和物联网(IoT)技术开发一个压力检测框架，这是十多年来技术的进步。方法:根据PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020指南，从Scopus、Web of Science等知名知识库中收集80余篇研究论文，并应用VOSviewer工具构建文献描述。讨论了近十年来使用的各种数据集和方法及其性能。结果:本研究收集了各种类型的压力源，各种机器学习和物联网算法和概念对各种数据集的影响以及各自的结果。结论:以清晰的表格形式讨论了各种可用数据集和多种算法的结果，以便更好地理解。由于各种研究空白，还提出了一种基于机器学习和物联网融合的方法，以便以经济有效的方式进行应力检测。
{"title":"Evolutionary Stress Detection Framework through Machine Learning and IoT (MLIoT-ESD)","authors":"Megha Bansal, Vaibhav Vyas","doi":"10.2174/0118722121267661231013062252","DOIUrl":"https://doi.org/10.2174/0118722121267661231013062252","url":null,"abstract":"Background: Life nowadays is full of stress due to lifestyle changes and the modernera race. Almost everyone around us is suffering from stress and anxiety. Mostly, stress identification is done by medical practitioners in a very late stage in which suitable help measures cannot be provided and hence result in suicides or early age deaths due to cardiac arrest, etc. One major reason behind the delay is the time required in stress identification by traditional approaches, and above that, the amount of time and financial support expected is always not feasible to be available. Hence, in this paper, we proposed an evolutionary research framework for stress identification by the usage of both machine learning and IoT. Here, we also conducted a pilot study on 83 records available over the decade since 2014 using PRISMA guidelines, and a bibliographic network visualization was also performed using VOS viewer. Objectives: This study aimed to develop a stress detection framework using Machine Learning and the Internet of Things (IoT) as technology advanced over a decade. Methods: More than 80 research papers from honorable repositories like Scopus and Web of Science were gathered according to the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020, and the VOSviewer tool was further applied to construct the bibliographic depictions. Various datasets and methods used over ten years with their performance were also discussed. Results: This research was conducted to gather various types of stressors, the impact of various Machine Learning and IoT algorithms and concepts on various datasets and their respective results. Conclusion: Various available datasets and results with multiple algorithms were discussed in a crisp tabular form for better understanding. A methodology based on an amalgamation of Machine Learning and IoT was also proposed due to various research gaps available so that stress detection could be done in a cost-effective way.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135167783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.2174/0118722121248136230928053214
Ramya T, Gopinath M.P
Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare facilities, it's essential to use the benefits of ubiquitous IoT services to assist patients by monitoring vital signs and automating functions. In healthcare, the current stateof-the-art equipment cannot detect many cancers early, and almost all humans have lost their lives due to this lethal sickness. Hence, early diagnosis of cancer is a significant difficulty for medical experts and researchers. Methods: The method for identifying cancer, together with machine learning and IOT, yield reliable results. In the Proposed model FCM system, the SVM methodology is reviewed to classify either benign or malignant disease. In addition, we applied a recursive feature selection to identify characteristics from the cancer dataset to boost the classifier system's capabilities. Results: This method is being applied in conjunction with fuzzy cluster-based augmentation, and classification can employ continuous monitoring to forecast lung cancer to improve patient care. In the process of effective image segmentation, the fuzzy-clustering methodology is implemented, which is used for the goal of obtaining transition region data. Conclusion: The Otsu thresholding method is applied to help recover the transition region from a lung cancer image. Furthermore, morphological thinning on the right edge and the segmentationimproving pictures are employed to increase segmentation performance. In future work, we intend to design a prototype to ensure real-time analysis to provide enhanced results. Thus, this work may open doors to carry patent-based outcomes.
{"title":"IoT Based Predictive Modeling Techniques for Cancer Detection in Healthcare Systems","authors":"Ramya T, Gopinath M.P","doi":"10.2174/0118722121248136230928053214","DOIUrl":"https://doi.org/10.2174/0118722121248136230928053214","url":null,"abstract":"Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare facilities, it's essential to use the benefits of ubiquitous IoT services to assist patients by monitoring vital signs and automating functions. In healthcare, the current stateof-the-art equipment cannot detect many cancers early, and almost all humans have lost their lives due to this lethal sickness. Hence, early diagnosis of cancer is a significant difficulty for medical experts and researchers. Methods: The method for identifying cancer, together with machine learning and IOT, yield reliable results. In the Proposed model FCM system, the SVM methodology is reviewed to classify either benign or malignant disease. In addition, we applied a recursive feature selection to identify characteristics from the cancer dataset to boost the classifier system's capabilities. Results: This method is being applied in conjunction with fuzzy cluster-based augmentation, and classification can employ continuous monitoring to forecast lung cancer to improve patient care. In the process of effective image segmentation, the fuzzy-clustering methodology is implemented, which is used for the goal of obtaining transition region data. Conclusion: The Otsu thresholding method is applied to help recover the transition region from a lung cancer image. Furthermore, morphological thinning on the right edge and the segmentationimproving pictures are employed to increase segmentation performance. In future work, we intend to design a prototype to ensure real-time analysis to provide enhanced results. Thus, this work may open doors to carry patent-based outcomes.","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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