Abstract The track state detection is of great significance to timely understand the operation state of track and find track defects and prevent operation accidents. This article initially analyzes the key technologies of track detection system and then proposes an image detection technology and image processing method for analyzing track detection at home and abroad, thus putting forward the scheme of track detection using image processing. The characteristics of onsite track images are analyzed, and a track state detection system based on track image preprocessing, image position correction, image defect comparison, and track section size measurement is designed in this article. Further in this article, a study of image linear transformation, noise filtering, defect recognition, and edge detection in track image processing is applied. Furthermore, a robust piecewise linear transformation is designed using the combination of image threshold transformation and image gray transformation. It reduces the loss of detailed information in the process of image processing. The center point of track bright band is determined by the image region segmentation method, which effectively reduces the error of image track measurement and improves the measurement accuracy.
{"title":"Application of vibration compensation based on image processing in track displacement monitoring","authors":"P. Yu, Honglin Wang","doi":"10.1515/pjbr-2022-0090","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0090","url":null,"abstract":"Abstract The track state detection is of great significance to timely understand the operation state of track and find track defects and prevent operation accidents. This article initially analyzes the key technologies of track detection system and then proposes an image detection technology and image processing method for analyzing track detection at home and abroad, thus putting forward the scheme of track detection using image processing. The characteristics of onsite track images are analyzed, and a track state detection system based on track image preprocessing, image position correction, image defect comparison, and track section size measurement is designed in this article. Further in this article, a study of image linear transformation, noise filtering, defect recognition, and edge detection in track image processing is applied. Furthermore, a robust piecewise linear transformation is designed using the combination of image threshold transformation and image gray transformation. It reduces the loss of detailed information in the process of image processing. The center point of track bright band is determined by the image region segmentation method, which effectively reduces the error of image track measurement and improves the measurement accuracy.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86941030","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}
I. Kansal, Vikas Khullar, Jyoti Verma, Renu Popli, Rajeev Kumar
Abstract The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is bringing about a new era of farming because they are growing more intelligent, recognizing causes of variation on the farm, consuming fewer resources, and optimizing their efficiency to more flexible jobs. The purpose of this article is to construct an IoT-Fog computing equipped robotic system for the categorization of weeds and soy plants during both the hazy season and the normal season. The used dataset in this article included four classes: soil, soybean, grass, and weeds. A two-dimensional Convolutional Neural Network (2D-CNN)-based deep learning (DL) approach was implemented for data image classification with dataset of height and width of 150 × 150 and of three channels. The overall proposed system is considered an IoT-connected robotic device that is capable of applying classification through the Internet-connected server. The reliability of the device is also enhanced as it is enabled with edge-based Fog computing. Hence, the proposed robotic system is capable of applying DL classification through IoT as well as Fog computing architecture. The analysis of the proposed system was conducted in steps including training and testing of CNN for classification, validation of normal images, validation of hazy images, application of dehazing technique, and at the end validation of dehazed images. The training and validation parameters ensure 97% accuracy in classifying weeds and crops in a hazy environment. Finally, it concludes that applying the dehazing technique before identifying soy crops in adverse weather will help achieve a higher classification score.
{"title":"IoT-Fog-enabled robotics-based robust classification of hazy and normal season agricultural images for weed detection","authors":"I. Kansal, Vikas Khullar, Jyoti Verma, Renu Popli, Rajeev Kumar","doi":"10.1515/pjbr-2022-0105","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0105","url":null,"abstract":"Abstract The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is bringing about a new era of farming because they are growing more intelligent, recognizing causes of variation on the farm, consuming fewer resources, and optimizing their efficiency to more flexible jobs. The purpose of this article is to construct an IoT-Fog computing equipped robotic system for the categorization of weeds and soy plants during both the hazy season and the normal season. The used dataset in this article included four classes: soil, soybean, grass, and weeds. A two-dimensional Convolutional Neural Network (2D-CNN)-based deep learning (DL) approach was implemented for data image classification with dataset of height and width of 150 × 150 and of three channels. The overall proposed system is considered an IoT-connected robotic device that is capable of applying classification through the Internet-connected server. The reliability of the device is also enhanced as it is enabled with edge-based Fog computing. Hence, the proposed robotic system is capable of applying DL classification through IoT as well as Fog computing architecture. The analysis of the proposed system was conducted in steps including training and testing of CNN for classification, validation of normal images, validation of hazy images, application of dehazing technique, and at the end validation of dehazed images. The training and validation parameters ensure 97% accuracy in classifying weeds and crops in a hazy environment. Finally, it concludes that applying the dehazing technique before identifying soy crops in adverse weather will help achieve a higher classification score.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76949318","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 Healthcare sector has become one of the challenging sectors to handle patient records as well as to provide better treatment to patients within a limited period. Covid-19 also exposed the limitations of the healthcare system due to the lack of better services. So, the involvement of information and communication technologies (ICTs) with the healthcare sector brings radical changes at global as well as local levels such as in hospitals and dispensaries. The article enlightened a novel survey technological paradigm that helps to facilitate the digital healthcare. With the use of technologies, the healthcare sectors are becoming more digital, innovative, patient-centric, and more effective. This article explores the proposed technological developments such as real-time health monitoring, generation of electronic health records, patient health record, mhealth, robotics, as well as robot sensors that are associated with healthcare sectors. This article also highlights the role of ICTs in different healthcare-related fields such as education, hospital management, health-related research, and data management as well as lightening the delivery levels of healthcare services. The article deals with the robotic applications in the healthcare field. This article categorizes the technologies as current and futuristic technological innovations enabling healthcare-as-a-service with benefits.
{"title":"Digital healthcare: A topical and futuristic review of technological and robotic revolution","authors":"Shilpa, Tarandeep Kaur, R. Garg","doi":"10.1515/pjbr-2022-0108","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0108","url":null,"abstract":"Abstract Healthcare sector has become one of the challenging sectors to handle patient records as well as to provide better treatment to patients within a limited period. Covid-19 also exposed the limitations of the healthcare system due to the lack of better services. So, the involvement of information and communication technologies (ICTs) with the healthcare sector brings radical changes at global as well as local levels such as in hospitals and dispensaries. The article enlightened a novel survey technological paradigm that helps to facilitate the digital healthcare. With the use of technologies, the healthcare sectors are becoming more digital, innovative, patient-centric, and more effective. This article explores the proposed technological developments such as real-time health monitoring, generation of electronic health records, patient health record, mhealth, robotics, as well as robot sensors that are associated with healthcare sectors. This article also highlights the role of ICTs in different healthcare-related fields such as education, hospital management, health-related research, and data management as well as lightening the delivery levels of healthcare services. The article deals with the robotic applications in the healthcare field. This article categorizes the technologies as current and futuristic technological innovations enabling healthcare-as-a-service with benefits.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80246292","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}
Narala Chitti Sai Sarita, Sanna Suresh Reddy, P. L. Sujatha
Abstract A fuzzy-integrated sliding mode-based hybrid controller (FISMHC) attributed to unified power quality conditioner (UPQC) was proposed in this study through implementation with solar integrated to fuel cell through incorporation of UPQC within sequence designed for active power filters of series and shunt configurations under shared structure of DC-link capacitor deployment. Furthermore, the proposed scheme with FISMHC UPQC (U-FISMHC) can achieve the following goals: (i) maintaining constant DC-link voltage in the absence of peak overshoot, (ii) performance evaluation under varied fluctuations in grid voltage, and (iii) decreasing source current and load voltage harmonics. In addition, the study compares U-FISMHC performance with distribution case over specific test conditions such as supply voltages, solar irradiation, and conditioned loads to demonstrate the proposed controller’s superior performance.
{"title":"Hybrid controller-based solar-fuel cell-integrated UPQC for enrichment of power quality","authors":"Narala Chitti Sai Sarita, Sanna Suresh Reddy, P. L. Sujatha","doi":"10.1515/pjbr-2022-0110","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0110","url":null,"abstract":"Abstract A fuzzy-integrated sliding mode-based hybrid controller (FISMHC) attributed to unified power quality conditioner (UPQC) was proposed in this study through implementation with solar integrated to fuel cell through incorporation of UPQC within sequence designed for active power filters of series and shunt configurations under shared structure of DC-link capacitor deployment. Furthermore, the proposed scheme with FISMHC UPQC (U-FISMHC) can achieve the following goals: (i) maintaining constant DC-link voltage in the absence of peak overshoot, (ii) performance evaluation under varied fluctuations in grid voltage, and (iii) decreasing source current and load voltage harmonics. In addition, the study compares U-FISMHC performance with distribution case over specific test conditions such as supply voltages, solar irradiation, and conditioned loads to demonstrate the proposed controller’s superior performance.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82837362","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}
Cecilia Roselli, Serena Marchesi, D. D. Tommaso, A. Wykowska
Abstract One of the key questions in human–robot interaction research is whether humans perceive robots as intentional agents, or rather only as mindless machines. Research has shown that, in some contexts, people do perceive robots as intentional agents. However, the role of prior exposure to robots as a factor potentially playing a role in the attribution of intentionality is still poorly understood. To this end, we asked two samples of high school students, which differed with respect to the type of education they were pursuing (scientific/technical vs. artistic) to complete the InStance Test, measuring individual tendency to attribute intentionality toward robots. Results showed that, overall, participants were more prone to attribute intentionality to robots after being exposed to a theoretical lecture about robots’ functionality and use. Moreover, participants’ scientific/technical education resulted in a higher likelihood of attribution of intentionality to robots, relative to those with artistic education. Therefore, we suggest that the type of education, as well as individually acquired knowledge, modulates the likelihood of attributing intentionality toward robots.
{"title":"The role of prior exposure in the likelihood of adopting the Intentional Stance toward a humanoid robot","authors":"Cecilia Roselli, Serena Marchesi, D. D. Tommaso, A. Wykowska","doi":"10.1515/pjbr-2022-0103","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0103","url":null,"abstract":"Abstract One of the key questions in human–robot interaction research is whether humans perceive robots as intentional agents, or rather only as mindless machines. Research has shown that, in some contexts, people do perceive robots as intentional agents. However, the role of prior exposure to robots as a factor potentially playing a role in the attribution of intentionality is still poorly understood. To this end, we asked two samples of high school students, which differed with respect to the type of education they were pursuing (scientific/technical vs. artistic) to complete the InStance Test, measuring individual tendency to attribute intentionality toward robots. Results showed that, overall, participants were more prone to attribute intentionality to robots after being exposed to a theoretical lecture about robots’ functionality and use. Moreover, participants’ scientific/technical education resulted in a higher likelihood of attribution of intentionality to robots, relative to those with artistic education. Therefore, we suggest that the type of education, as well as individually acquired knowledge, modulates the likelihood of attributing intentionality toward robots.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81495292","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 In recent years, there has been a noticeable rise in the mortality rate, and heart disease is a significant contributor to this trend. According to the California Association for Diabetes Investigation, by 2015, cardiovascular disease would be the primary cause of death in India, where 62 billion people live. Deficiencies in the heart’s ability to pump blood to and from the rest of the body are the leading cause of cardiovascular disease. The healthcare industry is a prime example of a sector poised to greatly benefit from the availability of massive amounts of data and analytical insights. Increasingly, it will be important to extract medical data to predict and treat the high fatality rate caused by heart attacks. Every day, humanity generates terabytes worth of data. Medical errors with dire effects can be avoided only with high-quality services. Hospitals can reduce the price of expensive clinical testing by using decision support systems. Hospitals in the modern-day use hospital information systems to keep track of patient records. The health care sector generates vast amounts of data, but little of it is really put to good use. It will be important to adopt a new strategy to reduce costs and make accurate predictions about heart disease. To determine which machine learning and deep learning approaches are most useful and accurate for predicting and classifying cardiac illnesses, this article reviews the existing literature on the topic and subsequently tries to detect the most probable factors leading to heart disease. This study introduces and models an artificial neural network methodology for identifying potential cardiovascular disease risk factors. In this study, we examine and present the various full and partial correlations among risk attributes. In addition, a number of risk variables are analysed to generate a predicted list of risk features most likely to result in cardiovascular disease.
近年来，死亡率明显上升，而心脏病是造成这一趋势的重要原因。根据加州糖尿病调查协会(California Association for Diabetes Investigation)的数据，到2015年，心血管疾病将成为印度的主要死因，印度有620亿人口。心脏向身体其他部位输送血液的能力不足是导致心血管疾病的主要原因。医疗保健行业就是一个典型的例子，该行业准备从大量数据和分析见解的可用性中受益匪浅。越来越重要的是，提取医疗数据，以预测和治疗由心脏病发作引起的高死亡率。人类每天都会产生数tb的数据。只有提供高质量的服务，才能避免造成严重后果的医疗事故。医院可以通过使用决策支持系统来降低昂贵的临床检测费用。现代医院使用医院信息系统来跟踪病人的记录。医疗保健行业产生了大量的数据，但很少有数据真正得到有效利用。采用一种新的策略来降低成本并对心脏病做出准确的预测将是很重要的。为了确定哪种机器学习和深度学习方法对预测和分类心脏病最有用和准确，本文回顾了有关该主题的现有文献，并随后试图检测导致心脏病的最可能因素。本研究介绍一种人工神经网路方法，并建立模型以辨识潜在的心血管疾病危险因素。在这项研究中，我们检查并提出各种风险属性之间的完全和部分相关性。此外，还分析了一些风险变量，以生成最可能导致心血管疾病的风险特征的预测列表。
{"title":"Early prediction of cardiovascular disease using artificial neural network","authors":"Jyotismita Talukdar, T. Singh","doi":"10.1515/pjbr-2022-0107","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0107","url":null,"abstract":"Abstract In recent years, there has been a noticeable rise in the mortality rate, and heart disease is a significant contributor to this trend. According to the California Association for Diabetes Investigation, by 2015, cardiovascular disease would be the primary cause of death in India, where 62 billion people live. Deficiencies in the heart’s ability to pump blood to and from the rest of the body are the leading cause of cardiovascular disease. The healthcare industry is a prime example of a sector poised to greatly benefit from the availability of massive amounts of data and analytical insights. Increasingly, it will be important to extract medical data to predict and treat the high fatality rate caused by heart attacks. Every day, humanity generates terabytes worth of data. Medical errors with dire effects can be avoided only with high-quality services. Hospitals can reduce the price of expensive clinical testing by using decision support systems. Hospitals in the modern-day use hospital information systems to keep track of patient records. The health care sector generates vast amounts of data, but little of it is really put to good use. It will be important to adopt a new strategy to reduce costs and make accurate predictions about heart disease. To determine which machine learning and deep learning approaches are most useful and accurate for predicting and classifying cardiac illnesses, this article reviews the existing literature on the topic and subsequently tries to detect the most probable factors leading to heart disease. This study introduces and models an artificial neural network methodology for identifying potential cardiovascular disease risk factors. In this study, we examine and present the various full and partial correlations among risk attributes. In addition, a number of risk variables are analysed to generate a predicted list of risk features most likely to result in cardiovascular disease.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"190 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86457711","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}
Amar Shukla, Ankit Verma, Hussain Falih Mahdi, T. Choudhury, T. Singh
Abstract Internet of Things (IoT) is a physical network of physical devices, such as widgets, structures, and other objects, which can store program, sensors, actuators, and screen configurations to allow the objects to assemble, control, display, and exchange data. The aim of this research was to develop an autonomous system with automated navigation. Using this approach, we are able to make use of deep neural networks for automatic navigation as well as the identification of pot holes and road conditions. Additionally, it displays potholes in traffic and the correct lane on the screen. The system stresses how important it is to select the path from one node to the next.
物联网(Internet of Things, IoT)是一个由物理设备组成的物理网络，如部件、结构和其他对象，它可以存储程序、传感器、执行器和屏幕配置，以允许对象组装、控制、显示和交换数据。这项研究的目的是开发一个自动导航的自主系统。使用这种方法，我们能够利用深度神经网络进行自动导航，以及识别坑洞和路况。此外，它还能在屏幕上显示交通坑洼和正确的车道。系统强调选择从一个节点到下一个节点的路径是多么重要。
{"title":"Path reader and intelligent lane navigator by autonomous vehicle","authors":"Amar Shukla, Ankit Verma, Hussain Falih Mahdi, T. Choudhury, T. Singh","doi":"10.1515/pjbr-2022-0117","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0117","url":null,"abstract":"Abstract Internet of Things (IoT) is a physical network of physical devices, such as widgets, structures, and other objects, which can store program, sensors, actuators, and screen configurations to allow the objects to assemble, control, display, and exchange data. The aim of this research was to develop an autonomous system with automated navigation. Using this approach, we are able to make use of deep neural networks for automatic navigation as well as the identification of pot holes and road conditions. Additionally, it displays potholes in traffic and the correct lane on the screen. The system stresses how important it is to select the path from one node to the next.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80891908","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 An optimization approach is described in the research study that deals with the issue of reconfiguration networks built with certain conditions of power loss reduction and reliability. Furthermore, the reconfigured networking system seeks optimization based on criteria affecting the limitations. This study optimises specific network faults subjecting resources with no supply during reconfiguration to avoid the effect and possess through active power losses. These goals were met using the mathematical method of the optimisation process. The mathematical formulation is generated first in the system development process. As a result, a comprehensive methodology using genetic algorithm, Grey Wolf optimization (GWO), and particle swarm optimization (PSO) was developed. Finally, intended methodologies were estimated. Based on the results, it is clear that the proposed hybrid GWO-PSO approach outperforms all other methods in terms of node voltage, reliability, line currents, and computational duration. Furthermore, when optimally sized distributed generations are placed in optimal locations, total loss is reduced by up to 63% and voltage profiles improve.
{"title":"Hybrid optimization to enhance power system reliability using GA, GWO, and PSO","authors":"Rachapalli Sireesha, Srinivasa Rao Coppisetty, Mallapu Vijaya Kumar","doi":"10.1515/pjbr-2022-0119","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0119","url":null,"abstract":"Abstract An optimization approach is described in the research study that deals with the issue of reconfiguration networks built with certain conditions of power loss reduction and reliability. Furthermore, the reconfigured networking system seeks optimization based on criteria affecting the limitations. This study optimises specific network faults subjecting resources with no supply during reconfiguration to avoid the effect and possess through active power losses. These goals were met using the mathematical method of the optimisation process. The mathematical formulation is generated first in the system development process. As a result, a comprehensive methodology using genetic algorithm, Grey Wolf optimization (GWO), and particle swarm optimization (PSO) was developed. Finally, intended methodologies were estimated. Based on the results, it is clear that the proposed hybrid GWO-PSO approach outperforms all other methods in terms of node voltage, reliability, line currents, and computational duration. Furthermore, when optimally sized distributed generations are placed in optimal locations, total loss is reduced by up to 63% and voltage profiles improve.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85932585","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}
Bharathiraja Nagu, Thiruneelakandan Arjunan, M. Bangare, Pradeepa Karuppaiah, Gaganpreet Kaur, Mohammed Wasim Bhatt
Abstract Improved Reliability and Low Latency Communication (IRLC) with Augmented Reality (AR) has become an emerging technology in today’s world. To minimize an accessory adaptation for Customer Equipment (CE) in AR, it may be feasible to offload the AR workload onto the onboard devices. Mobile-Edge Computation (MEC) will improve the throughput of a CE. MEC has caused enormous overhead or communication omissions on wireless media, making it difficult to choose the optimal payload proposition. The proposed system explores on-board devices that work together to achieve an AR goal. Code splitting is a Bayesian network used to examine the overall interdependence of efforts. From a longevity and endurance perspective, it is used to reduce the Probability of Supplier Failure (PSF) of an MEC-enabled AR environment. Weighed Particle Swarm Optimization (WPSO) was proposed despite the reality based on the emphasis on balancing the issue. As a result, a heuristic-based WPSO facilitates to improve the performance measures. A hybrid method could significantly increase the assertion of a predicted PSF in various network scenarios compared to the existing communication technologies. A preliminary iterative approach is suitable for AR operations and IRLC scenarios to generalize the attributes.
{"title":"Ultra-low latency communication technology for Augmented Reality application in mobile periphery computing","authors":"Bharathiraja Nagu, Thiruneelakandan Arjunan, M. Bangare, Pradeepa Karuppaiah, Gaganpreet Kaur, Mohammed Wasim Bhatt","doi":"10.1515/pjbr-2022-0112","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0112","url":null,"abstract":"Abstract Improved Reliability and Low Latency Communication (IRLC) with Augmented Reality (AR) has become an emerging technology in today’s world. To minimize an accessory adaptation for Customer Equipment (CE) in AR, it may be feasible to offload the AR workload onto the onboard devices. Mobile-Edge Computation (MEC) will improve the throughput of a CE. MEC has caused enormous overhead or communication omissions on wireless media, making it difficult to choose the optimal payload proposition. The proposed system explores on-board devices that work together to achieve an AR goal. Code splitting is a Bayesian network used to examine the overall interdependence of efforts. From a longevity and endurance perspective, it is used to reduce the Probability of Supplier Failure (PSF) of an MEC-enabled AR environment. Weighed Particle Swarm Optimization (WPSO) was proposed despite the reality based on the emphasis on balancing the issue. As a result, a heuristic-based WPSO facilitates to improve the performance measures. A hybrid method could significantly increase the assertion of a predicted PSF in various network scenarios compared to the existing communication technologies. A preliminary iterative approach is suitable for AR operations and IRLC scenarios to generalize the attributes.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75530703","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 To improve the accuracy of the mechanical fault diagnosis of the operating mechanism and fully exploit the characteristic information in the vibration signal of the high-voltage circuit breaker, a mechanical fault diagnosis method of the operating mechanism of the high-voltage circuit breaker based on the deep self-encoding network is proposed. First, the vibration signal of the switch operating mechanism is extracted, the wavelet packet conversion is performed, and the vibration signal of each frequency band is divided into equal times. The energy of the time–frequency subplane of the vibration signal is then calculated, and the time–frequency energy distribution is used as a switch. Finally, a breaker failure diagnostic model based on the deep self-coding network is established. Pretraining and tuning and a 126 kV high-voltage switch are used to simulate different types of faults and validate the method. Experimental results show that this method can acquire sample failure data and perform failure diagnosis, and the diagnosis accuracy rate reaches 97.5%. The deep self-coding network can fully pierce deep information on the switch vibration signal.
{"title":"Deep auto-encoder network for mechanical fault diagnosis of high-voltage circuit breaker operating mechanism","authors":"Qiuping Yang, Fang Hao","doi":"10.1515/pjbr-2022-0096","DOIUrl":"https://doi.org/10.1515/pjbr-2022-0096","url":null,"abstract":"Abstract To improve the accuracy of the mechanical fault diagnosis of the operating mechanism and fully exploit the characteristic information in the vibration signal of the high-voltage circuit breaker, a mechanical fault diagnosis method of the operating mechanism of the high-voltage circuit breaker based on the deep self-encoding network is proposed. First, the vibration signal of the switch operating mechanism is extracted, the wavelet packet conversion is performed, and the vibration signal of each frequency band is divided into equal times. The energy of the time–frequency subplane of the vibration signal is then calculated, and the time–frequency energy distribution is used as a switch. Finally, a breaker failure diagnostic model based on the deep self-coding network is established. Pretraining and tuning and a 126 kV high-voltage switch are used to simulate different types of faults and validate the method. Experimental results show that this method can acquire sample failure data and perform failure diagnosis, and the diagnosis accuracy rate reaches 97.5%. The deep self-coding network can fully pierce deep information on the switch vibration signal.","PeriodicalId":90037,"journal":{"name":"Paladyn : journal of behavioral robotics","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76123283","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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