Pub Date : 2021-11-29DOI: 10.36548/jsws.2021.3.006
S. Shakya
Remote and dangerous fields that are expensive, complex, and unreachable to reach human insights are examined with ease using the Wireless Sensor Network (WSN) applications. Due to the use of non-renewable sources of energy, challenges with respect to the network lifetime, fault tolerance and energy consumption are faced by the self-managed networks. An efficient fault tolerance technique has been provided in this paper as an effective management strategy. Using the network and communication nodes, revitalization and fault recognition techniques are used for handling diverse levels of faults in this framework. At the network nodes, the fault tolerance capability is increased by the proposed protocol model and management strategy. This enhances the corresponding data transmission in the network. When compared to the conventional techniques, the proposed model increases the network lifetime by five times. It is observed from the validation results that, with a 10% increase in the network lifetime, there is a 2% decrease in the fault tolerance proficiency of the network. The network lifetime and data transmission rate are improved while the network energy consumption is reduced significantly. The MATLAB environment is used for simulation purpose. In terms of energy consumption, network lifetime and fault tolerance, the proposed model offers optimal results.
{"title":"Pollination Inspired Clustering Model for Wireless Sensor Network Optimization","authors":"S. Shakya","doi":"10.36548/jsws.2021.3.006","DOIUrl":"https://doi.org/10.36548/jsws.2021.3.006","url":null,"abstract":"Remote and dangerous fields that are expensive, complex, and unreachable to reach human insights are examined with ease using the Wireless Sensor Network (WSN) applications. Due to the use of non-renewable sources of energy, challenges with respect to the network lifetime, fault tolerance and energy consumption are faced by the self-managed networks. An efficient fault tolerance technique has been provided in this paper as an effective management strategy. Using the network and communication nodes, revitalization and fault recognition techniques are used for handling diverse levels of faults in this framework. At the network nodes, the fault tolerance capability is increased by the proposed protocol model and management strategy. This enhances the corresponding data transmission in the network. When compared to the conventional techniques, the proposed model increases the network lifetime by five times. It is observed from the validation results that, with a 10% increase in the network lifetime, there is a 2% decrease in the fault tolerance proficiency of the network. The network lifetime and data transmission rate are improved while the network energy consumption is reduced significantly. The MATLAB environment is used for simulation purpose. In terms of energy consumption, network lifetime and fault tolerance, the proposed model offers optimal results.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80728745","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 : 2021-11-25DOI: 10.36548/jsws.2021.3.005
Edriss E. B. Adam, A. Sathesh
With modernization and technology enhancements on a global scale, environmental consciousness has also been increasing in recent days. Various technologies and automobile industries are vandalized with sustainable solutions and green technologies. Transportation via roadways is mostly preferred for distant travel as well, despite the advancements in airways and railways, due to less capital outlay, door to door service possibility in rural areas etc. The conventional fuel vehicles are a huge contributor to environmental pollution. Electric vehicles are an optimal solution to this issue. The lives of the common masses are not impacted largely by the electric vehicles despite their market commercialization since a few decades. It is due to certain challenges associated with the electrical vehicles. A 100% efficient perpetual machine does not exist yet. Predominantly, challenges related to charging, hinders the success of e-vehicles. Frequent charging is required in case of long-distance travel and other scenarios in the existing vehicles. Based on the respective governments, extensive changes are made in the infrastructure to overcome the issues at the charging stations. In this paper, an enhanced wireless charging module for electric vehicles is presented. The use of multiple coils is emphasized for building up energy and transmitting it. The inductive power transfer mechanism and efficiency of the system are improved with the design of a three-phase coil. The mechanism for assessment of the energy consumed in e-vehicles is also discussed.
{"title":"Three Phase Coil based Optimized Wireless Charging System for Electric Vehicles","authors":"Edriss E. B. Adam, A. Sathesh","doi":"10.36548/jsws.2021.3.005","DOIUrl":"https://doi.org/10.36548/jsws.2021.3.005","url":null,"abstract":"With modernization and technology enhancements on a global scale, environmental consciousness has also been increasing in recent days. Various technologies and automobile industries are vandalized with sustainable solutions and green technologies. Transportation via roadways is mostly preferred for distant travel as well, despite the advancements in airways and railways, due to less capital outlay, door to door service possibility in rural areas etc. The conventional fuel vehicles are a huge contributor to environmental pollution. Electric vehicles are an optimal solution to this issue. The lives of the common masses are not impacted largely by the electric vehicles despite their market commercialization since a few decades. It is due to certain challenges associated with the electrical vehicles. A 100% efficient perpetual machine does not exist yet. Predominantly, challenges related to charging, hinders the success of e-vehicles. Frequent charging is required in case of long-distance travel and other scenarios in the existing vehicles. Based on the respective governments, extensive changes are made in the infrastructure to overcome the issues at the charging stations. In this paper, an enhanced wireless charging module for electric vehicles is presented. The use of multiple coils is emphasized for building up energy and transmitting it. The inductive power transfer mechanism and efficiency of the system are improved with the design of a three-phase coil. The mechanism for assessment of the energy consumed in e-vehicles is also discussed.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83568771","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 : 2021-11-22DOI: 10.36548/jeea.2021.3.006
P. Karuppusamy
It is possible to transmit electricity wirelessly without the need for cables. Wireless power transmission makes it possible to link remote places that would otherwise be cut off from access to reliable electricity. A wireless connection to the power supply is expected in the future. This study describes the experimental results of Wireless Power Transfer (WPT) utilizing a transformer coupling approach and its future potential. This WPT device (WPTD) is used to transmit power using two procedures of energy transfer: radiofrequency coupling and transformer coupling, both of which are magnetic based, in principle. The distance between the transmitter and receiver of the system affects the amount of power that can be sent. Research is performed to establish how far apart the system's transmitter and receiver should be. Magnetic fields may transmit energy between two coils, but the distance between the two coils must be too close for this approach to work. Aside from that, it assesses the setting parameter of a value that has been tabulated using a certain application, in the findings and discussion parts.
{"title":"Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure","authors":"P. Karuppusamy","doi":"10.36548/jeea.2021.3.006","DOIUrl":"https://doi.org/10.36548/jeea.2021.3.006","url":null,"abstract":"It is possible to transmit electricity wirelessly without the need for cables. Wireless power transmission makes it possible to link remote places that would otherwise be cut off from access to reliable electricity. A wireless connection to the power supply is expected in the future. This study describes the experimental results of Wireless Power Transfer (WPT) utilizing a transformer coupling approach and its future potential. This WPT device (WPTD) is used to transmit power using two procedures of energy transfer: radiofrequency coupling and transformer coupling, both of which are magnetic based, in principle. The distance between the transmitter and receiver of the system affects the amount of power that can be sent. Research is performed to establish how far apart the system's transmitter and receiver should be. Magnetic fields may transmit energy between two coils, but the distance between the two coils must be too close for this approach to work. Aside from that, it assesses the setting parameter of a value that has been tabulated using a certain application, in the findings and discussion parts.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87904402","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 : 2021-11-19DOI: 10.36548/jsws.2021.3.003
Haoxiang Wang
The self-sustainable Wireless Sensor Networks (WSNs) face a major challenge in terms of energy efficiency as they have to operate without replacement of batteries. The benefits of renewable and green energy are taken into consideration for sensing and charging the battery in recent literatures using Energy Harvesting (EH) techniques. The sensors are provided with a reliable energy source through Wireless Charging (WC) techniques. Several challenges in WSN are addressed by combining these technologies. However, it is essential to consider the deployment cost in these systems. This paper presents a self-sustainable energy efficient WSN based model for Mobile Charger (MC) and Energy Harvesting Base Station (EHBS) while considering the cost of deployment. This system can also be used for low-cost microelectronic devices and low-cost Micro-Energy Harvesting (MEH) system-based applications. While considering the deployment cost, the network lifetime is maximized and an extensive comparison of simulation with various existing models is presented to emphasize the validity of the proposed model.
{"title":"Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application","authors":"Haoxiang Wang","doi":"10.36548/jsws.2021.3.003","DOIUrl":"https://doi.org/10.36548/jsws.2021.3.003","url":null,"abstract":"The self-sustainable Wireless Sensor Networks (WSNs) face a major challenge in terms of energy efficiency as they have to operate without replacement of batteries. The benefits of renewable and green energy are taken into consideration for sensing and charging the battery in recent literatures using Energy Harvesting (EH) techniques. The sensors are provided with a reliable energy source through Wireless Charging (WC) techniques. Several challenges in WSN are addressed by combining these technologies. However, it is essential to consider the deployment cost in these systems. This paper presents a self-sustainable energy efficient WSN based model for Mobile Charger (MC) and Energy Harvesting Base Station (EHBS) while considering the cost of deployment. This system can also be used for low-cost microelectronic devices and low-cost Micro-Energy Harvesting (MEH) system-based applications. While considering the deployment cost, the network lifetime is maximized and an extensive comparison of simulation with various existing models is presented to emphasize the validity of the proposed model.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75570029","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 : 2021-11-16DOI: 10.36548/jeea.2021.3.005
B. Vivekanandam
Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.
{"title":"Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder","authors":"B. Vivekanandam","doi":"10.36548/jeea.2021.3.005","DOIUrl":"https://doi.org/10.36548/jeea.2021.3.005","url":null,"abstract":"Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77964558","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 : 2021-11-15DOI: 10.36548/jeea.2021.3.004
J. Chen, Kong-Long Lai
Nature oriented power generation systems are considered as renewable energy sources. Renewable energy generations are safe to the environment and nature, in terms of minimal radiation and pollution. The space requirement, operational and maintenance cost of renewable energy generation stations are also comparatively lesser than the conventional generating stations. The new form of micro grid energy stations of 230Volt supply attract the small commercial users and the domestic users. The smart grid energy generation is widely employed in the place where the conventional energy supply is not available. Due to its simple construction process, the smart grid renewable energy stations are employed on certain national highways as charging stations for electric vehicles and as a maintenance centre. The motive of the proposed work is to alert the smart grid system with an intelligent algorithm for making an efficient energy generation process on various climatic changes. This reduces the energy wastage in the primary smart grid station and makes the system more reliable on all conditions. The performance of the proposed approach is compared with a traditional smart grid system which yielded a satisfactory outcome.
{"title":"Intelligent Automation System for Smart Grid Renewable Energy Generation on Climatic Changes","authors":"J. Chen, Kong-Long Lai","doi":"10.36548/jeea.2021.3.004","DOIUrl":"https://doi.org/10.36548/jeea.2021.3.004","url":null,"abstract":"Nature oriented power generation systems are considered as renewable energy sources. Renewable energy generations are safe to the environment and nature, in terms of minimal radiation and pollution. The space requirement, operational and maintenance cost of renewable energy generation stations are also comparatively lesser than the conventional generating stations. The new form of micro grid energy stations of 230Volt supply attract the small commercial users and the domestic users. The smart grid energy generation is widely employed in the place where the conventional energy supply is not available. Due to its simple construction process, the smart grid renewable energy stations are employed on certain national highways as charging stations for electric vehicles and as a maintenance centre. The motive of the proposed work is to alert the smart grid system with an intelligent algorithm for making an efficient energy generation process on various climatic changes. This reduces the energy wastage in the primary smart grid station and makes the system more reliable on all conditions. The performance of the proposed approach is compared with a traditional smart grid system which yielded a satisfactory outcome.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"146 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73913569","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 : 2021-11-10DOI: 10.36548/jeea.2021.3.003
S. Ayyasamy
People often use sarcasm to taunt, anger, or amuse one another. Scathing undertones can't be missed, even when using a simple sentiment analysis tool. Sarcasm may be detected using a variety of machine learning techniques, including rule-based approaches, statistical approaches, and classifiers. Since English is a widely used language on the internet, most of these terms were created to help people recognize sarcasm in written material. Convolutional Neural Networks (CNNs) are used to extract features, and Naive Bayes (NBs) are trained and evaluated on those features using a probability function. This suggested approach gives a more accurate forecast of sarcasm detection based on probability prediction. This hybrid machine learning technique is evaluated according to the stretching component in frequency inverse domain, the cluster of the words and word vectors with embedding. Based on the findings, the proposed model surpasses many advanced algorithms for sarcasm detection, including accuracy, recall, and F1 scores. It is possible to identify sarcasm in a multi-domain dataset using the suggested model, which is accurate and resilient.
{"title":"Construction of Hybrid Model for English News Headline Sarcasm Detection by Word Embedding Technique","authors":"S. Ayyasamy","doi":"10.36548/jeea.2021.3.003","DOIUrl":"https://doi.org/10.36548/jeea.2021.3.003","url":null,"abstract":"People often use sarcasm to taunt, anger, or amuse one another. Scathing undertones can't be missed, even when using a simple sentiment analysis tool. Sarcasm may be detected using a variety of machine learning techniques, including rule-based approaches, statistical approaches, and classifiers. Since English is a widely used language on the internet, most of these terms were created to help people recognize sarcasm in written material. Convolutional Neural Networks (CNNs) are used to extract features, and Naive Bayes (NBs) are trained and evaluated on those features using a probability function. This suggested approach gives a more accurate forecast of sarcasm detection based on probability prediction. This hybrid machine learning technique is evaluated according to the stretching component in frequency inverse domain, the cluster of the words and word vectors with embedding. Based on the findings, the proposed model surpasses many advanced algorithms for sarcasm detection, including accuracy, recall, and F1 scores. It is possible to identify sarcasm in a multi-domain dataset using the suggested model, which is accurate and resilient.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75106055","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}
In recent times, computing technologies have moved over to a new dimension with the advent of cloud platforms which provide seamless rendering of required services to consumers either in static or dynamic state. In addition, the nature of data being handled in today’s scenario has also become sophisticated as mostly real time data acquisition systems equipped with High-Definition capture (HD) have become common. Lately, cloud systems have also become prone to computing overheads owing to huge volume of data being imparted on them especially in real time applications. To assist and simplify the computational complexity of cloud systems, FoG platforms are being integrated into cloud interfaces to streamline and provide computing at the edge nodes rather at the cloud core processors, thus accounting for reduction of load overhead on cloud core processors. This research paper proposes a Two Stage Load Optimizer (TSLO) implemented as a double stage optimizer with one being deployed at FoG level and the other at the Cloud level. The computational complexity analysis is extensively done and compared with existing benchmark methods and superior performance of the suggested method is observed and reported.
{"title":"A Two Stage Task Scheduler for Effective Load Optimization in Cloud – FoG Architectures","authors":"J. Manoharan","doi":"10.36548/jei.2021.3.006","DOIUrl":"https://doi.org/10.36548/jei.2021.3.006","url":null,"abstract":"In recent times, computing technologies have moved over to a new dimension with the advent of cloud platforms which provide seamless rendering of required services to consumers either in static or dynamic state. In addition, the nature of data being handled in today’s scenario has also become sophisticated as mostly real time data acquisition systems equipped with High-Definition capture (HD) have become common. Lately, cloud systems have also become prone to computing overheads owing to huge volume of data being imparted on them especially in real time applications. To assist and simplify the computational complexity of cloud systems, FoG platforms are being integrated into cloud interfaces to streamline and provide computing at the edge nodes rather at the cloud core processors, thus accounting for reduction of load overhead on cloud core processors. This research paper proposes a Two Stage Load Optimizer (TSLO) implemented as a double stage optimizer with one being deployed at FoG level and the other at the Cloud level. The computational complexity analysis is extensively done and compared with existing benchmark methods and superior performance of the suggested method is observed and reported.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76723931","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 : 2021-11-08DOI: 10.36548/jitdw.2021.3.006
R. Kanthavel
Multimedia data in various forms is now readily available because of the widespread usage of Internet technology. Unauthorized individuals abuse multimedia material, for which they should not have access to, by disseminating it over several web pages, to defraud the original copyright owners. Numerous patient records have been compromised during the surge in COVID-19 incidents. Adding a watermark to any medical or defense documents is recommended since it protects the integrity of the information. This proposed work is recognized as a new unique method since an innovative technique is being implemented. The resilience of the watermarked picture is quite crucial in the context of steganography. As a result, the emphasis of this research study is on the resilience of watermarked picture methods. Moreover, the two-stage authentication for watermarking is built with key generation in the section on robust improvement. The Fast Fourier transform (FFT) is used in the entire execution process of the suggested framework in order to make computing more straightforward. With the Singular Value Decomposition (SVD) accumulation of processes, the overall suggested architecture becomes more resilient and efficient. A numerous quality metrics are utilized to find out how well the created technique is performing in terms of evaluation. In addition, several signal processing attacks are used to assess the effectiveness of the watermarking strategy.
{"title":"Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain","authors":"R. Kanthavel","doi":"10.36548/jitdw.2021.3.006","DOIUrl":"https://doi.org/10.36548/jitdw.2021.3.006","url":null,"abstract":"Multimedia data in various forms is now readily available because of the widespread usage of Internet technology. Unauthorized individuals abuse multimedia material, for which they should not have access to, by disseminating it over several web pages, to defraud the original copyright owners. Numerous patient records have been compromised during the surge in COVID-19 incidents. Adding a watermark to any medical or defense documents is recommended since it protects the integrity of the information. This proposed work is recognized as a new unique method since an innovative technique is being implemented. The resilience of the watermarked picture is quite crucial in the context of steganography. As a result, the emphasis of this research study is on the resilience of watermarked picture methods. Moreover, the two-stage authentication for watermarking is built with key generation in the section on robust improvement. The Fast Fourier transform (FFT) is used in the entire execution process of the suggested framework in order to make computing more straightforward. With the Singular Value Decomposition (SVD) accumulation of processes, the overall suggested architecture becomes more resilient and efficient. A numerous quality metrics are utilized to find out how well the created technique is performing in terms of evaluation. In addition, several signal processing attacks are used to assess the effectiveness of the watermarking strategy.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78316495","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 : 2021-11-08DOI: 10.36548/jeea.2021.3.002
S. Shakya
Wastage of electricity occurs in all places starting from a small house electrical loading to a heavy industrial electrical loading. KiloVolt-Ampere Reactive (KVAR) power metering devices are employed in industrial applications for measuring the energy utilization which measure the energy wastage along with it. This urges a consumer to pay for the unutilized or wasted energy as well. To avoid this, certain capacitor bank units are connected to the industrial application motor units. The right choice of capacitor rating are helpful in minimizing the wasted power observation in the KVAR meters. The selection of capacitor rating is analysed with respect to the power factor calculation. The power factor is a derivation of working power to the apparent power in an electrical system. An optimum power factor to be maintained in an electrical system is 1. The motive of the proposed work is to maintain the power factor by selecting an optimum capacitor bank on the operation of an electrical system at various load conditions. The requirement of capacitor bank values get changed with respect to the load given to an electrical system. A neural network based prediction model is employed in the work for estimating the right choice of capacitor bank. The efficiency of the proposed work is verified and found satisfied with a traditional capacitor bank operating system.
{"title":"An efficient Capacitor Bank Operating System for Single Phase Power Factor Correction using Neural Network Estimations","authors":"S. Shakya","doi":"10.36548/jeea.2021.3.002","DOIUrl":"https://doi.org/10.36548/jeea.2021.3.002","url":null,"abstract":"Wastage of electricity occurs in all places starting from a small house electrical loading to a heavy industrial electrical loading. KiloVolt-Ampere Reactive (KVAR) power metering devices are employed in industrial applications for measuring the energy utilization which measure the energy wastage along with it. This urges a consumer to pay for the unutilized or wasted energy as well. To avoid this, certain capacitor bank units are connected to the industrial application motor units. The right choice of capacitor rating are helpful in minimizing the wasted power observation in the KVAR meters. The selection of capacitor rating is analysed with respect to the power factor calculation. The power factor is a derivation of working power to the apparent power in an electrical system. An optimum power factor to be maintained in an electrical system is 1. The motive of the proposed work is to maintain the power factor by selecting an optimum capacitor bank on the operation of an electrical system at various load conditions. The requirement of capacitor bank values get changed with respect to the load given to an electrical system. A neural network based prediction model is employed in the work for estimating the right choice of capacitor bank. The efficiency of the proposed work is verified and found satisfied with a traditional capacitor bank operating system.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76896242","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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