Pub Date : 2023-09-20DOI: 10.1080/00051144.2023.2256521
V. Nagasumathy, B. Paulchamy
Gliomas constitute the prevalently seen brain tumours in humans. The real-time utilization of Computer Aided Diagnosis system depends on brain Magnetic Resonance Imaging (MRIs) has the ability of helping radiologists and professionals to identify the presence of glioma tumours. It is very difficult to segment brain tumours because of the brain image and it has a complex structure. A fully automated, accurate, segmentation and classification model is developed using a modified Graph Neural Network (MGNN) for brain tumours. Proposed work steps are, image registration, Shift-Invariant Shear let Transform (SIST), adaptive segmentation, feature extraction, and categorization of tumours. At first, image registration and SIST are carried out to improve image quality. Adaptive segmentation is then carried out utilizing Improved Fuzzy C-Means clustering. Next, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform is utilized for the extraction of features in brain MRI data. Finally, MGNN is introduced for the detection of anomalous tumour-infected MR and actual MR brain images. The findings are demonstrated that the proposed model leads in higher accuracy levels for both classification and segmentation.
{"title":"Detection of glioma on brain MRIs using adaptive segmentation and modified graph neural network based classification","authors":"V. Nagasumathy, B. Paulchamy","doi":"10.1080/00051144.2023.2256521","DOIUrl":"https://doi.org/10.1080/00051144.2023.2256521","url":null,"abstract":"Gliomas constitute the prevalently seen brain tumours in humans. The real-time utilization of Computer Aided Diagnosis system depends on brain Magnetic Resonance Imaging (MRIs) has the ability of helping radiologists and professionals to identify the presence of glioma tumours. It is very difficult to segment brain tumours because of the brain image and it has a complex structure. A fully automated, accurate, segmentation and classification model is developed using a modified Graph Neural Network (MGNN) for brain tumours. Proposed work steps are, image registration, Shift-Invariant Shear let Transform (SIST), adaptive segmentation, feature extraction, and categorization of tumours. At first, image registration and SIST are carried out to improve image quality. Adaptive segmentation is then carried out utilizing Improved Fuzzy C-Means clustering. Next, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform is utilized for the extraction of features in brain MRI data. Finally, MGNN is introduced for the detection of anomalous tumour-infected MR and actual MR brain images. The findings are demonstrated that the proposed model leads in higher accuracy levels for both classification and segmentation.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136373982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/00051144.2023.2244307
S. Balaji, T. Arunprasath, M. Pallikonda Rajasekaran, G. Vishnuvarthanan, K. Sindhuja
Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time.
{"title":"Computer-aided diagnostic system for breast cancer detection based on optimized segmentation scheme and supervised algorithm","authors":"S. Balaji, T. Arunprasath, M. Pallikonda Rajasekaran, G. Vishnuvarthanan, K. Sindhuja","doi":"10.1080/00051144.2023.2244307","DOIUrl":"https://doi.org/10.1080/00051144.2023.2244307","url":null,"abstract":"Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135063601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1080/00051144.2023.2246810
Poornima Pandian, Chithra Selvaraj
Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user’s protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms.
{"title":"An incentive-based dynamic energy efficient spectrum allocation for cognitive radio networks","authors":"Poornima Pandian, Chithra Selvaraj","doi":"10.1080/00051144.2023.2246810","DOIUrl":"https://doi.org/10.1080/00051144.2023.2246810","url":null,"abstract":"Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user’s protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/00051144.2023.2254981
Zhou Zheng
Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.
{"title":"Phase space load balancing priority scheduling algorithm for cloud computing clusters","authors":"Zhou Zheng","doi":"10.1080/00051144.2023.2254981","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254981","url":null,"abstract":"Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/00051144.2023.2254978
Lu Ming
With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.
{"title":"Searchable encryption algorithm in computer big data processing application","authors":"Lu Ming","doi":"10.1080/00051144.2023.2254978","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254978","url":null,"abstract":"With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/00051144.2023.2254975
V. Priya, V. Praveena, L. R. Sujithra
Transfer learning approaches in natural language processing have been explored and evolved as a potential solution for solving many problems in recent days. The current research on aspect-based summarization shows unsatisfactory accuracy and low-quality generated summaries. Additionally, the potential advantages of combining language models with parallel processing have not been explored in the existing literature. This paper aims to address the problem of aspect-based extractive text summarization using a transfer learning approach and an optimization method based on map reduce. The proposed approach utilizes transfer learning with language models to extract significant aspects from the text. Subsequently, an optimization process using map reduce is employed. This optimization framework includes an in-node mapper and reducer algorithm to generate summaries for important aspects identified by the language model. This enhances the quality of the summary, leading to improved accuracy, particularly when applied to electrical power system documents. By leveraging the strengths of natural language models and parallel data processing techniques, this model presents an opportunity to achieve better text summary generation. The performance metric used is accuracy, measured with the ROUGE tool, incorporating precision, recall and f-measure. The proposed model demonstrates a 6% improvement in scores compared to state-of-the-art techniques.
{"title":"A parallel optimization and transfer learning approach for summarization in electrical power systems","authors":"V. Priya, V. Praveena, L. R. Sujithra","doi":"10.1080/00051144.2023.2254975","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254975","url":null,"abstract":"Transfer learning approaches in natural language processing have been explored and evolved as a potential solution for solving many problems in recent days. The current research on aspect-based summarization shows unsatisfactory accuracy and low-quality generated summaries. Additionally, the potential advantages of combining language models with parallel processing have not been explored in the existing literature. This paper aims to address the problem of aspect-based extractive text summarization using a transfer learning approach and an optimization method based on map reduce. The proposed approach utilizes transfer learning with language models to extract significant aspects from the text. Subsequently, an optimization process using map reduce is employed. This optimization framework includes an in-node mapper and reducer algorithm to generate summaries for important aspects identified by the language model. This enhances the quality of the summary, leading to improved accuracy, particularly when applied to electrical power system documents. By leveraging the strengths of natural language models and parallel data processing techniques, this model presents an opportunity to achieve better text summary generation. The performance metric used is accuracy, measured with the ROUGE tool, incorporating precision, recall and f-measure. The proposed model demonstrates a 6% improvement in scores compared to state-of-the-art techniques.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-10DOI: 10.1080/00051144.2023.2253064
A. Abdul Hayum, J. Jaya, B. Paulchamy, R. Sivakumar
Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick’s features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches.
{"title":"A modified recurrent neural network (MRNN) model for and breast cancer classification system","authors":"A. Abdul Hayum, J. Jaya, B. Paulchamy, R. Sivakumar","doi":"10.1080/00051144.2023.2253064","DOIUrl":"https://doi.org/10.1080/00051144.2023.2253064","url":null,"abstract":"Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick’s features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.1080/00051144.2023.2241775
N. Priya, P. B. Pankajavalli
In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.
{"title":"High traffic communication congestion control for wireless sensor networks based on harmony search optimization","authors":"N. Priya, P. B. Pankajavalli","doi":"10.1080/00051144.2023.2241775","DOIUrl":"https://doi.org/10.1080/00051144.2023.2241775","url":null,"abstract":"In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48802493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.1080/00051144.2023.2253065
K. S. A. S. Murugan, M. Marsaline Beno, R. Sankar, Mahendran Ganesan
In micro-grids, energy management is described as an information and control system that assures that both the generating and distribution systems deliver electricity at the lowest operating costs. Renewable energy sources (RESs), including electric vehicles (EVs), can be successfully used and carbon emissions reduced by establishing a DC multi-microgrid system (MMGS), which includes renewable energy sources (RESs) and the distribution network. A Multi-Microgrid based Energy Management (MM-GEM) system is suggested to increase the economics of MMGS and minimize the distribution network's network loss. MMG is a network of dispersed generators, energy storage, and adjustable loads in a distribution system that is linked. Furthermore, its operation is deconstructed to reduce communication and control costs with the decentralized structure. “Aside from enhancing system resilience, the MMGEMS substantially impacts energy efficiency, power quality, and dependability". Typical MMGEMS functionality and architecture are shown in detail. This is followed by examining current and developing technologies for monitoring and interacting with data among the MMG clusters. In addition, a wide range of MMG energy planning and control systems for interactive energy trading, multi-energy management, and resilient operations are fully examined and researched. The economic effect of the EVs’ energy transfer over time and place is examined.
{"title":"An optimal approach to DC multi-microgrid energy management in electric vehicles (EV)","authors":"K. S. A. S. Murugan, M. Marsaline Beno, R. Sankar, Mahendran Ganesan","doi":"10.1080/00051144.2023.2253065","DOIUrl":"https://doi.org/10.1080/00051144.2023.2253065","url":null,"abstract":"In micro-grids, energy management is described as an information and control system that assures that both the generating and distribution systems deliver electricity at the lowest operating costs. Renewable energy sources (RESs), including electric vehicles (EVs), can be successfully used and carbon emissions reduced by establishing a DC multi-microgrid system (MMGS), which includes renewable energy sources (RESs) and the distribution network. A Multi-Microgrid based Energy Management (MM-GEM) system is suggested to increase the economics of MMGS and minimize the distribution network's network loss. MMG is a network of dispersed generators, energy storage, and adjustable loads in a distribution system that is linked. Furthermore, its operation is deconstructed to reduce communication and control costs with the decentralized structure. “Aside from enhancing system resilience, the MMGEMS substantially impacts energy efficiency, power quality, and dependability\". Typical MMGEMS functionality and architecture are shown in detail. This is followed by examining current and developing technologies for monitoring and interacting with data among the MMG clusters. In addition, a wide range of MMG energy planning and control systems for interactive energy trading, multi-energy management, and resilient operations are fully examined and researched. The economic effect of the EVs’ energy transfer over time and place is examined.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48996116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-31DOI: 10.1080/00051144.2023.2250639
K. Ragini, K. Gunaseelan, R. Dhanusuya
ABSTRACT Broadcasting in wireless channels causes security vulnerabilities since both the intended receiver and the eavesdropper may receive the information. Physical layer security (PLS) ensures the confidentiality of information transmitted wireless medium, even in the presence of eavesdroppers, without relying on cryptographic techniques implemented at higher layers. A PLS method for cooperative relay based Orthogonal Frequency Division Multiplexing (OFDM) with optimal relay selection and power optimization is proposed. In order to increase the overall system’s secrecy rate, a hybrid relaying and water filling based optimal power allocation is performed for multi-relay assisted OFDM-based wireless networks. By changing the eavesdroppers’ distances, the performance efficiency of the proposed system is verified. The analysis is carried out for both Full Duplex (FD) and Half Duplex (HD) systems and their performances are compared with existing equal power allocation technique. The proposed method combines relay selection and novel power optimization process to improve secrecy rate than the existing power allocation methods for both HD and FD systems.
{"title":"Physical layer security based on full duplex and half-duplex multi relay assisted OFDM system","authors":"K. Ragini, K. Gunaseelan, R. Dhanusuya","doi":"10.1080/00051144.2023.2250639","DOIUrl":"https://doi.org/10.1080/00051144.2023.2250639","url":null,"abstract":"ABSTRACT Broadcasting in wireless channels causes security vulnerabilities since both the intended receiver and the eavesdropper may receive the information. Physical layer security (PLS) ensures the confidentiality of information transmitted wireless medium, even in the presence of eavesdroppers, without relying on cryptographic techniques implemented at higher layers. A PLS method for cooperative relay based Orthogonal Frequency Division Multiplexing (OFDM) with optimal relay selection and power optimization is proposed. In order to increase the overall system’s secrecy rate, a hybrid relaying and water filling based optimal power allocation is performed for multi-relay assisted OFDM-based wireless networks. By changing the eavesdroppers’ distances, the performance efficiency of the proposed system is verified. The analysis is carried out for both Full Duplex (FD) and Half Duplex (HD) systems and their performances are compared with existing equal power allocation technique. The proposed method combines relay selection and novel power optimization process to improve secrecy rate than the existing power allocation methods for both HD and FD systems.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47083949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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