Pub Date : 2023-10-18DOI: 10.2174/0123520965254606231009091711
Tarun Jaiswal, Manju Pandey, Priyanka Tripathi
Introduction: An image captioning system is a crucial component in the domains of computer vision and natural language processing. Deep neural networks have been an increasingly popular tool for the generation of descriptive captions for photos in recent years. Method: However, these models frequently have the issue of providing captions that are unoriginal and repetitious. Beam search is a well-known search technique that is utilized for the purpose of producing descriptions for images in an effective and productive manner. The algorithm keeps track of a set of partial captions and expands them iteratively by choosing the probable next word throughout each step until a complete caption is generated. The set of partial captions, also known as the beam, is updated at each step based on the predicted probabilities of the next words. This research paper presents an image caption generation system based on beam search. In order to encode the image data and generate captions, the system is trained on a deep neural network architecture. Results: This architecture brings together the benefits of CNN with RNN. After that, the beam search method is executed in order to provide the completed captions, resulting in a more diverse and descriptive set of captions compared to traditional greedy decoding approaches. The experimental outcomes indicate that the suggested system is superior to the existing image caption generation techniques in terms of the precision and variety of the generated captions. Conclusion: This demonstrates the effectiveness of beam search in enhancing the efficiency of image caption generation systems.
{"title":"An Efficient Image Captioning Method Based on Beam Search","authors":"Tarun Jaiswal, Manju Pandey, Priyanka Tripathi","doi":"10.2174/0123520965254606231009091711","DOIUrl":"https://doi.org/10.2174/0123520965254606231009091711","url":null,"abstract":"Introduction: An image captioning system is a crucial component in the domains of computer vision and natural language processing. Deep neural networks have been an increasingly popular tool for the generation of descriptive captions for photos in recent years. Method: However, these models frequently have the issue of providing captions that are unoriginal and repetitious. Beam search is a well-known search technique that is utilized for the purpose of producing descriptions for images in an effective and productive manner. The algorithm keeps track of a set of partial captions and expands them iteratively by choosing the probable next word throughout each step until a complete caption is generated. The set of partial captions, also known as the beam, is updated at each step based on the predicted probabilities of the next words. This research paper presents an image caption generation system based on beam search. In order to encode the image data and generate captions, the system is trained on a deep neural network architecture. Results: This architecture brings together the benefits of CNN with RNN. After that, the beam search method is executed in order to provide the completed captions, resulting in a more diverse and descriptive set of captions compared to traditional greedy decoding approaches. The experimental outcomes indicate that the suggested system is superior to the existing image caption generation techniques in terms of the precision and variety of the generated captions. Conclusion: This demonstrates the effectiveness of beam search in enhancing the efficiency of image caption generation systems.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135889162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this field. Objective: This paper focuses on studying a high-precision method for transformer fault diagnosis, which can be easily and effectively applied to daily life. Methods: By changing the detection method, the traditional detection method can not distinguish the problem that the detection data are highly overlapping when identifying the same fault of the head and tail symmetric points, and the problem that the phase is too similar is changed. In order to solve the problem that the fault samples of transformer frequency response curve are scarce and the one-dimensional data cannot be read by partial deep learning method, the one-dimensional data of frequency response curve is first converted into characteristic index and then into a three-dimensional image by moving window calculation method and Gramian Angular difference field transformation. The fault classification is realized by a convolutional neural network. Results: The accuracy of the final model for slice classification reached 100%. Conclusion: Illustrative examples show that the method is distinguishable from different fault types. The traditional method only uses the amplitude of the frequency response curve, but this method displays the two features of the amplitude-phase together in the image. Compared with the traditional method, more features and samples are added to further improve the accuracy of the method. The accuracy of diagnosis results reached 100%, which showed the feasibility of the method.
{"title":"Fault Identification Method of Transformer Winding based on Gramian Angular Difference Field and Convolutional Neural Network","authors":"Shihao Yang, Zhenhua Li, Xinqiang Yang, Hairong Wu","doi":"10.2174/0123520965272942231009050206","DOIUrl":"https://doi.org/10.2174/0123520965272942231009050206","url":null,"abstract":"Background: As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this field. Objective: This paper focuses on studying a high-precision method for transformer fault diagnosis, which can be easily and effectively applied to daily life. Methods: By changing the detection method, the traditional detection method can not distinguish the problem that the detection data are highly overlapping when identifying the same fault of the head and tail symmetric points, and the problem that the phase is too similar is changed. In order to solve the problem that the fault samples of transformer frequency response curve are scarce and the one-dimensional data cannot be read by partial deep learning method, the one-dimensional data of frequency response curve is first converted into characteristic index and then into a three-dimensional image by moving window calculation method and Gramian Angular difference field transformation. The fault classification is realized by a convolutional neural network. Results: The accuracy of the final model for slice classification reached 100%. Conclusion: Illustrative examples show that the method is distinguishable from different fault types. The traditional method only uses the amplitude of the frequency response curve, but this method displays the two features of the amplitude-phase together in the image. Compared with the traditional method, more features and samples are added to further improve the accuracy of the method. The accuracy of diagnosis results reached 100%, which showed the feasibility of the method.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135889170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.2174/0123520965265629231010073736
Remna Radhakrishnan, Mariamma Chacko
Background: The recent trend in the all-electric ship (AES) electrical systems, especially in military vessels, is to move towards medium voltage direct current (MVDC) distribution. Bus voltage instability is a major issue in direct current (DC) distribution systems. Nowadays, direct current electric springs (DCES) are extensively used in low-voltage direct current (LVDC) microgrids to address voltage instability issues. This paper extends the use of a shunt DCES to stabilize the bus voltage in an MVDC grid. The work proposes an addition to the MVDC onboard ship distribution system architecture, described in IEEE 1709, by integrating a shunt DCES with a novel control strategy to stabilize the bus voltage under various loading conditions, including propulsion motor (PM) and online pulsed power load (PPL). Method: The shunt DCES is designed to provide current into the MVDC bus, which reduces the bus current ripple to attain a stable bus voltage with reduced ripple. A dual loop control with a battery management system (BMS) is proposed for the shunt DCES and simulated in MATLAB/Simulink. BMS is designed based on the state of charge (SOC) of the battery and bus current ripple extracted from the system's source and load side currents. The current supplied by the shunt DCES and the extracted ripple current validate the effectiveness of the proposed control. Total harmonic distortions (THDs) as a measure of voltage ripple of the MVDC bus voltage at different intervals are measured and compared for both systems, with and without shunt DCES. Result: It was observed that the shunt DCES could reduce the voltage ripple well below the permissible limit, which is 5 % as per IEEE 1709. Conclusion: The proposed control strategy could attain a reduction of 68-85 % in THD under peak to off-peak loading conditions with the addition of shunt DCES.
{"title":"A Shunt DC Electric Spring Control Strategy for MVDC Bus Voltage Stability Onboard AES","authors":"Remna Radhakrishnan, Mariamma Chacko","doi":"10.2174/0123520965265629231010073736","DOIUrl":"https://doi.org/10.2174/0123520965265629231010073736","url":null,"abstract":"Background: The recent trend in the all-electric ship (AES) electrical systems, especially in military vessels, is to move towards medium voltage direct current (MVDC) distribution. Bus voltage instability is a major issue in direct current (DC) distribution systems. Nowadays, direct current electric springs (DCES) are extensively used in low-voltage direct current (LVDC) microgrids to address voltage instability issues. This paper extends the use of a shunt DCES to stabilize the bus voltage in an MVDC grid. The work proposes an addition to the MVDC onboard ship distribution system architecture, described in IEEE 1709, by integrating a shunt DCES with a novel control strategy to stabilize the bus voltage under various loading conditions, including propulsion motor (PM) and online pulsed power load (PPL). Method: The shunt DCES is designed to provide current into the MVDC bus, which reduces the bus current ripple to attain a stable bus voltage with reduced ripple. A dual loop control with a battery management system (BMS) is proposed for the shunt DCES and simulated in MATLAB/Simulink. BMS is designed based on the state of charge (SOC) of the battery and bus current ripple extracted from the system's source and load side currents. The current supplied by the shunt DCES and the extracted ripple current validate the effectiveness of the proposed control. Total harmonic distortions (THDs) as a measure of voltage ripple of the MVDC bus voltage at different intervals are measured and compared for both systems, with and without shunt DCES. Result: It was observed that the shunt DCES could reduce the voltage ripple well below the permissible limit, which is 5 % as per IEEE 1709. Conclusion: The proposed control strategy could attain a reduction of 68-85 % in THD under peak to off-peak loading conditions with the addition of shunt DCES.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135890098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The idea of being able to communicate with an electronic device in a similar way as human beings is now the new big thing in the world of Artificial Intelligence. The fusion of AI and Cloud computing has given rise to a new technology that can understand and learn conversations in the natural language used by humans. In this Era, where automation is taking over the world, the invention of smart chat-bots has made it possible to imitate humans in various applications to reduce human effort and thereby perform at maximum efficiency. Objective: The objective is to replace a human-constituted assignment with an error-free technology. By using the intent modular concept of dialog flow, the role of the hotel receptionist is eliminated. The purpose of using an API of Google Cloud Platform namely Dialog flow in this project is to conveniently perform NLP (Natural Language Processing) i.e. training a robot to perform according to our instructions and understand the natural language spoken by humans and the hardware attached to the device enables the listening and speaking of the smart bot. Methods: Utilization of Dialog flow Enterprise Edition to make “Hotel Agent” with the use of intents comprising of a general hotel glossary. Results: Dialog flow as a natural language processing recognizer running on the processor Raspberry pie with Python as its constituent language. Finally, it is connected to Google Assistant to make it publicly available in the execution phase. Conclusion: The successful testing of the Artificial Intelligence-based device has ensured that manpower could be conveniently replaced by Machine Intelligence by using knowledge-based databases.
{"title":"Artificial Intelligence System-based Chatbot as a Hotel Agent","authors":"Javeria Ali, Ume Aymen Amjad, Wajeeha Iqbal Ansari, Fareeha Hafeez","doi":"10.2174/0123520965266459231016094630","DOIUrl":"https://doi.org/10.2174/0123520965266459231016094630","url":null,"abstract":"Background: The idea of being able to communicate with an electronic device in a similar way as human beings is now the new big thing in the world of Artificial Intelligence. The fusion of AI and Cloud computing has given rise to a new technology that can understand and learn conversations in the natural language used by humans. In this Era, where automation is taking over the world, the invention of smart chat-bots has made it possible to imitate humans in various applications to reduce human effort and thereby perform at maximum efficiency. Objective: The objective is to replace a human-constituted assignment with an error-free technology. By using the intent modular concept of dialog flow, the role of the hotel receptionist is eliminated. The purpose of using an API of Google Cloud Platform namely Dialog flow in this project is to conveniently perform NLP (Natural Language Processing) i.e. training a robot to perform according to our instructions and understand the natural language spoken by humans and the hardware attached to the device enables the listening and speaking of the smart bot. Methods: Utilization of Dialog flow Enterprise Edition to make “Hotel Agent” with the use of intents comprising of a general hotel glossary. Results: Dialog flow as a natural language processing recognizer running on the processor Raspberry pie with Python as its constituent language. Finally, it is connected to Google Assistant to make it publicly available in the execution phase. Conclusion: The successful testing of the Artificial Intelligence-based device has ensured that manpower could be conveniently replaced by Machine Intelligence by using knowledge-based databases.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136142206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.2174/0123520965248875231004060818
Yanli Yang, Xinlin Wang, Weisheng Pan
Background: Daily inspections of insulators are necessary because they are indispensable components for power transmission lines. Using deep learning to monitor insulators is a newly developed method. However, most deep learning-based detection methods rely on a large training sample set, which consumes computing resources and increases the workload of sample labeling. The selection of learning models to monitor insulators becomes problematic. Objective: Through comparative analysis, a model suitable for small-sample insulator learning is found to provide a reference for the research and application of insulator detection. objective: We intend to find a model suitable for small-sample learning of insulators, which can provide a reference for the research and application of insulator detection. Methods: This paper compares some of the latest deep learning models, YOLOv7, SSD, and DETR, for insulator detection based on small-sample learning. The small sample here means that the number of samples and their proportion to the total sample are relatively small. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in the natural background are selected to test the performance of these models. method: This paper compares some latest deep learning models which are the YOLOv7, the SSD, and the DETR, for insulator detection based on small-sample learning. Few public insulator datasets are available on the internet. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in natural background, are selected to test the performance of these models. Results: Tests on two public insulator image sets, InsulatorDataSet and TLP, show that the recognition rates of YOLOv7, DETR, and SSD are arranged from high to low. The DETR and the YOLOv7 have stable performance, while the SSD lacks stable performance on the learning time and recognition rate. Conclusion: The in-domain and cross-domain scenario tests show that YOLOv7 is more suitable for insulator detection under small-sample conditions among the three models. other: None
{"title":"Contrasting YOLOv7, SSD, and DETR on Insulator Identification under Small-Sample Learning","authors":"Yanli Yang, Xinlin Wang, Weisheng Pan","doi":"10.2174/0123520965248875231004060818","DOIUrl":"https://doi.org/10.2174/0123520965248875231004060818","url":null,"abstract":"Background: Daily inspections of insulators are necessary because they are indispensable components for power transmission lines. Using deep learning to monitor insulators is a newly developed method. However, most deep learning-based detection methods rely on a large training sample set, which consumes computing resources and increases the workload of sample labeling. The selection of learning models to monitor insulators becomes problematic. Objective: Through comparative analysis, a model suitable for small-sample insulator learning is found to provide a reference for the research and application of insulator detection. objective: We intend to find a model suitable for small-sample learning of insulators, which can provide a reference for the research and application of insulator detection. Methods: This paper compares some of the latest deep learning models, YOLOv7, SSD, and DETR, for insulator detection based on small-sample learning. The small sample here means that the number of samples and their proportion to the total sample are relatively small. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in the natural background are selected to test the performance of these models. method: This paper compares some latest deep learning models which are the YOLOv7, the SSD, and the DETR, for insulator detection based on small-sample learning. Few public insulator datasets are available on the internet. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in natural background, are selected to test the performance of these models. Results: Tests on two public insulator image sets, InsulatorDataSet and TLP, show that the recognition rates of YOLOv7, DETR, and SSD are arranged from high to low. The DETR and the YOLOv7 have stable performance, while the SSD lacks stable performance on the learning time and recognition rate. Conclusion: The in-domain and cross-domain scenario tests show that YOLOv7 is more suitable for insulator detection under small-sample conditions among the three models. other: None","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.
{"title":"Predicting Solar PV Output Based on Hybrid Deep Learning and Physical Models: Case Study of Morocco","authors":"Samira Abousaid, Loubna Benabbou, Hanane Dagdougui, Ismail Belhaj, Abdelaziz Berrado, hichame Bouzekri","doi":"10.2174/0123520965264083230926105355","DOIUrl":"https://doi.org/10.2174/0123520965264083230926105355","url":null,"abstract":"Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135919432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.2174/0123520965252489231002071659
Amrita Jyoti, Vikash Yadav, Mayur Rahul
Abstract: Blockchain technology is increasingly attracting young people because it is so well adapted to the digital age. A decentralised data management system is necessary for the blockchain idea in order to store and share data and transactions throughout the network. This study investigates various types of risks associated with blockchain technology. The research covers different aspects of blockchain, including the architecture, consensus mechanism, smart contracts, and underlying cryptographic algorithms. It also examines the risks associated with the adoption and implementation of blockchain in various industries, such as finance, healthcare, and supply chain management. Moreover, this study identifies several types of risks, including technical risks, such as scalability, interoperability, and security, as well as non-technical risks, such as regulatory compliance, legal liability, and governance issues. This study also discusses the potential impact of these risks on blockchain-based systems and the strategies that can be used to mitigate them.
{"title":"Blockchain Security Attacks, Difficulty, and Prevention","authors":"Amrita Jyoti, Vikash Yadav, Mayur Rahul","doi":"10.2174/0123520965252489231002071659","DOIUrl":"https://doi.org/10.2174/0123520965252489231002071659","url":null,"abstract":"Abstract: Blockchain technology is increasingly attracting young people because it is so well adapted to the digital age. A decentralised data management system is necessary for the blockchain idea in order to store and share data and transactions throughout the network. This study investigates various types of risks associated with blockchain technology. The research covers different aspects of blockchain, including the architecture, consensus mechanism, smart contracts, and underlying cryptographic algorithms. It also examines the risks associated with the adoption and implementation of blockchain in various industries, such as finance, healthcare, and supply chain management. Moreover, this study identifies several types of risks, including technical risks, such as scalability, interoperability, and security, as well as non-technical risks, such as regulatory compliance, legal liability, and governance issues. This study also discusses the potential impact of these risks on blockchain-based systems and the strategies that can be used to mitigate them.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136015159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.2174/0123520965262792231003061345
Wei Wei, Li Ye, Yi Fang, Yingchun Wang, Chenghao Zhang, Zhenhua Li, Yue Zhong
Background: In recent years, trade on credit has become increasingly common around the world, exposing companies in the supply chain to significantly increased financial risk due to extended billing periods. As an innovative financing model, supply chain finance (SCF) has received a lot of attention. background: The exhaust gas of traditional fuel vehicles is a major cause of environmental problems such as air pollution and global warming. In order to promote the low-carbon development of the energy system and contribute to the realization of carbon peaking and carbon neutrality, new energy electric vehicles quickly become an important part of the global new energy strategy by virtue of low-carbon, environmental protection, high-performance and other advantages. In recent years, China has attached great importance to breaking through the core technology of electric vehicles and improving product performance, and issued relevant policies to encourage and support the development of the industry. As a result, the industrialization of new energy charging vehicles has been accelerating. At the same time, the charging infrastructure of electric vehicles is also developing rapidly. The charging infrastructure is a variety of charging and changing facilities that provide energy supply for electric vehicles, and is an indispensable supporting infrastructure for the development of electric vehicles. The charging management platform needs to conduct power dispatching by region, so understanding the charging behavior of users can not only help relevant enterprises to develop business strategies, but also guide the infrastructure construction of the electric vehicle industry. Objective: The goal of this work is to examine the impact of supply chain finance on the performance of the automobile industry in the post-covid-19 era. objective: Forecasting the trading electricity can help relevant departments or enterprises better understand the charging behavior and habits of users, and further adjust and optimize the power supply, service and construction. Methods: After an in-depth understanding of the relevant theoretical literature, two models of inquiry are established in this paper, and the relevant data are collected from the CSMAR database for a sample of some enterprises in the automotive industry in the listed market, followed by an empirical analysis using the Stata 16.0. Then, the fixed effects model (FEM) and difference-indifference model (DID) are used to test the hypothesis. Results: The results show a significant impact of supply chain finance on the performance of automobile firms. It is effective in improving the flow of funds and contributes to the performance of enterprises in the automotive industry. Conclusion: In the context of the pandemic, supply chain finance can effectively help enterprises reduce the risk of bankruptcy due to capital rupture and provide a guarantee for the sustainable development of automobile industry enterprises. concl
{"title":"An Empirical Study on the Impact of Supply Chain Finance on the Performance of the Automobile Industry in the Post-covid-19 Era","authors":"Wei Wei, Li Ye, Yi Fang, Yingchun Wang, Chenghao Zhang, Zhenhua Li, Yue Zhong","doi":"10.2174/0123520965262792231003061345","DOIUrl":"https://doi.org/10.2174/0123520965262792231003061345","url":null,"abstract":"Background: In recent years, trade on credit has become increasingly common around the world, exposing companies in the supply chain to significantly increased financial risk due to extended billing periods. As an innovative financing model, supply chain finance (SCF) has received a lot of attention. background: The exhaust gas of traditional fuel vehicles is a major cause of environmental problems such as air pollution and global warming. In order to promote the low-carbon development of the energy system and contribute to the realization of carbon peaking and carbon neutrality, new energy electric vehicles quickly become an important part of the global new energy strategy by virtue of low-carbon, environmental protection, high-performance and other advantages. In recent years, China has attached great importance to breaking through the core technology of electric vehicles and improving product performance, and issued relevant policies to encourage and support the development of the industry. As a result, the industrialization of new energy charging vehicles has been accelerating. At the same time, the charging infrastructure of electric vehicles is also developing rapidly. The charging infrastructure is a variety of charging and changing facilities that provide energy supply for electric vehicles, and is an indispensable supporting infrastructure for the development of electric vehicles. The charging management platform needs to conduct power dispatching by region, so understanding the charging behavior of users can not only help relevant enterprises to develop business strategies, but also guide the infrastructure construction of the electric vehicle industry. Objective: The goal of this work is to examine the impact of supply chain finance on the performance of the automobile industry in the post-covid-19 era. objective: Forecasting the trading electricity can help relevant departments or enterprises better understand the charging behavior and habits of users, and further adjust and optimize the power supply, service and construction. Methods: After an in-depth understanding of the relevant theoretical literature, two models of inquiry are established in this paper, and the relevant data are collected from the CSMAR database for a sample of some enterprises in the automotive industry in the listed market, followed by an empirical analysis using the Stata 16.0. Then, the fixed effects model (FEM) and difference-indifference model (DID) are used to test the hypothesis. Results: The results show a significant impact of supply chain finance on the performance of automobile firms. It is effective in improving the flow of funds and contributes to the performance of enterprises in the automotive industry. Conclusion: In the context of the pandemic, supply chain finance can effectively help enterprises reduce the risk of bankruptcy due to capital rupture and provide a guarantee for the sustainable development of automobile industry enterprises. concl","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136253382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.2174/0123520965267339230928061410
Neha Sharma, Pankaj Dhiman
Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.
{"title":"Lightweight Privacy Preserving Scheme for IoT based Smart Home","authors":"Neha Sharma, Pankaj Dhiman","doi":"10.2174/0123520965267339230928061410","DOIUrl":"https://doi.org/10.2174/0123520965267339230928061410","url":null,"abstract":"Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-10DOI: 10.2174/0123520965262459231002051022
Xiaoguang Su
background: This paper proposes a synthetic aperture radar (SAR) target recognition method based on adaptive weighted decision fusion of multi-level deep features. methods: The trained ResNet-18 is employed to extract multi-level deep features from SAR images. Afterwards, based on the joint sparse representation (JSR) model, the multi-level deep features are represented to obtain the corresponding reconstruction error vectors. Considering the differences in the abilities of different levels of features to distinguish the target, the reconstruction error vectors are analyzed based on entropy theory, and their corresponding weights are adaptively obtained. Finally, the fused reconstruction error result is obtained through adaptively weighted fusion, and the target label is determined accordingly. results: Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under different conditions, and the proposed method is compared with published methods, including multi-feature decision fusion, JSR-based decision fusion and other types of ResNets. conclusion: The experimental results under standard operating condition (SOC) and extended operating conditions (EOCs) including depression angle variance and noise corruption validate the advantages of the proposed method.
{"title":"SAR Target Recognition Method Based on Adaptive Weighted Decision Fusion of Deep Features","authors":"Xiaoguang Su","doi":"10.2174/0123520965262459231002051022","DOIUrl":"https://doi.org/10.2174/0123520965262459231002051022","url":null,"abstract":"background: This paper proposes a synthetic aperture radar (SAR) target recognition method based on adaptive weighted decision fusion of multi-level deep features. methods: The trained ResNet-18 is employed to extract multi-level deep features from SAR images. Afterwards, based on the joint sparse representation (JSR) model, the multi-level deep features are represented to obtain the corresponding reconstruction error vectors. Considering the differences in the abilities of different levels of features to distinguish the target, the reconstruction error vectors are analyzed based on entropy theory, and their corresponding weights are adaptively obtained. Finally, the fused reconstruction error result is obtained through adaptively weighted fusion, and the target label is determined accordingly. results: Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under different conditions, and the proposed method is compared with published methods, including multi-feature decision fusion, JSR-based decision fusion and other types of ResNets. conclusion: The experimental results under standard operating condition (SOC) and extended operating conditions (EOCs) including depression angle variance and noise corruption validate the advantages of the proposed method.","PeriodicalId":43275,"journal":{"name":"Recent Advances in Electrical & Electronic Engineering","volume":"58 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136358648","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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