Pub Date : 2024-02-26DOI: 10.2174/0118722121294488240223075517
Congshan Li, Kefeng Zhao, Ping He, Zikai Zhen
A frequency control strategy is proposed based on additional virtual synchronous generator technology for voltage source converter-based multi-terminal high voltage direct current systems with wind power. This strategy addresses the system's inertia reduction and frequency stability issues caused by integrating large amounts of wind power through multi-terminal DC transmission. Firstly, the virtual synchronous generator mathematical model is constructed based on the system structure. Secondly, for the problem of zero rotational inertia of voltage source converter in a flexible DC transmission system, based on the P-U droop control method of the converter station, additional virtual synchronous control generation technology is applied to simulate the P-f droop characteristics of the synchronous generator by adding virtual rotational inertia, so that the converter has the inertial response of synchronous generator to realize primary frequency regulation. Finally, the simulation is verified on the PSCAD/ EMTDC platform with an example of a three-terminal parallel MTDC transmission system. the analyzed results demonstrate that the virtual synchronous generator control strategy is very valuable and useful for improving the frequency performance of the system.
{"title":"The Influence of Additional Virtual Synchronous Generator Technology in VSC-MTDC Systems with Wind Power on System Frequency","authors":"Congshan Li, Kefeng Zhao, Ping He, Zikai Zhen","doi":"10.2174/0118722121294488240223075517","DOIUrl":"https://doi.org/10.2174/0118722121294488240223075517","url":null,"abstract":"\u0000\u0000A frequency control strategy is proposed based on additional virtual synchronous\u0000generator technology for voltage source converter-based multi-terminal high voltage direct\u0000current systems with wind power.\u0000\u0000\u0000\u0000This strategy addresses the system's inertia reduction and frequency stability issues caused\u0000by integrating large amounts of wind power through multi-terminal DC transmission. Firstly, the\u0000virtual synchronous generator mathematical model is constructed based on the system structure. Secondly,\u0000for the problem of zero rotational inertia of voltage source converter in a flexible DC transmission\u0000system, based on the P-U droop control method of the converter station, additional virtual\u0000synchronous control generation technology is applied to simulate the P-f droop characteristics of the\u0000synchronous generator by adding virtual rotational inertia, so that the converter has the inertial response\u0000of synchronous generator to realize primary frequency regulation.\u0000\u0000\u0000\u0000Finally, the simulation is verified on the PSCAD/ EMTDC platform with an example of a\u0000three-terminal parallel MTDC transmission system.\u0000\u0000\u0000\u0000the analyzed results demonstrate that the virtual synchronous generator control strategy\u0000is very valuable and useful for improving the frequency performance of the system.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428674","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 : 2024-02-23DOI: 10.2174/0118722121269253240214075231
Anupama K N, R. Nagaraj
Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. To overcome the drawbacks, this work proposed a new method based on Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.
车载 Ad-hoc 网络(VANET)是路边车辆与车辆基础设施之间的无线通信。车载 Ad Hoc 网络(VANET)是一种前景广阔的技术,可有效管理交通并确保道路安全。然而,在开放访问环境中进行通信对安全和隐私问题提出了真正的挑战,这可能会影响 VANET 的大规模部署。车辆识别、分类、分配率和通信是以往方法中最具挑战性的领域。为了克服这些缺点,本研究提出了一种基于人工神经网络信任认证安全菅野模糊系统(AN2-TAS2FS)的新方法。车载 Ad HocNetworks(VANET)需要在车辆之间传输数据并使用交通安全指标。基于人工神经网络的信任认证安全杉野模糊系统(AN2-TAS2FS)使用对称密钥来提高 VANET 的安全性能。为了克服这些缺点,这项工作提出了一种基于人工神经网络的信任认证安全菅野模糊系统(AN2-TAS2FS)的新方法。车载 Ad Hoc 网络(VANET)需要在车辆之间传输数据,并使用交通安全指标改进集群安全路由协议(ICSRP)。基于人工神经网络的信任认证安全菅野模糊系统(AN2-TAS2FS)使用对称密钥来提高 VANET 的安全性能。在改进的基于集群的 VANET 路由协议中,每个节点都通过无线车辆-车辆(V2V)交换和路边单元(RSUs)之间的新寻址方案获得地址。仿真结果表明,在所提出的基于 ANN 的信任认证安全菅野模糊系统(AN2-TAS2FS)分析中,数据包传输率为 93%,延迟性能为 0.55sec,吞吐量性能为 94%,带宽为 55bits/sec,网络安全性为 92%,传输率为 89%,攻击检测率为 90%。
{"title":"Secure Vehicle-to-Vehicle Communication Using Routing Protocol Based On Trust Authentication Secure Sugeno Fuzzy Inference System Scheme","authors":"Anupama K N, R. Nagaraj","doi":"10.2174/0118722121269253240214075231","DOIUrl":"https://doi.org/10.2174/0118722121269253240214075231","url":null,"abstract":"\u0000\u0000Vehicular Ad-hoc Network (VANET) is wireless communication between\u0000Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising\u0000technology that effectively manages traffic and ensures road safety. However, communication in an\u0000open-access environment presents real challenges to security and privacy issues, which may affect\u0000large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and\u0000communication are the most challenging areas in previous methods. Vehicular communications face\u0000challenges due to vehicle interference and severe delays.\u0000\u0000\u0000\u0000To overcome the drawbacks, this work proposed a new method based on the Artificial Neural\u0000Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc\u0000Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators.\u0000Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust\u0000Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the\u0000security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating\u0000the node weights for data transferring; reduced the attacks accuracy of network malicious attacks.\u0000\u0000\u0000\u0000To overcome the drawbacks, this work proposed a new method based on Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET.\u0000\u0000\u0000\u0000In the improved cluster-based VANET routing protocol, each node obtains an address using a\u0000new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside\u0000Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation\u0000term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission.\u0000\u0000\u0000\u0000Simulation results show that in the proposed ANN-based Trust Authentication Secure\u0000Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance\u0000is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and\u0000the transmission ratio is 89%, attack detection is 90%.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437926","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 : 2024-02-22DOI: 10.2174/0118722121291771240216044918
P. Singha, Barsha Panda, Syed Benazir Firdaus, D. Ghosh
Artificial intelligence (AI) has made its own place in the present world. Almost in every field, AI is being utilized for betterment and advancement. Machine learning (ML) is a part of AI and has been applied extensively currently in various fields of science and technology including healthcare system. ML is the technique that uses AI to analyze, interpret and make decisions. To summarize the applications of ML in various healthcare systems in order to understand the strength and loopholes of the use of ML in medical science. The mechanisms and methods of ML approach in various medical issues have been analyzed and discussed. ML technique is being used to make decisions in medical cases, for determining the treatment regime of a particular patient, for designing and developing drugs, in personalized medicine, in designing and selecting diagnoses for any particular disease, for automated tracking of patient's recovery. Available clinical data and history are being used by ML techniques to compare, classify, select and execute results for any task being assigned. In a nutshell, ML uses earlier available information and data about the disease, the treatment protocols followed, and the results in correspondence with the clinical symptoms and pathological findings. Several achievements using ML in the healthcare system, yielded significant novel results that have been patented. There have been several thousand patents in the field of application of ML in healthcare systems from the years 2012 to 2023. Though, ML in healthcare comes with some risks and unknown possibilities yet, restricted and monitored application of ML in healthcare may hasten the healthcare system, save time, help to make efficient decisions in non-invasive ways, and may open up new possibilities in the healthcare system.
人工智能(AI)已在当今世界占据一席之地。几乎在每一个领域,人工智能都被用于改善和进步。机器学习(ML)是人工智能的一部分,目前已被广泛应用于包括医疗保健系统在内的各个科技领域。为了了解 ML 在医学科学中应用的优势和漏洞,我们总结了 ML 在各种医疗系统中的应用。ML 技术被用于在医疗案例中做出决策、确定特定病人的治疗方案、设计和开发药物、个性化医疗、设计和选择任何特定疾病的诊断方法、自动跟踪病人的康复情况。现有的临床数据和病史正被 ML 技术用于比较、分类、选择和执行所分配任务的结果。简而言之,ML 使用了有关疾病的早期可用信息和数据、所遵循的治疗方案以及与临床症状和病理结果相对应的结果。从 2012 年到 2023 年,在医疗保健系统中应用 ML 领域的专利已达数千项。尽管 ML 在医疗保健中的应用存在一些风险和未知的可能性,但在医疗保健中限制和监控 ML 的应用可能会加速医疗保健系统的发展,节省时间,有助于以非侵入性的方式做出高效决策,并为医疗保健系统开辟新的可能性。
{"title":"Machine Learning (ML) Techniques in Healthcare Systems: A Mini Review","authors":"P. Singha, Barsha Panda, Syed Benazir Firdaus, D. Ghosh","doi":"10.2174/0118722121291771240216044918","DOIUrl":"https://doi.org/10.2174/0118722121291771240216044918","url":null,"abstract":"\u0000\u0000Artificial intelligence (AI) has made its own place in the present world. Almost in every\u0000field, AI is being utilized for betterment and advancement. Machine learning (ML) is a part of AI\u0000and has been applied extensively currently in various fields of science and technology including\u0000healthcare system. ML is the technique that uses AI to analyze, interpret and make decisions.\u0000To summarize the applications of ML in various healthcare systems in order to understand the\u0000strength and loopholes of the use of ML in medical science.\u0000The mechanisms and methods of ML approach in various medical issues have been analyzed and\u0000discussed. ML technique is being used to make decisions in medical cases, for determining the\u0000treatment regime of a particular patient, for designing and developing drugs, in personalized medicine,\u0000in designing and selecting diagnoses for any particular disease, for automated tracking of patient's\u0000recovery. Available clinical data and history are being used by ML techniques to compare,\u0000classify, select and execute results for any task being assigned. In a nutshell, ML uses earlier available\u0000information and data about the disease, the treatment protocols followed, and the results in correspondence\u0000with the clinical symptoms and pathological findings.\u0000Several achievements using ML in the healthcare system, yielded significant novel results that have\u0000been patented. There have been several thousand patents in the field of application of ML in\u0000healthcare systems from the years 2012 to 2023.\u0000Though, ML in healthcare comes with some risks and unknown possibilities yet, restricted and monitored\u0000application of ML in healthcare may hasten the healthcare system, save time, help to make\u0000efficient decisions in non-invasive ways, and may open up new possibilities in the healthcare system.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140440840","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 : 2024-02-21DOI: 10.2174/0118722121293163240212030405
Seema Sharma, Narendra Singh Yadav
Web apps hold important information, such as login tokens and individual data, and cybercriminals repeatedly target attackers. Cross-site scripting is one of the most frequent vulnerabilities in web apps. Several techniques and patents are used to mitigate these vulnerabilities. Several 100 articles from a review of research papers published between 2005 and 2023 were considered. This paper reviewed different techniques and tools to detect cross-site scripting attacks, and it will be helpful to understand, analyze, and develop a strategy to deal with them. This paper focuses on different methods and tools for identifying cross-site scripting (XSS) attacks. Also, it depicts the strengths and shortcomings of the existing proposed method. Additionally, it will help to understand existing open issues or challenges faced by previous researchers.
{"title":"Review on Detection and Prevention Techniques of Scripting Attacks:\u0000Gaps, Challenges and Suggestions","authors":"Seema Sharma, Narendra Singh Yadav","doi":"10.2174/0118722121293163240212030405","DOIUrl":"https://doi.org/10.2174/0118722121293163240212030405","url":null,"abstract":"\u0000\u0000Web apps hold important information, such as login tokens and individual data, and cybercriminals\u0000repeatedly target attackers. Cross-site scripting is one of the most frequent vulnerabilities\u0000in web apps. Several techniques and patents are used to mitigate these vulnerabilities. Several\u0000100 articles from a review of research papers published between 2005 and 2023 were considered.\u0000This paper reviewed different techniques and tools to detect cross-site scripting attacks, and it will be\u0000helpful to understand, analyze, and develop a strategy to deal with them. This paper focuses on different\u0000methods and tools for identifying cross-site scripting (XSS) attacks. Also, it depicts the\u0000strengths and shortcomings of the existing proposed method. Additionally, it will help to understand\u0000existing open issues or challenges faced by previous researchers.\u0000","PeriodicalId":40022,"journal":{"name":"Recent Patents on Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442727","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}