Journal of Electrical Systems and Information Technology最新文献

{"title":"Implementation of partially tuned PD controllers of a multirotor UAV using deep deterministic policy gradient","authors":"Emmanuel Mosweu, Tshepo Botho Seokolo, Theddeus Tochukwu Akano, Oboetswe Seraga Motsamai","doi":"10.1186/s43067-024-00153-1","DOIUrl":"https://doi.org/10.1186/s43067-024-00153-1","url":null,"abstract":"The present methodology employed in classical control systems is characterized by high costs, significant processing requirements, and inflexibility. In conventional practice, when the controller exhibits instability after being implemented on the hardware, it is often adjusted to achieve stability. However, this approach is not suitable for mass-produced systems like drones, which possess diverse manufacturing tolerances and delicate stability thresholds. The aim of this study is to design and evaluate a controller for a multirotor unmanned aerial vehicle (UAV) system that is capable of adapting its gains in accordance with changes in the system dynamics. The controller utilized in this research employs a Simulink-constructed model that has been taught by reinforcement learning techniques, specifically employing a deep deterministic policy gradient (DDPG) network. The Simulink model of the UAV establishes the framework within which the agent engages in learning through interaction with its surroundings. The DDPG algorithm is an off-policy reinforcement learning technique that operates in continuous action spaces and does not require a model. The efficacy of the cascaded PD controllers and neural network tuner is evaluated. The results revealed that the controller exhibited stability during several flight phases, including take-off, hovering, path tracking, and landing manoeuvres.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737752","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}

{"title":"A comprehensive review for chronic disease prediction using machine learning algorithms","authors":"Rakibul Islam, Azrin Sultana, Mohammad Rashedul Islam","doi":"10.1186/s43067-024-00150-4","DOIUrl":"https://doi.org/10.1186/s43067-024-00150-4","url":null,"abstract":"","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"71 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643137","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}

{"title":"A semantic-based model with a hybrid feature engineering process for accurate spam detection","authors":"Chira N. Mohammed, Ayah M. Ahmed","doi":"10.1186/s43067-024-00151-3","DOIUrl":"https://doi.org/10.1186/s43067-024-00151-3","url":null,"abstract":"","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"43 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141648800","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}

{"title":"Optimally tuned cascaded FOPI-FOPIDN with improved PSO for load frequency control in interconnected power systems with RES","authors":"Yaw O. M. Sekyere, Francis B. Effah, Philip Y. Okyere","doi":"10.1186/s43067-024-00149-x","DOIUrl":"https://doi.org/10.1186/s43067-024-00149-x","url":null,"abstract":"In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective. To address these new challenges, this paper proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO. The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios. Governor dead band, communication time delay, and generation rate constraints are considered in one of the scenarios for more realistic evaluation. Again, the controller’s robustness to uncertain model parameters is validated by varying the parameters of the three-area test system by ± 50%. The simulation results obtained confirm the controller’s robustness and its superiority over the comparison LFC strategies in terms of the above performance metrics.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574293","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}

{"title":"Deep learning model for detection of hotspots using infrared thermographic images of electrical installations","authors":"Ezechukwu Kalu Ukiwe, Steve A. Adeshina, Tsado Jacob, Bukola Babatunde Adetokun","doi":"10.1186/s43067-024-00148-y","DOIUrl":"https://doi.org/10.1186/s43067-024-00148-y","url":null,"abstract":"Hotspots in electrical power equipment or installations are a major issue whenever it occurs within the power system. Factors responsible for this phenomenon are many, sometimes inter-related and other times they are isolated. Electrical hotspots caused by poor connections are common. Deep learning models have become popular for diagnosing anomalies in physical and biological systems, by the instrumentality of feature extraction of images in convolutional neural networks. In this work, a VGG-16 deep neural network model is applied for identifying electrical hotspots by means of transfer learning. This model was achieved by first augmenting the acquired infrared thermographic images, using the pre-trained ImageNet weights of the VGG-16 algorithm with additional global average pooling in place of conventional fully connected layers and a softmax layer at the output. With the categorical cross-entropy loss function, the model was implemented using the Adam optimizer at learning rate of 0.0001 as well as some variants of the Adam optimization algorithm. On evaluation, with a test IRT image dataset, and a comparison with similar works, the research showed that a better accuracy of 99.98% in identification of electrical hotspots was achieved. The model shows good score in performance metrics like accuracy, precision, recall, and F1-score. The obtained results proved the potential of deep learning using computer vision parameters for infrared thermographic identification of electrical hotspots in power system installations. Also, there is need for careful selection of the IR sensor’s thermal range during image acquisition, and suitable choice of color palette would make for easy hotspot isolation, reduce the pixel to pixel temperature differential across any of the images, and easily highlight the critical region of interest with high pixel values. However, it makes edge detection difficult for human visual perception which computer vision-based deep learning model could overcome.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510339","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}

{"title":"The use of Efficientnet_b0 network to identify COVID-19 in chest X-ray images","authors":"Michael Ayitey Junior, Peter Appiahene, Yaw Marfo Missah, Vivian Akoto-Adjepong","doi":"10.1186/s43067-024-00143-3","DOIUrl":"https://doi.org/10.1186/s43067-024-00143-3","url":null,"abstract":"A newly discovered coronavirus called COVID-19 poses the greatest threat to mankind in the twenty-first century. Mortality has dramatically increased in all cities and countries due to the virus's current rate of spread. A speedy and precise diagnosis is also necessary in order to treat the illness. This study identified three groups for chest X-ray images: Covid, normal, and pneumonia. This study's objective is to present a framework for categorizing chest X-ray images into three groups of pneumonia, normal, and Covid scenarios. To do this, chest X-ray images from the Kaggle database which have been utilized in previous studies were obtained. It is suggested to use an Efficientnet_b0 model to identify characteristics in raw data hierarchically. An unedited X-ray image of the chest is enhanced for more reasonable assumptions in order to apply the proposed method in real-world situations. With an overall accuracy of 93.75%, the proposed network correctly identified the chest X-ray images to the classes of Covid, viral pneumonia, and normal on the test set. 90% accuracy rate for the test dataset was attained for the viral pneumonitis group. On the test dataset, the Normal class accuracy was 94.7%, while the Covid class accuracy was 96%. The findings indicate that the network is robust. In addition, when compared to the most advanced techniques of identifying pneumonia, the concluded findings from the suggested model are highly encouraging. Since the recommended network is successful at doing so utilizing chest X-ray imaging, radiologists can diagnose COVID-19 and other lung infectious infections promptly and correctly.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510340","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}

{"title":"Electrical and thermal performance analysis of hybrid photovoltaic/thermal water collector using meta-heuristic optimization","authors":"Heba S. Aggour, D. Atia, Hanaa M. Farghally, M. Soliman, M. Omar","doi":"10.1186/s43067-024-00146-0","DOIUrl":"https://doi.org/10.1186/s43067-024-00146-0","url":null,"abstract":"","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"139 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350975","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}

{"title":"Unveiling the evolution of generative AI (GAI): a comprehensive and investigative analysis toward LLM models (2021–2024) and beyond","authors":"Zarif Bin Akhtar","doi":"10.1186/s43067-024-00145-1","DOIUrl":"https://doi.org/10.1186/s43067-024-00145-1","url":null,"abstract":"","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"89 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352769","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}

{"title":"A low-power 8-bit 1-MS/s single-ended SAR ADC in 130-nm CMOS for medical devices","authors":"Dina M. Ellaithy","doi":"10.1186/s43067-024-00147-z","DOIUrl":"https://doi.org/10.1186/s43067-024-00147-z","url":null,"abstract":"","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"142 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350676","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}

{"title":"Hunter–Prey Optimization Algorithm: a review","authors":"Pappu Soundarya Lahari, Varaprasad Janamala","doi":"10.1186/s43067-024-00144-2","DOIUrl":"https://doi.org/10.1186/s43067-024-00144-2","url":null,"abstract":"The Hunter–Prey Optimization Algorithm (HPO) is a nature-inspired optimization technique influenced by the predator–prey relationships observed in nature. Over the years, HPO has gained attention as a promising method for solving complex optimization problems. This review article provides a comprehensive analysis and a bibliographic study of the Hunter–Prey Optimization Algorithm. It explores its origins, underlying principles, applications, strengths, weaknesses, and recent developments in detail. By delving into various facets of HPO, this review aims to shed light on its effectiveness and potential, inspiring the researchers to address real-world optimization challenges.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253569","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}