B. Valarmathi, Jain Kshitij, Rajpurohit Dimple, N. Gupta, Y. H. Robinson, G. Arulkumaran, Tadesse Mulu
Drone examination has been overall quickly embraced by NDMM (natural disaster mitigation and management) division to survey the state of impacted regions. Manual video analysis by human observers takes time and is subject to mistakes. The human identification examination of pictures caught by drones will give a practical method for saving lives who are being trapped under debris during quakes or in floods and so on. Drone investigation for research and security and search and rescue (SAR) should involve the drone to filter the impacted area using a camera and a model of unmanned area vehicles (UAVs) to identify specific locations where assistance is required. The existing methods (Balmukund et al. 2020) used were faster-region based convolutional neural networks (F-RCNNs), single shot detector (SSD), and region-based fully convolutional network (R-FCN) for the detection of human and recognition of action. Some of the existing methods used 700 images with six classes only, whereas the proposed model uses 1996 images with eight classes. The proposed model is used YOLOv3 (you only look once) algorithm for the detection and recognition of actions. In this study, we provide the fundamental ideas underlying an object detection model. To find the most effective model for human recognition and detection, we trained the YOLOv3 algorithm on the image dataset and evaluated its performance. We compared the outcomes with the existing algorithms like F-RCNN, SSD, and R-FCN. The accuracies of F-RCNN, SSD, R-FCN (existing algorithms), and YOLOv3 (proposed algorithm) are 53%, 73%, 93%, and 94.9%, respectively. Among these algorithms, the YOLOv3 algorithm gives the highest accuracy of 94.9%. The proposed work shows that existing models are inadequate for critical applications like search and rescue, which convinces us to propose a model raised by a pyramidal component extracting SSD in human localization and action recognition. The suggested model is 94.9% accurate when applied to the proposed dataset, which is an important contribution. Likewise, the suggested model succeeds in helping time for expectation in examination with the cutting-edge identification models with existing strategies. The average time taken by our proposed technique to distinguish a picture is 0.40 milisec which is a lot better than the existing method. The proposed model can likewise distinguish video and can be utilized for real-time recognition. The SSD model can likewise use to anticipate messages if present in the picture.
无人机检查已被NDMM(自然灾害缓解和管理)部门全面迅速接受，以调查受影响地区的状况。人工视频分析需要时间，而且容易出错。对无人机拍摄的照片进行人类识别检查，将为在地震或洪水等期间拯救被困在废墟下的生命提供一种实用的方法。用于研究和安全以及搜救(SAR)的无人机调查应涉及无人机使用摄像头和无人驾驶区域车辆(uav)模型过滤受影响区域，以确定需要援助的特定位置。现有的方法(Balmukund et al. 2020)使用更快的基于区域的卷积神经网络(F-RCNNs)、单镜头检测器(SSD)和基于区域的全卷积网络(R-FCN)来检测人类和识别动作。现有的一些方法只使用了包含6个类的700幅图像，而该模型使用了包含8个类的1996幅图像。所提出的模型使用YOLOv3(只看一次)算法来检测和识别动作。在这项研究中，我们提供了目标检测模型的基本思想。为了找到最有效的人类识别和检测模型，我们在图像数据集上训练YOLOv3算法并评估其性能。我们将结果与现有的算法如F-RCNN、SSD和R-FCN进行了比较。现有算法F-RCNN、SSD、R-FCN和YOLOv3的准确率分别为53%、73%、93%和94.9%。在这些算法中，YOLOv3算法的准确率最高，达到94.9%。我们的研究表明，现有的模型在搜索和救援等关键应用中是不够的，这使我们有理由提出一种基于锥体成分提取SSD的人体定位和动作识别模型。该模型在数据集上的准确率为94.9%，这是一个重要的贡献。同样，建议的模型成功地帮助使用现有策略的前沿识别模型检查期望时间。该方法识别图像的平均时间为0.40毫秒，大大优于现有方法。该模型同样可以区分视频，并可用于实时识别。SSD模型同样可以用于预测图片中出现的消息。
{"title":"Human Detection and Action Recognition for Search and Rescue in Disasters Using YOLOv3 Algorithm","authors":"B. Valarmathi, Jain Kshitij, Rajpurohit Dimple, N. Gupta, Y. H. Robinson, G. Arulkumaran, Tadesse Mulu","doi":"10.1155/2023/5419384","DOIUrl":"https://doi.org/10.1155/2023/5419384","url":null,"abstract":"Drone examination has been overall quickly embraced by NDMM (natural disaster mitigation and management) division to survey the state of impacted regions. Manual video analysis by human observers takes time and is subject to mistakes. The human identification examination of pictures caught by drones will give a practical method for saving lives who are being trapped under debris during quakes or in floods and so on. Drone investigation for research and security and search and rescue (SAR) should involve the drone to filter the impacted area using a camera and a model of unmanned area vehicles (UAVs) to identify specific locations where assistance is required. The existing methods (Balmukund et al. 2020) used were faster-region based convolutional neural networks (F-RCNNs), single shot detector (SSD), and region-based fully convolutional network (R-FCN) for the detection of human and recognition of action. Some of the existing methods used 700 images with six classes only, whereas the proposed model uses 1996 images with eight classes. The proposed model is used YOLOv3 (you only look once) algorithm for the detection and recognition of actions. In this study, we provide the fundamental ideas underlying an object detection model. To find the most effective model for human recognition and detection, we trained the YOLOv3 algorithm on the image dataset and evaluated its performance. We compared the outcomes with the existing algorithms like F-RCNN, SSD, and R-FCN. The accuracies of F-RCNN, SSD, R-FCN (existing algorithms), and YOLOv3 (proposed algorithm) are 53%, 73%, 93%, and 94.9%, respectively. Among these algorithms, the YOLOv3 algorithm gives the highest accuracy of 94.9%. The proposed work shows that existing models are inadequate for critical applications like search and rescue, which convinces us to propose a model raised by a pyramidal component extracting SSD in human localization and action recognition. The suggested model is 94.9% accurate when applied to the proposed dataset, which is an important contribution. Likewise, the suggested model succeeds in helping time for expectation in examination with the cutting-edge identification models with existing strategies. The average time taken by our proposed technique to distinguish a picture is 0.40 milisec which is a lot better than the existing method. The proposed model can likewise distinguish video and can be utilized for real-time recognition. The SSD model can likewise use to anticipate messages if present in the picture.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78698104","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}
Appropriate data analysis technology can make people use the online degree education, obtain the data and information generated in the learning management system, and provide a useful decision basis for optimizing the teaching and management process of online degree education. Data analysis technology can help English teachers better grasp students’ learning situations and progress and optimize management. First, data analysis methods and decision tree algorithms are analyzed. Second, in data mining technology, the C4.5 decision tree method is used to construct an English score prediction model. Through the analysis of English learning-related information such as questionnaires and collected student test score data, the prediction of English teaching performance is analyzed from the perspective of teachers’ in-depth intervention. The survey results are shown as follows: (1) The model is simulated and tested. The model’s prediction accuracy is 98.20%, 99.10%, 99.40%, 98.70%, and 98.90%, higher than the standard accuracy of 97.5%. Additionally, the average response efficiency of the model is 99.42%, which can be used. (2) The failure rate of boys’ final grades is 11%, and the failure rate of female students’ final grades is 10%. There is only a 1% difference in the final grade failure rate between male and female students. The effect of gender on teaching performance is less pronounced. (3) As the number of practice questions increases, the rate of failing grades decreases. Thus, the data suggest that the number of practice questions affects instructional performance. (4) Teachers’ intervention can improve students’ English achievement. Increasing the intensity of the intervention also improves student achievement. Therefore, the follow-up research should increase the number of practice questions and teacher intervention in English teaching. The English teaching achievement prediction suggestion based on big data analysis is put forward, providing a reference for prediction management.
{"title":"English Teaching Achievement Prediction by Big Data Analysis under Deep Intervention","authors":"Junfang Guo","doi":"10.1155/2023/9542465","DOIUrl":"https://doi.org/10.1155/2023/9542465","url":null,"abstract":"Appropriate data analysis technology can make people use the online degree education, obtain the data and information generated in the learning management system, and provide a useful decision basis for optimizing the teaching and management process of online degree education. Data analysis technology can help English teachers better grasp students’ learning situations and progress and optimize management. First, data analysis methods and decision tree algorithms are analyzed. Second, in data mining technology, the C4.5 decision tree method is used to construct an English score prediction model. Through the analysis of English learning-related information such as questionnaires and collected student test score data, the prediction of English teaching performance is analyzed from the perspective of teachers’ in-depth intervention. The survey results are shown as follows: (1) The model is simulated and tested. The model’s prediction accuracy is 98.20%, 99.10%, 99.40%, 98.70%, and 98.90%, higher than the standard accuracy of 97.5%. Additionally, the average response efficiency of the model is 99.42%, which can be used. (2) The failure rate of boys’ final grades is 11%, and the failure rate of female students’ final grades is 10%. There is only a 1% difference in the final grade failure rate between male and female students. The effect of gender on teaching performance is less pronounced. (3) As the number of practice questions increases, the rate of failing grades decreases. Thus, the data suggest that the number of practice questions affects instructional performance. (4) Teachers’ intervention can improve students’ English achievement. Increasing the intensity of the intervention also improves student achievement. Therefore, the follow-up research should increase the number of practice questions and teacher intervention in English teaching. The English teaching achievement prediction suggestion based on big data analysis is put forward, providing a reference for prediction management.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78077149","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}
Accurate load forecasting (LF) plays an important role in the operation and decision-making process of the power grid. Although the stochastic and nonlinear behavior of loads is highly dependent on consumer energy requirements, that demands a high level of accuracy in LF. In spite of several research studies being performed in this field, accurate load forecasting remains an important consideration. In this article, the design of a hybrid short-term load forecasting model (STLF) is proposed. This work combines the features of an artificial neural network (ANN), ensemble forecasting, and a deep learning network. RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. The predictions made from RBFNNs have been computed and produced as the forecast of each activated cluster. This framework is known as fuzzy-RBFNN. This proposed framework is outlined to anticipate one-week ahead load demand on an hourly basis, and its accuracy is determined using two case studies, i.e., Hellenic and Cretan power systems. Its results are validated while comparing with four benchmark models like multiple linear regression (MLR), support vector machine (SVM), ML-SVM, and fuzzy-RBFNN in terms of accuracy. To demonstrate the performance of RBF-CNN, SVMs replace the RBF-CNN regressor, and this model is identified as an ML-SVM having 3 layers.
{"title":"Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting","authors":"M. Yadav, M. Jamil, M. Rizwan, Richa Kapoor","doi":"10.1155/2023/8669796","DOIUrl":"https://doi.org/10.1155/2023/8669796","url":null,"abstract":"Accurate load forecasting (LF) plays an important role in the operation and decision-making process of the power grid. Although the stochastic and nonlinear behavior of loads is highly dependent on consumer energy requirements, that demands a high level of accuracy in LF. In spite of several research studies being performed in this field, accurate load forecasting remains an important consideration. In this article, the design of a hybrid short-term load forecasting model (STLF) is proposed. This work combines the features of an artificial neural network (ANN), ensemble forecasting, and a deep learning network. RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. The predictions made from RBFNNs have been computed and produced as the forecast of each activated cluster. This framework is known as fuzzy-RBFNN. This proposed framework is outlined to anticipate one-week ahead load demand on an hourly basis, and its accuracy is determined using two case studies, i.e., Hellenic and Cretan power systems. Its results are validated while comparing with four benchmark models like multiple linear regression (MLR), support vector machine (SVM), ML-SVM, and fuzzy-RBFNN in terms of accuracy. To demonstrate the performance of RBF-CNN, SVMs replace the RBF-CNN regressor, and this model is identified as an ML-SVM having 3 layers.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78597135","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}
Mohamed Bouni, Badr Hssina, K. Douzi, Samira Douzi
The economic prosperity of a country is highly dependent on agriculture. The use of technology in agriculture has greatly contributed to the economic prosperity of industrialized countries and is crucial for the growth of emerging countries. One major challenge in agriculture is the detection and control of plant diseases, which can greatly affect food production and population well-being. Plant illnesses have a substantial effect on plant productivity and quality. The detection of various types of diseases in plants with bare eyes is time consuming and a difficult task with little precision. Mainly our primary concern is tomato crops. The economic demand for tomatoes has grown dramatically over time. The complicated task of controlling tomato infection requires ongoing care during the crop cycle and consumes a considerable amount of the total cost of production. To classify tomato diseases, we made the use of the pretrained deep neural networks and automation, which are crucial for this method. Digital image processing can be used to monitor plant disease. Deep learning has made remarkable improvements in digital image processing in recent years, surpassing the older techniques. This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. The CNN’s backbone comprises AlexNet, ResNet, VGG-16, and DenseNet. The Adam and RmsProp optimization methods examine these networks’ relative performance, demonstrating that the DenseNet model with the RmsProp optimization approach achieves the most significant outcomes with the best accuracy of 99.9%.
{"title":"Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction","authors":"Mohamed Bouni, Badr Hssina, K. Douzi, Samira Douzi","doi":"10.1155/2023/5051005","DOIUrl":"https://doi.org/10.1155/2023/5051005","url":null,"abstract":"The economic prosperity of a country is highly dependent on agriculture. The use of technology in agriculture has greatly contributed to the economic prosperity of industrialized countries and is crucial for the growth of emerging countries. One major challenge in agriculture is the detection and control of plant diseases, which can greatly affect food production and population well-being. Plant illnesses have a substantial effect on plant productivity and quality. The detection of various types of diseases in plants with bare eyes is time consuming and a difficult task with little precision. Mainly our primary concern is tomato crops. The economic demand for tomatoes has grown dramatically over time. The complicated task of controlling tomato infection requires ongoing care during the crop cycle and consumes a considerable amount of the total cost of production. To classify tomato diseases, we made the use of the pretrained deep neural networks and automation, which are crucial for this method. Digital image processing can be used to monitor plant disease. Deep learning has made remarkable improvements in digital image processing in recent years, surpassing the older techniques. This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. The CNN’s backbone comprises AlexNet, ResNet, VGG-16, and DenseNet. The Adam and RmsProp optimization methods examine these networks’ relative performance, demonstrating that the DenseNet model with the RmsProp optimization approach achieves the most significant outcomes with the best accuracy of 99.9%.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86929729","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}
Aiming at the problems of low recognition accuracy and large memory occupation when using point cloud information for power operation violation, A power operation violation recognition method based on point cloud data preprocessing and deep learning under the architecture of Internet of things (IoT) is proposed. First, voxel filtering and statistical filtering methods are used to properly simplify the power operation point cloud data on the premise of ensuring the quality of reverse modeling, and the moving least square method is used to smooth the point cloud to obtain a complete and closed three-dimensional model; second, the process of power operation violation behavior recognition is divided into two stages. In the first stage, PointRCNN extracts the semantic features of each point, separates the front scenic spots, and extracts the preselection box. In the second stage, the candidate box is refined by integrating the semantic features and classification confidence of the first stage to obtain a more accurate bounding box. Finally, the experiments show that the average accuracy of the proposed method is the highest, with an average accuracy of 0.919 in the simple difficulty scenario, 0.897 in the medium difficulty scenario, and 0.839 in the difficult difficulty scenario, which are higher than those of the compared methods. Therefore, the proposed method can effectively improve the accuracy of power operation violation identification.
{"title":"Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT","authors":"Shibo Yang, W. Fu, Lishuo Zhang, Zhaolei Wang","doi":"10.1155/2023/6859102","DOIUrl":"https://doi.org/10.1155/2023/6859102","url":null,"abstract":"Aiming at the problems of low recognition accuracy and large memory occupation when using point cloud information for power operation violation, A power operation violation recognition method based on point cloud data preprocessing and deep learning under the architecture of Internet of things (IoT) is proposed. First, voxel filtering and statistical filtering methods are used to properly simplify the power operation point cloud data on the premise of ensuring the quality of reverse modeling, and the moving least square method is used to smooth the point cloud to obtain a complete and closed three-dimensional model; second, the process of power operation violation behavior recognition is divided into two stages. In the first stage, PointRCNN extracts the semantic features of each point, separates the front scenic spots, and extracts the preselection box. In the second stage, the candidate box is refined by integrating the semantic features and classification confidence of the first stage to obtain a more accurate bounding box. Finally, the experiments show that the average accuracy of the proposed method is the highest, with an average accuracy of 0.919 in the simple difficulty scenario, 0.897 in the medium difficulty scenario, and 0.839 in the difficult difficulty scenario, which are higher than those of the compared methods. Therefore, the proposed method can effectively improve the accuracy of power operation violation identification.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81037246","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}
Shrish Bajpai, Divyakant Sharma, Monauwer Alam, V. Chandel, A. Pandey, S. Tripathi
The wavelet transform is widely used in the task of hyperspectral image compression (HSIC). They have achieved outstanding performance in the compression of a hyperspectral (HS) image, which has attracted great interest. However, transform based hyperspectral image compression algorithm (HSICA) has low-coding gain than the other state of art HSIC algorithms. To solve this problem, this manuscript proposes a curvelet transform based HSIC algorithm. The curvelet transform is a multiscale mathematical transform that represents the curve and edges of the HS image more efficiently than the wavelet transform. The experiment results show that the proposed compression algorithm has high-coding gain, low-coding complexity, at par coding memory requirement, and works for both (lossy and lossless) compression. Thus, it is a suitable contender for the compression process in the HS image sensors.
{"title":"Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors","authors":"Shrish Bajpai, Divyakant Sharma, Monauwer Alam, V. Chandel, A. Pandey, S. Tripathi","doi":"10.1155/2023/8961271","DOIUrl":"https://doi.org/10.1155/2023/8961271","url":null,"abstract":"The wavelet transform is widely used in the task of hyperspectral image compression (HSIC). They have achieved outstanding performance in the compression of a hyperspectral (HS) image, which has attracted great interest. However, transform based hyperspectral image compression algorithm (HSICA) has low-coding gain than the other state of art HSIC algorithms. To solve this problem, this manuscript proposes a curvelet transform based HSIC algorithm. The curvelet transform is a multiscale mathematical transform that represents the curve and edges of the HS image more efficiently than the wavelet transform. The experiment results show that the proposed compression algorithm has high-coding gain, low-coding complexity, at par coding memory requirement, and works for both (lossy and lossless) compression. Thus, it is a suitable contender for the compression process in the HS image sensors.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84380188","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}
This work aims to solve the specific problem in the Power Internet of Things (PIoT). PIoT is vulnerable to monitoring, tampering, forgery, and other attacks during frequent data interaction under the background of big data, leading to a severe threat to the power grid’s Information Security (ISEC). Cryptosystems can solve ISEC problems, such as confidentiality, data integrity, authentication, identity recognition, data control, and nonrepudiation. Thereupon, this work expounds on cryptography from public-key encryption and digital signature and puts forward the model of network information attack. Then, the security of the two cryptograms is certified against the two cyberattack modes. On this basis, an Identity-based Combined Encryption and Signature (IBCES) ensemble scheme is proposed by combining public-key encryption with the digital signature. Finally, the security of the proposed IBCES’s encryption and the signature schemes is verified, and the results prove their feasibility. The results show that the proposed IBCEs are effective and feasible, fully meeting the information confidentiality requirements. Additionally, smart grid against Information Security (ISEC) algorithms must comprehensively consider network resources and computing power. This work creatively combines the two cryptosystems. The proposal breaks the traditional key segmentation principle by applying the same key to different cryptosystems and ensures the independent security of the two cryptosystems. The conclusion provides technical support for future research on cryptography.
{"title":"Information Security Protection of Internet of Energy Using Ensemble Public Key Algorithm under Big Data","authors":"Baode Lin, Zhenwei Geng, Fengrong Yu","doi":"10.1155/2023/6853902","DOIUrl":"https://doi.org/10.1155/2023/6853902","url":null,"abstract":"This work aims to solve the specific problem in the Power Internet of Things (PIoT). PIoT is vulnerable to monitoring, tampering, forgery, and other attacks during frequent data interaction under the background of big data, leading to a severe threat to the power grid’s Information Security (ISEC). Cryptosystems can solve ISEC problems, such as confidentiality, data integrity, authentication, identity recognition, data control, and nonrepudiation. Thereupon, this work expounds on cryptography from public-key encryption and digital signature and puts forward the model of network information attack. Then, the security of the two cryptograms is certified against the two cyberattack modes. On this basis, an Identity-based Combined Encryption and Signature (IBCES) ensemble scheme is proposed by combining public-key encryption with the digital signature. Finally, the security of the proposed IBCES’s encryption and the signature schemes is verified, and the results prove their feasibility. The results show that the proposed IBCEs are effective and feasible, fully meeting the information confidentiality requirements. Additionally, smart grid against Information Security (ISEC) algorithms must comprehensively consider network resources and computing power. This work creatively combines the two cryptosystems. The proposal breaks the traditional key segmentation principle by applying the same key to different cryptosystems and ensures the independent security of the two cryptosystems. The conclusion provides technical support for future research on cryptography.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87162843","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}
Ginbar Ensermu, M. Vijayashanthi, Suresh Merugu, A. Shaik, B. Premalatha, G. Devadasu
The development of a network termed microgrid (MG) has been motivated owing to augmentation in renewable energy source (RES) infiltration along with the utilization of enhanced power electronic technologies. Recently, more popularity has been gained by the hybrid MG (HMG). Maintaining the power system’s (PS) small-signal stability (SSS) is highly complicated during the energy enhancement of RES. The enhancement of the SSS has been focused on by numerous existing methodologies; however, the optimal solution was not obtained by those methodologies. A new controller with the assistance of bell-curved squirrel search optimization (BCSSO) is proposed to address the aforementioned issue. Initially, for PSs such as photovoltaic (PV), wind turbines, along with fuel cells, a mathematical model is ascertained. Then, in this, the converter design has been developed. The PV’s maximum power flow is recognized by maximum power point tracking (MPPT) in the bidirectional switched buck-boost converter (BSBBC), which is utilized in this research, and by utilizing the fuzzy ruled linear quadratic Gaussian (FRLQG), the converters are controlled to assure safe operation along with soft dynamics. By employing the BCSSO, the parameters are modified in this controller which in turn ameliorates the SSS. The experiential evaluation of the proposed system’s performance is analogized with the existing methodologies. Consequently, the outcomes confirmed that a better performance was attained by the proposed methodology than the prevailing works.
{"title":"An FRLQG Controller-Based Small-Signal Stability Enhancement of Hybrid Microgrid Using the BCSSO Algorithm","authors":"Ginbar Ensermu, M. Vijayashanthi, Suresh Merugu, A. Shaik, B. Premalatha, G. Devadasu","doi":"10.1155/2023/8404457","DOIUrl":"https://doi.org/10.1155/2023/8404457","url":null,"abstract":"The development of a network termed microgrid (MG) has been motivated owing to augmentation in renewable energy source (RES) infiltration along with the utilization of enhanced power electronic technologies. Recently, more popularity has been gained by the hybrid MG (HMG). Maintaining the power system’s (PS) small-signal stability (SSS) is highly complicated during the energy enhancement of RES. The enhancement of the SSS has been focused on by numerous existing methodologies; however, the optimal solution was not obtained by those methodologies. A new controller with the assistance of bell-curved squirrel search optimization (BCSSO) is proposed to address the aforementioned issue. Initially, for PSs such as photovoltaic (PV), wind turbines, along with fuel cells, a mathematical model is ascertained. Then, in this, the converter design has been developed. The PV’s maximum power flow is recognized by maximum power point tracking (MPPT) in the bidirectional switched buck-boost converter (BSBBC), which is utilized in this research, and by utilizing the fuzzy ruled linear quadratic Gaussian (FRLQG), the converters are controlled to assure safe operation along with soft dynamics. By employing the BCSSO, the parameters are modified in this controller which in turn ameliorates the SSS. The experiential evaluation of the proposed system’s performance is analogized with the existing methodologies. Consequently, the outcomes confirmed that a better performance was attained by the proposed methodology than the prevailing works.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85737672","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}
UASs (Unmanned Air Systems) are universally used in many activities, spanning from leisure-commercial to military applications. Accordingly, as the number of UASs operating in the sky increases, so does the need to detect and identify them, in order to ensure their legitimate use. This paper introduces a continuous wave (CW) Doppler radar implementation that can be used to provide early warning for flying-by small UASs. By applying Fast Fourier Transform (FFT) to the returned signal’s Doppler frequency, estimations can be made regarding the presence of aerial bodies inside an Area of Interest (AoI). Achieving reliable detection with a low false alarm rate (FAR) while keeping the size and power demands of the system to minimum was a challenge that was successfully met. The proposed system was extensively tested in outdoor environments; measurement results are presented and parameters such as radar power, antenna gain, and noise are discussed.
{"title":"Detection of Low RCS Unmanned Air Systems Using K-Band Continuous Wave Doppler Radar","authors":"Alexandros Kyritsis, R. Makri, N. Uzunoglu","doi":"10.1155/2023/5683661","DOIUrl":"https://doi.org/10.1155/2023/5683661","url":null,"abstract":"UASs (Unmanned Air Systems) are universally used in many activities, spanning from leisure-commercial to military applications. Accordingly, as the number of UASs operating in the sky increases, so does the need to detect and identify them, in order to ensure their legitimate use. This paper introduces a continuous wave (CW) Doppler radar implementation that can be used to provide early warning for flying-by small UASs. By applying Fast Fourier Transform (FFT) to the returned signal’s Doppler frequency, estimations can be made regarding the presence of aerial bodies inside an Area of Interest (AoI). Achieving reliable detection with a low false alarm rate (FAR) while keeping the size and power demands of the system to minimum was a challenge that was successfully met. The proposed system was extensively tested in outdoor environments; measurement results are presented and parameters such as radar power, antenna gain, and noise are discussed.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76849746","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}
J. Rodríguez-Flores, V. Herrera, Javier Gavilanes, Andres Morocho-Caiza, J. Hernández-Ambato
This paper addresses the synchronization and operation of a 140 kVA inverter system connected to the main grid as part of a decentralized microgeneration system. The considerations for the supply of electrical energy stored in battery banks, mostly of photovoltaic origin, involve a study of the details of a rigid nonlinear system, which parallels the generation and distribution standards typical of hydroelectric and thermoelectric plants. Considering aspects related to power electronics operation, this paper presents both the modeling and the controlling aspects necessary to synchronize and ensure a stable operation of the microgeneration systems when connected to the main grid. Statistical processing was developed to guarantee synchronization between the systems without presenting electric shocks by simulating the magnetic link in asynchronous generators to meet this aim. The proposed model simulates the increase in power by a phase shift by maintaining a constant frequency based on a Chirp wave generator. The proposed process considers a generation power baseband operation. A Mamdani-type fuzzy proportional-integral controller is used to determine the power setpoint, which sets the Chirp generator phase shift setpoint, which includes a Mamdani fuzzy proportional-type controller. Both controllers are connected in a cascade. The applied correlational technique to achieve the synthesis of the sinusoid and the synchronization presented optimal performance when using 17 samples per signal period. The design of the transformer primarily, guaranteed a phase shift of −4.3018°, allowed for a THD below 2.75%.
{"title":"Synchronization and Optimal Operation of a 140 kVA Inverter in On-Grid Mode Using Mamdani Controllers in Cascade","authors":"J. Rodríguez-Flores, V. Herrera, Javier Gavilanes, Andres Morocho-Caiza, J. Hernández-Ambato","doi":"10.1155/2023/8617388","DOIUrl":"https://doi.org/10.1155/2023/8617388","url":null,"abstract":"This paper addresses the synchronization and operation of a 140 kVA inverter system connected to the main grid as part of a decentralized microgeneration system. The considerations for the supply of electrical energy stored in battery banks, mostly of photovoltaic origin, involve a study of the details of a rigid nonlinear system, which parallels the generation and distribution standards typical of hydroelectric and thermoelectric plants. Considering aspects related to power electronics operation, this paper presents both the modeling and the controlling aspects necessary to synchronize and ensure a stable operation of the microgeneration systems when connected to the main grid. Statistical processing was developed to guarantee synchronization between the systems without presenting electric shocks by simulating the magnetic link in asynchronous generators to meet this aim. The proposed model simulates the increase in power by a phase shift by maintaining a constant frequency based on a Chirp wave generator. The proposed process considers a generation power baseband operation. A Mamdani-type fuzzy proportional-integral controller is used to determine the power setpoint, which sets the Chirp generator phase shift setpoint, which includes a Mamdani fuzzy proportional-type controller. Both controllers are connected in a cascade. The applied correlational technique to achieve the synthesis of the sinusoid and the synchronization presented optimal performance when using 17 samples per signal period. The design of the transformer primarily, guaranteed a phase shift of −4.3018°, allowed for a THD below 2.75%.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90049824","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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