Traditional analytical approaches for stability assessment of inverter-based resources(IBRs),often requiring detailed knowledge of IBR internals,become impractical due to IBRs’proprietary *** measurements,relying on ...
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Traditional analytical approaches for stability assessment of inverter-based resources(IBRs),often requiring detailed knowledge of IBR internals,become impractical due to IBRs’proprietary *** measurements,relying on electromagnetic transient simulation or laboratory settings,are not only time-intensive but also operationally inflexible,since various non-linear control loops make IBRs’admittance models operating-point ***,such admittance measurements must be performed repeatedly when operating point *** avoid time-consuming and cumbersome measurements,admittance estimation for arbitrary operating points is highly ***,existing admittance estimation algorithms usually face challenges in versatility,data demands,and *** this challenge,this letter presents a simple and efficient admittance estimation method for blackboxed IBRs,by utilizing a minimal set of seven operating points to solve a homogeneous linear equation *** studies demonstrate this proposed method ensures high accuracy across various types of *** accuracy is satisfying even when non-negligible measurement errors exist.
Autonomous driving has been significantly advanced in today’s society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is underst...
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Autonomous driving has been significantly advanced in today’s society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is understanding traffic density to enable intelligent traffic management. With the rapid improvement in deep neural networks (DNNs), the accuracy of density estimation has markedly improved. However, there are two main issues that remain unsolved. First, current DNN-based models are excessively heavy, characterized by an overwhelming number of training parameters (millions or even billions) and substantial computational complexity, indicated by a high number of FLOPs. These requirements for storage and computation severely limit the practical application of these models, especially on edge devices with limited capacity and computational power. Second, despite the superior performance of DNN models, their effectiveness largely depends on the availability of large-scale data for training. Growing privacy concerns have made individuals increasingly hesitant to allow their data to be publicly used for model training, particularly in vehicle-related applications that might reveal personal movements, which leads to data isolation issues. In this article, we address these two problems at once with a systematic framework. Specifically, we introduce the proxy model distributed learning (PMDL) model for traffic density estimation. PMDL model is composed of two main components. First, we introduce a proxy model learning strategy that transfers fine-grained knowledge from a larger master model to a lightweight proxy model, i.e., a proxy model. Second, we design a distributed learning strategy that trains multiple proxy models with privacy-aware local data and seamlessly aggregates these models via a global parameter server. This ensures privacy protection while significantly improving estimation performance compared to training models with limited, isolated data. We tested t
Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for e...
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Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classification ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.
The inherent characteristics of fingerprint pores, including their immutability, permanence, and uniqueness in terms of size, shape, and position along ridges, make them suitable candidates for fingerprint recognition...
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The inherent characteristics of fingerprint pores, including their immutability, permanence, and uniqueness in terms of size, shape, and position along ridges, make them suitable candidates for fingerprint recognition. In contrast to only a limited number of other landmarks in a fingerprint, such as minutia, the presence of a large number of pores even in a small fingerprint segment is a very attractive characteristic of pores for fingerprint recognition. A pore-based fingerprint recognition system has two main modules: a pore detection module and a pore feature extraction and matching module. The focus of this paper is on the latter module, in which the features of the detected pores in a query fingerprint are extracted, uniquely represented and then used for matching these pores with those in a template fingerprint. Fingerprint recognition systems that use convolutional neural networks (CNNs) in the design of this module have automatic feature extraction capabilities. However, CNNs used in these modules have inadequate capability of capturing deep-level features. Moreover, the pore matching part of these modules heavily relies only on the Euclidean distance metric, which if used alone, may not provide an accurate measure of similarity between the pores. In this paper, a novel pore feature extraction and matching module is presented in which a CNN architecture is proposed to generate highly representational and discriminative hierarchical features and a balance between the performance and complexity is achieved by using depthwise and depthwise separable convolutions. Furthermore, an accurate composite metric, encompassing the Euclidean distance, angle, and magnitudes difference between the vectors of pore representations, is introduced to measure the similarity between the pores of the query and template fingerprint images. Extensive experimentation is carried out to demonstrate the effectiveness of the proposed scheme in terms of performance and complexity, and it
Brain cancer is a disease of the brain caused by a brain tumor. A brain tumor is the development of cells in the brain that grow in an unregulated and unnatural manner. Patients may suffer irreversible brain damage or...
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The rapid increase in coffee consumption has led to a significant expansion in production scale and variety within the agricultural regions of the global coffee belt. Recent coffee harvested in varies specious and pro...
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Large language models (LLMs) have become quite popular in recent years due to their remarkable capacity to produce language that is human-like. The creation and use of LLMs, however, also bring up serious ethical ques...
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One of the important topics in remote sensing is land use land cover classification. This paper presents a framework that aims to correctly classify land use land cover into seven different landscape characteristics s...
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The smart world currently employs smart devices that are inextricably linked with everyday life. These smart devices are lightweight due to their small size, low memory capacity, low-power batteries, and limited compu...
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Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and ***,monitor-ing...
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Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and ***,monitor-ing the video continually at a quicker pace is a challenging *** a consequence,security cameras are useless and need human *** primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful *** anomalous action does not occur at a high-er rate than usual *** detect the object in a video,first we analyze the images pixel by *** digital image processing,segmentation is the process of segregating the individual image parts into *** performance of segmenta-tion is affected by irregular illumination and/or low *** factors highly affect the real-time object detection process in the video surveillance *** this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient *** results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video *** proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.
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