This paper presents a novel method for accurately estimating the cumulative capacity credit(CCC)of renewable energy(RE)*** data from the main interconnected system(MIS)of Oman for 2028,where a substantial increase in ...
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This paper presents a novel method for accurately estimating the cumulative capacity credit(CCC)of renewable energy(RE)*** data from the main interconnected system(MIS)of Oman for 2028,where a substantial increase in RE generation is anticipated,the method is introduced alongside the traditional effective load carrying capability(ELCC)*** ensure its robustness,we compare CCC results with ELCC calculations using two distinct standards of reliability criteria:loss of load hours(LOLH)at 24 hour/year and 2.4 hour/*** method consistently gives accurate results,emphasizing its exceptional accuracy,efficiency,and simplicity.A notable feature of the method is its independence from loss of load probability(LOLP)calculations and the iterative procedures associated with analytic-based reliability ***,it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE *** innovation is of particular significance to prospective independent power producers(IPPs)in the RE sector,offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data,often restricted by privacy concerns.
The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernie...
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The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power *** realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and...
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The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power *** realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method(MPM),this paper propos es an improved MPM-based parameter identification with syn *** MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coeffi cients of the matrix pencil to ensure the accuracy of the identi fied parameters,since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation ***,the identification and recovery of bad data are proposed by uti lizing the difference in temporal continuity of the synchropha sors before and after noise *** proposed parameter identification is verified with synthetic,simulated,and actual measured phase measurement unit(PMU)*** with the existing MPM,the improved MPM achieves better accuracy for parameter identification of each component in SSOs,better real-time performance,and significantly reduces the effect of bad data.
In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of N (similar) control tasks. Each task involves controlling a line...
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In optical applications where avalanche photodiodes (APDs) provide the benefit of high sensitivity, Sb-based materials systems such as AlInAsSb and AlGaAsSb have shown extremely low excess noise factors. The Monte Car...
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Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity ...
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Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as fac...
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Image inpainting consists of filling holes or missing parts of an image. Inpainting face images with symmetric characteristics is more challenging than inpainting a natural scene. None of the powerful existing models ...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.
App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(M...
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App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.
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