A new multi-period transmission planning method is presented in this article to choose optimal solutions among the suggested HVAC and HVDC lines for installation in consecutive periods over a long-term planning horizo...
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Traffic modeling and prediction are indispensable to future extensive data-driven automated intelligent cellular *** contributes to proactive and autonomic network control operations within cellular *** methodologies ...
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Traffic modeling and prediction are indispensable to future extensive data-driven automated intelligent cellular *** contributes to proactive and autonomic network control operations within cellular *** methodologies typically rely on established prediction models designed for univariate and multivariate time series ***,these approaches often demand a substantial volume of training data and extensive computational resources for prediction model *** this study,we introduce a dual-step transfer learning(DSTL)-based prediction model specifically designed for the prediction of multivariate spatio-temporal cellular *** technique involves the categorization of gNodeBs(gNBs)into distinct clusters based on their traffic pattern *** of training the prediction model individually on each gNB,a base model is trained on the aggregated dataset of all the gNBs within a base cluster using a combination of recurrent neural network(RNN)and bidirectional long-short term memory(RNN-BLSTM)*** the first-step transfer learning(TL),the base model is provided to the gNBs within the base cluster and to the other clusters,where it undergoes the process of fine-tuning the intra-cluster aggregated *** the model is trained on the aggregated dataset within each cluster,it is provided to the gNBs within the respective cluster in the second-step *** model received by each gNB through the proposed DSTL technique either necessitates minimal fine-tuning or,in some cases,requires no further *** conduct extensive experiments on a real-world Telecom Italia cellular traffic *** results demonstrate that the proposed DSTL-based prediction model achieves a mean absolute percentage error of 2.97%,9.85%,and 9.73%in predicting spatio-temporal Internet,calling,and messaging traffic,respectively,while utilizing less computational resources and requiring less training time than traditional model training and
The cybersecurity of the power grid has gained increasing attraction in today's smart grid system. The dynamic load-altering attack (DLAA), which causes under-frequency trips by injecting an attacking load, and th...
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Instruction-tuned large language models have demonstrated remarkable capabilities in following human instructions across various domains. However, their proficiency remains notably deficient in many low-resource langu...
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Diffusion models have become a popular choice for representing actor policies in behavior cloning and offline reinforcement learning. This is due to their natural ability to optimize an expressive class of distributio...
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Diffusion models have become a popular choice for representing actor policies in behavior cloning and offline reinforcement learning. This is due to their natural ability to optimize an expressive class of distributions over a continuous space. However, previous works fail to exploit the score-based structure of diffusion models, and instead utilize a simple behavior cloning term to train the actor, limiting their ability in the actor-critic setting. In this paper, we present a theoretical framework linking the structure of diffusion model policies to a learned Q-function, by linking the structure between the score of the policy to the action gradient of the Q-function. We focus on off-policy reinforcement learning and propose a new policy update method from this theory, which we denote Q-score matching. Notably, this algorithm only needs to differentiate through the denoising model rather than the entire diffusion model evaluation, and converged policies through Q-score matching are implicitly multi-modal and explorative in continuous domains. We conduct experiments in simulated environments to demonstrate the viability of our proposed method and compare to popular baselines. Source code is available from the project website: https://***/qsm. Copyright 2024 by the author(s)
Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement...
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Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement in realistic multipath environments. Array decorrelation techniques have been proposed, achieving correlation reductions by either tilting the antenna beams or shifting the phase centers away from each other. Hence, these methods are mainly limited to MIMO terminals with small arrays. To avoid such problems, this work proposes a decorrelation optimization technique based on phase correcting surface(PCS)that can be applied to large MIMO arrays, enhancing their MIMO performances in a realistic(non-isotropic)multipath environment. First, by using a near-field channel model and an optimization algorithm, a near-field phase distribution improving the MIMO capacity is obtained. Then the PCS(consisting of square elements)is used to cover the array's aperture, achieving the desired near-field phase *** examples demonstrate the effectiveness of this PCS-based near-field optimization technique. One is a1 × 4 dual-polarized patch array(working at 2.4 GHz)covered by a 2 × 4 PCS with 0.6λ center-to-center distance. The other is a 2 × 8 dual-polarized dipole array, for which a 4 × 8 PCS with 0.4λ center-to-center distance is designed. Their MIMO capacities can be effectively enhanced by 8% and 10% in single-cell and multi-cell scenarios, respectively. The PCS has insignificant effects on mutual coupling, matching, and the average radiation efficiency of the patch array, and increases the antenna gain by about 2.5 dB while keeping broadside radiations to ensure good cellular coverage, which benefits the MIMO performance of the *** proposed technique offers a new perspective for improving large MIMO arrays in realistic multipath in a statistical sense.
This paper presents Deeper, a design for a decentralized exchange that enhances liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Shallow li...
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The triple active bridge (TAB) is a promising 3-port DC-DC converter technology which performs bi-directional power transfer with galvanic isolation. Due to intensive interactions between parameter selection and a TAB...
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The capacity to generalize to future unseen data stands as one of the utmost crucial attributes of deep neural networks. Sharpness-Aware Minimization (SAM) aims to enhance the generalizability by minimizing worst-case...
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