With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducte...
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Microwave imaging has long been reported for locating buried anomalies inside a bulk object as relying upon external measurements of microwave data. The approach is noninvasive, low-cost, portable, and the use of non-...
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In this correspondence, we consider vehicular edge computing systems in which computing tasks arrive randomly to vehicles over time and are offloaded to one of the edge servers. Unlike previous work that assumes ideal...
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Forecasting bitcoin prices using deep learning techniques has gained significant attention recently. Despite the success of Generative Adversarial Networks (GANs) across various domains, there is a crucial issue in bi...
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Integrating Power Electronics Converters (PECs)-based renewable energy sources can establish a weak grid due to the substantial inertia reduction leading to severe frequency fluctuations. In this paper, a control stra...
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Distributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate futu...
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Distributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate future wireless networking and a wide range of Internet-of-Things (IoT) applications. In distributed edge learning, multiple edge devices train a common learning model collaboratively without sending their raw data to a central server, which not only helps to preserve data privacy but also reduces network traffic. However, distributed edge training and edge inference typically still require extensive communications among devices and servers connected by wireless links. As a result, the salient features of wireless networks, including interference and channels’ heterogeneity, time-variability, and unreliability, have significant impacts on the learning performance.
This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control form...
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This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.
Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughin...
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Continuous phase modulation (CPM) has extensive applications in wireless communications due to its high spectral and power efficiency. However, its nonlinear characteristics pose significant challenges for detection i...
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Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nod...
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