This paper presents a novel framework aimed at enhancing the experimental methodology within cognitive psychology, integrating both informal and formal components to accommodate varied reasoning approaches. The framew...
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作者：
Salama, Wessam M.Aly, Moustafa H.Department of Computer Engineering
Faculty of Engineering Pharos University Canal El Mahmoudia Street Beside Green Plaza Complex 21648 Alexandria Egypt OSA Member
Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Marine Transport Alexandria1029 Egypt
Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent...
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Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to improve the channel estimation and lower the error rate. In order to mitigate inter-symbol interference and map the datasets, this paper introduces M-quadrature amplitude modulation (16-QAM) and orthogonal frequency division multiplexing (OFDM), which is based on quadrature phase shift keying (QPSK). Additionally, the existence or lack of cyclic prefixes forms the basis of our simulation. Additionally, the suggested models are investigated using pilot samples 2, 4, 8, and 64. Labeled OFDM signal samples, where the labels match the signal received after applying OFDM and passing through the medium, are used to train the proposed models. The DenseNet121 functions as a powerful feature extractor to extract intricate spatial information from received signal data. Sequential models like as RNN, LSTM, and GRU are used to model temporal dependencies in the retrieved features. RF is also utilized to exploit non-linear relationships and interactions between features to further increase prediction accuracy and reduce bit error rate (BER). By comparing the models using key metrics like accuracy, bit error rate (BER), and mean squared error (MSE), superior performance is attained based on the DenseNet121_RNN_GRU_RF model. Additionally, the DLMs are assessed against traditional methods like minimal mean square error (MMSE) and least squares (LS). Using the DenseNet121_RNN_GRU_RF model indicates a considerable gain over alternative architectures, with an improvement of 36.3% over DensNet121-RNN-LSTM-RF, according to a comparison of the suggested models without cyclic prefix for OFDM_QPSK. The improvement in percentages of roughly 63.3% over DensNet121-RNN-LSTM, 68.18% over De
In this paper, we present a novel approach to protein folding and drug discovery leveraging Grover’s algorithm. Our protein folding methodology focuses on accurately determining the phi and psi angles to achieve opti...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
In this paper, we present a novel approach to protein folding and drug discovery leveraging Grover’s algorithm. Our protein folding methodology focuses on accurately determining the phi and psi angles to achieve optimal protein configurations, ensuring the system attains a low energy state. In drug discovery, our algorithm accommodates variable primary structures, incorporating a critical examination step for comprehensive analysis. Detailed simulations demonstrate the efficacy of our approach in identifying potential drug candidates with high accuracy. Enhancements to Grover’s algorithm are realized through the integration of the Assembly-ToQuantumCompiler, optimizing quantum circuit designs for increased efficiency. Additionally, we introduce the Quantum Binary Neural Network (QBNN) to improve search precision and computational performance. By combining quantum algorithms with advanced compilers and neural networks, we aim to revolutionize computational biology and accelerate drug discovery. These algorithms improve as quantum computers improve.
The participation of renewable energy resources in modern power systems has been increasing due to their positive impacts on diversifying power supplies with environmental benefits. Despite these advantages, the syste...
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ISBN:
(数字)9798331541125
ISBN:
(纸本)9798331541132
The participation of renewable energy resources in modern power systems has been increasing due to their positive impacts on diversifying power supplies with environmental benefits. Despite these advantages, the system's vulnerability to frequency deviation increases due to the low inertia and intermittent characteristics of these sources. This paper proposes a simple Integral controller in which the frequency control parameter is tuned by reinforcement learning using a Deep Deterministic Policy Gradient model under changing inertia conditions due to the effects of renewable energy resources. The system's frequency will return to its nominal value faster by the improvement of secondary control. The simulation results of the proposed method show significant improvement in the system's frequency control under constantly changing inertia and load. The proposed method has a significant potential to improve the system's robustness under constantly changing inertia and load conditions.
The Internet of Things (IoT) is transforming academic research and will impact various industries, including healthcare. IoT has transformed healthcare from blast-shaped outdated centralized methods to decentralized s...
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Degree reduction of ball Said-Ball (BSB) surface is a complex and unsolved problem in computer-aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geo...
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This paper focuses on analyzing the impact of the magnetic properties of Soft Magnetic Composites (SMC) on the performance of electric motors for traction applications. A sensitivity analysis is done to select the par...
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Frequency stability is crucial for the proper operation of the power grids. The changing landscapes in power systems, raised by continuously increasing demand and rapid decommissioning of conventional generation, have...
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ISBN:
(数字)9798331541125
ISBN:
(纸本)9798331541132
Frequency stability is crucial for the proper operation of the power grids. The changing landscapes in power systems, raised by continuously increasing demand and rapid decommissioning of conventional generation, have changed the frequency dynamics significantly. Load frequency control (LFC) is a mechanism for regulating frequency and ensuring stability by balancing supply and load. Conventional control strategies, which require accurate representation of the mathematical methods, can face difficulty in modeling the complexity of power dynamics. Therefore, this paper proposes a model-free strategy utilizing deep reinforcement learning (DRL) for implementing the LFC mechanism without the involvement of a central controlling. The problem is formulated as a deep deterministic policy gradient (DDPG) to find an optimal solution for regulating frequency control operations at primary and secondary stages. The proposed framework enables the generating unit, acting as an agent, to identify the optimal actions for maintaining system stability by analyzing generation and load patterns. The DRL-based method utilizes the test signal to change the dynamics of the system, and the agent modeled using the recurrent neural network (RNN) framework inside DDPG learns to balance the generation according to the load and ensures restoration of the deviated frequency. The performance of the implemented method is validated with the proportional and integral (PI) controller.
The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases,...
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Computed Tomography is an important tool for diagnosing brain-related trauma or acute events, and brain tissue segmentation is a critical step in this task. In this study, we attempt to explore the performance of CT b...
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