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Year: 2023, Volume: 23, Number: 4
... improved the efficacy of intrusion detection. Adaptive adversaries who comprehend the infrastructure of computer networks. The administrators of underlying principles of ML techniques can initiate attacks these crucial ... Advances in Electrical and Computer Engineering Volume 23, Number 4, 2023 A Robust Network Intrusion Detection System Using Random Forest Based Random Subspace Ensemble to Defend Against Adversarial ...
12. Transformer Core Saturation Fault Analysis using Current Sensor Signals and Thermal Image Features
Year: 2023, Volume: 23, Number: 4
... taken as a reference for detection of anomalies and images are processed with Multi Resolution wavelet- Deep faults. Signal analysis techniques are applied in CT signals Convolutional Neural Network. Energy band-based values from current sensor signal and current sensor thermal image and extract relevant information regarding inrush current Haralick features are used for differentiating transformer and harmonics ...
Year: 2023, Volume: 23, Number: 4
... operation capabilities [5 ,6]. High-performance wireless networks of diverse wireless standards and various error correction depend ... FPGA). The presented RSCI demonstrates remarkable Local Area Network (WLAN) is proposed in [15] ... individual and Polar Code," IEEE Wireless Communications and Networking resources. This is compared with the resource utilization ...
Year: 2023, Volume: 23, Number: 4
... ]. The nonlinear properties inherent to the substantial need for enhanced efficiency and in Schottky diodes facilitate the up/down conversion and reliability in utilizing spectrum within 5G networks, frequency multiplication of incident signals. These diode numerous broadcasting systems are opting to integrate the nonlinearities serve as a foundation for the generation of millimeter wave spectrum ...
15. Transfer Learning Based Convolutional Neural Network for Classification of Remote Sensing Images
Year: 2023, Volume: 23, Number: 4
... are shown monitoring etc., Presently, Convolutional Neural Networks in Fig. 1. (CNN) which ... , Number 4, 2023 Transfer Learning Based Convolutional Neural Network for Classification of Remote Sensing Images Meena Prakash RAMASAMY1 ... 2015, I. Zinicovscaia, "Processing, neural network-based modeling of doi:10.1109/lgrs.2015.2475299. biomonitoring ...
Year: 2023, Volume: 23, Number: 4
... combined on the semiconductor devices, and hence, it can be with a reconfigurable series resonant converter and LCC or employed for PV powered utility pole applications. LLC network, to obtain constant frequency control [8-10]. MATLAB/Simulink software is used to model these For automotive applications, [11-13] shows a resonant ...
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... frequency regulation in low-inertia power grids," Sustain. Energy, [24] X. Hou et al., "Distributed hierarchical control of AC microgrid Grids Networks, vol. 27, p. 100496, Sep. 2021. operating in grid-connected, islanded and their transition modes," doi:10.1016/J.SEGAN.2021.100496 ...
Year: 2023, Volume: 23, Number: 3
... works mostly use 3D convolutional video. The next cross-modal interaction module fuses the neural networks [20-21] or based on dual-stream networks features of the two modalities, ... in Electrical and Computer Engineering Volume 23, Number 3, 2023 Video Moment Localization Network Based on Text Multi-semantic Clues Guidance Guangli WU, Tongjie XU School of Cyberspace ...
Year: 2023, Volume: 23, Number: 3
... parameters of the algorithm for each PV the best results from the algorithm, the network must be system should be redefined. trained many times [19-20]. ... Liu, J. W. Huang, J. H. Chen, "Neural-network-based the array was operated at this point. The method was tested maximum power ...
Year: 2023, Volume: 23, Number: 3
... to as "traffic prediction," which can neural networks. mitigate several health issues caused by traffic jams ... real-time data to make a short-term learning models or neural network architectures for time prediction of traffic flow during morning ... and 24%, respectively. Additionally, the neural network-based modeling of biomonitoring studies data and LSTM model has ...
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