In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However...
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In this paper, a novel flow control strategy which is the inlet throttled pump was used to design an angular velocity control system for rotary actuator. Inlet throttled systems have good performance in addition to th...
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A second-order sliding mode control is used for high-order uncertain plants using equivalent control approach to improve the performance of controlsystems. They combine backstepping with quasi-continuous controller a...
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In this paper, the tracking control problem for networked control system (NCS) under communication delays is investigated. In order to realize the tracking of the NCS, an event-triggered predictive control strategy is...
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The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on...
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In this paper, a FIWARE based control implementation framework for supervisory control of inputoutput models, in Discrete Event System (DES) form, will be introduced, through the case study of an industrial product tr...
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With the prevalence of new wearable devices and personal sensing, model fitting from real-world human-generated data has become a topic of interest in the fields of bioengineering, sports science, and medical engineer...
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Adaptive FRIT (A-FRIT) with exponential for-getting (EF) has been proposed for time-varying systems to improve the data dependence of FRIT, which is a direct data-driven tuning method. However, the EF -based method is...
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Longer training times pose a significant challenge in Artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative ...
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Longer training times pose a significant challenge in Artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network.
Two new modifications of second-order low-frequency discrete-analog filters (DAFs) based on switched capacitors with two electronic keys have been developed and investigated. The first modification contains a resistiv...
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