Load noise prediction of a power transformer

In this article, we propose a new regression equation to predict the noise of a power transformer based on the winding vibration under a loading condition. A regression between load noises and tank vibrations for multiple transformers with different rated powers was confirmed through measurements and regression analysis. A regression equation for load noise and winding vibration was derived considering the fact that the winding vibration level is proportional to the tank vibration level. The electromagnetic force, which is the excitation force of the winding, was obtained using the equivalent magnetic circuit network method to obtain the winding vibration required for the regression equation. Subsequently, the obtained force was applied to a finite element model for the winding to achieve the vibration response. The winding vibration obtained through these methods is closely correlated with the load noise, and the amount of winding vibration transferred to the tank could be changed according to the distance between the tank and the winding. Accordingly, an equation for predicting the load noise was established considering the winding vibration and the correlation factors according to the distance of the transmission path. The proposed prediction equation is considerably more accurate than the previous prediction equation. </jats:p

Integrate-and-Fire Neuron With Li-Based Electrochemical Random Access Memory Using Native Linear Current Integration Characteristics

Neuromorphic computing has gained a considerable research interest due to its potential in realizing highly efficient parallel computations. However, the existing neuromorphic architectures face various drawbacks. In this study, we present an integrate-and-fire (I&F) neuron using a Li-based electrochemical random access memory (Li-ECRAM) to achieve exceptional area efficiency and low-power neuromorphic computing. The proposed Li-ECRAM neuron employs a significantly reduced number of transistors when compared to other novel nonvolatile memory-based I&F neurons due to linear current integration characteristics and a high linear conductance response to the input current. As the integration-type Li-ECRAM is linear, it eliminates the requirement of a nonlinear compensating circuit. Therefore, a Li-ECRAM-based neuron has a simple structure comprising Li-ECRAM, reset transistor, inverter, and pulse generator. Furthermore, we also evaluate the operation speed and energy consumption of the proposed neuron, demonstrating the potential for high-speed and low-power operation. The proposed neuron can be applied in large-scale neuromorphic hardware applications due to the scalability and low energy consumption of Li-ECRAM. IEEE11Nsciescopu

Nonvolatile Frequency-Programmable Oscillator With NbO₂ and Li-Based Electro-Chemical Random Access Memory for Coupled Oscillators-Based Temporal Pattern Recognition System

In this letter, we propose a compact frequency programmable oscillator with an NbO2-based insulator-metal transition (IMT) device and three-terminal Li-based electro-chemical RAM (Li-ECRAM) for coupled oscillators-based temporal pattern recognition system. Owing to the non-volatility, multilevel characteristics, and linear conductance modulation of Li-ECRAM, our proposed oscillator exhibits a large number of programmable frequencies (45) and high controllability by applying pulses to the Li-ECRAM to attain the target frequency. Furthermore, we demonstrated injection locking phenomenon in our proposed oscillators, which can be utilized for the frequency detection of the injected signal. Finally, we simulated four-coupled oscillators system for the frequency classification of the input temporal signal with multiple frequencies and amplitude noise. These results demonstrate the feasibility of a temporal pattern recognition system composed of our proposed compact frequency-programmable oscillators.11Nsciescopu

An On-Chip Learning Method for Neuromorphic Systems Based on Non-Ideal Synapse Devices

In this paper, we propose an on-chip learning method that can overcome the poor characteristics of pre-developed practical synaptic devices, thereby increasing the accuracy of the neural network based on the neuromorphic system. The fabricated synaptic devices, based on Pr1&minus;xCaxMnO3, LiCoO2, and TiOx, inherently suffer from undesirable characteristics, such as nonlinearity, discontinuities, and asymmetric conductance responses, which degrade the neuromorphic system performance. To address these limitations, we have proposed a conductance-based linear weighted quantization method, which controls conductance changes, and trained a neural network to predict the handwritten digits from the standard database MNIST. Furthermore, we quantitatively considered the non-ideal case, to ensure reliability by limiting the conductance level to that which synaptic devices can practically accept. Based on this proposed learning method, we significantly improved the neuromorphic system, without any hardware modifications to the synaptic devices or neuromorphic systems. Thus, the results emphatically show that, even for devices with poor synaptic characteristics, the neuromorphic system performance can be improved

Impact of Operating Temperature on Pattern Recognition Accuracy of Resistive Array-Based Hardware Neural Networks

In hardware neural networks (HNNs), different operating temperatures cause variation in conductance of resistive arrays, and they can significantly distort the information of the synaptic weights, leading to a considerable loss in pattern recognition accuracy. In this study, a WOx-based resistive device is characterized with varying ambient temperatures, and 1k-bit synapse arrays are evaluated. A systematic analysis of the impact of operating temperature on the array-based HNNs is executed using neural network simulations. Moreover, we propose a temperature compensator (TC) that can mitigate anomalous array behavior without modifying the readout circuitry. Our results have demonstrated successful accuracy recovery of the array-based HNN over a wide range of operating temperatures.11Nsciescopu

Excellent Synapse Characteristics of 50 nm Vertical Transistor with WOx channel for High Density Neuromorphic system

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Improved On-chip Training Efficiency at Elevated Temperature and Excellent Inference Accuracy with Retention (> 108 s) of Pr0.7Ca0.3MnO3-x ECRAM Synapse Device for Hardware Neural Network

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