Performance Estimation of Induction Motor using Artificial Neural Network

Lee, Ho‐Young; Lee, Jong-Ll; Kwon, Soon-O; Lee, Su-Woong

doi:10.1109/iwssip.2018.8439459

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…These techniques help models deal with out-of-distribution data, make informed decisions under uncertainty, and improve safety in critical applications. Uncertainty estimation may be useful in detecting adversarial examples [13], adapting models to changing environments [14], and facilitating robust model selection through ensemble techniques [15].…”

Section: Robustness and Uncertainty Estimationmentioning

confidence: 99%

Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness

Pintor,

Demontis,

Biggio

2023

ESANN 2023 Proceesdings

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In recent years, machine learning has become the most effective way to analyze massive data streams. However, machine learning is also subject to security and reliability issues. These aspects require machine learning to be thoroughly tested before being deployed in unsupervised scenarios, such as services intended for consumers. The goal of this session is to discuss open challenges, both theoretical and practical, related to the security and safety of machine learning. The session will try to address the following challenges: (i) the implementation of efficient tests for Machine Learning in the context of robustness to attacks and natural drifts of data; and (ii) the design of robust and efficient models able to function in the wild and mitigate or detect adversarial attacks. ContextMachine learning (ML) has rapidly transformed various industries, from powering recommendation systems to driving autonomous vehicles. As ML adoption grows, so does the need for rigorous evaluation and trustworthiness of ML models. In the last years, these models have been rapidly increasing in size and complexity, as well as the amount of data used for their training. This demands for testing techniques able to effectively cover the attack surface of these systems and properly test the resilience of ML to unseen and undesirable attacks [1]. To address this open problem, we formulate the following research challenge:Research Challenge 1 Implementation of efficient tests for Machine Learning in the context of robustness to attacks and natural drifts of data.

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Section: Robustness and Uncertainty Estimationmentioning

confidence: 99%

Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness

Pintor,

Demontis,

Biggio

2023

ESANN 2023 Proceesdings

View full text Add to dashboard Cite

show abstract

“…The probability masses of softmax-classifiers are often spoiled by overconfident posteriors even for out-of-distribution samples (Lee et al, 2018;Geifman et al, 2018). Therefore, the model's energy surface is often used as a straightforward approximation to the confidence surface (Liu et al, 2020).…”

Section: Energy-induced Confidencementioning

confidence: 99%

5. Polyphonie als Passion: Johann Sebastian Bach

Schmidt¹

2022

Zwischen Tönen

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Deep neural networks are applied in more and more areas of everyday life. However, they still lack essential abilities, such as robustly dealing with spatially transformed input signals. Approaches to mitigate this severe robustness issue are limited to two pathways: Either models are implicitly regularised by increased sample variability (data augmentation) or explicitly constrained by hard-coded inductive biases. The limiting factor of the former is the size of the data space, which renders sufficient sample coverage intractable. The latter is limited by the engineering effort required to develop such inductive biases for every possible scenario. Instead, we take inspiration from human behaviour, where percepts are modified by mental or physical actions during inference. We propose a novel technique to emulate such an inference process. This is achieved by traversing a sparsified inverse transformation tree during inference using parallel energy-based evaluations. Our proposed inference algorithm, called Inverse Transformation Search (ITS), is model-agnostic and equips the model with zero-shot pseudo-invariance to spatially transformed inputs. We evaluated our method on several benchmark datasets, including a synthesised ImageNet test set. ITS outperforms the utilised baselines on all zero-shot test scenarios.

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“…In particular, ANN-based controllers and estimators have been widely used in identification and control of power converters and motor drives [43]. As an example, they can be used to estimate the rotor speed, rotor-flux, and torque of induction motors [44]- [46], in addition to the identification and estimation of the stator current of induction motor drives [47]. Several ANN-based methods have also been used in the control of power converters, as presented in [48]- [51].…”

Section: Introductionmentioning

confidence: 99%

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

et al. 2019

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Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANNbased control strategy.Index Terms-Three-phase inverter, model predictive control, artificial neural network, UPS systems.

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Performance Estimation of Induction Motor using Artificial Neural Network

Cited by 12 publications

References 9 publications

Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness

Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness

5. Polyphonie als Passion: Johann Sebastian Bach

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

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