Optimal DNN architecture search using Bayesian Optimization Hyperband for arrhythmia detection

Han, Seungwoo; Eom, Heesang; Kim, Juhyeong; Park, Cheolsoo

doi:10.1109/wptc48563.2020.9295590

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Compared with memory-less grid search and random search methods, Bayesian optimization can find better parameters in fewer iterations. However, when the search space of hyperparameters is complex, Bayesian optimization tends to focus on the most promising region during the exploration process, which may lead to being trapped in local optima [65][66].…”

Section: Research On Models' Hyperparameter Optimizationmentioning

confidence: 99%

CGAOA-AttBiGRU: A novel deep learning framework for forecasting CO 2 emissions

Liu,

Wu,

Shan

et al. 2024

Preprint

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Background: Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on such as Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU), which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. Methods: To address the aforementioned issues, this paper proposed a framework for predicting CO2 emissions, named as CGAOA-AttBiGRU. In this framework, Attentional-Bidirectional Gate Recurrent Unit (AttBiGRU) is a prediction model that uses BiGRU units to extract bidirectional temporal features from the data, and adopts an attention mechanism to adaptively weight the bidirectional temporal features, thereby improving prediction accuracy. CGAOA is an improved Arithmetic Optimization Algorithm (AOA) used to optimize the 5 key hyperparameters of the AttBiGRU. Results: We first validated the optimization performance of the improved CGAOA algorithm on 24 benchmark functions. Then, CGAOA was used to optimize AttBiGRU and compared with 12 optimization algorithms. The results indicate that the AttBiGRU optimized by CGAOA has the best predictive performance. Finally, the optimized AttBiGRU was compared with the BiGRU, LSTM, GRU, ANN, and SVM on predicting CO2 emissions from 4 sectors in China. The experimental results showed that AttBiGRU is significantly better than the comparison models. Conclusions: This paper proposed a novel framework, CGAOA-AttBiGRU, for predicting CO2 emissions. This framework not only includes predictive models, but also optimization method for the models. This study will guide researchers to not only focus on proposing new predictive models, but also on model optimization.

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Section: Research On Models' Hyperparameter Optimizationmentioning

confidence: 99%

CGAOA-AttBiGRU: A novel deep learning framework for forecasting CO 2 emissions

Liu,

Wu,

Shan

et al. 2024

Preprint

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“…The tuning process of hyperparameters with the Bayesian optimization algorithm is graphically demonstrated in Figure 4. The whole process consists of two parts: the training model process and the Bayesian optimization process [40]. The black box in Figure 4 is the model training process, which mainly achieves the training and testing of the proposed deep model.…”

Section: Proposed Residual Dilated Cnn Modelmentioning

confidence: 99%

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM

Wang,

Song,

Lai

2023

Sensors

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The early detection of an inter-turn short circuit (ITSC) fault is extremely critical for permanent magnet synchronous motors (PMSMs) because it can lead to catastrophic consequences. In this study, a model-based transfer learning method is developed for ITSC fault detection. The contribution can be summarized as two points. First of all, a Bayesian-optimized residual dilated CNN model was proposed for the pre-training of the method. The dilated convolution is utilized to extend the receptive domain of the model, the residual architecture is employed to surmount the degradation problems, and the Bayesian optimization method is launched to address the hyperparameters tuning issues. Secondly, a transfer learning framework and strategy are presented to settle the new target domain datasets after the pre-training of the proposed model. Furthermore, motor fault experiments are carried out to validate the effectiveness of the proposed method. Comparison with seven other methods indicates the performance and advantage of the proposed method.

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“…Several new algorithms have been proposed to solve the Neural Architecture Search problem, but many of them require significant computational resources. The approach includes different techniques such as Reinforcement Learning (RL) (Zoph e Le 2016), (Baker et al 2016), (Hsu et al 2018), (Tian et al 2020), Bayesian optimization (Han et al 2020), (Real et al 2019), evolutionary algorithms (Szwarcman, Civitarese e Vellasco 2019), (Awad, Mallik e Hutter 2020), (Ottelander et al 2021) or its variation, the quantum-inspired evolutionary algorithms (QIEA), which show promising results when it comes to faster convergence. The first proposal for QIEA used important principles of quantum computing such as the quantum bit, the linear superposition of states and the quantum rotation gate.…”

Section: List Of Algorithmsmentioning

confidence: 99%

Enhanced Q-Nas for Image Classification

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Optimal DNN architecture search using Bayesian Optimization Hyperband for arrhythmia detection

Cited by 8 publications

References 7 publications

CGAOA-AttBiGRU: A novel deep learning framework for forecasting CO 2 emissions

CGAOA-AttBiGRU: A novel deep learning framework for forecasting CO 2 emissions

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM

Enhanced Q-Nas for Image Classification

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