Novel suboptimal approaches for hyperparameter tuning of deep neural network [under the shelf of optical communication]

Amirabadi, Mohammad Ali; Kahaei, Mohammad Hossein; Nezamalhosseini, S. Alireza

doi:10.1016/j.phycom.2020.101057

Cited by 47 publications

(16 citation statements)

References 36 publications

(30 reference statements)

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“…The purpose of a deep learning LSTM-autoencoder network is to gather and extract composite information from large time-series datasets using many hidden layers [ 47 ]. However, choosing suitable hyperparameters is a complex task and significantly affects the model’s performance [ 48 ]. For example, more hidden layers or more neurons in a network does not necessarily increase the performance of the network.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Bampoula

Siaterlis

Nikolakis

et al. 2021

Sensors

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Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Bampoula

Siaterlis

Nikolakis

et al. 2021

Sensors

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“…However, they have high computational complexity, especially with a wide range of parameter values that need to be tuned. In this regard, other researchers have explored alternative techniques, such as the suboptimal grid search [64]. In this paper, we used the grid search technique for hyperparameter tuning for LSTM and SVR, since it was necessary to optimize a large number of models with different behaviour.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach

Jossa-Bastidas

Zahia

Fuente-Vidal

et al. 2021

IJERPH

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The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition.

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“…These variables are: The grid search methodology has been considered to determine the internal structure of both denoising approaches, i.e., their hyperparameters. This methodology has been considered as one of the most effective methods to determine the ANNs hyperparameters testing different net configurations [58,59]. Here, it has been considered to determine the number of hidden layers and hidden neurons per layer of the two denoising approaches.…”

Section: Denoising Architecturesmentioning

confidence: 99%

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Pisa

Morell

Vilanova

et al. 2021

Sensors

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Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.

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Novel suboptimal approaches for hyperparameter tuning of deep neural network [under the shelf of optical communication]

Cited by 47 publications

References 36 publications

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

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