An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Predescu, Alexandru; Truică, Ciprian-Octavian; Apostol, Elena-Simona; Mocanu, Mariana; Lupu, Ciprian

doi:10.3390/math8060887

Cited by 17 publications

(5 citation statements)

References 56 publications

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“…As regards the choice of the DL framework, there are numerous open-source frameworks [ 98 , 99 ], such as keras [ 100 ], tensorflow [ 101 ], and pytorch [ 102 ]. In the developing of DL models, the Keras framework offers a high level in build blocks by using particular libraries, such as TensorFlow, dedicated for operations characterized by a low level [ 103 ]. In this context, we have used the Keras DL library with a sequential model applied to the binary classification.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…Stored data on human behavior in relation to water consumption and usage are also important in stored historical data. Based on histories, current and future decisions can be made on the optimal operation and development of these networks [21,22]. Unfortunately, there are few localities where these aspects are developed correctly and coherently [22,24].…”

Section: Water Infrastructure: Current Status In Cluj Napoca and Fact...mentioning

confidence: 99%

“…Based on histories, current and future decisions can be made on the optimal operation and development of these networks [21,22]. Unfortunately, there are few localities where these aspects are developed correctly and coherently [22,24]. The problem of the presence of sensors and monitoring data transfer and storage systems is incipient in most localities in Romania.…”

Section: Water Infrastructure: Current Status In Cluj Napoca and Fact...mentioning

confidence: 99%

Water management issues in the context of music festivals

Giurgea

Safta

Lupu

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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In recent years, festivals, especially music festivals have become important vectors for promoting their host cities. Festivals are complex phenomena with high economic and socio-cultural impact. High CO2 emissions and huge resource consumption are commonly related to the festivals’ negative environmental impact. Ensuring the suitable amount of water for a large-scale event is essential for both the audience experience and the city residents, especially when their access to water is hindered by excessive water usage for the festival. Therefore, an accurate estimation of water consumption during festivals is a prerequisite for rigorous water planning. In the Covid-19 world, due to the worsening crisis of resources and to the increasing pressure exerted by audiences asking for more eco-conscious events, festival organizers are called to get together with other stakeholders and re-examine the water management issues. Water companies have already started to embrace digital solutions. Their further implementation will result in a large amount of environmental data that could contribute to a paradigm shift concerning festival sustainability. The present paper was conceived around this topic, with the goal of increasing the awareness of all the actors involved in festival operational processes. Outcomes of a desk research and a case study focused on the UNTOLD and Electric Castle music festivals are presented.

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“…Another perspective based on machine learning for water control and monitoring is presented in [36] with the aim of creating a multimodel control supervisor for pumping stations that includes increased adaptability to changing operating conditions. Khoi Nguyen et al proposed in [37] a proactive approach to water demand awareness and management.…”

Section: Introductionmentioning

confidence: 99%

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

Arsene

Predescu

Pahonțu

et al. 2022

Water

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Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine learning has been proven for the design of intelligent solutions in this field. This paper presents an approach for monitoring and predicting water consumption from the most important water outlets in a household based on a proposed IoT solution. Data processing pipelines were defined, including K-means clustering and evaluation metrics, extracting consumption events, and training classification methods for predicting consumption sources. Continuous water consumption monitoring offers multiple benefits toward improving decision support by combining modern processing techniques, algorithms, and methods.

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An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Cited by 17 publications

References 56 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Water management issues in the context of music festivals

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

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