Optimization of Neural Network Structure for Smart House Systems

Kazarian, Artem; Teslyuk, Vasyl

doi:10.1109/ukrcon.2019.8879772

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scope of “smart” house systems can be expanded by their usage for commercial purposes through their integration into the infrastructure of different functional-purpose premises (offices, shops, warehouses, government agencies, shopping and entertainment complexes, etc.) [ 16 , 17 , 18 ].…”

Section: Related Workmentioning

confidence: 99%

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Teslyuk

Kazarian

Kryvinska

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Teslyuk

Kazarian

Kryvinska

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…e most commonly used AI algorithms are artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM). In [9][10][11], the ANN was used to analyze sensor data and output control decisions. In [12][13][14], the RNN was used to analyze the behavior of buildings, such as abnormality detection and energy consumption measurement.…”

Section: Introductionmentioning

confidence: 99%

Establishing an AI Model on Data Sensing and Prediction for Smart Home Environment Control Based on LabVIEW

Yao

Huang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this study, the authors aimed to realize a smart home using an AI model that can be integrated with the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) application to realize environment control. The collected input data were outdoor temperature, indoor temperature, humidity, illumination, and indoor person count. The output control decisions included control of air conditioners, dehumidifiers, power curtains, and lights. An artificial neural network was utilized to process the input data for machine learning for the objective of achieving a comfortable environment. In addition, the control decision predictions made by this AI model were analyzed for model loss and model accuracy. This study implemented the model. Specifically, LabVIEW was used to design the sensing component, data display, and control interface, and Python was used to establish the intelligent model. Moreover, by using the web publishing tool built into LabVIEW, remote sensing and control were fulfilled in this implementation.

show abstract

“…In other word, the term smart house refers to provide a communication network around the house that has ability to connect different appliances and allow them to be controlled automatically and remotely either via local Wi-Fi or online connection using internet [2].…”

Section: Introductionmentioning

confidence: 99%

An Approach for controlling Household Electrical devices using Bluetooth communication system via Smartphone devices

Babban

2020

Technium

View full text Add to dashboard Cite

Technologies software applications and hardware devices in general play a big role in everything in our life, which make things to be done quickly and easily comparing to some few years ago. Even though, these rapid developments in technologies comprises electrical devices that we are use in daily manner as well. In this research, an idea is applied to provide a smart house technology which can control any electrical for any domestic device in households, this is done by using Bluetooth networking connection through using mobile smartphone. The main goal of this research is to show that there are possibilities to control any electrical devices in home via controlling them remotely and at any time and at anywhere around the home. So, it provides easy accessibility to switch on or off devices, also it delivers safety use of those electrical devices.

show abstract

Optimization of Neural Network Structure for Smart House Systems

Cited by 5 publications

References 8 publications

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Establishing an AI Model on Data Sensing and Prediction for Smart Home Environment Control Based on LabVIEW

An Approach for controlling Household Electrical devices using Bluetooth communication system via Smartphone devices

Contact Info

Product

Resources

About