scholarly journals IoT for smart home system

2021 ◽  
Vol 23 (2) ◽  
pp. 733
Author(s):  
Puji Catur Siswipraptini ◽  
Rosida Nur Aziza ◽  
Iriansyah Sangadji ◽  
Indrianto Indrianto ◽  
Riki Ruli A. Siregar ◽  
...  
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Smart Home ◽  
Solar Panel ◽  
Energy Sources ◽  
Neural Network Method ◽  
Smart Appliances ◽  
The Neural Network ◽  
Network Method ◽  
Linear Neural Network

<p>This paper examines the integration of smart home and solar panel system that is controlled and monitored using IoT (internet ofthings). To enable the smart home system to monitor the activity within the house and act according to the current conditions, it is equipped with several sensors, actuators and smart appliances. All of these devices have to be connected to a communication network, so they can communicate and provide services forthe smart home’s in habitants. The smart home system was first introduced to provide comfort and convenience, but later it should also address many other things, e.g. the importance of the efficient use of energy or electricity and hybrid use of energy sources. A solar panel is added to the smart home prototype and its addition is studied. Adaptive linear neural network is implemented in the prototype as an algorithm for predicting decisions based on the current conditions. The construction of the proposed integrated systemis carried out through several procedures, i.e. the implementation of the adaptive linear neural network (ADALINE) as the neural network method, the design of the prototype and the testing process. This prototype integrates functionalities of several household appliances into one application controlled by an Android-based framework.</p>

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

scholarly journals Comparative analysis of the geometrized histogram method and the neural network method for road markings recognition

2021 ◽  
pp. 1-22
Author(s):  
Aleksei Valerievich Podoprosvetov ◽  
Dmitry Anatolevich Anokhin ◽  
Konstantin Ivanovich Kiy ◽  
Igor Aleksandrovich Orlov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Computational Complexity ◽  
Video Sequences ◽  
Histogram Method ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Accuracy Speed

This paper compares two approaches to determining road markings from video sequences, namely, the method of finding the markings using geometrized histograms and the method based on neural networks. An independent open dataset TuSimple is used to conduct a comparative analysis of the algorithms. Since the investigated methods have different architectures, their work is evaluated according to the following metrics: Accuracy, speed (relative FPS), general computational complexity of the algorithm (TFlops).

scholarly journals Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes

2019 ◽  
Vol 125 ◽  
pp. 15006
Author(s):  
Taufik Mawardi Sinaga ◽  
M. Syamsu Rosid ◽  
M. Wahdanadi Haidar
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Frequency ◽  
Seismic Attributes ◽  
Seismic Inversion ◽  
Carbonate Reservoir ◽  
Neural Network Method ◽  
Time Migration ◽  
The Neural Network ◽  
Network Method

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

Sign in / Sign up

Export Citation Format

Share Document