Design and Analysis of an Intelligent Flow Transmitter Using Artificial Neural Network

Sinha, Sujata; Mandal, Nirupama

doi:10.1109/lsens.2017.2701409

Cited by 15 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…COMPUTATIONAL STEP Bioinspired computing (BIC) has aroused great interest among researchers as they are powerful methods to tackle a series of engineering and industrial problems, such as the search for optimization concerning cost and energy consumption, improving network performance and efficiency [20], [21]. Some algorithms stand out, such as Genetic algorithms [22], Artificial neural networks [23], Particle swarm optimization [24], Flower pollination algorithm [25], and the BAT algorithm [26] and the Cuckoo Search algorithm [27].…”

Section: A Measurement Campaignsmentioning

confidence: 99%

Adjusting Large-Scale Propagation Models for the Amazon Region Using Bioinspired Algorithms at 1.8 and 2.6 GHz Frequencies

Carvalho

Batalha

Neto

et al. 2021

J. Microw. Optoelectron. Electromagn. Appl.

View full text Add to dashboard Cite

In this work, a new method employs Bioinspired Computational (BIC) optimization from the genetic algorithm, bat algorithm, and flower pollination algorithm. Robust and accurate modeling of the input parameters adjusts the propagation models Stanford University Interim, Electronic Communication Committee, and Floating Interception that consider environments with characteristics specifically of urban regions in the Amazon. The lack of research related to the development of propagation models for Amazonian environments motivated this work. Thus, this application proves the effectiveness of using BIC techniques for modeling the communication channel. Measurement campaigns were carried out in the city of Belem, Brazil, for large-scale channel modeling on the frequencies of 1.8 and 2.6 GHz, belonging to the long-term evolution or fourth-generation mobile communications system (4G). After being adjusted by the optimum values calculated by the BIC techniques used, the models showed better results compared to modeling without optimization. Additionally, it was verified an error reduction of about 80% concerning the metrics root-mean-square error and standard deviation.

show abstract

Section: A Measurement Campaignsmentioning

confidence: 99%

Adjusting Large-Scale Propagation Models for the Amazon Region Using Bioinspired Algorithms at 1.8 and 2.6 GHz Frequencies

Carvalho

Batalha

Neto

et al. 2021

J. Microw. Optoelectron. Electromagn. Appl.

View full text Add to dashboard Cite

show abstract

“…For example, Ramil et al [7] combined a back propagation (BP) neural network with a Kalman filter to model the upstream and downstream data of the water flow to predict the water level downstream of the water flow. Sunita et al [8] conducted a study of water catchments in the UK using an improved artificial neural network. Imrie et al [9] built a complex flood early warning model using neural networks and achieved good prediction results, which provided help for flood prevention.…”

Section: Introductionmentioning

confidence: 99%

Predicting Typhoon Flood in Macau Using Dynamic Gaussian Bayesian Network and Surface Confluence Analysis

Zou,

Chu,

Dai

et al. 2024

Mathematics

View full text Add to dashboard Cite

A typhoon passing through or making landfall in a coastal city may result in seawater intrusion and continuous rainfall, which may cause urban flooding. The urban flood disaster caused by a typhoon is a dynamic process that changes over time, and a dynamic Gaussian Bayesian network (DGBN) is used to model the time series events in this paper. The scene data generated by each typhoon are different, which means that each typhoon has different characteristics. This paper establishes multiple DGBNs based on the historical data of Macau flooding caused by multiple typhoons, and similar analysis is made between the scene data related to the current flooding to be predicted and the scene data of historical flooding. The DGBN most similar to the scene characteristics of the current flooding is selected as the predicting network of the current flooding. According to the topography, the influence of the surface confluence is considered, and the Manning formula analysis method is proposed. The Manning formula is combined with the DGBN to obtain the final prediction model, DGBN-m, which takes into account the effects of time series and non-time-series factors. The flooding data provided by the Macau Meteorological Bureau are used to carry out experiments, and it is proved that the proposed model can predict the flooding depth well in a specific area of Macau under the condition of a small amount of data and that the best predicting accuracy can reach 84%. Finally, generalization analysis is performed to further confirm the validity of the proposed model.

show abstract

“…Their outputs are independent of excitation frequency and temperature. Sinha and Mandal [26] have designed a smart flow transmitter using a rotameter and hall probe sensor as a secondary sensor. In this method, the temperature dependency of a hall probe sensor has been compensated using ANN.…”

Section: Introductionmentioning

confidence: 99%

ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

Lata

Mandal

2020

IET Science, Measurement &Amp; Technology

Self Cite

View full text Add to dashboard Cite

The proposed liquid level transmitter using a force resistive sensor as a secondary sensor is simple, cost-effective, and applicable for all types of liquid. The force resistive sensor is located at the bottom of a liquid measuring tank. The pressure exerted by the liquid height on the measuring tank changes the force resistive sensor's resistance. In the long term application, due to the creeping behaviour of force resistive sensor, the proposed level transmitter has low repeatability, hysteresis, and nonlinearity. In this study, the artificial neural network (ANN) has been used to eliminate the hysteresis and non-linearity present in the proposed liquid level transmitter. The performance of the proposed technique has been experimentally verified. The hysteresis and non-linearity error has been decreased from 7 to 0.89% of full-scale observation (FSO) and 9 to 0.14%, respectively.

show abstract

Design and Analysis of an Intelligent Flow Transmitter Using Artificial Neural Network

Cited by 15 publications

References 15 publications

Adjusting Large-Scale Propagation Models for the Amazon Region Using Bioinspired Algorithms at 1.8 and 2.6 GHz Frequencies

Adjusting Large-Scale Propagation Models for the Amazon Region Using Bioinspired Algorithms at 1.8 and 2.6 GHz Frequencies

Predicting Typhoon Flood in Macau Using Dynamic Gaussian Bayesian Network and Surface Confluence Analysis

ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

Contact Info

Product

Resources

About