The Use of Wireless Networks and Mobile Applications in the Control of Electric Machines

Muc, Аdаm; Bielecka, Agata; Iwaszkiewicz, Jan

doi:10.3390/app13010532

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Figure 2, the hardware design for monitoring temperature and humidity in the incubator is depicted. The primary device utilized for this purpose is the NodeMCU-12E microcontroller board [31]. This board features a 2.4 GHz Wi-Fi module and employs the ESP-8266 32-bit microcontroller with 11 digital input/output (I/O) pins and 1 analog I/O pin [32], [33].…”

Section: Hardware Designmentioning

confidence: 99%

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

Ady Aryanto,

Maneetham,

Crisnapati

2024

IJEECS

View full text Add to dashboard Cite

This research aims to develop a monitoring system and temperature prediction model in neonatal premature infant incubators by applying the internet of things (IoT) concept and the 1-dimensional convolutional neural network (1D-CNN) method. The system is designed by integrating sensors, actuators, and microcontrollers connected through Wi-Fi network with message queue telemetry transport (MQTT) protocol. Sensor data in the incubator is stored in a database and displayed in real-time on a web application. The data in the database is also used for creating a temperature prediction model in the incubator. Test results indicate that the best model configuration consists of 5 neurons in the first layer, 20 neurons in the second layer, and a dense layer with 100. The evaluation of this model yields a high level of accuracy with an root mean square error (RMSE) of 0.200 °C, MSE of 0.004 °C, mean absolute error (MAE) of 0.152 °C, and mean absolute percentage error (MAPE) of 0.4%. Based on the error values obtained between the predicted and actual values from each evaluation technique in the model, it can be concluded that the range between the real and predicted values is approximately 0.2 °C. Overall, this research contributes to improving the quality of care for premature infants.

show abstract

Section: Hardware Designmentioning

confidence: 99%

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

Ady Aryanto,

Maneetham,

Crisnapati

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…The control and supervision of the autonomous barge is performed by a remote operator who has the ability to view data from all on-board devices [35], and has the ability to establish direct control over the barge in special cases.…”

Section: Proposed Structure Of the Sensor System And Motion Control O...mentioning

confidence: 99%

Modeling of an Autonomous Electric Propulsion Barge for Future Inland Waterway Transport

Łebkowski,

Koznowski

2023

Energies

View full text Add to dashboard Cite

International trade is continuously rising, leading to an increase in the flow of goods passing through transportation hubs, including air and sea. In addition, the aging fleet of inland vessels necessitates renewal through the construction of new vessels, presenting opportunities for the adoption of modern transport technologies. Autonomous barges can transport bulk and containerized cargo between the central port of a specific region and smaller satellite ports, enabling the dispersal of goods over a wider area. Equipping autonomous barges with advanced sensors, such as LIDAR, computer vision systems that operate in visible light and thermal infrared, and incorporating advanced path finding and cooperation algorithms may enable them to operate autonomously, subject only to remote supervision. The purpose of this study is to explore the potential of autonomous electric propulsion barges in inland waterway transport. Given the increasing demand for efficient and sustainable transport solutions as a result of various new policies, which have set new ambitious goals in clean transportation, this study aims to develop a proposition of an electric propulsion hybrid drive inland waterway barge, and compare it to a conventional diesel-powered barge. The methodology involves the creation of a simulation model of an inland waterway class IV electric barge, equipped with advanced sensors and autonomous control systems. The barge’s navigation is managed through a multi-agent system, with evolutionary algorithms determining a safe passage route. This research also utilizes a proprietary networked ship traffic simulator, based on real inland vessel recorded routes, to conduct the autonomous navigation study. The energy consumption of the barge on a route resulting from the ship traffic simulation is then examined using the mathematical model using the OpenModelica package. As a result of the study, the proposed hybrid propulsion system achieved a 16% reduction in fuel consumption and CO2 emissions, while cutting engine operation time by more than 71%. The findings could provide valuable insights into the feasibility and efficiency of autonomous electric propulsion barges, potentially helping future developments in inland waterway transport.

show abstract

A Prototype of IoT Medication Management System for Improved Adherence

Yun,

Chae,

Jun Choi

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The Use of Wireless Networks and Mobile Applications in the Control of Electric Machines

Cited by 4 publications

References 22 publications

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

Modeling of an Autonomous Electric Propulsion Barge for Future Inland Waterway Transport

A Prototype of IoT Medication Management System for Improved Adherence

Contact Info

Product

Resources

About