Fault Tolerance Smart Incubator With IoT Control and Alerts

Petkov, Emiliyan; Kalushkov, Teodor; Shipkovenski, Georgi; Radoeva, Rositsa; Valcheva, Donika

doi:10.1109/ismsit56059.2022.9932792

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The first hypotheses is that heat loss factors account for the control system performance testing supporting system decision making [9], [37]. The second hypotheses is heat transfer due to negligible radiation [38]- [40], and the third hypotheses is that the fan curves and the interpolations around the setpoint are accurate enough in various operating conditions resulting in the incubator equation as shown on (1) [41].…”

Section: Methodsmentioning

confidence: 99%

An enhancement to the FLC-based baby incubator system using genetic algorithm

Budiyanto,

Silalahi,

Gunawan

et al. 2023

J.INFOTEL

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This research problem focuses on treating premature babies due to hypothermia so that the baby must be put in an incubator for several days. Conventional intensive care method in premature babies, namely skin-to-skin care method between mother and child. Meanwhile, the latest technological developments, the method is already based on electrical-Internet of Things (IoT) engineering. This research proposes the design of an IoT-based prototype known as a smart incubator. This prototype has been equipped with a real-time monitoring system and system settings using the mamdani fuzzy inference system control method and combined using the Genetic Algorithm (GA) method. The results showed that the ideal temperature range in the smart incubator was 33° C with an accuracy of 99.97% and was in accordance with the fuzzy membership degree in the range of 29° C ≤x≤ 37° C. Furthermore, the ideal relative humidity range in the smart incubator was 60% with an accuracy of 98.60% and was in accordance with the fuzzy membership degree in the range of 59 ≤x≤ 65. Then, the noise range in the smart incubator is 37.9dB to 56.8dB with an accuracy of 96.44% and has been appropriate at the fuzzy membership degree. At a maximum distance of 50cm, it takes 8 seconds for the prototype to detect movement as a safety measure.

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Section: Methodsmentioning

confidence: 99%

An enhancement to the FLC-based baby incubator system using genetic algorithm

Budiyanto,

Silalahi,

Gunawan

et al. 2023

J.INFOTEL

View full text Add to dashboard Cite

show abstract

“…The reader should note that this is a development of another study on smart incubators. Prior publication on development of Smart Egg Incubator (SEI) can be seen at [17].…”

Section: E Expected Benefitsmentioning

confidence: 99%

Fault Tolerance Smart Egg Incubation System with Computer Vision

Petkov¹,

Kalushkov²,

Valcheva³

et al. 2023

IJACSA

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Reliability of incubators is one of their most important specifications. Development of wireless, cloud and computer vision based technologies gives new possibilities for work process control and increasing fault tolerance. Regardless of whether the hatching is in the field of mass production or in the breeding of rare species of birds, detecting a critical situation and sending timely notifications can prevent serious losses.Experience shows that network isolated solutions are not reliable enough and good management requires complex algorithms that are beyond the capabilities of a local, single controller. Even with the duplication of some sensors and actuators, incubators without external connection are high risk due to the fact that their controller is a central point in the architecture and can fail, leaving the farmer with no alert about the accident. The report presents a solution that uses periodic checks from cloud structures on the condition and operability of the incubator. In parallel, a video surveillance system analyzes the internal environment and the condition of hatching chicks. When potential and real risks occur, the system sends notifications to the responsible persons even to his or her wrist. Additionally, the proposed smart egg incubation methodology has been found to reduce the amount of time required for farmers to oversee the incubation process by up to 50%, allowing them to focus on other important tasks while still ensuring optimal hatching conditions for their eggs. Overall, the proposed methodology offers a significant improvement in egg incubation efficiency and reliability, with potential applications in both commercial and personal settings.

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Fault Tolerance Smart Incubator With IoT Control and Alerts

Cited by 2 publications

References 8 publications

An enhancement to the FLC-based baby incubator system using genetic algorithm

An enhancement to the FLC-based baby incubator system using genetic algorithm

Fault Tolerance Smart Egg Incubation System with Computer Vision

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