Tsukamoto Fuzzy Inference System on Internet of Things-Based for Room Temperature and Humidity Control

Sunardi, Sunardi; Yudhana, Anton; Furizal, None

doi:10.1109/access.2023.3236183

Cited by 9 publications

(9 citation statements)

References 59 publications

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“…Meanwhile, the average humidity in the entire test was at 80.16% (Wet set), meaning that humidity has not been able to be controlled under normal conditions. Even so, these results have given better results than the results in previous studies [28]. In previous studies, the temperature could not be controlled to normal conditions by using a device in the form of a fan in a closed room.…”

Section: B Discussionmentioning

confidence: 85%

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Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.

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Section: B Discussionmentioning

confidence: 85%

“…In addition to some of the literature studies that have been described, this research is also based on previous studies that used fans as a control mechanism [28]. This study shows that a fan that is turned in a closed room does not have too significant an impact in lowering the room temperature.…”

Section: Introductionmentioning

confidence: 92%

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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“…The structure for sensor data storage in the database is outlined in Table 2. The data from the DHT22 devices comprise two main types: temperature and humidity [40]. In our research, we employed 4 DHT22 sensors, which resulted in a total of 8 data points encompassing both temperature and humidity.…”

Section: Data Managementmentioning

confidence: 99%

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

Ady Aryanto,

Maneetham,

Crisnapati

2024

IJEECS

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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.

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“…On the other hand, other researchers have used data-based approaches to estimate and detect collisions. Sharkawy et al [14] reported that they achieved an effective classification for force sensor signals received from a robot using a pattern recognition neural network (PR-NN). The PR-NN method resulted in high-accuracy results.…”

Section: Related Workmentioning

confidence: 99%

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

Mahmoud,

Abdel-Jaber,

Sharkawy

2024

Automation

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In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links “Link 1, Link 2, and Link 3” was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature.

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Tsukamoto Fuzzy Inference System on Internet of Things-Based for Room Temperature and Humidity Control

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 9 publications

References 59 publications

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

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

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

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