The Design of Household Appliances Controller in Smart Home

Mao, Ming; Yuan, Jinpeng; Chen, Zhi Cheng

doi:10.4028/www.scientific.net/amm.249-250.1214

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…In the field of clinical cardiology, one study has shown that deep learning algorithms clearly outperform clinicians in predicting prognosis and future events in patients with pulmonary hypertension [30]. With the development of IoT technology, IoT will gradually become the basis of home appliances [31]. To sum up, many artificial intelligence diagnostic algorithms have been developed to detect diseases, but their prediction accuracy for hypertension is lower.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method

Zhu

Wang

et al. 2021

Sustainability

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Hypertension has become the greatest risk factor for death in elderly populations. As factors influencing cardiovascular disease, indoor environmental parameters pose potential risks for older adults. In this study, elderly residents in Dalian (Liaoning Province, China) urban dwellings were selected as the research subjects, and the environmental parameters of the dwellings’ main activity rooms and the blood pressure parameters of the older adults were measured. Based on the Long Short-Term Memory (LSTM) deep learning algorithm and Bayesian fitting method, a hypertension disease model was established using the long-term environmental parameters to predict the hypertension risk of older adults in their building’s environment. The results showed that temperature, humidity, and some air quality parameters had an impact on blood pressure under single environmental factor, and the comprehensive environmental risks of high systolic blood pressure, high diastolic blood pressure, and high blood pressure were 16.44%, 0%, and 16.44% for the male elderly and 14.11%, 7.14%, and 17.55% for the female elderly, respectively. By comparing the results for the blood pressure measurement and prediction, it can be observed that the risk error of hypertension obtained by the algorithm maintains the variables’ relationship, and the result of the algorithm is reliable in this period. This technology can provide a basis for measuring environmental parameters and will be conducive to the development of an ecological smart building environment.

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Section: Introductionmentioning

confidence: 99%

Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method

Zhu

Wang

et al. 2021

Sustainability

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“…The network layer has been constructed to process and transfer all of the information acquired in the perception layer through the mobile communication system or internet etc. The application layer is able to analyze and process all of the information acquired in the perception layer and the transmission network to make correct control and decision for the intelligent management, application and services [4].…”

Section: Introductionmentioning

confidence: 99%

Research on the Temperature & Humidity Monitoring System in the Key Areas of the Hospital Based on the Internet of Things

Guo¹,

Wang²,

Xiao³

et al. 2016

IJSH

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This paper designs an internet of things-based temperature & humidity monitoring system in the key areas of the hospital to address the problem that the temperature and humidity monitoring systems are independent from each other in the hospital. The establishment of a hospital-level temperature and humidity monitoring platform is able to realize the integrated monitoring and management on such areas as ICU, pharmacy and operation room etc, where both of the temperature and humidity must be monitored. In every temperature and humidity acquisition node, the collected environmental data about the indoor humidity and temperature will be sent over the 433M wireless sensor network to the internet of things (IoT) gateway, through which data will be uploaded via wifi or the wired access network within the hospital to the background server, where in addition to data collection and analysis, various reports will be generated and control commands will be transferred. Then all of the medical staffs can make a real-time inquiry and control on the environmental data by connecting their intelligent terminals (such as cell phone) to the server. Actually when the humidistat thermostat controller is designed according to the DDC principle and the integral-separation PID control algorithm, the control on the central airconditioning units is able to realize the real-time control of constant temperature and humidity. Also a prototype system has been established to conduct the functional verification. The experiment shows that this system is able to run steadily with precise data collection and reliable control.

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“…are used to identify things and collect information. In the network domain, information acquired in the sensing domain can be processed and transferred through the mobile communication system and the internet etc, while in the application domain, information will be analyzed and processed to server for the correct control and decision making for the purpose to achieve the intelligent management, application and service [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Home Healthcare Management System Based on the Improved Internet of Things Architecture

Guo¹,

Dong²,

Zhang³

et al. 2015

IJSH

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The Design of Household Appliances Controller in Smart Home

Cited by 3 publications

References 7 publications

Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method

Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method

Research on the Temperature & Humidity Monitoring System in the Key Areas of the Hospital Based on the Internet of Things

Research on Home Healthcare Management System Based on the Improved Internet of Things Architecture

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