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Temperature

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Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts.

Nature communications
This study aims to develop and validate prediction models for the number of all heatstroke cases, and heatstrokes of hospital admission and death cases per city per 12 h, using multiple weather information and a population-based database for heatstro...

Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.

PloS one
INTRODUCTION: Dioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of great medicinal and economic importance due to the presence of the bioactive compound diosgenin. In the present study, response surface methodology (RS...

Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite.

Sensors (Basel, Switzerland)
The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for ...

Nuclear Quantum Effect and Its Temperature Dependence in Liquid Water from Random Phase Approximation via Artificial Neural Network.

The journal of physical chemistry letters
We report structural and dynamical properties of liquid water described by the random phase approximation (RPA) correlation together with the exact exchange energy (EXX) within density functional theory. By utilizing thermostated ring polymer molecul...

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor.

Sensors (Basel, Switzerland)
In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed...

Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.

PloS one
In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search alg...

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process.

Sensors (Basel, Switzerland)
In industries that implement the technology of induction soldering, various sensors, including non-contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution tha...

Forecasting Air Temperature on Edge Devices with Embedded AI.

Sensors (Basel, Switzerland)
With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network...

Evaluation of multilayer perceptron neural networks and adaptive neuro-fuzzy inference systems for the mass transfer modeling of Echium amoenum Fisch. & C. A. Mey.

Journal of the science of food and agriculture
BACKGROUND: Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first-order Takagi-Sugeno-type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed-drying process of Echium amoenum Fisch. & ...

Deep Learning of Binary Solution Phase Behavior of Polystyrene.

ACS macro letters
Predicting binary solution phase behavior of polymers has remained a challenge since the early theory of Flory-Huggins, hindering the processing, synthesis, and design of polymeric materials. Herein, we take a complementary data-driven approach by bu...