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Temperature

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Effect of process parameters on the temperature changes during robotic bone drilling.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
In medical surgery, bone drilling is an inevitable procedure. The thermal necrosis in the drilling process can affect post-operative recovery. In this study, the method of drill bit precooling is proposed in bone drilling with robot assisted system. ...

Intelligent temperature modeling in robotic cortical bone milling process based on teaching-learning-based optimization algorithm.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Bone milling is one of the most important and sensitive biomechanical processes in the field of medical engineering. This process is used in orthopedic surgery, dentistry, treatment of fractures, and bone biopsy. The use of automatic numerical contro...

Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in an Agrometeorological Station in Castile and León, Spain.

Sensors (Basel, Switzerland)
This study evaluates the predictive modeling of the daily ambient temperature (maximum, T; average, T; and minimum, T) and its hourly estimation (T, …, T) using artificial neural networks (ANNs) for agricultural applications. The data, 2004-2010, wer...

A Temperature Compensation Method for aSix-Axis Force/Torque Sensor Utilizing Ensemble hWOA-LSSVM Based on Improved Trimmed Bagging.

Sensors (Basel, Switzerland)
The performance of a six-axis force/torque sensor (F/T sensor) severely decreased when working in an extreme environment due to its sensitivity to ambient temperature. This paper puts forward an ensemble temperature compensation method based on the w...

Deep learning based analysis of microstructured materials for thermal radiation control.

Scientific reports
Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospace and space applications. However, due to the vast design space available for microstructures with ...

A Long Short-Term Memory Network for Plasma Diagnosis from Langmuir Probe Data.

Sensors (Basel, Switzerland)
Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the traditional diagnosis theory is affected by many factors, and it is difficult to obtain accurate diagnosis results. In this study, a long short-term memory (LSTM) appr...

Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling.

Environmental science and pollution research international
Machines learning models have recently been proposed for predicting rivers water temperature (T) using only air temperature (T). The proposed models relied on a nonlinear relationship between the T and T and they have proven to be robust modelling to...

Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India.

Environmental science and pollution research international
Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict t...

ECMWF short-term prediction accuracy improvement by deep learning.

Scientific reports
This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main i...

Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models.

Environmental science and pollution research international
Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, mult...