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Related Concept Videos

Temperature Measurement Sites01:14

Temperature Measurement Sites

A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
Thermometers and Temperature Scales01:22

Thermometers and Temperature Scales

Any physical property that depends consistently and reproducibly on temperature can be used as the basis of a thermometer. For example, volume increases with temperature for most substances. This property is the basis for the common alcohol thermometer and the original mercury thermometers. Other properties used to measure temperature include electrical resistance, color, and the emission of infrared radiation.
As many physical properties depend on temperature, the variety of thermometers is...
Temperature and Thermal Equilibrium01:11

Temperature and Thermal Equilibrium

Heat and temperature are essential concepts for everyone every day. The study of heat and temperature is part of an area of physics known as thermodynamics. It is not always easy to distinguish heat and temperature.
The concept of temperature has evolved from the common concepts of hot and cold. The scientific definition of temperature explains more than just our sense of hot and cold. Temperature is operationally defined as the quantity measured with a thermometer. Furthermore, temperature is...
Heating and Cooling Curves02:44

Heating and Cooling Curves

When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
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Thermosensation01:43

Thermosensation

Peripheral thermosensation is the perception of external temperature. A change in temperature (on the surface of the skin and other tissues) is detected by a family of temperature-sensitive ion channels called Transient Receptor Potential, or TRP, receptors. These receptors are located on free nerve endings. Those detecting cold temperatures are closer to the surface of the skin than the nerve endings detecting warmth. These thermoTRP channels, while temperature selective, have relatively...
Temperature Dependent Deformation01:12

Temperature Dependent Deformation

In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added together...

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Related Experiment Videos

The hybrid grey-based models for temperature prediction.

Y P Huang1, T M Yu

  • 1Dept. of Comput. Sci. & Eng., Tatung Inst. of Technol., Taipei.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1997
PubMed
Summary

This study enhances grey-based models for temperature prediction by integrating statistical and fuzzy methods. The hybrid approach significantly improves prediction accuracy for monthly temperatures, offering a more reliable forecasting tool.

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Area of Science:

  • Environmental Science
  • Data Science
  • Statistical Modeling

Background:

  • Accurate temperature prediction is crucial for various applications.
  • Traditional grey models, like GM(1, 1), have limitations in prediction accuracy.
  • Enhancing grey models with statistical and fuzzy techniques can address these limitations.

Purpose of the Study:

  • To improve the prediction capability of the grey model (GM(1, 1)).
  • To explore the effectiveness of integrating standard normal distribution, linear regression, and fuzzy techniques into grey-based models.
  • To evaluate the performance of hybrid grey models for monthly temperature prediction.

Main Methods:

  • Preprocessing data using standard normal distribution for normalization.
  • Integrating linear regression and fuzzy techniques with the grey model (GM(1, 1)).
  • Developing and testing hybrid grey models for temperature forecasting.

Main Results:

  • Hybrid grey models showed reduced prediction errors compared to the standard model.
  • The combination of statistical preprocessing and fuzzy logic with the grey model yielded satisfactory prediction accuracy.
  • Performance was compared against neural network predictions.

Conclusions:

  • Hybrid grey-based models offer enhanced temperature prediction accuracy.
  • Integrating statistical normalization and fuzzy logic is a promising approach for improving grey model forecasting.
  • The proposed hybrid methodologies provide a robust alternative for temperature time-series analysis.