The present disclosure provides a forehead thermometer that displays different colors according to detected temperatures and a control circuit thereof, which belong to the technical field of forehead thermometers. The forehead thermometer includes a forehead thermometer body, wherein the forehead thermometer body comprises a housing, an indicator light, a liquid crystal display (LCD) screen comprising a backlight board which is adapted to light up in different colors according to different detected temperatures. The LCD screen is disposed on an upper end face of the housing, and the indicator light is disposed on a side of the housing, and wherein the indicator light is a transparent light guide body capable of guiding light, and an outer end face of the indicator light is located outside the housing; and a light guide plate capable of guiding the light emitted from the backlight board to the indicator light is fixedly disposed in the housing.

Disclosed are a method and a system for determining a background water temperature of thermal discharge from operating nuclear power plants based on the remote sensing. The system includes a station selection module, a model construction module, a background water temperature calculation module and a temperature rise calculation module; the general idea: constructing linear regression coefficients between water temperature reference station and water temperature estimation stations before the operation of the nuclear power plant based on historical satellite remote sensing water temperature data, and establishing a linear relationship model to calculate the background water temperature of the water temperature estimation of the operating nuclear power plant. The specific implementation route: the station selection module is connected with the model construction module, the model construction module is connected with the background water temperature calculation module, and the background water temperature calculation module is connected with the temperature rise calculation module.

The present disclosure relates to a method for measuring full-field strain of an ultra-high temperature (UHT) object based on a digital image correlation method. The temperature range is from normal temperature to 3500 degrees Celsius. The method includes the steps of selecting a proper high-temperature-resistant speckle material, tantalum carbide powder, according to the characteristics of the object to be measured. First, polishing a to-be-measured surface of a tungsten test piece to remove an oxide layer, then mixing the tantalum carbide (TaC) powder and absolute ethanol to form a paste according to a mass ratio of 1:2. Making randomly distributed speckles from the mixture on the to-be-measured surface of the test piece which has been processed. In order to improve firmness and stability of the newly made speckles, performing curing treatment to the speckles.

A conformable garment may fit snugly against, and may exert pressure against, skin in a region of a user's body. The garment may house multiple sensors that touch the user's skin. Each sensor may exposed to the user's skin through a hole in an inner surface of the garment. The garment may include elongated channels. Flexible, stretchable wiring may pass through a hollow central region of each channel. This wiring may provide electrical power to the sensors, and may enable wired communication between the sensors and a main hub. Each sensor may include an integrated chip and may be encapsulated in a waterproof material. Each sensor may output electrical signals that encode digital data and that are transmitted, via the wiring, to a main hub housed in the garment. The encapsulated sensors and the wiring may remain in the garment when the garment is washed.

The present disclosure discloses a wearable device to indicate a hazardous condition. The said device comprises one or more thermochromic paint coating layers, each indicative of a colour based on variations in temperature, one or more thermoelectric couples to regulate the temperature of corresponding one or more thermochromic paint coating layers, and a control module. The control module is configured to receive one or more parameters from one or more sensors associated with the one or more thermoelectric couples, determine one of presence and absence of at least one hazardous condition by comparing the one or more parameters with corresponding threshold parameters and configure the one or more thermoelectric couples to regulate temperature of the corresponding one or more thermochromic paint coating layers and to dynamically control indication of the colour, based on one of the presence and absence of the at least one hazardous condition.

A method for identifying corrosion under insulation (CUI) in a structure comprises receiving thermographs from the structure using an infrared camera, applying filters to the thermograph using a first machine learning system, initially determining a CUI classification based on output from the filters, and validating the initial CUI classification by an inspection of the structure. The first machine learning system is trained using results of the validation. Outputs of the first machine learning system and additional structural and environmental data are fed into a second machine learning system that incorporates information from earlier states into current states. The second machine learning system is trained to identify CUI according to changes in the outputs of the first machine learning system and the additional data over time until a second threshold for CUI classification accuracy is reached. CUI is thereafter identified using the first and second machine learning systems in coordination.

A method for identifying corrosion under insulation (CUI) in a structure comprises receiving thermographs from the structure using an infrared camera, applying filters to the thermograph using a first machine learning system, initially determining a CUI classification based on output from the filters, and validating the initial CUI classification by an inspection of the structure. The first machine learning system is trained using results of the validation. Outputs of the first machine learning system and additional structural and environmental data are fed into a second machine learning system that incorporates information from earlier states into current states. The second machine learning system is trained to identify CUI according to changes in the outputs of the first machine learning system and the additional data over time until a second threshold for CUI classification accuracy is reached. CUI is thereafter identified using the first and second machine learning systems in coordination.

A method for identifying corrosion under insulation (CUI) in a structure comprises receiving thermographs from the structure using an infrared camera, applying filters to the thermograph using a first machine learning system, initially determining a CUI classification based on output from the filters, and validating the initial CUI classification by an inspection of the structure. The first machine learning system is trained using results of the validation. Outputs of the first machine learning system and additional structural and environmental data are fed into a second machine learning system that incorporates information from earlier states into current states. The second machine learning system is trained to identify CUI according to changes in the outputs of the first machine learning system and the additional data over time until a second threshold for CUI classification accuracy is reached. CUI is thereafter identified using the first and second machine learning systems in coordination.

A system and method for fast temperature measurement during a cooking process uses a high temperature thermometer with a user settable memory feature. The thermometer includes a processor and a memory for storing recommended safe cooking temperatures for a plurality of food items, for example, different meats. A plurality of keys on the thermometer provide an input mechanism for a user of the thermometer to select a type of food item being cooked and display the recommended safe cooking temperature, or to change the stored cooking temperature for a food item.

A method for identifying corrosion under insulation (CUI) in a structure comprises receiving thermographs from the structure using an infrared camera, applying filters to the thermograph using a first machine learning system, initially determining a CUI classification based on output from the filters, and validating the initial CUI classification by an inspection of the structure. The first machine learning system is trained using results of the validation. Outputs of the first machine learning system and additional structural and environmental data are fed into a second machine learning system that incorporates information from earlier states into current states. The second machine learning system is trained to identify CUI according to changes in the outputs of the first machine learning system and the additional data over time until a second threshold for CUI classification accuracy is reached. CUI is thereafter identified using the first and second machine learning systems in coordination.