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.

A gauge device is provided and includes an analog display to signify a measured valued, a motor and worm gear arrangement which is configured to retain a current state of the analog display when de-energized or to change the analog display when energized and a control system. The control system is configured to normally de-energize the motor and worm gear arrangement and to energize the motor and worm gear arrangement only in accordance with a calculated state of the analog display differing from the current state by at least a threshold level.

A gauge device is provided and includes an analog display to signify a measured valued, a motor and worm gear arrangement which is configured to retain a current state of the analog display when de-energized or to change the analog display when energized and a control system. The control system is configured to normally de-energize the motor and worm gear arrangement and to energize the motor and worm gear arrangement only in accordance with a calculated state of the analog display differing from the current state by at least a threshold level.

A device for measuring a weight and a length of a fish, the device comprising: a communication unit configured to transmit information to an external terminal; a fixing unit configured to hang and fix the fish; an elastic unit connected to the fixing unit and configured to generate an elastic force in response to the weight of the fish hanging on the fixing unit; a weight measuring unit configured to measure the elastic force using a weight sensor; and a control unit configured to generate information on the weight of the fish on the basis of the measured elastic force.

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 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 fluorescing marker is used in order to mark (for example) a leaf of a multi-leaf collimator and/or the reference points within the field of view. The markers are illuminated with light tuned to cause the markers to fluoresce at a wavelength different to that of the illuminating light. The fluorescence is then detected by a camera. This method allows the image to be captured by the camera with increased contrast. Accordingly, the present invention provides a multi-leaf collimator for a radiotherapeutic apparatus, comprising at least one leaf having a fluorescent marker. The fluorescent marker will usually emit light of a wavelength longer than the incident light, allowing suitable filters to be provided in order to distinguish the light emitted by the markers. A suitable material for use in the fluorescent markers is ruby. The present invention also provides a radiotherapeutic apparatus comprising a multi-leaf collimator as defined above, and a camera arranged to view the fluorescent markers. A source of illumination for the fluorescent markers is ideally monochromatic, or nearly so. The camera can have a filter arranged to substantially prevent light of the wavelength emitted by the source of illumination from entering the camera, thereby improving the contrast of the image. The radiotherapeutic apparatus can also comprise a source of illumination that is optically co-located with a radiation source, to allow the radiation field that will be emitted to be checked visually by an operator. The co-located source is preferably substantially monochromatic, emitting substantially no light at the wavelength of the fluorescent markers. A filter can then be placed over an output of the radiotherapeutic apparatus, for blocking light of the wavelength of the fluorescent markers and thereby enhancing the contrast of the image that is taken of the fluorescent markers.