摘要:
A lighting system (100; 120) comprises at least one lamp (10) and a processing unit (20) for estimating an end-of-life of the at least one lamp (10). The processing unit (20) is configured to receive a lamp burning time during which the at least one lamp is turned on and a forecasted temperature over a selected period of time at a location of the at least one lamp. The processing unit (20) is configured to estimate the end of life of the at least one lamp based on the lamp burning time and the forecasted temperature. By using the forecasted temperature, use of dedicated lamp sensors measuring internal parameters of the lamp usable for the end-of-life estimate may be avoided and the estimate may be simplified.
摘要:
A lighting system (100; 120) comprises at least one lamp (10) and a processing unit (20) for estimating an end-of-life of the at least one lamp (10). The processing unit (20) is configured to receive a lamp burning time during which the at least one lamp is turned on and a forecasted temperature over a selected period of time at a location of the at least one lamp. The processing unit (20) is configured to estimate the end of life of the at least one lamp based on the lamp burning time and the forecasted temperature. By using the forecasted temperature, use of dedicated lamp sensors measuring internal parameters of the lamp usable for the end-of-life estimate may be avoided and the estimate may be simplified.
摘要:
A pollution estimation system (100) for estimating pollution level caused by exhaust gas of motor vehicles is provided. The system comprises an acoustic sensor interface (110) arranged to obtain use-data from an acoustic sensor comprising an audio sample of a motor vehicle sound and a trained exhaust gas model unit (120) arranged to receive as input the audio sample of the use-data and to apply a trained exhaust gas model to the received audio sample to produce an estimated pollution level associated with the received audio sample. The trained exhaust gas model has been obtained by training an exhaust gas model on multiple training items using a machine learning algorithm, the multiple training items comprising multiple audio samples of motor vehicle sounds obtained from one or more acoustic sensors and associated pollution levels.