Abstract: A noise generation cause identifying method and a noise generation cause identifying device are provided. A response correcting process corrects a sound signal obtained through a sound signal obtaining process based on obtained model information so that a frequency response of the obtained sound signal approaches a frequency response of a learning sound signal. A variable obtaining process obtains a variable output from a map by inputting the corrected sound signal to the map. A cause identifying process identifies a generation cause of a sound picked up by a microphone using the variable obtained through the variable obtaining process.

Abstract: A mapping, which is as a subject of learning, is a learning model that uses a sound signal as an input variable and outputs a variable indicating the cause of a sound in a vehicle. A learning method includes a signal correction process that corrects a sound signal by superimposing a noise signal on the sound signal, and an update process that updates the mapping through machine learning in which the sound signal corrected in the signal correction process serves as training data and a cause of a sound paired with the sound signal serves as teaching data.

Abstract: An information processing device acquires character strings in respective cells that are included in a first table and a second table in document data. When acquiring a character string, the device may identify the characters included in the character string by using a pre-trained model that has been trained in advance through machine learning. The device determines whether the second table corresponds to the first table based on similarities between character strings in cells that are included in the first table and character strings in cells that are included in the second table. When determining that the second table corresponds to the first table, the device identifies a difference between the character strings in the cells that are included in the first table and the character strings in the cells that are included in the second table and correspond to the cells included in the first table.

Abstract: An image processing apparatus includes a control unit configured to execute processing for converting a red green blue (RGB) format in which colors of a first reference image not including liquid leakage are expressed into a hue saturation value (HSV) format, and for extracting a second reference image consisting of S components from the first reference image having colors expressed in the HSV format, processing for converting the RGB format in which colors of a first detection target image are expressed into the HSV format, and extracting a second detection target image consisting of S components from the first detection target image having colors expressed in the HSV format, and processing for generating a third reference image and a third detection target image by smoothing the second reference image and the second detection target image, respectively.

Abstract: A noise generation cause identifying method and a noise generation cause identifying device are provided. A response correcting process corrects a sound signal obtained through a sound signal obtaining process based on obtained model information so that a frequency response of the obtained sound signal approaches a frequency response of a learning sound signal. A variable obtaining process obtains a variable output from a map by inputting the corrected sound signal to the map. A cause identifying process identifies a generation cause of a sound picked up by a microphone using the variable obtained through the variable obtaining process.

Abstract: A mapping, which is as a subject of learning, is a learning model that uses a sound signal as an input variable and outputs a variable indicating the cause of a sound in a vehicle. A learning method includes a signal correction process that corrects a sound signal by superimposing a noise signal on the sound signal, and an update process that updates the mapping through machine learning in which the sound signal corrected in the signal correction process serves as training data and a cause of a sound paired with the sound signal serves as teaching data.