Abstract: The present disclosure provides a method and a system for acquiring seismic data of a four-component ocean bottom node (OBN). The method is implemented by the system, comprising controlling installations of a plurality of ocean bottom submerged buoys and a plurality of four-component OBN seismic data acquisition instruments and sending positioning signals and timing signals to the plurality of ocean bottom submerged buoys through armored opto-electronic composite cables. The method also includes obtaining real-time and uninterrupted water temperature data, pressure data, density data, and salt saturation data along the armored opto-electronic composite cables from the ocean surface to locations of the plurality of ocean bottom submerged buoys, and calculating real-time and three-dimensional data of waters of a whole measurement work area through interpolation.

Abstract: An object of the present invention is to provide a water level measurement device and a shoreline extraction method each of which is capable of stably measuring the water level. A pixel selection unit (11) selects a pixel of interest (302) from a designated area (301) designated from a captured image (300), and an identification image extraction unit (12) extracts identification images (303), (304) each coming in contact with the pixel of interest (302). The identification unit (13) calculates an identification strength indicating a degree to which an area corresponding to each of the identification images (303), (304) is a water area, on a basis of the result of machine learning related to identification between the water area and a non-water area.

Abstract: A congestion degree estimating unit (12) which estimates, for each image frame, a congestion degree of an area in which each object detected by an object detecting unit (11) is present is provided. An object selecting unit (13) refers to an estimation result of the congestion degree by the congestion degree estimating unit (12) for each image frame, and selects an object present in an area the congestion degree of which is equal to or lower than a first threshold out of one or more objects detected by the object detecting unit (11).

Abstract: An image feature map generating unit (3) generates, on the basis of feature amounts extracted from a plurality of images successively captured by a camera (109), an image feature map which is an estimated distribution of the object likelihood on each of the images. An object detecting unit (4) detects an object on the basis of the image feature map generated by the image feature map generating unit (3).

Abstract: An object of the present invention is to provide a water level measurement device and a shoreline extraction method each of which is capable of stably measuring the water level. A pixel selection unit (11) selects a pixel of interest (302) from a designated area (301) designated from a captured image (300), and an identification image extraction unit (12) extracts identification images (303), (304) each coming in contact with the pixel of interest (302). The identification unit (13) calculates an identification strength indicating a degree to which an area corresponding to each of the identification images (303), (304) is a water area, on a basis of the result of machine learning related to identification between the water area and a non-water area.

Abstract: A data processing unit (101) processes input data using a neural network. A compression controlling unit (102) generates quantization information that defines quantization steps. An encoding unit (103) encodes network configuration information including parameter data which is quantized using the quantization steps determined by the compression controlling unit (102), and the quantization information, to generate compressed data.

Abstract: The learning model building unit (2) builds a learning model for a convolution neural network, by extracting characteristics of abnormality included in a sample image from the convolution neural network using a kernel having a shape corresponding to the shape of the abnormality included in the sample image and by learning the extracted characteristics.

Abstract: A congestion degree estimating unit (12) which estimates, for each image frame, a congestion degree of an area in which each object detected by an object detecting unit (11) is present is provided. An object selecting unit (13) refers to an estimation result of the congestion degree by the congestion degree estimating unit (12) for each image frame, and selects an object present in an area the congestion degree of which is equal to or lower than a first threshold out of one or more objects detected by the object detecting unit (11).

Abstract: An on-board device (100) is installed on a vehicle (1) in which an automated driving system (300) is installed. A request acquisition unit (111) acquires a transfer request requesting transfer of a control right that is a right to control the automated driving system (300), from any passenger among a plurality of passengers riding in the vehicle (1). A control right management unit (112) determines presence or absence of a driving qualification of a transfer requester who is the requesting passenger of the transfer request and decides whether the control right is to be transferred to the transfer requester or not, based on the presence or absence of the driving qualification of the transfer requester.

Abstract: An image feature map generating unit (3) generates, on the basis of feature amounts extracted from a plurality of images successively captured by a camera (109), an image feature map which is an estimated distribution of the object likelihood on each of the images. An object detecting unit (4) detects an object on the basis of the image feature map generated by the image feature map generating unit (3).

Abstract: A number-of-operations information storage unit (113) stores number-of-operations information in which a plurality of conditions of the vehicle is defined and number of acceleration operations and number of deceleration operations performed by a vehicle are described for each condition defined.

Abstract: A passenger state determining unit (12) determines whether a passenger is sleeping, the passenger being a person riding in a vehicle other than a driver. An operation performing unit (20) performs drowsiness prevention operation for preventing the driver from feeling drowsy, when it is determined by the passenger state determining unit (12) that the passenger is sleeping.

Abstract: A number-of-operations information storage unit (113) stores number-of-operations information in which a plurality of conditions of the vehicle is defined and number of acceleration operations and number of deceleration operations performed by a vehicle are described for each condition defined.