Disclosed are fibers comprising one or more branded fibers which exhibit surface markings in a repeated pattern along the length of the branded fibers. The branded fibers can be incorporated into yarns or fiber bands to represent supply chain information of the yarns, fiber bands, and/or articles made from the yards or fiber bands. In a specific example, branded fibers can be incorporated into an acetate tow band The branded fibers can be recovered from a cigarette filter, the repeated pattern decoded, and supply chain information associated with the acetate tow used to make the cigarette filter, such as manufacturer, customer, ship to location, and even the acetate tow bale, can be obtained.

A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

A learning model generation method is a method for generating a learning model for use in machine learning to automatically examine the number of target objects accommodated in a container. The method includes: by a device that generates the learning model, a step of inputting model data which represents a shape of the container and a shape of a target object in an image; a step of creating, by using the model data, a plurality of unit formative assemblies each having a plurality of target objects arranged in a specific array and arranging the unit formative assemblies in a container area corresponding to the container in a specific formation to create shape image data of the container accommodating the target objects at a specific density; and a step of creating training image data for use in establishing the learning model by applying processing of giving a real texture of each of the container and the target object to the shape image data.

A learning model generation method is a method for generating a learning model for use in machine learning to automatically examine the number of target objects accommodated in a container. The method includes: by a device that generates the learning model, a step of inputting model data which represents a shape of the container and a shape of a target object in an image; a step of creating, by using the model data, a plurality of unit formative assemblies each having a plurality of target objects arranged in a specific array and arranging the unit formative assemblies in a container area corresponding to the container in a specific formation to create shape image data of the container accommodating the target objects at a specific density; and a step of creating training image data for use in establishing the learning model by applying processing of giving a real texture of each of the container and the target object to the shape image data.

A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

A guidance processing apparatus (100) includes an information acquisition unit (101) that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit (102) that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

w An estimation method is provided which includes: calculating a plurality of complex transfer functions, based on reception signals respectively received by N reception antenna elements during a predetermined period, the complex transfer functions each representing propagation characteristics between a transmission antenna element and the N reception antenna elements; extracting a variation component corresponding to each of the N reception antenna elements, from the calculated complex transfer functions, the variation component being caused by a living body; calculating a correlation matrix based on the variation component corresponding to each of the N reception antenna elements: calculating eigenvalues of the correlation matrix calculated in the calculating of the correlation matrix; and estimating the number of living bodies in a predetermined method, using the eigenvalues calculated in the calculating of the eigenvalues.