Abstract: A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

Abstract: A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

Abstract: An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.

Abstract: A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

Abstract: A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

Abstract: A data processing device includes a state value calculation unit which calculates a state value of which the value increases as much as a state with a high transition probability for each state of the state transition model, an action value calculation unit which calculates an action value, of which the value increases as a transition probability increases for each state of the state transition model and each action that the agent can perform, a target state setting unit which sets a state with great unevenness in the action value among states of the state transition model to a target state that is the target to reach by action performed by the agent, and an action selection unit which selects an action of the agent so as to move toward the target state.

Abstract: There is provided an information processing apparatus including a current waveform acquisition unit which acquires a current waveform from when a predetermined electric appliance is used, a communication unit which transmits the acquired current waveform of the electric appliance to a server apparatus, and receives control information on a character corresponding to the electric appliance from the server apparatus, and a display control unit which performs control of causing a predetermined display unit to display the character based on the received control information on the character.

Abstract: A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

Abstract: A data processing apparatus includes an obtaining unit for obtaining time-series data, an activity model learning unit for learning an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data, a recognition unit for recognizing a current user activity state by using the learned activity model, and a prediction unit for predicting a user activity state after a predetermined time elapses from a current time from the recognized current user activity state, wherein the prediction unit predicts the user activity state as an occurrence probability, and calculates the occurrence probabilities of the respective states on the basis of the state transition probability of the stochastic state transition model to predict the user activity state, while it is presumed that observation probabilities of the respective states at the respective times of the stochastic state transition model are an equal probability.

Abstract: There is provided an information processing apparatus including a current waveform acquisition unit which acquires a current waveform from when a predetermined electric appliance is used, a communication unit which transmits the acquired current waveform of the electric appliance to a server apparatus, and receives control information on a character corresponding to the electric appliance from the server apparatus, and a display control unit which performs control of causing a predetermined display unit to display the character based on the received control information on the character.

Abstract: An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.

Abstract: A data processing device includes a state value calculation unit which calculates a state value of which the value increases as much as a state with a high transition probability for each state of the state transition model, an action value calculation unit which calculates an action value, of which the value increases as a transition probability increases for each state of the state transition model and each action that the agent can perform, a target state setting unit which sets a state with great unevenness in the action value among states of the state transition model to a target state that is the target to reach by action performed by the agent, and an action selection unit which selects an action of the agent so as to move toward the target state.

Abstract: A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

Abstract: A data processing device includes a parameter estimation unit and a structure adjustment unit. The structure adjustment unit notes each state of an HMM as a noted state, obtains, for the noted state, a value corresponding to an eigen value difference which is a difference between a partial eigen value sum and a total eigen value sum, as a target degree value indicating a degree for selecting the noted state as a division target or a mergence target, selects a state having the target degree value larger than a division threshold value, as a division target, and selects a state having the target degree value smaller than a mergence threshold value, as a mergence target.

Abstract: A data processing apparatus includes an obtaining unit configured to obtain time-series data from a wearable sensor, an activity model learning unit configured to learn an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data, a recognition unit configured to recognize a current user activity state by using the activity model of the user obtained by the activity model learning unit, and a prediction unit configured to predict a user activity state after a predetermined time elapses from a current time from the current user activity state recognized by the recognition unit.