Abstract: According to one embodiment, an anomaly factor diagnosis apparatus includes an identifier configured to identify a first measurement data item corresponding to a first time point when an anomaly factor occurs in a power generation apparatus, among measurement data items in the power generation apparatus, based on anomaly factor occurrence data identifying the first time point; a distribution generator configured to calculate a first distribution function representing a distribution of a feature value of the first measurement data item; and an evaluator configured to calculate an evaluation value of possibility of occurrence of the anomaly factor in the power generation apparatus, based on the first distribution function, and measurement data items to be tested in the power generation apparatus.

Abstract: An information processing device includes a hardware processor. The processor functions to generate first data to be input to a model used for deducing an effect of a case where a measure is executed with input of observation data. The first data represents the observation data obtained without execution of the measure. The processor functions to receive first parameters affecting the effect. Each first parameter is used in an estimation process of estimating the observation data when the measure is executed. The processor functions to execute the estimation process and generate second data representing the observation data. The processor functions to estimate the effect by using the first/second data. The processor functions to learn the model by using the first/second data. The processor functions to evaluate performance of the learned model by comparing the effect estimated by using the first/second data and the effect estimated by the learned model.

Abstract: According to one embodiment, an anomaly factor diagnosis apparatus includes an identifier configured to identify a first measurement data item corresponding to a first time point when an anomaly factor occurs in a power generation apparatus, among measurement data items in the power generation apparatus, based on anomaly factor occurrence data identifying the first time point; a distribution generator configured to calculate a first distribution function representing a distribution of a feature value of the first measurement data item; and an evaluator configured to calculate an evaluation value of possibility of occurrence of the anomaly factor in the power generation apparatus, based on the first distribution function, and measurement data items to be tested in the power generation apparatus.

Abstract: According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.

Abstract: According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.

Abstract: According to one embodiment, the flow line recognition system includes a first device, a first recording unit, a second device, a second recording unit and a generation unit. The first device detects a position of a moving object in a monitoring area by laser scanning. The second device detects a position of the moving object in a specific area, which is a part of the monitoring area, more accurately than the first device. The generation unit generates flow line information indicative of a path of the moving object moving in the monitoring area, based on the moving object information for the monitoring area recorded in the first recording unit, and the moving object information for the specific area recorded in the second recording unit.

Abstract: A trajectory processing apparatus comprises a trajectory database configured to store a position coordinate of a movable body detected from a camera image in association with data that specifies the camera image from which the movable body is detected, and a camera image database configured to store the camera image. A control section fetches the position coordinate of the movable body and the specifying data for the camera image from which the movable body is detected from the trajectory database. Further, the position coordinate of the movable body fetched from the trajectory database is displayed in a display section as a trajectory of the movable body. Furthermore, the control section acquires from the camera image database the camera image specified by the specifying data fetched from the trajectory database. Moreover, this camera image is displayed in the display section.

Abstract: The invention provides a personal behavior analysis apparatus capable of statistically analyzing a personal behavior. To analyze personal behaviors, a population data file is provided that stores flow-line data for individual persons as a population acquired by tracking the behaviors of persons within a monitored area. After input of a condition for narrowing down the flow-line data as analysis objects is accepted via a condition input section, the population data file is searched based on the condition and flow-line data meeting the condition is acquired. The acquired flow-line data is stored in a subpopulation data file as a subpopulation. A control section of the apparatus calculates statistics values related to personal behavior analysis using the flow-line data stored in the subpopulation data file and displays them on a display section.

Abstract: A trajectory processing apparatus comprises a trajectory database configured to store a position coordinate of a movable body detected from a camera image in association with data that specifies the camera image from which the movable body is detected, and a camera image database configured to store the camera image. A control section fetches the position coordinate of the movable body and the specifying data for the camera image from which the movable body is detected from the trajectory database. Further, the position coordinate of the movable body fetched from the trajectory database is displayed in a display section as a trajectory of the movable body. Furthermore, the control section acquires from the camera image database the camera image specified by the specifying data fetched from the trajectory database. Moreover, this camera image is displayed in the display section.

Abstract: A trajectory processing apparatus comprises a trajectory database configured to store a position coordinate of a movable body detected from a camera image in association with data that specifies the camera image from which the movable body is detected, and a camera image database configured to store the camera image. A control section fetches the position coordinate of the movable body and the specifying data for the camera image from which the movable body is detected from the trajectory database. Further, the position coordinate of the movable body fetched from the trajectory database is displayed in a display section as a trajectory of the movable body. Furthermore, the control section acquires from the camera image database the camera image specified by the specifying data fetched from the trajectory database. Moreover, this camera image is displayed in the display section.

Abstract: According to one embodiment, the flow line recognition system includes a first device, a first recording unit, a second device, a second recording unit and a generation unit. The first device detects a position of a moving object in a monitoring area by laser scanning. The second device detects a position of the moving object in a specific area, which is a part of the monitoring area, more accurately than the first device. The generation unit generates flow line information indicative of a path of the moving object moving in the monitoring area, based on the moving object information for the monitoring area recorded in the first recording unit, and the moving object information for the specific area recorded in the second recording unit.

Abstract: A behavior prediction apparatus includes an input unit inputting sole behaviors of a human and simultaneous occurrence probability of behaviors, a behavior discrimination unit specifying a behavior pattern based on correspondence between behaviors corresponding to simultaneous occurrence probability and actual behaviors, an information generation/recording unit generating behavior history information of the specified behavior pattern, and to generate and record information of each of a behavior transition probability, a behavior time and a behavior occurrence probability based on the behavior history information, a behavior prediction unit obtaining a starting behavior from the behavior occurrence probability, select another behavior pattern in order of behavior occurrence probability, and add the behavior time to the selected behavior pattern to output behavior prediction information, and a social value calculation unit obtaining a social value per unit time using the behavior prediction information, social

Abstract: According to an aspect of the present invention, there is provided with a behavior prediction apparatus including: an area attribute database which stores area attributes of each of a plurality of areas in an activity area of customers; an inter-area attribute database which stores inter-area attributes between two areas; a behavior history database which stores behavior history of the customers in the activity area; a movement frequency database generator which generates a movement frequency database representing a movement frequency between the two areas on the basis of the individual behavior history database; and a movement frequency model estimator which estimates a value of a parameter in a movement frequency model having area attributes and inter-area attributes as its input and a movement frequency as its output.

Abstract: According to an aspect of the present invention, there is provided with a behavior determining apparatus including: an elemental-behavior extracting unit configured to extract, from trajectory data in which positions of a traveling body in an observation area are held at predetermined time intervals, L (L is an integer equal to or larger than 1) elemental behavior quantities among a stopping time, a stop frequency, a walking time, a running time, a direction change frequency, a sum of angle change quantities at the times when the traveling body changes a traveling direction, a sum of absolute values of angle change quantities at the times when the traveling body changes a traveling direction, a gravitational center set vector, and a pre-stop average speed; and a behavior determining unit configured to determine a behavior of the traveling body from the L elemental behavior quantities.

Abstract: The invention provides a personal behavior analysis apparatus capable of statistically analyzing a personal behavior. To analyze personal behaviors, a population data file is provided that stores flow-line data for individual persons as a population acquired by tracking the behaviors of persons within a monitored area. After input of a condition for narrowing down the flow-line data as analysis objects is accepted via a condition input section, the population data file is searched based on the condition and flow-line data meeting the condition is acquired. The acquired flow-line data is stored in a subpopulation data file as a subpopulation. A control section of the apparatus calculates statistics values related to personal behavior analysis using the flow-line data stored in the subpopulation data file and displays them on a display section.

Abstract: A flow line creation section generates flow line data indicative of a trajectory of a customer moving in a monitored area. The generated flow line data is stored in a flow line database. A customer extraction section extracts image data including the customer's face image from a video captured by a camera. The extracted image data is stored in a customer image database. A matching section matches the flow line data stored in the flow line database individually with the image data including the customer's face image corresponding to the flow line data, out of the image data stored in the customer image database. Data indicative of a correlation between the matched flow line data and image data is stored in a matching list database.

Abstract: A control section edits a complete flow line as an entire trajectory in a sales area for each customer, based on data for a flow line generated from a camera image obtained by shooting the sales area. The complete flow line is reproduced as trajectories of customers by a display section. The control section accepts input of information on transaction settlements by the customers. When the information is input through an input section, it is added to the complete flow line data of the customers being reproduced and is stored into a complete flow line database. The control section matches the complete flow line data stored in the complete flow line database with transaction data specified by the information on the transaction settlements added to the complete flow line data.

Abstract: A flow line recognition system comprises a first information recording unit which records a position of a mobile object in a monitoring area together with a time, a second information recording unit which records a position of the mobile object in a specified area together with a time, and a flow line information generation unit which generates flow line information of the mobile object on the basis of the monitoring area mobile object information and the specified area mobile object information.

Abstract: A flow line storage stores flow line data of each customer obtained by tracking a behavior of each customer in a store, by adding time data on an estimated time of payment made by each customer. A transaction data storage stores transaction data for each customer including records of items purchased by each customer, by adding time data on an estimated time of payment made by each customer. A combination unit determines a combination in which a difference between time data is minimum, for each piece of flow line data stored in the flow line storage and each piece of transaction data stored in the transaction data storage. A correlating unit correlates the flow line data and transaction data combined by the combination unit.

Abstract: A customer shopping pattern analysis apparatus includes a correlating information storage section, and a sub-area information storage section. Upon receiving specifications of a particular sub-area as analysis conditions, flow line data of customers who passed through the particular sub-area are extracted based on information specifying the particular sub-area in the sub-area information storage section and based on the flow line data of each of the customers. In addition, transaction data correlated with the flow line data extracted is specified with reference to data in the correlating information storage section. Then, information about correlations between the flow line data extracted and the transaction data is created. From the thus created information, the apparatus analyzes the shopping patterns of the customers in the shop.