Abstract: An anomaly determination method, in a production management system that manages a production line including first, second, and third production devices, includes: obtaining, from among first, second, and third operating status data of the first, second, and third production devices, respectively, at least the first and third operating status data; when it is determined that the second operating status data was not obtained, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device, and generating dummy operating status data including the planned production quantity and the dummy operation timepoint; performing anomaly determination processing on production processes as a whole based on the first and third operating status data and the dummy operating status data; and outputting determination result information representing a result of the anomaly determination processing to display the determination result information on a display device included in

Abstract: An anomaly determination method, in a production management system that manages a production line including first, second, and third production devices, includes: obtaining, from among first, second, and third operating status data of the first, second, and third production devices, respectively, at least the first and third operating status data; when determined that the second operating status data was not obtained, predicting a dummy operation timepoint and a dummy wait time based on operation timepoints and wait times of preceding and subsequent processes and generating dummy operating status data corresponding to the second operating status data including the dummy operation timepoint and wait time; performing anomaly determination processing on production processes as a whole based on the first, third, and dummy operating status data; and outputting determination result information representing a result of the anomaly determination processing on a display device in the production management system.

Abstract: An information processing method includes: calculating a probability density distribution of a work time that is at least part of a setup time, based on production performance data read from storage, the setup time being time taken for a setup work that is performed between lots; determining whether the work time is anormal, based on the probability density distribution; and outputting a result of the determining.

Abstract: A skill evaluation device includes: a first extractor that obtains first information indicating one or more types of first products, machines used in the production, and skill indicators of workers involved in the production; a model generator that generates an estimation model for estimating a standard probability distribution of a skill indicator; an aggregator that calculates a skill indicator by aggregating production records of the specified worker; an indicator estimator that estimates a probability distribution of the skill indicator for the specified worker by providing, as input data to the estimation model, second information indicating: one or more types of second products the specified worker has been involved in production; and one or more machines used by the specified worker; and an evaluator that calculates a deviation degree between the probability distribution estimated by the indicator estimator and the skill indicator calculated by the aggregator.

Abstract: A data analysis device, for analyzing efficiency of an assembly line operation including processes, includes: an input interface configured to acquire log data indicating a history of the processes respectively performed for each time of the assembly line operation; and a control circuit configured to generate analysis information indicating an analysis result of the history indicated by the log data, based on information generated by a probability model to calculate probability distribution for the assembly line operation.

Abstract: A system for generating a forecast including a classifier module for receiving from a user, at least one feature and classifying the at least one feature into a plurality of priority groups based on a user preference. The system further includes an artificial intelligence (AI) forecast module in communication with the classifier module for processing the plurality of priority groups with at least one feature. The AI forecast module derive a learning from classification of the at least one feature into the plurality of priority groups; and generate the forecast based on the learning.

Abstract: A quality estimation device for generating information on quality with which a plurality of unit products are obtained by using a plurality of facilities to pass at least one step, includes: a memory that stores quality control data associating the facilities passed for each of the unit products in the step when the unit products are obtained, with quality for the obtained unit products; and a circuit that controls calculation processing based on the quality control data stored in the memory. The circuit extracts a plurality of pass records from the quality control data, the plurality of pass records each indicating a series of facilities passed by a unit product in the plurality of unit products and quality for the unit product, and generates facility quality information indicating quality with respect to a facility in the plurality of facilities by the calculation processing, based on the extracted pass records.

Abstract: One embodiment can provide a system for detecting anomaly for high-dimensional sensor data associated with one or more machines. During operation, the system can obtain sensor data from a set of sensor associated with one or machines, apply data exploration techniques on the sensor data to automatically process sensor data to identify a subset of feature sensors from the available set of feature sensors, apply an unsupervised machine-learning technique to the identified subset of feature sensors and the target sensor to learn a set of pair-wise univariate models, and determine whether and how an anomaly occurs in the operation of the one or more machines based on the set of pair-wise univariate models.

Abstract: A power transmitting apparatus includes: a converter which converts an input power into an AC power with a predetermined frequency; and a first code modulator which code-modulates at least part of the AC power with a first modulation code to generate a first code-modulated power.

Abstract: A long-term predictor predicts long-term predicted power indicating temporal changes in consumed power of a customer using a long-term prediction model. A short-term predictor predicts short-term predicted power indicating temporal changes in the consumed power of the customer immediately after a current time, using a short-term prediction model, based on temporal changes in the consumed power of the customer immediately before the current time. A controller controls charging and discharging of a power storage apparatus at intervals based on the long-term predicted power and the short-term predicted power. The controller predicts temporal changes in charging power, discharging power, and charged electric energy of the power storage apparatus, and when the charged electric energy is predicted to reaches a lower limit due to discharging of the power storage apparatus, the controller charges power from the power network to the power storage apparatus in advance.

Abstract: A reference capacity determiner determines a reference capacity of each power storage apparatus, under an assumption that power is not transmitted/received among the customer facilities. An actual capacity determiner determines an actual capacity of each power storage apparatus, under an assumption that power is transmitted/received among the customer facilities. A characteristic value determiner determines a first number of charging/discharging cycles, under an assumption that power is not transmitted/received among the customer facilities and each power storage apparatus has the reference capacity.

Abstract: A long-term predictor circuit predicts a long-term predicted power indicating temporal variations in consumed power of a customer, using a long-term prediction model indicating the variations for each moment of clock times. A short-term predictor circuit predicts a short-term predicted power using a short-term prediction model indicating the variations over a time interval before and after a change in a consumed power of each load apparatus, based on the variations over a time interval immediately before a current time, the short-term predicted power indicating the variations over a time interval immediately after the current time. A controller circuit controls charging and discharging of a battery apparatus by setting a charging power or a discharging power based on the long-term predicted power, and controls discharging of the battery apparatus by setting a discharging power based on the short-term predicted power.

Abstract: A reference capacity determiner determines a reference capacity of each power storage apparatus, under an assumption that power is not transmitted/received among the customer facilities. An actual capacity determiner determines an actual capacity of each power storage apparatus, under an assumption that power is transmitted/received among the customer facilities. A characteristic value determiner determines a first number of charging/discharging cycles, under an assumption that power is not transmitted/received among the customer facilities and each power storage apparatus has the reference capacity.

Abstract: One embodiment provides a system for facilitating anomaly detection and characterization. During operation, the system determines, by a computing device, a first set of testing data which includes a plurality of data points, wherein the first set includes a data series for a first variable and one or more second variables. The system identifies anomalies by dividing the first set into a number of groups and performing an inter-quartile range analysis on data in each respective group. The system obtains, from the first set, a second set of testing data which includes a data series from a recent time period occurring before a current time, and which further includes a first data point from the identified anomalies. The system classifies the first data point as a first type of anomaly based on whether a magnitude of a derivative of the second set is greater than a first predetermined threshold.

Abstract: One embodiment can provide a system for detecting anomaly for high-dimensional sensor data associated with one or more machines. During operation, the system can obtain sensor data from a set of sensor associated with one or machines, apply data exploration techniques on the sensor data to automatically process sensor data to identify a subset of feature sensors from the available set of feature sensors, apply an unsupervised machine-learning technique to the identified subset of feature sensors and the target sensor to learn a set of pair-wise univariate models, and determine whether and how an anomaly occurs in the operation of the one or more machines based on the set of pair-wise univariate models.

Abstract: A long-term predictor predicts long-term predicted power indicating temporal changes in consumed power of a customer using a long-term prediction model. A short-term predictor predicts short-term predicted power indicating temporal changes in the consumed power of the customer immediately after a current time, using a short-term prediction model, based on temporal changes in the consumed power of the customer immediately before the current time. A controller controls charging and discharging of a power storage apparatus at intervals based on the long-term predicted power and the short-term predicted power. The controller predicts temporal changes in charging power, discharging power, and charged electric energy of the power storage apparatus, and when the charged electric energy is predicted to reaches a lower limit due to discharging of the power storage apparatus, the controller charges power from the power network to the power storage apparatus in advance.

Abstract: A power controller apparatus predicts demand power of a load apparatus, generated power of a power generator apparatus, and inherent stored energy of a battery apparatus. The power controller apparatus calculates excess electric energy and deficient electric energy achieved when the inherent stored energy reaches higher- and lower-limit stored energies, respectively. The power controller apparatus combines at least one first customer facility and at least one second customer facility having the excess electric energy and the deficient electric energy, respectively, and thus, forms at least one group including the first and second customer facilities. For each group, the power controller apparatus determines transmitting power from the first customer facility to the second customer facility, so that stored energy of each of the battery apparatuses of the first and second customer facilities is equal to or less than a first threshold, and equal to or more than a second threshold.

Abstract: A management apparatus is configured to acquire status information of a virtual infrastructure on which a first management service operates, perform mutual communication with a second management service that has a function identical to a function of the first management service and that operates on the virtual infrastructure, identify a status for an item used for identifying a problem area based on a communication status, the status information of the virtual infrastructure, and a communication status with the second management service, acquire, from the second management service, a status identified for the item based on status information of the virtual infrastructure and a communication status of the mutual communication, identify, based on the identified status and the acquired status for the item, whether the problem area is either the first management service or the second management service, and perform a restoration operation corresponding to the identified problem area.

Abstract: Each one of code modulators modulates first power to generate a code-modulated wave using a modulation code based on a code sequence, and transmits the code-modulated wave to one of code demodulators. Each one of code demodulator receives the code-modulated wave from one of the code modulators, and demodulates the received code-modulated wave to generate second power using a demodulation code based on a code sequence identical to the code sequence of the modulation code used. When power is transmitted between each one of transmitter and receiver pairs, a controller selects and assigns code sequences to the transmitter and receiver pairs, so as to reduce an average of an absolute value of a total current of the code-modulated waves of the transmitter and receiver pairs in a transmission path, as compared to a reference value, the average being averaged over a predetermined time.

Abstract: One embodiment provides a system for facilitating anomaly detection and characterization. During operation, the system determines, by a computing device, a first set of testing data which includes a plurality of data points, wherein the first set includes a data series for a first variable and one or more second variables. The system identifies anomalies by dividing the first set into a number of groups and performing an inter-quartile range analysis on data in each respective group. The system obtains, from the first set, a second set of testing data which includes a data series from a recent time period occurring before a current time, and which further includes a first data point from the identified anomalies. The system classifies the first data point as a first type of anomaly based on whether a magnitude of a derivative of the second set is greater than a first predetermined threshold.