Mitsubishi Electric Corporation

Sony Corporation

Takaaki Nakamura

Kiyoaki Takiguchi

Makoto Imamura

Kazuhiko Aida

Norio Hirai

Osamu Nasu

Masashi Ohkubo

Takahiko Masuzaki

Toshiyuki Kuriyama

Tetsushi Ishida

Takahisa Hirata

A machining dimension prediction apparatus includes a trend acquirer that acquires, for each workpiece, trend information indicating a trend of a state of a machining tool during a machining period of machining performed by the machining tool, a feature calculator that calculates, based on the trend information, a feature using the trend of the state in each of sections included in the machining period, a measurement value acquirer that acquires a measurement value of a dimension of each workpiece after being machined, a section specifier that specifies, as a specific section of the sections, a section including a calculated feature having a greatest degree of relevance to the measurement value, and a predictor that predicts, when a new-target workpiece is machined, a dimension of the new-target workpiece after being machined based on the feature calculated using the trend of the state in the specific section.

Machining dimension prediction apparatus, machining dimension prediction system, machining dimension prediction method, and recording medium

An event waveform extracting unit () extracts an event waveform from time-series data. A co-occurrence rate calculating unit () calculates co-occurrence rates of event waveforms among the time-series data. A grouping unit () classifies the time-series data into groups depending the co-occurrence rates of the event waveforms. An event information generating unit () determines the time at which the periods during which event waveforms occur overlap with each other among the time-series data included in each group, and generates event information identifying an event related to the event waveforms on the basis of the determined time.

Time-series data processing device, time-series data processing system, and time-series data processing method

The data processing device includes: an extraction condition input unit receiving waveform data including a change point of a state of a device, parameter information about the waveform data, and transition information of the device; a similarity calculation unit calculating similarity between time-series data of the device and the waveform data; an operating mode determination unit setting the state of the device on the basis of the transition information of the device; a change point detection unit detecting the change point from the time-series data of the device on the basis of the calculated similarity and the determined state of the device, and setting a start time of a segment which is a subsequence of the time-series data and an end time of the segment; and an information output unit outputting, as segment information, the state of the device, the start time and the end time of the segment.

Data processing device and data processing method

A time-series data processing device () includes: a protruding data extracting unit () for extracting, from time-series data () which is a sequence of values obtained from sequential observation with the elapse of time, protruding data including an ascending leg a value of which continuously rises with respect to time and a descending leg a value of which continuously drops with respect to time; an occurrence pattern defining unit () for defining an occurrence pattern of protruding data in the time-series data (); and an occurrence pattern detecting unit () for detecting, as a segment (), one or more pieces of protruding data matching the occurrence pattern defined by the occurrence pattern defining unit (), from among a set of pieces of protruding data extracted by the protruding data extracting unit ().

Time-series data processing device

A principal component calculating unit () for calculating a principal component of a plurality of sensor data collected from a device, and a physical quantity for failure determination calculating unit () for calculating a physical quantity to be used for failure determination on the basis of the plurality of sensor data are provided. Further, an operation state classification unit () for generating classification, for each operation state, of the sensor data collected from the device, using the principal component calculated by the principal component calculating unit () and the physical quantity calculated by the physical quantity for failure determination calculating unit () is provided.

Operation state classification apparatus

An information processing apparatus includes a data acquisition unit to acquire input data that is time-series data; a sampling error upper limit calculation unit to calculate, when similar learning subsequences selected from among a plurality of learning subsequences extracted from learning data that is time-series data are integrated to generate a sample subsequence. The information processing apparatus further includes a sampling error upper limit using data taken from the input data, the sampling error upper limit being an upper limit on dissimilarity between the learning subsequences to be integrated; and a sample subsequence generation unit to generate the sample subsequence from the learning data using the sampling error upper limit.

Information processing apparatus and information processing method

An initial segment set generation unit () generates an initial segment set (F) from training time-series data (S). An initial segment set sorting unit () sorts initial segments included in the initial segment set by the order of feature quantities. A sample segment set generation unit () generates a sample segment set (E) from the initial segment set. A sample segment set sorting unit () sorts sample segments included in the sample segment set by the order of the feature quantities. A search result distance calculation unit () calculates a search result distance (Z) for each of test segments by using the sample segment set.

Time-series data search device and computer readable medium

An apparatus and a method for personal identification include an image pickup section for picking up an image of a blood vessel pattern of a wrist, a storage unit for storing in advance an image of a blood vessel pattern of a certain person as a registered image, and a control section (CPU) for the computation of degrees of similarity according to data differences between the image of the blood vessel pattern picked up by the image pickup section and the registered image to determine identification according to the relationship between the computed degrees of similarity and a threshold, thereby eliminating the need to carry out processing by a characteristic extraction section or the like.

Apparatus, method, and program for personal identification

A walking gait detection apparatus includes a microphone for picking up low-frequency-band sounds that are transmitted through the body of a pedestrian while walking and an analyzer for performing analysis. Accordingly, the gait of the pedestrian is detected. It is also possible to distinguish the gait pattern on the basis of the stance-phase time of a foot sole and the signal intensity, for example. The gait detection apparatus can accurately estimate the stride length on the basis of a detected gait cycle, the height of the pedestrian, and signals detected during walking. On the basis of low-frequency sounds picked up by the microphone while the pedestrian is walking, the pedestrian can be identified.

Gait detection system, gait detection apparatus, device, and gait detection method

A gait detection apparatus includes a microphone for picking up low-frequency-band sounds which are transmitted through the body of a pedestrian while walking and an analyzer for performing analysis. Accordingly, the gait of the pedestrian is detected. It is also possible to distinguish the gait pattern on the basis of the stance-phase time of a foot sole, the signal intensity, etc. The gait detection apparatus can accurately estimate the step length on the basis of a detected gait cycle, the height of the pedestrian, and signals detected during walking. On the basis of low-frequency-region sounds picked up by the microphone while the pedestrian is walking, the pedestrian can be identified.

In the case that a telephone communication line of a portable type wireless telephone terminal is formed between a data transmission terminal apparatus and a data acquisition apparatus with respect to a data communication counter party, the data transmission terminal apparatus sends out data as a dial signal via the wireless telephone terminal to the telephone communication line. As a result, the data is received in the form of the DTMF signal to the data acquisition apparatus.

Data transmission terminal apparatus, data communicating method, and data communication system

A GPS receiver communicates with a management center through a portable telephone terminal. The management center is connected to a telephone unit and a facsimile unit in a house through a telephone line. When the user presses a predetermined button with the telephone unit, a request for location information of the receiver is transmitted to the management center. The management center transmits the location information request as a DTMF signal to the receiver. The receiver measures the location thereof. The measured result and time information of a navigation message thereof are transmitted as a DTMF signal. When the management center determines that the message is old corresponding to the time information, the management center compensates the measured result. The management center transmits the location information to the telephone unit. The management center transmits image data with map information to the facsimile unit.

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Tokyo, Japan

Takaaki Nakamura