Nec Corporation

Murtuza Petladwala

Shigeru Koumoto

Ryota Suzuki

Shingo Takahashi

Shigeru Kasai

Shohei Kinoshita

Eisuke Saneyoshi

Yuko Ohta

Mitsuru Sendoda

A vehicle weight estimation apparatusincludes: a collecting unitthat collects vibration information indicating vibration output from a sensorinstalled to a structure having a vibration-generating structure that generates vibration upon passage of a vehicle; an axle response extracting unitthat detects, by using the vibration information, axle responses indicating the passage of one or more axles of the vehicleover the vibration-generating structure; an axle index calculating unitthat calculates axle indices corresponding to the individual axles based on the axle responses; a converting unitthat converts the axle indices into axle weights by referring to conversion information that is stored in advance and that indicates a relationship between axle indices and axle weights; and a vehicle weight calculating unitthat calculates the weight of the vehicle by totaling the axle weights of the individual axles.

Vehicle weight estimation apparatus, vehicle weight estimation method, and computer readable recording medium

An apparatus includes a signal acquisition part acquires oscillation signals from sensors provided under lanes of a bridge and close to an expansion joint, a signal separation part applies BSS to the oscillation signals to estimate source oscillation signals respectively separated in the plurality of lanes, and adjusts amplitude of the source oscillation signals to output amplitude adjusted oscillation signals, and a vehicle estimation part estimates, from the amplitude adjusted oscillation signal, a response oscillation due to a vehicle passing on the lane of interest to detect and count vehicles passing on the lane.

Vehicle detection apparatus, method and program

Provided a method comprising: obtaining waveform data sets of a periodic electric waveform signal, with a length set to one cycle time; calculating a frequency spectrum for each waveform data set; extracting and separating odd and even frequency harmonics to create odd and even frequency harmonic matrices on which a canonical correlation analysis (CCA) being applied to obtain CCA features; performing linear transformation on the CCA features to obtain linear transformed features; generating a model based on the linear transformed features; performing magnitude quantization of frequency spectrums of waveform data sets to identify normal and anomalous waveform signals.

Anomaly detection apparatus, method and computer-readable medium

Provided an apparatus configured to calculate a periodicity of the time series data using at least one of a periodogram of the time series data and an autocorrelation of the time series data; generate a plurality of subsequences from the time series data, a length of each subsequence set to the periodicity, calculate feature values of the plurality of subsequences, and categorize the plurality of subsequences, based on the features values thereof, into one or more groups; and execute analysis of the time series data, based on the categorization result.

Periodicity analysis apparatus, method and program recording medium

Provided an apparatus configured to calculate a periodicity of time series data, generate a plurality of subsequences, from the time series data, a length of each subsequence set to the periodicity, calculate feature values of the plurality of subsequences; categorize the plurality of subsequences, based on the feature values thereof, into one or more groups, find a periodicity of the subsequences belonging in common to one group, based on an occurrence order of the subsequences belonging in common to the one group and perform missing event detection by identifying the subsequence, occurrence of which is expected according to the periodicity of the subsequences belonging in common to the one group, but not found.

Event monitoring apparatus, method and program recording medium

Provided an apparatus that receives time series data from a data storage unit storing time series of sample data or feature values calculated from the sample data, computes a measure indicating change and repetition characteristics of the time series data, based on sample value distribution thereof, selects a state model structure to be used for model learning and estimation, from state models including a fully connected state model and a one way direction state model, based on the measure and stores the selected state model in a model storage unit.

Model structure selection apparatus, method, disaggregation system and program

A feature transformation apparatus includes at least a combination storage part that stores a combination with respect to a set of features, wherein data is approximately represented as a sum of the combination of the features; and a transformation part that transforms at least the combination so as not to change the sum of the combination of the set of features.

Feature transformation apparatus and method, and recording medium

Provided a method including applying a Fourier Transform to an AC current waveform measured to perform conversion thereof to a frequency domain; adjusting entire phase components of frequency spectra obtained as a result of the Fourier Transform, such that a phase component of an AC power supply frequency becomes zero; and applying an inverse Fourier Transform to the frequency spectra with the entire phase components thereof adjusted to obtain a current waveform in a time domain.

Measurement apparatus and method

An anomaly detection apparatus estimates time series data of a first signal of each appliance by disaggregating an aggregate signal that is a sum of first signals of a plurality of appliances into each first signal of an individual appliance, calculates a residual by subtracting sum of the estimated first signals of the plurality of appliances from the aggregate signal, and obtains value of the second signal at a time point at which the residual indicates presence of anomaly, and checks if there is match of pair of value of the estimated first signal and estimated state of the appliance, with any one of the one or more pairs of value of the first signal and state of the appliance, stored in a table, in association with the value of the second signal at the time point at which the residual indicates presence of anomaly to identify appliance with anomaly.

Growing community of inventors

Tokyo, Japan

Murtuza Petladwala