Abstract: An object of the present disclosure is to provide a traffic event detection apparatus, traffic event detection system, a method and a non-transitory computer readable medium capable of detecting traffic events correctly. A traffic event detection apparatus includes at least one memory configured to store instructions and at least one processor configured to execute the instructions to: estimate a trajectory of a moving object based on an oscillation signal by using deep neural network, while the oscillation signal is induced by traffic of the moving object; extract a timestamp of the moving object based on the trajectory of the moving object; and extract a part of the oscillation signal corresponding to the timestamp of the moving object.

Abstract: 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.

Abstract: Provided an apparatus including: a signal acquisition part that acquires an oscillation signal from a sensor that detects an oscillation induced in a target object; and an estimation part that obtains a feature value for each frame of the oscillation signal by applying Fourier transform to each frame extracted by a window of a predetermined length to calculate the feature value for the each frame in a frequency domain, and performs Gaussian mixture model-clustering on a time series of the feature values for respective frames to estimate one or more clusters, each of which is modeled with a Gaussian probability distribution best fit to the time series, and detect one or more events by detecting one or more corresponding clusters, a probability density value thereof greater than a predetermined threshold value.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present invention provides an apparatus and method enabling to perform a 2-dimensional trajectory analysis applicable for a three-phase system. A signal analysis apparatus generates a three-row and N-column waveform matrix constituted by three N dimensional row vectors having respectively N samples of first to third phase current values obtained from three-phase current signals measured in a three-phase system, where N is the number of samples of each of the three-phase current signals in one cycle of an AC power supply; applies transformation to the waveform matrix to obtain a two-row and N-column matrix constituted by the first and second N dimensional row vectors; performs normalization in amplitude of the first and second N dimensional row vectors, respectively; selects a grid size based on the sample number N; and maps a two dimensional trajectory made up from the first and second normalized N dimensional row vectors on a grid with the grid size selected.

Abstract: A vehicle weight estimation apparatus 10 includes: a collecting unit 11 that collects vibration information indicating vibration output from a sensor 25 installed to a structure having a vibration-generating structure that generates vibration upon passage of a vehicle 30; an axle response extracting unit 12 that detects, by using the vibration information, axle responses indicating the passage of one or more axles of the vehicle 30 over the vibration-generating structure; an axle index calculating unit 13 that calculates axle indices corresponding to the individual axles based on the axle responses; a converting unit 14 that 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 unit 15 that calculates the weight of the vehicle by totaling the axle weights of the individual axles.

Abstract: 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.

Abstract: An unsupervised disaggregation method includes estimating, from an observation matrix X, by using a latent feature model approach, a binary matrix Z and a latent feature matrix W; calculating a dot product of the matrix W and a D dimensional vector x; repeating, for each row of the matrix W, checking that the dot product value for the row of the matrix W with the vector x is negative to discard the row from the matrix W and a corresponding column from the binary matrix Z; if any discarded row present in the matrix W, updating the matrices W and Z using new matrix Wnew and Znew including respectively un-discarded rows of the matrix W, and un-discarded columns of the matrix Z to iterate from the estimation of the matrices W and Z from the matrix X, until no row discarded in the matrix W.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A waveform disaggregation apparatus includes a storage apparatus that stores, as a model of an operation state of a unit, a first state transition model including a segment in which each state transition occurs along a one directional single path; and an estimation section that receives a composite signal waveform of a plurality of units including a first unit that operates based on the first state transition model and that at least based on the first state transition model, performs estimation of a signal waveform of the first unit from the composite signal waveform to separate the signal waveform therefrom.