Various embodiments include a method for determining the mixing ratio R of a fluid comprising a mixture of at least two different fluids for a technical process in a device comprising: irradiating an ultrasonic signal with a transmission level along a measuring distance running inside a measuring section; measuring a receiving level of the ultrasonic signal at one end of the measuring distance; determining an ultrasonic attenuation of the ultrasonic signal attenuated by the fluid based at least on the transmission and receiving levels of the ultrasonic signal; measuring a temperature of the fluid flowing through the measuring section; and determining a mixing ratio of the at least two different fluids from the determined ultrasonic attenuation and from the measured fluid temperature.

Because of the increase of the obesity related diseases, it is desirable to be able to detect a fatty liver and quantify the content in fat for the fatty liver. Known methods are biopsy and magnetic resonance imaging. However, biopsy is an invasive method and magnetic resonance imaging is a complicated method to carry out. The inventors propose a new ultrasonic method which is more compliant with a regular control of the content in fat for the fatty liver for a subject. This method notably relies on a smart exploitation of the coherence properties of ultrasound pulses applied to the liver. This method has already been validated on sane subjects as providing accurate measurements, notably for fat content.

Method for determining the geometry of one or more real, examined defects of a metallic, in particular magnetizable object, in particular a pipe or a tank, by means of at least two reference data sets of the object generated on the basis of different, non-destructive measurement methods,

wherein the object is at least partially represented on or by an at least two-dimensional, preferably three-dimensional, object grid, in an EDP unit,
wherein an output defect geometry, in particular on the object grid or an at least two-dimensional defect grid, is generated by inversion of at least parts of the reference data sets, in particular by at least one neural network (NN) trained for this object, a respective prediction data set for the non-destructive measurement methods used in the generation of the reference data sets is calculated on the basis of the output defect geometry by a simulation routine, a comparison of at least parts of the prediction data sets with at least parts of the reference data sets is carried out and, depending on at least one accuracy measure, the method for determining the geometry of the defect is terminated or an iterative adjustment of the output defect geometry to the geometry of the real defect(s) is carried out, as well as methods for determining a load limit (FIG. 1).

To provide an anomalous sound detection training technique by which a feature amount extraction function for detecting anomalous sound can be generated irrespective of whether training data for anomalous signals is available or not. An anomalous sound detection training apparatus includes: a first function updating unit 3 that updates a feature amount extraction function and an feature amount inverse transformation function, which are input, based on an optimization index of a variational autoencoder; an acoustic feature extraction unit 4 that extracts an acoustic feature of normal sound based on training data for normal sound; a normal sound model updating unit 5 that updates a normal sound model by using the acoustic feature that is extracted; a threshold updating unit 6 that obtains a threshold φ.sub.ρ corresponding to a false positive rate ρ, which has a predetermined value, by using the training data for normal sound and the feature amount extraction function that is input; and a second function updating unit 8 that updates the feature amount extraction function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φ.sub.ρ that is obtained, and repeatedly performs processing of each of the above-mentioned units.

A non-destructive testing method for flexural strength of fine ceramic, an apparatus, and a storage medium, including adjusting an uncut intact fine ceramic test sample to an ultrasonic testing position, and fixing the test sample; adjusting an ultrasonic testing instrument, controlling and adjusting the positions of ultrasonic testing probes of the ultrasonic testing instrument until the ultrasonic testing probes, the fine ceramic test sample and the resiling direction are located on the same plane, performing ultrasonic testing on the test sample, and collecting ultrasonic testing data of the test sample; adjusting the position of the fine ceramic test sample until a resilience testing rod and the test sample are located on the same plane and fixed, performing resilience testing on the test sample, and collecting resilience testing data of the test sample; and building a data model, or substituting testing data into the pre-built data model.

The invention concerns a method for the non-destructive mapping of a component, in order to determine an elongation direction of the elongate microstructure of the component at at least one point of interest, characterised in that it comprises at least two successive intensity measurement steps comprising the following steps: a sub-step of rotating a linear transducer into a plurality of angular positions, said linear transducer comprising a plurality of transducer elements, a sub-step of emitting a plurality of elementary ultrasonic beams at each angular position, a sub-step of measuring a plurality of backscattered signals resulting from the backscattering of the elementary ultrasonic beams by said elongate microstructure, the intensity measurement steps making it possible to obtain two series of intensities measured according to two axes of rotation, and in that the method comprises a step of combining the measured series of intensities so as to determine the elongation direction of the microstructure at said at least one point of interest.

A method for testing of a population of monocrystalline components is provided. The method includes obtaining a plurality of component parameters including a crystal angle of each monocrystalline component with respect to a coordinate axis, a three-dimensional geometry, and a material. The method further includes determining a statistical parameter of the crystal angle, and generating a simulation model of the monocrystalline component based on the statistical parameter, the three-dimensional geometry, and the material. The method further includes determining at least one probe parameter based on the simulation model and a predetermined region of interest. The method further includes determining anisotropic delay laws based on the statistical parameter and the probe parameter, and controlling at least one probe based on the anisotropic delay laws to emit ultrasonic waves towards the region of interest in order to test the monocrystalline component for one or more abnormalities.

A compatibility prediction method includes predicting compatibility between a prediction target food and a prediction target drink using: a model for predicting the compatibility between the prediction target food and the prediction target drink, and measurements that are values related to predetermined information obtained when a measuring instrument measures aroma components of the prediction target food and the prediction target drink or calculations calculated based on the measurements.

An inspection device includes a first data storage unit configured to store a first data which is time series according to a state of an inspection object, a second data generation unit configured to generate second data, which is a spectrogram including a first frequency component, a time component, and an amplitude component by performing short-time Fourier transform on the first data, a third data generation unit configured to generate third data including the first frequency component, a second frequency component, and the amplitude component by performing Fourier transform on time-amplitude data for each first frequency component in the second data, respectively, and a determination unit configured to determine the state of the inspection object based on the third data.

The present invention discloses a method for onset time detection of acoustic emission signals based on histogram distance. The method comprises the following steps: acquiring an acoustic emission signal; dividing the signal into two intervals with a sliding point k as the demarcation point; obtaining the relative frequency histograms of two adjacent intervals; obtaining histogram distance of the relative frequency histograms of two adjacent intervals; moving the sliding point k to the next element to obtain two new intervals and generating new histograms of the two new intervals and calculating the histogram distance of two new intervals; searching for the point which gives the maximum value of the histogram distances, and the corresponding time to this point is regarded as the onset time.