A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

A technological solution for analyzing a sequence of ultrasound scan images of an asset and diagnosing a health condition of a section of the asset. The solution includes receiving, by a machine learning platform, an ultrasound scan image of the section of the asset; analyzing, by the machine learning platform, the ultrasound scan image to detect any aberrations in the section; generating, by the machine learning platform, an aberration label for each detected aberration in the section; labeling, by the machine learning platform, the section of the asset with a section condition label; and, rendering, by a display device, the section conditional label. The section condition label can be based on each detected aberration in the section. The section condition label can include at least one of an aberration area ratio, a total number of aberrations, and the aberration label for each detected aberration in the section of the asset.

A technological solution for analyzing a sequence of noisy or incoherent ultrasound scan images of an asset that includes a composite material having internal defects or voids and diagnosing a health condition of a section of the asset. The solution includes receiving, by an input-output interface, an ultrasound scan image of the section of the asset that contains noise or incoherence resulting from signal attenuation due to the composite material in the section of the asset; preprocessing, by a denoising unit, the ultrasound scan image to remove the noise or incoherence and output a denoised ultrasound image; analyzing, by a machine learning platform, the denoised ultrasound scan image to detect any aberrations in the section; evaluating, by the machine learning platform, any detected aberrations; generating, by the machine learning platform, a degree of health of the section of the asset based on any detected aberrations; and generating, by an image rendering unit, an image rendering signal to cause a computer resource asset to display the denoised ultrasound scan image on a display device.

A processing state detecting device for detecting a processing state of a workpiece processed by laser processing includes: a sound collecting unit that measures sound while the workpiece is being processed by laser processing; an installation position evaluating unit that determines whether an installation position of the sound collecting unit needs to be changed, on the basis of the sound measured by the sound collecting unit; and an evaluation result informing unit that provides information on a result of evaluation of the installation position evaluating unit.

A system for detecting machining errors for a laser machining system for machining a workpiece includes: a detection unit for detecting image data and height data of a machined workpiece surface; and a computing unit. The computing unit is designed to generate an input tensor based on the detected image data and height data and to determine an output tensor on the basis of the input tensor using a transfer function. The output tensor contains information on a machining error.

Techniques are provided for detecting wafer defects. Example techniques include exciting a wafer using an acoustic signal to cause the wafer to exhibit vibrations, measuring one or more of linear frequency response metrics or nonlinear frequency responses metrics associated with the vibrations, and identifying any defects in the wafer based at least in part on one or more of the linear frequency response metrics or nonlinear frequency responses metrics. In embodiments, the wafer includes bismuth telluride (Bi.sub.2Te.sub.3).

An aspect includes a vehicle that includes rail inspection sensors configured for capturing transducer data describing the rail, and a processor configured for receiving and processing the transducer data in near-real time to determine whether the captured transducer data identifies a suspected rail flaw. The processing includes inputting the captured transducer data to a machine learning system that has been trained to identify patterns in transducer data that indicate rail flaws. The processing also includes receiving an output from the machine learning system, the output indicating whether the captured transducer data identifies a suspected rail flaw. An alert is transmitted to an operator of the vehicle based at least in part on the output indicating that the captured transducer data identifies a suspected rail flaw. The alert includes a location of the suspected rail flaw and instructs the operator to stop the vehicle and to perform a repair action.

A photoacoustic measurement setup having an infrared radiator that is suitable for radiating broadband light with periodically modulated energy/intensity. The infrared radiator is configured to change an excitation spectra of a radiated broadband light, and a gas volume is heated by the radiated broadband light to generate an acoustic wave within the gas volume. The photoacoustic measurement setup also includes an acoustic sensor, which is suitable for measuring the acoustic wave generated in the gas volume.

A system for measuring a gas concentration, the system including: a first oscillator including a first surface for placement in a sampling location, wherein the first oscillator oscillates at a frequency greater than 20,000 Hz but less than 300,000,000 Hz; a first counter to accumulate a count of oscillations of the first oscillator; and a comparator to calculate a difference between the accumulated counts of the first oscillator and a reference, wherein the difference calculated by the comparator is sampled at a frequency of less than 100 Hz.

The present invention concerns an array processing image reconstruction method comprising: receiving a set of echo waveforms as measurements from the object of interest; defining a measurement model linking an unknown image of the object to the measurements; defining a data fidelity functional using the measurement model; defining a regularisation functional using a trained non-linear mapping, the regularisation functional comprising prior knowledge about the unknown image; defining an optimisation problem involving the data fidelity functional and the regularisation functional for obtaining a first image estimate of the unknown image; and solving the optimisation problem to obtain the first image estimate.