A method for classifying a glass object via acoustic analysis by a classifying apparatus is provided. The method including: receiving, by a processor, sound data of a knock sound generated by applying a knocking operation on the glass object; determining, by the processor, a type of the glass object by performing a knock-sound analysis to the sound data, wherein the type of the glass object includes an organic glass and an inorganic glass; if the type of the glass object is determined as the inorganic glass, receiving, by the processor, echo data of an echo induced by applying an ultrasonic-echo operation on the glass object; and determining, by the processor, a further type of the glass object by performing an echo-decay analysis to the echo data, wherein the further type of the glass object includes a crystal glass, a borosilicate glass and a soda-lime glass.

An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

Methods of detecting pipeline weakening are described herein. The methods include creating a pressure wave in a fluid flowing in a pipeline using an input transducer located at a first position along the pipeline; measuring the pressure wave using an output transducer positioned at a second position along the pipeline that is spaced from the first position, and generating an output signal based on the pressure wave; analyzing the output signal to determine a stiffness of a sidewall of the pipeline positioned between the input transducer and output transducer; and determining if the sidewall includes a defect based on the stiffness of the sidewall, including analyzing a frequency response of the output signal to detect the defect.

Methods of detecting pipeline weakening are described herein. The methods include creating a pressure wave in a fluid flowing in a pipeline using an input transducer located at a first position along the pipeline; measuring the pressure wave using an output transducer positioned at a second position along the pipeline that is spaced from the first position, and generating an output signal based on the pressure wave; analyzing the output signal to determine a stiffness of a sidewall of the pipeline positioned between the input transducer and output transducer; and determining if the sidewall includes a defect based on the stiffness of the sidewall, including analyzing a frequency response of the output signal to detect the defect.

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.

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.

This method comprises: generating, only using the device, a low-frequency signal that makes the structure vibrate, generating a high-frequency signal in the structure, measuring a vibratory signal caused by the generated low-frequency and high-frequency signals at the same time then adaptively re-sampling these measurements to obtain a re-sampled vibratory signal the power spectrum of which comprises: a first frequency range [u.sub.BFmin; u.sub.BFmax] of width larger than 5 Hz that contains 95% of the power of the low-frequency signal, a second frequency range [u.sub.HFmin; u.sub.HFmax] of width systematically smaller than u.sub.BFmin that contains 95% of the power of the low-frequency signal, signaling a defect in the structure if an additional power lobe is detected outside of the ranges [u.sub.BFmin; u.sub.BFmax] and [u.sub.HFmin; u.sub.HFmax].

Disclose herein is a portable platform based on a direct digital synthesizer (DDS) is investigated for the orthogonal SAW sensor, integrating signal synthesis, gain control, phase/amplitude measurement, and data processing in a small, portable electronic system. The disclosed platform allows for simultaneous removal of non-specific binding proteins, and mixing, as well as improved incubation time.

Disclose herein is a portable platform based on a direct digital synthesizer (DDS) is investigated for the orthogonal SAW sensor, integrating signal synthesis, gain control, phase/amplitude measurement, and data processing in a small, portable electronic system. The disclosed platform allows for simultaneous removal of non-specific binding proteins, and mixing, as well as improved incubation time.