When evaluating the quality of photoplethysmography (PPG) signal (52) measured from a patient monitor (e.g., a finger sensor or the like), multiple features of the PPG signal are extracted and analyzed to facilitate assigning a score to the PPG signal or portions (e.g., heartbeats) thereof. Heartbeats in the PPG signal are segmented out using concurrently captured electrocardiograph (ECG) signal (50), and for each heartbeat, a plurality of extracted features are analyzed. If all extracted features satisfy one or more predetermined criteria for each feature, then the heartbeat waveform is compared to a predefined heartbeat template. If the waveform matches the template (e.g., within a predetermined match percentage or the like), then the heartbeat is classified as “clean.” If the heartbeat does not patch the template, or if one or more of the extracted features fails to satisfy its one or more pre-determined criteria, the heartbeat is classified as “noisy.”

A method for predicting a clamp force using a convolutional neural network includes: generating a cepstrum image from a signal processing analysis apparatus; extracting a characteristic image by multiplying a predetermined weight value to pixels of the generated cepstrum image through artificial intelligence learning; extracting, as a representative image, the largest pixel from the extracted characteristic image; synthesizing an image by synthesizing the extracted representative image information; and predicting a clamp force by comparing the synthesized image with a predetermined value.

The present disclosure provides a leakage measurement error compensation method based on cloud-edge collaborative computing, implemented on a communication network formed by interconnection between a leakage current edge monitoring terminal and a power consumption management cloud platform, and including the following steps: monitoring, by the leakage current edge monitoring terminal, leakage current data, and sending the leakage current data to the power consumption management cloud platform; iteratively training, by the power consumption management cloud platform, a pseudo-leakage compensation model by using the received leakage current data, continuously updating pseudo-leakage model parameters, and feeding the pseudo-leakage model parameters back to the leakage current edge monitoring terminal; and processing, by the leakage current edge monitoring terminal, the leakage current data according to the pseudo-leakage compensation model parameters, so as to eliminate the influence of a pseudo-leakage phenomenon in the leakage current data.

Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1 computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.

A method is provided for hyperspectral inversion of a phosphorus content of rubber tree leaves. The method includes: acquiring hyperspectral data of to-be-detected rubber tree leaves; extracting key wavelengths of the rubber tree leaves according to the hyperspectral data and a pre-established wavelength extraction model, where the key wavelengths are related to the phosphorus content of the rubber tree leaves, and the pre-established wavelength extraction model is obtained by learning and training hyperspectral sample data and sample phosphorus content data pairs in a pre-established sample database by adopting a competitive adaptive reweighted sampling (CARS) algorithm and a successive projection algorithm (SPA); and inputting the key wavelengths into a pre-established phosphorus content prediction model to calculate the phosphorus content of the to-be-detected rubber tree leaves. Moreover, the CARS algorithm and the SPA are comprehensively applied to extract the key wavelengths closely related to the phosphorus content of the rubber tree leaves.

A numerical processing method for a nucleic acid mass spectrum, including: step S1, recalibrating a single mass spectrum, for each detection point of a sample, obtaining a plurality of mass spectra corresponding to different positions of the detection point, each mass spectrum being recalibrated by using anchor peaks with an expected mass-to-charge ratio; step S2, synthesizing the mass spectra, where the mass spectra corresponding to the different positions of the detection point are synthesized into a unitary mass spectrum of the detection point; step S3: performing wavelet filtering on the unitary mass spectrum to eliminate high-frequency noise and a baseline through a wavelet-based digital filter; and step S4: extracting a peak feature value, performing peak fitting to obtain a fitted curve of the unitary mass spectrum, and obtaining a peak height, a peak width, a peak area, a mass offset, and a signal-noise ratio based on the fitted curve.

The present invention provides a method and a system for quickly eliminating signal spikes of structural health monitoring in civil engineering, including the following steps: (1) quickly identifying, by using a threshold method, a spike position in a time domain; (2) extracting spike features in a time-frequency domain through wavelet transform for a signal within a set range near the spike position; and (3) eliminating spike feature components in wavelet coefficients, and effectively eliminating a spike through inverse wavelet transform. The method and system combine the advantages of a high calculation speed of a time domain method and high resolution of a time-frequency domain method, which can make an algorithm fast and accurate. In addition to eliminating a spike, the method and system also retain wanted signal components, and have good applicability to structural health monitoring signals in civil engineering with complex time-frequency characteristics and a large amount of data.

A method for diagnosing and predicting operation conditions of large-scale equipment based on feature fusion and conversion, including: collecting a vibration signal of each operating condition of the equipment, and establishing an original vibration acceleration data set of the vibration signal; performing noise reduction on the original vibration acceleration data set, and calculating a time domain parameter; performing EMD on a de-noised vibration acceleration and calculating a frequency domain parameter; constructing a training sample data set through the time domain parameter and the frequency domain parameter; establishing a GBDT model, and inputting the training sample data set into the GBDT model; extracting a leaf node number set from a trained GBDT model; performing one-hot encoding on the leaf node number set to obtain a sparse matrix; and inputting the sparse matrix into a factorization machine to obtain a prediction result.

A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.

A tire-side device is attached to a tire included in a vehicle and applied to a tire apparatus for estimating a condition of a road surface on which the vehicle travels. The tire-side device includes: a vibration detector outputting a detection signal according to a level of vibration of the tire; a controller having a feature quantity extraction device extracting a feature quantity of the detection signal in one rotation of the tire; and a transmitter transmitting road surface data including the feature quantity extracted by the feature quantity extraction device.