Systems, devices, and methods described herein may involve receiving movement data associated with a subject, the movement data collected during repetitive movement of the subject; generating a phase portrait based on the movement data; calculating a phase portrait metric of a characteristic of the phase portrait; and assigning the subject to a movement phenotype based on the phase portrait metric.

A method for maintaining a tube heat exchanger includes: obtaining measurement signals resulting from the passage of a measurement probe in the tubes of the heat exchanger, determining a reference time series corresponding to an average, at each instant, of the measurement signals, synchronising each measurement signal with the reference time series by applying a dynamic time warping, DTW, to said measurement signal and the reference time series, and searching for a potential anomaly by measuring a potential local deviation of a measurement signal with respect to the other measurement signals.

The present disclosure provides a device and methods to control a temperature of an integrated circuit (IC). For example, a device may include a circuit (e.g., an IC), a power monitor, a temperature sensor, and a controller. In some examples, temperature may be estimated based on power measured by a dynamic power monitor (DPM). In some cases, the estimated temperatures may be corrected based on temperature sensed by a temperature sensor on the IC. The power may be measured in shorter time periods and/or more frequent time periods compared to a time periods that the temperature sensor senses temperature. Accordingly, the temperature of an IC may be detected and adjusted more frequently based on the power measurements, and the temperature estimates may be adjusted for accuracy based on sensed temperatures.

Sampling operations enable a computer vision tool to regulate downstream tasks. The sampling operations can indicate which frames of a video sequence should be processed by different downstream tasks. For example, a computer vision tool receives encoded data for a given frame and uses the encoded data to determine inputs for machine learning models in different channels. The computer vision tool provides the inputs to the machine learning models, respectively, and fuses results from the machine learning models. In this way, the computer vision tool determines a set of event indicators for the given frame. Based at least in part on the event indicator(s) for the given frame, the computer vision tool regulates downstream tasks for the given frame (e.g., selectively performing or skipping downstream tasks for the given frame, or otherwise adjusting how and when downstream tasks are performed for the given frame).

One or more embodiments of the present disclosure relate to a system, a device, and a method for motion monitoring. The method includes obtaining an action signal of a user; identifying a first target interval in the action signal, the first target interval corresponding to a target action of the user; extracting, from the first target interval, a second target interval that is capable of reflecting at least one motion cycle of the target action; and evaluating the target action according to the second target interval.

A method for an anomaly detection is provided. The method may include acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density, extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data, and predicting a conditional score for the data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score.

A conditional temporal diffusion model-based method and apparatus for generating a time series of an industrial device, including: acquiring parameter indicator data for the time series of the industrial device; using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series; inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model; denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant; and inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device.

A geothermal management system may receive time-series data for operation of the geothermal energy system. A geothermal management system may calibrate a physical model using the time-series data. A geothermal management system may apply the physical model to a pre-determined comparison parameter to generate a performance indicator. A geothermal management system may identify an operating status of the geothermal energy system based on the performance indicator.

A brain functional connectivity correlation value clustering device for clustering subjects having a prescribed attribute on the basis of brain measurement data obtained from a plurality of facilities, wherein a plurality of MRI devices capture resting state fMRI image data of a healthy cohort and a patient cohort; a computing system 300 performs generation of an identifier as ensemble learning of supervised learning between harmonized component values of correlation matrixes and disease labels of each of the subjects, selects, during the ensemble learning, features for clustering in accordance with importance from the features specified for generating an identifier for a disease label, and performs multiple co-clustering by unsupervised learning.

Methods and systems are disclosed for generating optimal data predictions in time series data signals based on empirically-optimized model selection, noise filtering, and window size selection using machine learning models. For example, the system may receive a first subset of time series data. The system may receive a prediction horizon. The system may generate a feature input based on the first subset of time series data and the prediction horizon. The system may input the feature input into a machine learning model, wherein the machine learning model includes multiple components. The system may receive an output from the machine learning model. The system may generate for display, on a user interface, a prediction for the first subset of time series data at the prediction horizon based on the output.