10 results about "Data series" patented technology

A plunger lift management system may measure a casing pressure data series of an annular space between a casing and a tubing. A plunger lift management system may measure a tubing pressure data series of the tubing. A plunger lift management system may generate a pressure differential data series based on the casing pressure data series and the tubing pressure data series. A plunger lift management system may, based on the pressure differential data series, a weight of a plunger, and a length of the tubing, identifying a weight of liquid lifted by the plunger.

A method (200) for assessing the condition of a road (120) comprises steps of periodically acquiring (205) vertical velocity data on board a vehicle (105) traveling on the road (120); generating (215) several overlapping data series (405-450) of predetermined length from the acquired data; recreating (220) each data series (405-450) using a capsule autoencoder (138); determining (225) a mean absolute error between each data series (405-450) and its corresponding newly created data series (405-450); and detecting (230) an anomaly on the road (120) when the mean absolute error exceeds a predetermined threshold.

The invention provides a design method of a curve groove of a zoom cam lever and a zoom mechanism, and the method comprises the steps: obtaining data columns of the moving distances of a zoom lens group and a compensation lens group through optical software simulation, arranging the data columns, determining data and data serial numbers of a circle of rotation of the zoom cam lever, and calculating the curve groove of the zoom cam lever; and by adjusting the radius of the zoom cam lever or calculating the rotation angle of the corresponding point, it is ensured that all the pressure angles in the data column are not larger than the allowable pressure angle. According to the width of the curve groove and the minimum wall thickness of the curve groove, the minimum moving distance of one circle of curve winding is limited, so that the inner wall of the curve groove is smooth in the whole process and not prone to deformation, it is guaranteed that excellent imaging quality is obtained, smooth movement of the zoom guide pin can be guaranteed, a zoom mechanism is not prone to being blocked or even stuck, jumping of an optical axis is reduced, and the service life of the zoom mechanism is prolonged. And the driving torque of the motor for driving the zoom cam rod to rotate is reduced.

This invention discloses a method for assessing the safety status of a material yard based on multi-source sensing data, belonging to the field of data processing technology. The method includes acquiring multi-source sensing data from various location units within a continuous sampling time and constructing a data time series; establishing adjacency relationships between location units based on spatial coordinates; correcting the data based on the differences between adjacent time points and the spatial differences between adjacent location units to obtain a target data series; calculating spatial difference indices for each location unit under various data types based on the target data series; determining weight values ​​based on the magnitude of the spatial difference index differences; weighted fusion of the spatial difference indices to obtain the overall spatial status value of the material yard; and performing spatial difference concentration analysis based on this spatial status value to determine the safety status level. This invention, through time and spatial consistency correction and multi-source fusion analysis, achieves a quantitative assessment and graded output of the overall safety status of the material yard, improving the ability to identify local spatial anomalies and the overall risk characterization capability.

The application discloses a bridge structure parameter correction model establishment and prediction method based on time domain data, which comprises the following steps: selecting at least one type of to-be-corrected parameter of a bridge structure; taking each type of to-be-corrected parameter as a training sample; obtaining a series of acceleration time history data of each type of to-be-corrected parameter corresponding to each measuring point position and taking the series as a sampling point; compressing and converting the series of acceleration time history data into accumulated intensity values and inputting data, so as to construct a training sample set; establishing a blind Kriging model; inputting the training sample set into the blind Kriging model; determining important variables by using a Bayesian variable selection algorithm; starting iteration from 0 by setting a parameter k in the blind Kriging model; finally finding an optimal value of k; finding other corresponding parameters of the blind Kriging model from the optimal value; substituting the parameters into the blind Kriging model; and obtaining a final blind Kriging model. The bridge structure parameter can be corrected based on the time domain data.

Method for checking and, if necessary, improving the setting of at least one parameter of a control unit monitoring function of a vehicle diagnostic system, taking into account at least one robustness indicator, in particular taking into account four robustness indicators, - where at least one robustness parameter R z [n] is calculated according to the following equation: R z [ n ] = 1 r max_pos / max_neg ⋅ N 0 ∑ i = n n+N 0 ri - where r max_pos / max_neg the largest positive or largest negative distance value when monitoring against a positive or a negative deviation is, - where N0 is the number of data points in the data series that represents the minimum time, in particular the debounce time, for error detection, - where n is an element of a data series of consecutive feature values, - where r ithe distance of a characteristic value from a positive or negative error threshold, determined on the basis of the raw distance, - where the setting of at least one parameter is retained if the calculated value R z or the calculated values ​​R z above a minimum robustness index R z,min lies / lie - and where the setting of at least one parameter is adjusted when the calculated value R z or the calculated values ​​R z below the minimum robustness index R z,min lies / lie or the calculated value R z or the calculated values ​​R z the minimum robustness index R z,min corresponds / correspond to, - wherein, in order to check the setting of at least one parameter, the results of the control unit monitoring function are calculated by inputting an input data set of a functioning system and an input data set of a non-functioning system into the control unit monitoring function, characterized in that - that, to check the setting of at least one parameter, the results determined by the control unit monitoring function are compared with certain values ​​of a characteristic of a functioning system, and, to monitor against a positive error threshold exceedance, a value R is calculated. z,max_threshold,False Fail to check whether a functioning system is incorrectly identified as non-functional, - that, in order to verify the setting of at least one parameter, the results obtained by the control unit monitoring function are compared with certain values ​​of a characteristic of a functioning system for monitoring against a negative error threshold undercutting by calculating a value R z,min_threshold,False Fail to check whether a functioning system is incorrectly identified as non-functional, - that, in order to verify the setting of at least one parameter, the results determined by the control unit monitoring function are compared with certain values ​​of a feature of a non-functional system for monitoring against a positive error threshold undercutting by calculating a value R z,max_threshold,False Pass to check whether a non-functional system is incorrectly identified as functional, - and that, to verify the setting of at least one parameter, the results obtained by the control unit monitoring function are compared with certain values ​​of a feature of a non-functional system for monitoring against a negative error threshold exceedance by calculating a value R z,min_threshold,False Pass It should be checked whether a non-functional system is incorrectly identified as functional.

This document describes a method (400) and a system (101) for training a generative model to model operational profiles. The method (400) comprises training an initial model based on a plurality of input data samples and processing an input training data sample from a plurality of input training data samples to generate a data series profile corresponding to the input training data sample. Furthermore, the method comprises processing the data series profile using one or more pairs of models to generate profiles of one or more parameters. Finally, the method comprises determining the initial loss value by comparing the generated profiles of one or more parameters with the corresponding profiles of one or more ideal parameters.Finally, the procedure involves training the generative model by repeating the above steps for the remaining input training data samples until the determined first loss value is less than a first threshold loss value, in order to train the second, third, and fourth models.

The present disclosure relates to a computer-implemented method suitable for identifying or classifying microbes, the method for use with a system comprising a cartridge for receiving a sample, the cartridge comprising an array of wells, each well comprising an electrochemical transducer; wherein the method comprises: interrogating each of the electrochemical transducers, wherein interrogation of the electrochemical transducers comprises, for each well, generating a plurality of data series by repeatedly measuring a respective plurality of dependent variables; and determining, based on the plurality of data series from each well, an identification or classification of a microbe in each well.

Introduced here are diabetes management platforms able to examine glucose measurements associated with a subject to discover patterns indicative of similar behaviors / circumstances. More specifically, a diabetes management platform can acquire multiple data series generated by a glucose monitoring device that monitors the blood glucose level of an individual over multiple time intervals. The diabetes management platform can then apply a similarity algorithm to produce a distance measure (also referred to as a “similarity measure”) for each data series. For example, the diabetes management platform may, for each data series, perform dynamic time warping to produce a distance measure with respect to each of the other data series. The diabetes management platform can identify patterns indicative of similar behaviors / circumstances based on these distance measures.

The utility model provides a data collection folder for engineering management, which belongs to the field of collection folders and comprises a middle framework, a double-partition storage component is arranged on the middle framework, and a data query component is arranged on the middle framework. According to the utility model, through the arrangement of the double-partition storage assembly, when in use, detailed files are independently placed, so that the files are not confused, the placement of the files is more standard and more regular, the files in the small storage folder are convenient to put in and take out through a drawing groove, and the independent placement is safer and more stable; through the arranged data query assembly, numbers are marked on the theme block and the subdivision plate during use, data series represented by the marked numbers on the theme block are clarified through recording paper, and then data types represented by the marked numbers on the subdivision plate are clarified continuously. Therefore, a directory effect is formed, so that a user can quickly find required data, and the working efficiency is improved.