8 results about "Permissible Value" patented technology

A computer-implemented method for correcting initial NOTAM-messages using a trained machine-learning algorithm, comprising: inputting a plurality of initial NOTAM-messages from a plurality of data sources, identifying, from the E-field and using the trained machine-learning algorithm, a plurality of permissible values for the traffic, the purpose, scope, first NOTAM-code-component, and second NOTAM-code-component; selecting a preferred value for one or more of the traffic, purpose, scope, first NOTAM-code-component, and second NOTAM-code-component, wherein selecting of the preferred value is done using a set of probabilities; and setting the selected value for the traffic, purpose, scope, first NOTAM-code-component, and second NOTAM-code-component as corrected values for the traffic, purpose, scope, first NOTAM-code-component, and second NOTAM-code-component to produce corrected NOTAM-message. The initial NOTAM-messages comprises at least a Q-field and an E-field. The E-field comprises a string describing a content of the initial NOTAM-messages.

A method comprises storing an ontology for a data store, wherein the ontology comprises a plurality of data object types and a plurality of object property types. The method also comprises storing a validator for a particular object property type of the plurality of object property types, wherein the validator specifies a permitted value for the particular object property type, and storing a parser definition corresponding to the particular object property type, wherein the parser definition specifying a permitted format for the particular object property type. In addition, the method comprises receiving user input that changes a particular data object type of the plurality of data object types, including changing the particular object property type. Finally, the method comprises updating the validator for the particular object property type, and updating the parser definition corresponding to the particular object property type.

The invention relates to the technical field of water conservancy and hydropower engineering safety monitoring, and discloses a safety monitoring data abnormal value identification method based on a sliding window and morphological logic, and the method comprises the steps: processing an original data sequence through a window sliding at a fixed odd length, calculating a median and a median absolute deviation, and estimating a standard deviation; generating a dynamic upper and lower threshold sequence; comparing with a dead zone tolerance threshold value, converting into a logic vector, identifying a connected region with continuous truth values, and secondarily marking all data points in the region with the length exceeding the maximum allowable value as abnormal to obtain an abnormal value logic sequence; and performing linear interpolation processing on the data points marked as abnormal values in the abnormal value logic sequence to obtain a cleaned data sequence without abnormal values. According to the method, more than 95% of invalid data can be efficiently eliminated while the integrity of real signals is reserved, a high-reliability data basis is provided for data analysis and structural safety evaluation, and the method has important engineering application value.

Sensor device (1) with a microcontroller unit (20) and a sensor (7) including a transducer (2;60), which is coupleable to a device (19) and generates a signal (sX(t)) indicative of a physical quantity, and a processing circuit (3) including: a conversion stage (16) which generates samples (X[n]) of the physical quantity; a data generation stage (12,40,42,44) which generates data vectors (DV[k]) as a function of the samples (X[n]), each data vector (DV[k]) being formed by programmable quantity values; and a decision stage (12,46). The microcontroller unit (20) programs the decision stage (12,46) so that it classifies the data vectors (DV[k]) by executing a decision tree having a structure and thresholds. In a configuration mode, the microcontroller unit (20) programs the data generation stage (12,40,42,44); in a calibration mode, the microcontroller unit (20) acquires a corresponding set of data vectors (DV[k]), determines, for each programmable quantity, a corresponding range of admissible values and programs the thresholds as a function of the ranges of admissible values; in a detection mode, the decision stage (12,46) classifies the data vectors (DV[k]), by executing the decision tree on the basis of the programmed thresholds.

The embodiments concern a method comprising determining at least one parameter value, wherein the at least one parameter value comprises one or more of following: a unit type, a scalability layer identifier, and a temporal sublayer identifier; deriving a one-byte header for a unit comprised in a bitstream, wherein the at least one parameter value is represented by at least one syntax element and pre-defined information; if the one-byte header has an allowed value among one or more allowed values, the method comprises determining a header for the unit to be the one-byte header, wherein the header comprises a first byte, and to include the header in the bitstream The embodiments also concern technical equipment for implementing the method.

The invention relates to the technical field of thread gauge traceability management and control, in particular to a thread gauge code traceability management and control method and system, and the method comprises the steps: designing and printing a unique code for each thread gauge; using data are recorded through the codes, frequency, load and environment conversion coefficients are calculated, and the abrasion loss is calculated according to accumulation. By comparing the abrasion loss with the maximum allowable value, the relative abrasion degree and the number of remaining use times can be obtained, and therefore the thread gauge needing to be inspected is determined. Meanwhile, the system monitors the reject ratio, the abrupt change degree and the number of times of continuous disqualification, carries out early warning and determines an inspection object. And after inspection, the basic wear rate constant is corrected according to the difference between the verification result and the predicted value, and the next calibration period is dynamically adjusted. The corrected constant and the new period are fed back to the rule base, prediction and decision of the next period are continuously optimized, and closed-loop dynamic optimization is formed. According to the invention, the accuracy of thread gauge code traceability control is improved.

Disclosed are a point cloud encoding and decoding method, and a decoder. The method includes: acquiring geometric information and attribute information of an input point cloud; determining a maximum allowable value of a sampling period when the input point cloud is subjected to level-of-detail (LOD) division; determining a preset value of the sampling period on the basis of the maximum allowable value of the sampling period; processing the input point cloud according to the preset value of the sampling period and the geometric information, so as to obtain at least one refinement layer and at least one detail layer; and encoding the attribute information by using the at least one refinement layer and the at least one detail layer, so as to generate a code stream, and writing the preset value of the sampling period into the code stream.

Make effective use of the power storage device. [Solution] The control system 30 includes an information acquisition unit 51 that acquires the health H(t) of the storage device at a target time within the operation period, and an arithmetic processing unit 52 that calculates a second value in accordance with the health H(t) of the storage device so that the capacity degradation of the storage device expected at the end of the operation period does not exceed an allowable value when the number of operation cycles of the storage device increases from a first value to a second value at the target time.