A data system and processes that generate structural characteristics and analytics such as various elevations and heights of a structure. Information produced in on-demand fashion and can be certified. It combines human intelligence with machine intelligence to achieve optimal results. Information produced by the system are significant for many purposes, including Flood Risk Assessment, Flood Insurance Rating, and Flood Impacting Threshold (FIT) determination. The system further generates various derivatives such as Flood Impacting Threshold Score (FITS), precise Flood Risk Ratings (e.g. PrecisionRating,) building conditions and valuation. The system generates such information on-demand by computer vision, artificial intelligence, sensors, image analysis, statistical analysis, and mathematical analysis through a Graphic User Interface (GUI) or machine-to-machine, and Application Programming Interface (API.)

The present invention provides an improved Smith predictive controller-based aero-engine H∞ algorithm, and belongs to the technical field of aero-engine control and simulation. The present invention first establishes a reasonable small deviation linear model for an aero-engine nonlinear model, and selects the state space model data of a certain operating condition as the controlled object for controller design; selects appropriate performance index weighting function parameters, solves the H.sub.∞ output feedback controller, and adjusts the parameters to basically meet the control requirements; and designs a Smith predictive compensator with an improved structure based on a closed-loop feedback control system designed according to the H.sub.∞ control law to constitute a compound controller, adds a deviation correction controller designed according to the PID control law to the control system to stabilize the controlled object in view that the prediction model and parameters of the controlled object have large deviations from the real model and parameters, and makes adaptive corrections by comparing the output signals of the controlled object and the model so as to further enhance the robustness of the system.

A circuit comprises: a register configured to be a linear finite state machine and comprising storage elements, injection devices, one or more input channels for injecting variables using the injection devices, and one or more feedback devices; a plurality of phase shifters, each of the plurality of phase shifters configured to receive signals from a unique segment of the register; scan chains, serial inputs of the scan chains configured to receive signals from outputs of the plurality of phase shifters, wherein the one or more input channels are coupled to the injection devices at injection points in the register, each of the injection points being assigned to one of the one or more input channels based on lifespan values for the injection points, the injection points being determined based on one or more predetermined requirements.

A method for detecting an open/short circuit on a PCB design layout includes: reading PCB data of a to-be-checked PCB design layout, to output an image of each PCB layer included in the PCB design layout; performing a first connectivity analysis on the image of each PCB layer to classify pad patterns connected with each other in the same layer into a corresponding child network group; performing a second connectivity analysis to classify child network groups in which pad patterns connected by the same electroplated hole, into a corresponding parent network group; reading IPC netlist data of the PCB design layout, to obtain a netlist network group in which each pad pattern is; and determining whether a netlist network relationship of the pad patterns is consistent with a network relationship obtained after the second connectivity analysis in order to determine whether there is an open/short circuit.

Computer-implemented methods and processing systems for estimating a probability of failure for different severity levels for an Automated Driving System (ADS) feature in a virtual test environment are provided. The estimation of a probability of crash of different severities may be enabled by utilizing a limit state function (LSF) that attains increasingly negative or positive values after crash (e.g., when TTC=0 or PET=0). This may be achieved by defining a function for severity that is more negative for more severe crashes. The LSF may include a function of the delta speed at collision (i.e., minus delta speed at collision).

A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.

A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.

A fault aware analog model (FAAM) system is disclosed. The FAAM system comprises a FAAM builder module comprising a model construction module configured to receive a reference dataset associated with a circuit block. The reference dataset comprises a set of data values that defines input to output relationship of the circuit block for both in spec and out of spec operation of the circuit block. The reference data set is derived based on data associated with a ground truth representation of the circuit block. In some embodiments, the model construction module is further configured to generate a FAAM comprising a behavioral model of the circuit block, based on the reference dataset, wherein the FAAM is configured to approximate the input to output relationship of the circuit block that is defined by the set of data values in the reference dataset.

Various aspects of the disclosed technology relate to predicting physical failure analysis-oriented diagnosis resolution. Fault simulation is performed on a circuit design to derive test responses for a set of faults and test patterns for testing circuits fabricated according to the circuit design. The set of faults is grouped into groups of equivalent faults based on the test responses. A group of equivalent faults consists of faults having the same test responses for all test patterns in the test patterns that can activate the faults. A PFA (physical failure analysis)-oriented diagnosis resolution evaluation value is computed by averaging weighted sizes of the groups of equivalent faults. The weight factors for the groups of equivalent faults with sizes greater than a certain number being smaller than the weight factors for rest of the groups of equivalent faults.

A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.