A method of additive manufacturing machine (AMM) build process control includes obtaining AMM machine and process parameter settings, accessing sensor data for monitored physical conditions in the AMM, calculating a difference between expected AMM physical conditions and elements of the monitored conditions, providing the machine and process parameter settings, monitored conditions, and differences to one or more material property prediction models, computing a predicted value or range for the monitored conditions, comparing the predicted value or range to a predetermined target range, based on a determination that predicted value(s) are within the predetermined range, maintaining the machine and process parameter settings, or based on a determination that one or more of the predicted value(s) is outside the predetermined range, generating commands to compensate the machine and process parameter settings, and repeating the closed feedback loop at intervals of time during the build process. A system and a non-transitory medium are also disclosed.

A method for generating a manipulated data signal for misleading a first machine learning system, which is designed to ascertain a semantic segmentation of a received one-dimensional or multi-dimensional data signal, the method having the following steps: a) ascertaining a desired semantic segmentation of the manipulated data signal; and b) generating the manipulated signal as a function of the received data signal and the ascertained desired semantic segmentation as well as an estimated semantic segmentation of the manipulated data signal.

An information processing apparatus includes an acquisition unit that acquires a physical sensor output value output from a plurality of physical sensors installed in a substrate processing apparatus; a prediction unit that predicts a virtual sensor output value of a virtual sensor corresponding to a prediction target physical sensor by using a statistical model or a physical model, based on a degree of similarity between the physical sensor output value and data learned by the statistical model; an abnormality determination unit that compares the physical sensor output value of the prediction target physical sensor with the virtual sensor output value of the virtual sensor, thereby determining whether an abnormality occurs in the physical sensor; and an instruction unit that switches from a control based on the physical sensor output value to a control based on the virtual sensor output value when determined that the abnormality occurs in the physical sensor.

Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.

Driving parameters (e.g., speed, heading direction) that an autonomous driving vehicle (ADV) likely utilize as target driving parameters are grouped into a number of ranges and one of the driving parameters in each range is selected as a driving parameter representative or a target driving parameter representing the respective range or segment. For each of the target driving parameters representing the ranges, a particle swarm optimization method is utilized to derive a set of most optimized coefficients for a controller (e.g., speed controller, steering controller) for controlling an ADV. A driving parameter to coefficient (parameter/coefficient) mapping table is generated to map a particular driving parameter representing a range of driving parameter to a set of one or more coefficients of a particular controller. The parameter/coefficient mapping table is utilized at real-time to configure a controller in response to a particular target driving parameter using the corresponding coefficients.

A method and device for evaluating the performance of an industrial control loop based on full loop reconstruction simulations. The method comprises: performing reconstruction simulation on control modules one by one except a controlled object in the loop, and judging the correctness of the reconstructed modules; establishing a mathematical model of the object, connecting the mathematical model to the reconstructed modules to complete reconstruction of the entire loop, and optimizing the mathematical model of the object to obtain an optimized model of the object; adjusting parameters of the modules according to a control performance index, and performing simulation calculation on the reconstructed loop using the parameters to obtain an ideal value of the reconstructed performance control index for evaluating the performance of the loop. The loop is reconstructed, the influence of the modules, a PID controller, a filter, a piece-wise linear function and a deadband, on the performance is evaluated.

A control parameter is adjusted without the need to operate a driver of a control target. A control device (10) includes a model identification unit (12), a parameter determination unit (132), a data evaluation unit (131) and an operation data calculation unit (133). The model identification unit (12) identifies a physical model of an operation of the control target. The operation data calculation unit (133) calculates operation data by using the physical model and the control parameter. The data evaluation unit (131) calculates an evaluation value by using normative data of the operation of the control target and the operation data. The parameter determination unit (132) determines the control parameter by using the evaluation value.

Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.

A method of bending a metallic article comprising at least the following steps is described: a) executing a relative movement between a bending head having at least one bending group and the metallic article, and b) bending the metallic article by means of an activation of the bending group. During step b), at least a first actuation device actuates the bending group according to a respective motion profile and during the step a), at least a second actuation device actuates a relative movement between the bending head and the metallic article according to a respective motion profile. The method also comprises a step of preparing, during which a respective initial motion profile of the first actuation device and/or of the second actuation device is modified for obtaining the respective motion profile in function of a first parameter and of a second parameter.

A system identification device includes: a direct feedthrough term identification unit that receives an impulse response of a dynamic system; a block Hankel matrix generation unit; a singular value decomposition unit that, by singular value decomposition of the block Hankel matrix, outputs a first orthogonal matrix, a second orthogonal matrix, and a singular value; a system dimension determination unit that, on the basis of the first orthogonal matrix, second orthogonal matrix, singular value, and search range, identifies a system matrix other than a direct feedthrough term, and from a comparison of the actual system characteristics and system characteristics calculated on the basis of the system matrix and direct feedthrough term, determines the system dimension; and a system matrix identification unit that identifies a system matrix other than the direct feedthrough term on the basis of the first orthogonal matrix, second orthogonal matrix, singular value, and system dimension.