A computer-implemented method for determining criticality values of a technical system. The method includes: specifying a reliability of the technical system that is to be satisfied; providing a fuzzy fault tree for the technical system, the fuzzy fault tree comprising a fuzzy top event and multiple fuzzy basic events and logical programmable fuzzy AND/OR operators; transforming the fuzzy fault tree into a flexible neural network comprising a tree structure; determining an optimized flexible neural network by carrying out a learning method for optimizing the flexible neural network, the optimized flexible neural network achieving the reliability of the technical system that is to be satisfied; deriving criticality values of the fuzzy basic events from the optimized flexible neural network.

A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.

A method executed at least partially by a processor includes creating a plurality of groups of paths from a plurality of paths in an integrated circuit (IC) layout diagram. Each group among the plurality of groups has a unique dominant feature among a plurality of features of the plurality of paths. The dominant feature of a group among the plurality of groups is slack. The method further includes testing at least one path in a group among the plurality of groups. The method also includes, in response to the testing indicating that the at least one path fails, modifying at least one of the IC layout diagram, at least a portion of at least one library having cells included in the IC layout diagram, or a manufacturing process for manufacturing an IC corresponding to the IC layout diagram.

A process to design an attitude control system (ACS) controller in each of a plurality of joined entities includes identifying a worst case configuration as a design-to configuration as one or more configurations in a given set S of configurations required for a spacecraft. For the design-to configuration, the process includes deriving one or more system equations in a functional form of equations to determine intermediate design parameters that represent effective proportional and derivative gains of the combined controller, Kp and Kd, respectively. The process also includes determining the design parameters of the ACS controller, namely, gains Kq and Kω and stiffness and damping coefficients, Ks and Cd respectively of all the interfaces between each of the plurality of joined entities, from the intermediate design parameters Kp and Kd. The process further includes programming the ACS controller with selected values of the design parameters for matrices Kq and Kω and selecting springs with stiffness Ks and dampers with damping coefficient Cd for all interfaces between each of the plurality of joined entities. The process includes iterating the computer-implemented process after incrementing a convergence requirement parameter σthreshold when the control performance is not acceptable and until the system achieves acceptable performance, and programming the ACS controller for each of the plurality of joined entities.

Methods and systems for generating a floorplan for a circuit are disclosed. A netlist graph of the circuit and block features associated with blocks of the circuit are obtained. A reinforcement learning (RL) agent is used to generate a sequence of corner block list (CBL) actions. Each CBL action is generated by: generating a current state embedding representing a current state of the floorplan; and inputting the current state embedding to a policy network of the RL agent to generate a predicted output vector, which is used to generate the CBL action. After each CBL action is generated, the current CBL representation of the floorplan and the block features are updated to reflect the state of the floorplan after applying the CBL action. The CBL representation is outputted as a final floorplan after all blocks have been placed.

Training a neural network processor is described for providing diagnostic information of a controlled liquid chromatography pump unit. The training includes executing a sequence of operations wherein the neural network processor is trained with input signals obtained from a simulated version of the controlled liquid chromatography pump unit and associated sensors, while modifying the simulated version of the liquid chromatography pump unit to a pump fault simulation signal. Dependent on a value of the pump fault simulation signal, the simulated version of the liquid chromatography pump unit simulates operation of the liquid chromatography pump unit free from faults or the operation thereof with one or more pump faults. The trained neural network processor obtained therewith is thereafter integrated with a controlled liquid chromatography pump unit to provide for auto-diagnostic capabilities or used in a separate diagnostic unit for diagnosing one or more controlled liquid chromatography pump units not having auto-diagnostic capabilities.

Method and system for selecting an E/E arrangement from a plurality of E/E arrangement variants. An input block receives wiring and component data for a plurality of E/E arrangement variants. For each E/E arrangement variant, a calculation block generates a first data record for a first current path starting with a first wire from a starting component and conducting through one or more intervening components to an end point. An output block generates a first data log of the sequence of wires and intervening components along the first current path and any alternate current paths stemming from the intervening components. The output block further calculates a failure probability value for each E/E arrangement and selects the E/E arrangement from the plurality of E/E arrangement variants based on, in part, their calculated failure probability values.

An information processing device includes processing circuitry configured to obtain a plurality of sets of data related to a processing target, group relationships among the sets of data that are obtained, based on group information set in advance, calculate degrees of importance indicating strengths of cause-and-effect relationships among sets of data included in each group, and calculate, based on the degrees of importance, estimation values indicating the cause-and-effect relationships among sets of data.

A computer system may receive a layout of a data center, the layout of the data center identifying physical locations of a plurality of server racks, electrical distribution feeds, and uninterruptible power supplies. The computer system may receive a fault domain configuration for the datacenter, the fault domain configuration identifying virtual locations of a plurality of logical fault domains for distributing one or more instances so that the instances are stored on independent physical hardware devices within a single availability fault domain. The computer system may determine the configuration for the data center by assigning the plurality of fault domains to a plurality of electrical zones, wherein each electrical zone provides a redundant electrical power supply across the plurality of logical fault domains in an event of a failure of one or more electrical distribution feeds. The computer system may display the configuration for the data center on a display.

Systems, methods and computer program products leveraging digital twin modeling and cognitive computing to predict lubrication replacement for a physical asset. Predictions of lubrication replacement consider one or more various parameters such as operating conditions, usage parameters, the surrounding environment, overall health and state of repair of the physical asset, lubricant properties and historically collected data from the physical asset (or similarly comparable assets). Timing for optimal lubrication replacement is identified using the collected data of the physical asset, along with historical data, to simulate changes in a state of lubricants and lubricated parts within a physical asset using digital twin modeling to make predictions how one or more actions upon the physical asset impact the health, stability and/or longevity of the lubricant's lifespan. Based on the simulation results, recommended action(s) suitable for increasing and optimizing the overall life of the lubrication are provided and/or implemented.