Abstract: A machine and process for identifying an effect on a performance of a vehicle by a component thereof. The process may include a processor that may include a vehicle performance processor generating a simulation configuration file for a model of a mechanical system that controls the performance of the vehicle and for a power system that controls the mechanical system of the vehicle; providing a visual interface for selecting a degraded mode of a component within the mechanical system or the power system; generating a simulation configuration file introducing the degraded mode of a selected component within the model; running, using a modification scripting of the degraded mode of the selected component, a simulation generating performance data for the vehicle; and transforming the performance data to a visualization of the performance of the vehicle.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component monitors an integrated design application and generates feature vectors that represent operations of the integrated design application. The security component supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the integrated design application. When anomalous behavior is detected in the operations of the integrated design application, the security component generates and transfers an alert that characterizes the anomalous behavior.

Abstract: According to this invention, it is possible to reduce the load of the user in a work of operating a model described in a hardware description language, and allow the user to readily make a change. This invention provides an information processing apparatus including a hardware processor that emulates, by hardware, operations corresponding to a model described in a hardware description language, and a control unit that controls, in accordance with instructions of a user received from a user terminal, at least one of inputs to the hardware processor and outputs from the hardware processor.

Abstract: The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

Abstract: A distributed security system can include instances of a compute engine that can execute either locally in security agents on client devices or as cloud instances in a security network. Event data can be processed by elements of the distributed security system according to centrally-defined ontological definitions and/or configurations. Bounding managers of local security agents can control how much event data is sent to the security network. A storage engine in the security network can store event data received from client devices, can route event data to other elements of the security network, including cloud instances of the compute engine. An experimentation engine of the security network can also at least temporarily adjust other elements of the distributed security system during experiments or tests.

Abstract: In various examples disclosed herein, provided is a method for mapping data models to facilitate code generation. The method can include receiving a first file comprising a first data model of a first group of components and a second file comprising a second data model of a second group of components. The method can also include converting the first data model into a first intermediate data model. The method can also include converting the second data model into a second intermediate data model. The method can also include mapping the first intermediate data model to the second intermediate data model to generate a map of matching elements. The method can also include generating code to be executed based at least in part on the map of matching elements.

Abstract: One variation of a method for emulating a known attack on a computer network includes: generating a set of data packets by recombining packet fragments within a packet capture file representing packet fragments transmitted between machines during a prior malicious attack on a second network; defining transmission triggers for transmission of the set of data packets between pairs of agents connected to a target network based on timestamps of packet fragments in the packet capture file; initiating transmission of the set of data packets between the pairs agents according to the set of transmission triggers to simulate the malicious attack on the target network; and, in response to absence of a security event related to the simulation in a log of a security technology deployed on the target network, generating a prompt to reconfigure the security technology to respond to the malicious attack.

Abstract: Computational/communication system security tools are provided. Such tools report at least one multi-dimensional (or multi-component) data-object (based on the monitored events) to an administrator of the system. The multiple components of the data object provide multiple risk indicators (e.g., risk scores) along various dimensions of security for such systems. Thus, tools provide multi-dimensional monitoring and reporting of risks and security threats to computational/communication systems. The tools may also provide at least one risk mitigation action (e.g., quarantining and/or prohibiting particular risky entities, entity groups, and/or entity activities) based on the enhanced monitoring and detection methods presented herein.

Abstract: A method includes displaying a first user interface, displaying a first simulated operating state of a smart home device in a status display area in response to selecting a first control, receiving an association success notification, displaying a second user interface, and sending a control instruction to the smart home device in response to selecting a second control. The first user interface includes the first control for controlling the smart home device and a status display area of the smart home device. The second user interface includes the second control for controlling the smart home device and the status display area of the smart home device. The control instruction instructs the smart home device to enter a first operating state according to the selecting the second control, and displaying a second simulated operating state of the smart home device in the status display area of the second user interface.

Abstract: The invention relates to methods of simulation of a plurality of processors running on a plurality of cores, to multi-core microprocessor systems in which such methods may be carried out, and to computer program products configured to perform a simulation of a plurality of processors, running on a plurality of cores. According to a first aspect of the invention, there is provided a method of running a plurality of simulated processors on a plurality of cores, in which simulation of the processors is performed in parallel on the plurality of cores.

Abstract: A system includes a processor configured to perform operations, including determining, for each respective software defect of software defects identified in a software product, corresponding attribute values that represent a software development history of the respective software defect, and determining, for each respective defect, using a machine learning model, and based on the corresponding attribute values, a corresponding escalation value representing a likelihood of the respective defect being escalated for resolution after release of the software product. The machine learning model may have been trained using corresponding software development histories of training defects that were escalated for resolution after release of a prior version of the software product.

Abstract: An apparatus and method for determining a probability datum is provided. The apparatus includes a processor and a memory connected to the processor. The memory contains instructions configuring a processor to receive a first datum that describes actions of the user, and to receive a second datum that describes a target attainment value, and to classify the first datum and the second datum to various labels, each label identified by a label value. Classifying includes generating a gap datum by as a function of the first datum and the second datum and categorizing the first datum with a label based on the gap datum and the label value of each label. The processor may determine the probability datum by using each label to select a respective probability generation model configured to determine the probability datum of the first datum matching the second datum per label based on the gap datum.

Abstract: Various embodiments are disclosed of a compilation with optimization for multi-processor system with processing elements optimized for high performance and low power dissipation and an associated method of programming the processing elements. Application source code may be initially compiled into an intermediate representation. Following the initial compilation, resources may be mapped and communication synthesis performed. Simulation and debug may be performed prior to loading an executable image onto the multi-processor system. At each step, checks may be performed for possible optimizations, and one or more steps repeated using results of the checks.

Abstract: An embodiment device for controlling a service robot includes a communication device configured to communicate with the service robot and a controller configured to detect a present situation of the service robot by using various pieces of sensor information acquired from the service robot, determine a task appropriate to the present situation of the service robot, wherein the task includes one or more behaviors, and control the service robot based on an action set for each of the one or more behaviors of the task.

Abstract: A method, system, and non-transitory storage medium with instructions that implement a method to receive an electronic payment; select a selected payee's address from an address array, the address array storing a plurality of addresses of different payees in a plurality of locations in the address array, each different payee being entitled to a share of the payment; and transmit the payment in its entirety to the selected payee via the selected payee's address. The payee address selection may include stepping through a linearly populated address array, stepping through a randomly populated address array, or randomly selecting the address in the address array.

Abstract: There may be provided a method for model-based risk assessment of a computerized network, the method may include obtaining a model of the computerized network, the model comprises risk assessments building blocks that represent (a) risk related dependencies between members of sets of assets of the computerized network, and (b) probabilities of occurrence of the risk related dependencies; and simulating, for at least some of the assets of the computerized network, and based on at least a portion of the model, one or more attacks to provide one or more model-based risk assessment results.

Abstract: Managing data center recovery from failure events can include a failure event platform having aspects provided via a user interface that integrates multiple failure and recovery management and execution features. The features can include, among others, application drift monitoring between production and recovery environments, real-time health checks of system components, user-modifiable scripting for prioritizing and customizing data center recovery actions, and a recovery execution tool.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to generate a graphics processing unit (GPU) long instruction trace (GLIT). An example apparatus includes at least one memory, and at least one processor to execute instructions to at least identify a first routine based on an identifier of a second routine executed by the GPU, the first routine based on an emulation of the second routine, execute the first routine to determine a first value of a GPU state of the GPU, the first routine having (i) a first argument associated with the second routine and (ii) a second argument corresponding to a second value of the GPU state prior to executing the first routine, and control a workload of the GPU based on the first value of the GPU state.

Abstract: A dataflow graph for an application has operation units that are configured to be producers and consumers of tensors. A write access pattern of a particular producer specifies an order in which the particular producer generates elements of a tensor, and a read access pattern of a corresponding consumer specifies an order in which the corresponding consumer processes the elements of the tensor. The technology disclosed detects conflicts between the producers and the corresponding consumers that have mismatches between the write access patterns and the read access patterns. A conflict occurs when the order in which the particular producer generates the elements of the tensor is different from the order in which the corresponding consumer processes the elements of the tensor. The technology disclosed resolves the conflicts by inserting buffers between the producers and the corresponding consumers.

Abstract: An anomaly detection system according to an embodiment is an anomaly detection system that executes anomaly detection of each of one or a plurality of simulators by using a controller. The controller causes each simulator to start a process of simulation, to transmit to the controller an existence notification indicative of existence of the simulator, at a predetermined cycle until an end of the process of simulation, and to transmit to the controller an end notification indicative of an end of the process if the process ends; determines that the simulator that is a transmission source of the existence notification is normal; determines that the process of the simulator that is a transmission source of the end notification ends, upon receiving the end notification; and detects that an anomaly occurs in the simulator.

Abstract: Some embodiments process log data into traffic patterns, using call graph normalization and aggregation. Traffic patterns are characterized, by comparing traffic patterns, finding redundancies, locating coverage gaps, delimiting impact regions, spotting optimization candidates, performing timing analyses, predicting traffic flow, or other operations. Characterizations are prioritized, and priorities help guide operational changes in a computing system. Gaps between the traffic patterns of different environments or different scenarios may be located, leading to changes in signoff coverage or testing coverage or both. Deployment traffic anomalies may trigger rollback. Traffic patterns indicative of cyberattacks may trigger security countermeasures.

Abstract: A method is provided for creating and executing a control program for controlling an automation system having a controller and a web server connected to the controller. The method includes creating a first version of a program code of a control program for the automation system in an input module of a web-based development environment executed in a web browser, in a code creating step; executing a translation module of the web-based development environment on the web server and translating the program code into a program code of a binary language, in a translating step; and executing the program code in the binary language with the aid of the controller of the automation system, in an executing step. An automation system is also provided.

Abstract: A method, device, and system for configuring a coating machine for coating a surface of a product using a coating substance are provided. The method includes determining a value associated with one or more parameters from a plurality of parameters associated with the coating operation. The method also includes predicting a value associated with at least one attribute associable with the coating substance based on the determined value associated with the one or more parameters using a trained machine learning model. The method includes configuring the coating machine for coating the surface using the coating substance based on the predicted value associated with the at least one attribute associable with the coating substance. The method also includes initiating a coating operation at the configured coating machine for coating the surface of the product using the coating substance.

Abstract: It is disclosed a PLC Program analysis method where a program (PROG) is translated (TRANS) into a model (MOD) in a logical framework, from which properties (Prop) are determined. Said properties coupled with user specifications (IntProp) are verified by an automated solver (SMT). If contraposition of a property (Prop) is satisfiable, counter-examples (PROOF NOK) representative of model inputs and internal memory values is provided. Counter-examples (PROOF NOK) are translated into error initial configurations (IniConf) of said model. Execution of the model is simulated (EXE) with said model error initial configurations (IniConf), and error intermediary configurations (AST-IntConf) of said model simulation are recorded up to said property violation. Error initial and intermediary configurations (Lad-IniConf, Lad-IntConf) of said original program (PROG) are derived from error initial configurations (IniConf) of said model and error intermediary configurations (AST-IntConf) of said model simulation and displayed.

Abstract: A method includes: accessing an attack record defining actions representing a previous known attack on a second computer network; initializing an attack graph; for each action, defining a set of behaviors—analogous to the action and executable by an asset on a target network to emulate an effect of the action on the second computer network—and storing the set of behaviors in a node in the attack graph; connecting nodes in the attack graph according to an order of actions in the known attack; scheduling the asset to selectively execute analogous behaviors stored in the set of nodes in the attack graph; accessing alerts generated by a set of security tools deployed on the target network; and characterizing vulnerability of the target network based on alerts, in the set of alerts, indicating detection and prevention of behaviors executed by the asset according to the attack graph.

Abstract: A method of providing a calendar service includes obtaining time-series data, determining whether the time-series data correspond to an outlier, obtaining, based on the determination that the time-series data correspond to an outlier, non-time-series data linked to the time-series data corresponding to the outlier, and displaying at least one of the non-time-series data and the time-series data corresponding to the outlier on a calendar page.

Abstract: Systems and methods may generate code, for a model, with one or more service access points generated and at locations in the code based on an analysis of model constraints and deployment specifications (e.g., RTE specifications or OS specifications). The systems and methods may analyze the model and identify a functionality that needs an RTE service. The system and methods may receive deployment specifications. The systems and methods may generate code for the model, where an analysis of model constraints and the deployment specifications determine which service access points are generated and where in the code the service access points are located. In an embodiment, the code may be executed by different RTEs. In an embodiment, the systems and methods may determine, based on the analysis of the model constraints and the deployment specification, one or more admissible implementations for an RTE service that may be implemented in different ways.

Abstract: Techniques described herein relate to implementing mutation testing of software applications associated with continuous integration (CI) systems. A mutation test system may determine one or more portions of modified source code within an application codebase. Mutated applications may be generated based on the modified source code, and a mutation test system may determine subsets application test suites for execution based on the portions of the modified source code and/or other factors. In various examples, the mutation test system may use mappings between portions of source code and test subsets, and/or machine-learned models or heuristics-based techniques to determine subsets of test suites based on discreet source code modifications.

Abstract: Aspects are described herein that are capable of providing a vehicle configuration for a vehicle that is optimized to maximize the customer's performance priorities while additionally complying with regulatory emissions requirements and equipment regulations. A machine learning (ML) predictive model is trained based on simulations run on combinations of vehicle configurations and routes and on real-world telematics data, and used to determine a vehicle configuration optimized for a representative route.

Abstract: Systems and methods automatically rescale an original electric motor model so that it models an electric motor of a different size. The original electric motor model may be coupled to a motor controller model, and the systems and methods may also rescale the original motor controller model to produce a rescaled motor controller model matched to the rescaled electric motor model. The original electric motor model may include motor parameters and motor lookup tables, and the original motor controller model may include controller parameters and controller lookup tables. Rescaling factors indicating the size of the new electric motor being modeled may be received, and ratios may be computed as a function of the rescaling factors. Original motor parameters and controller parameters may be rescaled based on the ratios. Original motor lookup tables and controller lookup tables may be reshaped based on the ratios.

Abstract: An API classification unit (231) extracts as omittable internal data processing, from among internal data processing included in execution data processing which is data processing executed by an outside data processing device which is a data processing device outside, internal data processing that is required to be executed when the outside data processing device executes the execution data processing, but is omittable from the execution when a data processing apparatus (10) executes the execution data processing, due to a difference in an execution environment between the outside data processing device and the data processing apparatus (10). An execution unit (235) executes the execution data processing while omitting the execution of the omittable internal data processing extracted by the API classification unit (231).

Abstract: The present disclosure provides a scalable acceleration of data processing in Machine Learning pipeline which is unavailable in conventional methods. Initially, the system receives a dataset and a data processing code. A plurality of sample datasets are obtained based on the received dataset using a sampling technique. A plurality of performance parameters corresponding to each of the plurality of sample datasets are obtained based on the data processing code using a profiling technique. A plurality of scalable performance parameters corresponding to each of a plurality of larger datasets are predicted based on the plurality of performance parameters and the data processing code using a curve fitting technique. Simultaneously, a plurality of anti-patterns are located in the data processing code using a pattern matching technique.

Abstract: A system and method for detecting code initialization actions is utilized for performing static analysis of an application code including an external initialization component. The method includes receiving an application code including an external initialization code component, the application code deployed in a cloud computing environment; detecting in the application code an anchor point, the anchor point including a call to another code object; inserting a hook into the application code based on a location of the anchor point in the application code; and receiving a result from the hook in response to executing the application code.

Abstract: In a computer-implemented method for improving performance of a database optimizer, a processor calculates a first execution time for a database query executing through a first access path of a database system. A processor generates a second access path. A processor determines a second execution time via a simulation of the database query through the second access path. A processor determines that the second execution time is less than the first execution time. A processor, responsive to determining that the second execution time is less than the first execution time, sends the second access path to the database optimizer.

Abstract: Devices and techniques for loading contexts in a coarse-grained reconfigurable array processor are described herein. A system or apparatus may include context load circuitry operable to load context for a coarse-grained reconfigurable array processor, where the context load circuitry is configured to: (a) receive a kernel identifier; (b) access a first registry to obtain a context mask base address; (c) determine a context mask address from the context mask base address and the kernel identifier; (d) access a second registry to obtain a context state base address; (e) determine a context state address from the context state base address and the kernel identifier; (f) use a context mask at the context mask address to determine corresponding active context state; and (g) load the corresponding active context state into the coarse-grained reconfigurable array processor.

Abstract: The present technology is directed to visualizing a Wi-Fi signal propagation in 3-D at various heights and locations. The present technology can calculate a radio frequency (RF) propagation pattern for a Wi-Fi access point (AP) based on a RF propagation model for the Wi-Fi AP and overlay the RF propagation pattern for the Wi-Fi AP over a visualization of the building plan to present a 3-D visualization of the RF propagation pattern of the 3-D space. In particular, the present technology can project a plurality of ray-paths in various directions in a 3-D space originated from the Wi-Fi AP and determine whether the ray-paths interface with objects defined in the building plan. The present technology can segment the respective ray-path into contiguous segments of substantially uniform mediums for each ray-path that interface with the objects and determine a RF signal strength at points along the contiguous segments of the ray-paths.

Abstract: An approach is provided that receives event data that correspond to detected activities performed by a user on one of a set of one or more computer systems. The detected activities are performed by the user over a time duration. The approach analyzes the event data using time-based models. Each of the time-based models correspond to a different time interval that is included in the time duration. The analysis results in time-based risk scores pertaining to the user for each of the different time intervals. An action is then performed based on an overall security risk score of the user with the overall security risk score of the user being calculated based on the different time-based risk scores.

Abstract: A system and method for generating a netlist of a memory device includes receiving a logical netlist file including memory instances and placement information for the memory device. Each memory instance includes leaf cells. Further, a location of a first leaf cell and a location of a second leaf cell of the leaf cells of a first memory instance is determined based on the placement information. A first net segment between the first leaf cell and the second leaf cell is generated based on the location and parasitic elements of the first leaf cell and the location and parasitic elements of the second leaf cell. A parasitic netlist is generated based on the first net segment and the parasitic elements of the first leaf cell and parasitic elements of the second leaf cell.

Abstract: A method for generating an upgrade recommendation for a communication network includes: analyzing a dataset to generate a request category, in which the dataset comprises at least a request, a response to the request, and a corresponding response time, in which the request and the response are communicated over the communication network; generating a network slice corresponding to the request category; executing an emulation using the network slice and the request to generate an emulated response time for the request; comparing the emulated response time against the corresponding response time; determining, based on the comparison, a response time variation for the request; and generating the upgrade recommendation based on the response time variation.

Abstract: An example system includes a processor to monitor activity on a database server to generate an events stream. The processor can convert the events stream into a time series that approximates activity load at the database server using an exponential smoothing. The processor can also send the time series to a streaming analytics engine.

Abstract: The present disclosure relates to a method and apparatus for predicting application service response time in a communication system. Particularly, the present disclosure relates to a method, apparatus and system for predicting response time required to provide an application service to a user in response to the user's request of the application service in a server of a communication system. A method for predicting application service response time according to an embodiment of the present disclosure includes: data collection for collecting network data associated with a user application service, in response to a request for the application service by a user device; and data analysis for performing, by utilizing the collected data, a response time predictive analysis, in order to predict a user's application service response time. The data analysis selects an optimal prediction model by initially applying a response time prediction model and testing the initial prediction model that is applied.

Abstract: An attribute-value extraction unit (101) extracts as a plurality of model-generation attribute values, a plurality of attribute values belonging to an attribute associated with a monitoring subject for anomaly detection. A division-data generation unit (102) extracts for each model-generation attribute value, a normal event associated with the model-generation attribute value from normal data indicating a plurality of normal events each of which is found out to be normal, each of which is associated with one attribute value of the plurality of attribute values, and each of which includes a plurality of characteristics, and generates for each model-generation attribute value, division data indicating the extracted normal event. A characteristic selection unit (103) selects a combination of characteristics to be used for generation of a normal model to be used for anomaly detection, from a plurality of characteristics included in a plurality of normal events indicated in a plurality of pieces of division data.

Abstract: A method for evaluating an updated analytical procedure in a monitoring system, comprising a plurality of monitoring device arranged to monitor similar environments, is provided. The method comprises identifying available processing resources, selecting a first monitoring device for which available processing resources have been identified, selecting a second monitoring device, acquiring monitoring data by the second monitoring device, and performing a current analytical procedure on the monitoring data. The method further comprises sending the monitoring data to the first monitoring device, performing, in the first monitoring device, an updated analytical procedure on the monitoring data, and evaluating the updated analytical procedure based on the outcome of the current analytical procedure and the updated analytical procedure.

Abstract: In an approach for predicting an optimal system upgrade, a processor detects an upgrade is available for a system of a first user computing device. A processor gathers a set of live data from an environment of the first user computing device using a process mining software. A processor compares the set of live data to a set of historical data stored in a knowledge corpus to identify a second user computing device similar to the first user computing device. Responsive to identifying the second user computing device, a processor performs a pattern recognition to generate one or more critical recommendations associated with the upgrade for the system of the first user computing device. A processor schedules the upgrade for the system of the first user computing device during a period of time, wherein the period of time is scheduled consistent with the one or more critical recommendations.

Abstract: There is provided a method for programming an industrial robot, where distributors and integrators can present accessories that run successfully at end users. Also the developer can define customized installation screens and program nodes for the end user. There is provided a software platform, where the developer can define customized installation screens and program nodes for the end user thereby extending an existing robot system with customized functionalities by still using the software platform available in the robot system. Hereby a robot developer can define customized installation screens and program nodes for the end user. These can, for example, encapsulate complex new robot programming concepts, or provide friendly hardware configuration interfaces.

Abstract: A program is executed in a first mode of operation in a controlled environment in accordance with normal operations without malicious behavior. An acceptable behavior model is generated based on a plurality of sequences of events that occur during the normal operation of the program. The acceptable behavior model is indicative of normal behavior of the program that occurs during the normal operation. Then the program is executed in a second mode of operation in an operational environment. An operational sequence of events (determined during the second mode of operation) is compared with the acceptable behavior model. When there is a match between the operational sequence of events and the acceptable behavior model, execution in the second mode of operation continues. When there is not a match between the operational sequence of events and the acceptable behavior model, execution in the second mode of operation is halted.

Abstract: This application relates to a non-communication electronic warfare system design analysis system based on engineering modeling. In one aspect, the system includes a scenario unit transmitting a simulation threat signal corresponding to an input scenario and a threat signal simulator transmitting an actual threat signal. The system may also include an electronic warfare support receiving model unit allocating a jamming technique. The system may further include an electronic warfare support receiving analyzer allocating the jamming technique to the received actual threat signal and an electronic attack jamming model unit modelling a device for generating a simulation jamming signal. The system may further include an electronic attack jamming generator generating an actual jamming signal and a simulation situation demonstration controller analyzing performance by using the actual jamming signal and the actual threat signal.

Abstract: A computer-implemented method of machine-learning is described that includes obtaining a test dataset of scenes. The test dataset belongs to a test domain. The method includes obtaining a domain-adaptive neural network. The domain-adaptive neural network is a machine-learned neural network taught using data obtained from a training domain. The domain-adaptive neural network is configured for inference of spatially reconfigurable objects in a scene of the test domain. The method further includes determining an intermediary domain. The intermediary domain is closer to the training domain than the test domain in terms of data distributions. The method further includes inferring, by applying the domain-adaptive neural network, a spatially reconfigurable object from a scene of the test domain transferred on the intermediary domain. Such a method constitutes an improved method of machine learning with a dataset of scenes comprising spatially reconfigurable objects.

Abstract: The disclosure is to constitute, while reducing a cost for collecting training data used in machine learning that makes a control module acquire an ability to control a robot device, the control module operatable in an actual environment by the machine learning. A learning device according to one aspect of the present invention executes machine learning of an extractor by using a first learning data set constituted by a combination of simulation data and first environmental information and a second learning data set constituted by a combination of actual data and second environmental information. Further, a learning device according to one aspect of the present invention executes machine learning of a controller by using a third learning data set constituted by a combination of third environmental information, state information, and a control command.

Abstract: A method for testing a basic parameterization of a component in an automation system is provided. The method includes: starting a test run of the component in the automation system with the basic parameterization, measuring of a measured value data record during trial operation, access to a machine learning module comprising a pre-trained neural network, wherein the pre-trained neural network is pre-trained to calculate a target parameterization for the respective component for a measured value data set, wherein the basic parameterization is compared with the calculated target parameterization and in the event of deviation a result message for adapting the basic parameterization is provided, and receipt of the provided result message for adaptation of the basic parameterization.