Abstract: Industrial automation systems are often inflexible, which can result in delays that are costly and inconvenient. In particular, it is recognized herein that the engineering phase of automation system implementation currently represents a significant portion of the overall cost of an automation system. As described herein, automation system configurations can be automatically generated. For example, a discover match use (DMU) system described herein can reduce engineering time while providing design flexibility.

Abstract: A hyperlink message for machine-to-machine (M2M) or machine-to-human (M2H) communication has a semantic metadata tag, a content field, an executable specification field. Executable specification instructs a machine to perform a task associated with the machine related data, and post to a machine social media platform results of the task as content for the hyperlink message. The hyperlink message posting is visible and available to other participating machines on the machine social media platform to read and contribute related content as a hyperlink discussion of M2M or M2H communication.

Abstract: A hyperlink message for machine-to-machine (M2M) or machine-to-human (M2H) communication has a semantic metadata tag, a content field, an executable specification field. Executable specification instructs a machine to perform a task associated with the machine related data, and post to a machine social media platform results of the task as content for the hyperlink message. The hyperlink message posting is visible and available to other participating machines on the machine social media platform to read and contribute related content as a hyperlink discussion of M2M or M2H communication.

Abstract: A computer-implemented method for learning structural relationships between nodes of a graph includes generating a knowledge graph comprising nodes representing a system and applying a graph-based convolutional neural network (GCNN) to the knowledge graph to generate feature vectors describing structural relationships between the nodes. The GCNN comprises: (i) a graph feature compression layer configured to learn subgraphs representing embeddings of the nodes of the knowledge graph into a vector space, (ii) a neighbor nodes aggregation layer configured to derive neighbor node feature vectors for each subgraph and aggregate the neighbor node feature vectors with their corresponding subgraphs to yield aggregated subgraphs, and (iii) a subgraph convolution layer configured to generate the feature vectors based on the aggregated subgraphs. Functional groups of components included in the system may then be identified based on the plurality of feature vectors.

Abstract: System and method optimize recyclability of an electronic device during manufacturing design A manufacturing design software produces engineering bill of materials, manufacturing bill of materials, and bill of process for the manufacturing design. Recycling process plan engine constructs a recycling process plan for the electronic device according to the manufacturing design. A virtual model of recycling processes is constructed by mapping needed skills to corresponding recycling equipment using a library of recycling equipment information. Recycling process plan engine uses the virtual model to simulate the recycling plan and optimizes each recycling process, and the overall sequence of recycling processes, according to an objective function. Evaluator module receives key performance indicator values from a virtual model simulation and calculates the value of the objective function based on key performance indicators.

Abstract: Various embodiments include a semantic-based causal event probability analysis method. The method may include: generating a cause event instance and corresponding effect event instances thereof according to user requirements and based on a cause event template and an effect event template; assigning each cause event or effect event instance to a parent node, wherein the event instance comprises a plurality of entities and a mutual relationship between the entities; and calculating a probability of a cause event instance or an effect event instance having a common parent node. A probability that a first cause event instance causes a first effect event instance equals: P R E 1 R C E 1 C = P C E 1 R R E 1 C ? P R E 1 R P C E 1 C P(CE1(R) |RE1(C)) represents a probability of the first cause event occurring when the first effect event occurs. P(RE1(R)) represents a probability of the first effect event occurring among all effect events.

Abstract: A system and method adaptively control a heterogeneous system of systems. A graph convolutional network (GCN) that receive a time series of graphs representing topology of an observed environment at a time moment and state of a system. Embedded features are generated having local information for each graph node. Embedded features are divided into embedded states grouped according to a defined grouping, such as node type. Each of several reinforcement learning algorithms are assigned to a unique group and include an adaptive control policy in which a control action is learned for a given embedded state. Reward information is received from the environment with a local reward related to performance specific to the unique group and a global reward related to performance of the whole graph responsive to the control action. Parameters of the GCN and adaptive control policy are updated using state information, control action information, and reward information.

Abstract: A system and a method provide a global view of an automation system for any industrial controller in a network. The method comprises providing a distributed version control runtime system for managing industrial controller process images in that an automation engineering process provides non-linear workflows. The method further comprises providing an engineering system having a first industrial controller program database of an industrial controller program. The method further comprises providing a first industrial controller having a first process image including a second industrial controller program database of an industrial controller program and a first historian database. The method further comprises providing a second industrial controller having a second process image including a third industrial controller program database of an industrial controller program and a second historian database.

Abstract: Methods and systems for dynamic network link prediction include generating a dynamic graph embedding model for capturing temporal patterns of dynamic graphs, each of the graphs being an evolved representation of the dynamic network over time. The dynamic graph embedding model is configured as a neural network including nonlinear layers that learn structural patterns in the dynamic network. A dynamic graph embedding learning by the embedding model is achieved by optimizing a loss function that includes a weighting matrix for weighting reconstruction of observed edges higher than unobserved links. Graph edges representing network links at a future time step are predicted based on parameters of the neural network tuned by optimizing the loss function.

Abstract: A method for representing knowledge in a cognitive engineering system (CES) includes receiving information relating to an automation engineering project from an engineering tool, storing the received information in a cognitive engineering graph (CEG) storing a plurality of previously generated CEGs for previous automation engineering projects, and establishing a communication path between the CEG storing the received information and the plurality of previously generated CEGs. The method may further include applying machine learning to the stored CEG based on the received information and the stored plurality of previously generated CEGs. The machine learning may analyze the CEG to identify at least one pattern that is representative of a given object from the automation engineering project. The CES may automatically add an element to the CEG based on the received information and a query from a user. Further, the user may request a change made by the CES be reversed.

Abstract: Computerized engineering tool and methodology to develop neural skills for computerized autonomous systems, such as a robotics system (50), are provided. A disclosed computerized engineering tool (10) may involve an integrated arrangement of respective modular functionalities arranged in a closed loop, such as may include a physics engine (14), a neural data editor (16), an experiment editor (18), a neural skills editor (20), and a machine learning environment (22). Disclosed embodiments are conducive to cost-effectively simplifying development efforts involving neural skills, such as by reducing the time involved to develop the neural skills involved in any given robotics system and by reducing the level of expertise involved to develop neural skills.

Abstract: A system and method for modeling human behavior includes receiving, by a classifier module, sensor data from one or more sensors monitoring human behavior associated with a work task and to identify the type of human behavior based on a trained neural network. A prediction module receives the identified type of human behavior from the classifier and generates prediction data representing predicted next one or more human actions based on a time series of position vectors learned by the trained neural network. A rendering module translates the prediction data into a visual rendering for a virtual human simulation model.

Abstract: A system and a method provide an Artificial Intelligence (AI) companion for each Function Block in a Programmable Logic Controller (PLC) program to integrate AI in automation systems. Multiple function blocks and system function blocks are grouped into a logic group. A control problem is broken down from a top level into logical partitions as several functions that are programmed as Function Blocks in a PLC program. Each Function Block and the entire PLC program are integrated with an associated AI Companion. A runtime system for the AI Companion provides new runtime capabilities. An approach to implementing the AT Companions is provided. A method of controlling an automation process is also provided.

Abstract: A system for checking security vulnerabilities for automation system design includes a security database, an Internet crawler application, and security service application. The security database stores descriptions of known software vulnerabilities related to an automation system. The Internet crawler application is configured to systematically browse the Internet to find new software vulnerabilities related to the automation system and index the new software vulnerability into the security database. The security service application retrieves, from the security database, potential software vulnerabilities related to a hardware/software configuration of the automation system. The security service application also identifies policies related to the potential vulnerabilities. Each policy describes a potential vulnerability and action to be performed in response to detection of the potential vulnerabilities.

Abstract: A system for exchanging data in an automation environment is provided. The system includes at least one programmable logic controller (PLC A) containing program instructions executable by the at least one programmable logic controller and a queue block (50) configured to dynamically exchange data between the program instructions of the PLC and a data consumer (PLC C, PLC D).

Abstract: Applications of artificial intelligence (AI) in industrial automation have focused mainly on the runtime phase due to the availability of large volumes of data from sensors. Methods, systems, and apparatus that can use machine learning or artificial intelligence (AI) to complete automation engineering tasks are described herein.

Abstract: A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

Abstract: A system and method to apply deep learning techniques to an automation engineering environment are provided. Big code files and automation coding files are retrieved by the system from public repositories and private sources, respectively. The big code files include examples general software structure examples to be utilized by the method and system to train advanced automation engineering software. The system represents the coding files in a common space as embedded graphs which a neural network of the system uses to learn patterns. Based on the learning, the system can predict patterns in the automation coding files. From the predicted patterns executable automation code may be created to augment the existing automation coding files.

Abstract: A computer-implemented method for quantifying assurance of a software system includes collecting artifacts of the software system generated during phases of the software system's engineering lifecycle. A graph of graphs (GoG) is constructed encoding the artifacts. Each subgraph in the GoG is a semantic network corresponding to a distinct assurance requirement. The GoG is used to calculate a component assurance value for each software component for each distinct assurance requirement. A system assurance value is calculated based on the component assurance values. An architectural view of the software system is presented showing at least one of the component assurance values and the system assurance values.

Abstract: A computer-implemented method for detecting cyber-attacks affecting a computing device includes retrieving a plurality of sensor datasets from a plurality of sensors, each sensor dataset corresponding to involuntary emissions from the computing device in a particular modality and extracting a plurality of features from the plurality of sensor datasets. One or more statistical models are applied to the plurality of features to identify one or more events related to the computing device. Additionally, a domain-specific ontology is applied to designate each of the one or more events as benign, failure, or a cyber-attack.