Abstract: A computing system may include a fail-safe learning engine configured to access camera data captured by multiple cameras positioned within an environment during a learning phase, generate training data based on the camera data captured by the multiple cameras, and construct a human detection model using the training data. The computing system may also include a fail-safe trigger engine configured to access camera data captured by the multiple cameras positioned within the environment during an active phase, and the camera data captured during the active phase may include a target object. The fail-trigger engine may further be configured to provide, as an input to the human detection model, the camera data that includes the target object and execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

Abstract: A computing system that includes a neural network can receive a statement written in natural language text. The neural network can determine an operation intended by the statement. Based on the operation, the computing system can determine one or more parameters that correspond to the operation. Based on the operation, the computing system can identify a template in a target domain-specific language. Further, the computing system can populate the template with the operation and the one or more parameters. so as to automatically generate source code in the target domain-specific language, from the statement written in natural language text.

Abstract: A computing system may include a fail-safe learning engine configured to access camera data captured by multiple cameras positioned within an environment during a learning phase, generate training data based on the camera data captured by the multiple cameras, and construct a human detection model using the training data. The computing system may also include a fail-safe trigger engine configured to access camera data captured by the multiple cameras positioned within the environment during an active phase, and the camera data captured during the active phase may include a target object. The fail-trigger engine may further be configured to provide, as an input to the human detection model, the camera data that includes the target object and execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

Abstract: A method for providing cybersecurity to an industrial system having multiple components includes monitoring communications across the components of the system and detecting a deviation in an expected communication state in a first component. Upon detection of a deviation, communication states of at least one other component in the system is observed. If a deviation is also detected in the other component, then the deviation is deemed to be unauthorized. If no other deviations are detected in other components, the original deviation is deemed to be benign. Detecting a deviation includes comparing an expected communication state based on a baseline to the current communication state, the communication state may be based on a communication's content or a pattern of communication actions.

Abstract: Methods and systems are provided for evaluating robustness of engineering components. An example method includes receiving an engineering problem definition for a multidimensional engineering domain of an engineering component, in which the engineering problem definition includes a description of a plurality of engineering features of the engineering component. Executing an optimal stratified sampling algorithm that obtains a plurality of probabilistic samples from the multidimensional engineering domain. Selecting optimal combinations of higher fidelity models, reduced order models and response surfaces for execution of the probabilistic samples. Executing the probabilistic samples using the selected combinations to determine a respective engineering response for each engineering feature of the plurality of engineering features.

Abstract: Technical solutions are described for performing sensitivity analysis for engineering systems in spatial-temporal domain. An example method includes receiving a set of process parameters and retrieving a multidimensional dataset containing historical values of the process parameters and corresponding output values. The method further includes selecting a sampling algorithm to divide the multidimensional dataset into multiple subspaces, and selecting multiple samples (xi), one sample from each subspace. The method further includes perturbing the samples, computing a first effect (EEi) on an output value (y), and computing a second effect (SEEii) of perturbing a pair of samples (xi and xj) on the output value. The method further includes computing a sensitivity coefficient of the process parameters on the output value using the second effect for xi and xj, the first effect for xi, and the first effect for xj. An automatic visualization scheme for the global sensitivity results is also provided.

Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method includes identifying, by a processing device, a transition feature of the object based at least in part on point cloud data corresponding to the object. The method further includes performing, by the processing device, a geometric analysis on the transition feature of the object based at least in part on a curvature deviation. The method further includes generating a fitted parametric surface for the object based at least in part on the transition feature of the object and results of the geometric analysis on the transition feature of the object.

Abstract: Method and apparatus to provide protocol translation and selectable data exchange in a client/server system are provided. A tag list is extracted from a legacy client device 116 connected to a legacy server 114 using a protocol corresponding to the legacy server. A configuration manager device 120 is used to configure the extracted tag list to obtain a selected tag list excerpt of the extracted tag list. The configuring device is arranged to map the selected tag list excerpt to a configuration adapted for a respective 112 server, and to define contextualization. Server 112 provides industrial automation services using a next-generation protocol, e.g., OPC UA or MTConnect. A tag list is generated to configure server 112. A stream of data points of the selected tag list excerpt of the tag list extracted from the legacy client device is transferred to one or more client devices 124 connected to server 112.

Abstract: Methods and systems are provided for evaluating robustness of engineering components. An example method includes receiving an engineering problem definition for a multidimensional engineering domain of an engineering component, in which the engineering problem definition includes a description of a plurality of engineering features of the engineering component. Executing an optimal stratified sampling algorithm that obtains a plurality of probabilistic samples from the multidimensional engineering domain. Selecting optimal combinations of higher fidelity models, reduced order models and response surfaces for execution of the probabilistic samples. Executing the probabilistic samples using the selected combinations to determine a respective engineering response for each engineering feature of the plurality of engineering features.

Abstract: Technical solutions are described for performing sensitivity analysis for engineering systems in spatial-temporal domain. An example method includes receiving a set of process parameters and retrieving a multidimensional dataset containing historical values of the process parameters and corresponding output values. The method further includes selecting a sampling algorithm to divide the multidimensional dataset into multiple subspaces, and selecting multiple samples (xi), one sample from each subspace. The method further includes perturbing the samples, computing a first effect (EEi ) on an output value (y), and computing a second effect (SEEii) of perturbing a pair of samples (xi and xj) on the output value. The method further includes computing a sensitivity coefficient of the process parameters on the output value using the second effect for xi and xj, the first effect for xi, and the first effect for xj. An automatic visualization scheme for the global sensitivity results is also provided.

Abstract: Method and apparatus to provide protocol translation and selectable data exchange in a client/server system are provided. A tag list is extracted from a legacy client device 116 connected to a legacy server 114 using a protocol corresponding to the legacy server. A configuration manager device 120 is used to configure the extracted tag list to obtain a selected tag list excerpt of the extracted tag list. The configuring device is arranged to map the selected tag list excerpt to a configuration adapted for a respective 112 server, and to define contextualization. Server 112 provides industrial automation services using a next-generation protocol, e.g., OPC UA or MTConnect. A tag list is generated to configure server 112. A stream of data points of the selected tag list excerpt of the tag list extracted from the legacy client device is transferred to one or more client devices 124 connected to server 112.

Abstract: An imaging device for determining particle size distribution including a sample receptacle containing a sample and an imager capable of capturing a plurality of images of the sample in a region of observation. The imaging device further includes a radiation source provided linearly opposite to the imager and a base platform that supports the imager and the radiation source.

Abstract: An apparatus and a method for processing slurry are provided. The apparatus includes a support frame including a vertical member and a horizontal member. The apparatus also includes a mounting unit mounted on the vertical member, a mixing chamber disposed on the horizontal member, a controller mounted on the mounting unit, and a stirrer attached to the controller. The mounting unit is operable to position the stirrer with respect to the mixing chamber such that the stirrer is in contact with a slurry in the mixing chamber during the mixing operation.

Abstract: An imaging device for determining particle size distribution including a sample receptacle containing a sample and an imager capable of capturing a plurality of images of the sample in a region of observation. The imaging device further includes a radiation source provided linearly opposite to the imager and a base platform that supports the imager and the radiation source.

Abstract: An apparatus and a method for processing slurry are provided. The apparatus includes a support frame including a vertical member and a horizontal member. The apparatus also includes a mounting unit mounted on the vertical member, a mixing chamber disposed on the horizontal member, a controller mounted on the mounting unit, and a stirrer attached to the controller. The mounting unit is operable to position the stirrer with respect to the mixing chamber such that the stirrer is in contact with a slurry in the mixing chamber during the mixing operation.

Abstract: A packing apparatus for packing a substance in a container includes a filling unit configured to facilitate filling of the substance in the container and a compacting unit configured to compactly pack the filled substance in the container. The filling unit includes a moveable filling member moveable from at least a first filling position to a second filling position. The compacting unit includes a moveable compacting member moveable from at least a first compacting position to a second compacting position. At least one of the first compacting position and the second compacting position of the moveable compacting member is in relation to at least one of the first filling position and the second filling position of the moveable filling member. The moveable compacting member is moveable with a preconfigured compaction force that is a function of one or more properties of the container.