A computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method includes generating one or more solid CAD feature includes each representing a respective 3D shape. The method also includes, for each solid CAD feature, determining one or more respective freehand drawings each representing the respective 3D shape, and inserting in the dataset, one or more training samples. Each training sample includes the solid CAD feature and a respective freehand drawing. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.

An apparatus includes circuitry; and a memory storing computer-executable instructions that cause the circuitry to execute acquiring training data, in which image data in a manual and text data in the manual are input data, and in which work procedure information is output data, the work procedure information being supplemented by adding, to the text data in the manual, text data that is generated based on the image data; performing machine learning by using the training data; and generating a machine learning model that outputs the work procedure information in response to receiving input of the image data in the manual and the text data in the manual.

An image processing method implemented by a computer includes extracting feature points from captured images that are sequentially generated by an image capture device and include at least a first captured image and a second captured image generated prior to the first captured image, determining whether the number of feature points extracted from the first captured image exceeds a threshold value, and specifying a location of the first captured image relative to the second captured image upon determining that the number of the feature points extracted from the first captured image is below the threshold value.

An example computing system is configured to: (i) access a drawing associated with a construction project; (ii) identify, in the drawing, a set of candidate textual elements that potentially represent a title of the drawing; (iii) for each candidate textual element, (a) determine a respective dataset comprising values for a set of data variables that are potentially predictive of whether the candidate textual element is the title of the drawing, and (b) input the respective dataset into a machine-learning model that functions to (1) evaluate the respective dataset and (2) output, based on the evaluation, a respective score indicating a likelihood that the candidate textual element represents the title of the drawing; and (iv) based on the respective scores for the candidate textual elements that are output by the machine-learning model, select one given candidate textual element as the title of the drawing.

An example computing system is configured to: (i) access a drawing associated with a construction project; (ii) identify, in the drawing, a set of candidate textual elements that potentially represent a title of the drawing; (iii) for each candidate textual element, (a) determine a respective dataset comprising values for a set of data variables that are potentially predictive of whether the candidate textual element is the title of the drawing, and (b) input the respective dataset into a machine-learning model that functions to (1) evaluate the respective dataset and (2) output, based on the evaluation, a respective score indicating a likelihood that the candidate textual element represents the title of the drawing; and (iv) based on the respective scores for the candidate textual elements that are output by the machine-learning model, select one given candidate textual element as the title of the drawing.

Systems and methods are disclosed for automatically detecting a design element in a design document. One method comprises receiving a design document and generating an enhanced design document based on the received design document. The enhanced design document may be generated by augmenting additional information to the design document using machine learning techniques. In response to receiving a user input, one or more design elements in the enhanced design document may be determined, and additional information associated with the determined one or more design elements may be displayed to the user.

A machine learning (ML) based asset monitoring system that automatically determines damage mechanisms (DMs) and generates automatically updated visualizations of assets that include equipment and lines of a processing plant is disclosed. The asset monitoring system is communicatively coupled to the assets of the plant and continuously receives process parameters associated with the various processes and equipment in the plant. Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system. DMs are predicted for each of the assets using a ML model based on the process parameters and the corrosion loops. The data regarding the DMs, CLs and the process parameters are used to obtain equipment risk rankings for the assets. Multi-dimensional visualizations of the assets that display the state of the plant assets in real-time are generated.

A machine learning (ML) based asset monitoring system that automatically determines damage mechanisms (DMs) and generates automatically updated visualizations of assets that include equipment and lines of a processing plant is disclosed. The asset monitoring system is communicatively coupled to the assets of the plant and continuously receives process parameters associated with the various processes and equipment in the plant. Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system. DMs are predicted for each of the assets using a ML model based on the process parameters and the corrosion loops. The data regarding the DMs, CLs and the process parameters are used to obtain equipment risk rankings for the assets. Multi-dimensional visualizations of the assets that display the state of the plant assets in real-time are generated.

Concepts and technologies disclosed herein are directed to pictograms as digitally recognizable tangible controls. According to one aspect disclosed herein, a user system can include a processing component and a memory component. The memory component can include instructions of a pictogram digitization module. The user system can capture, via a camera component, an image containing a pictogram that is a digitally recognizable tangible manifestation of a digital control. The user system can determine, via the pictogram digitization module, the digital control associated with the pictogram. The user system can implement, via the pictogram digitization module, the digital control. The digital control can include a digital content, an action, or a context. The user system can create, via the pictogram digitization module, a digital interface that includes the digital control. In some embodiments, the pictogram includes a formal pictogram. In other embodiments, the pictogram includes an informal pictogram.

Concepts and technologies disclosed herein are directed to pictograms as digitally recognizable tangible controls. According to one aspect disclosed herein, a user system can include a processing component and a memory component. The memory component can include instructions of a pictogram digitization module. The user system can capture, via a camera component, an image containing a pictogram that is a digitally recognizable tangible manifestation of a digital control. The user system can determine, via the pictogram digitization module, the digital control associated with the pictogram. The user system can implement, via the pictogram digitization module, the digital control. The digital control can include a digital content, an action, or a context. The user system can create, via the pictogram digitization module, a digital interface that includes the digital control. In some embodiments, the pictogram includes a formal pictogram. In other embodiments, the pictogram includes an informal pictogram.