The computer-implemented method for glass bending includes obtaining a deviation of a real shape of a glass from a desired shape of the glass, the glass is produced by a glass bending process; determining a variation of at least one parameter associated with the glass bending process, at least in part based on the deviation of the real shape from the desired shape; and adjusting the at least one parameter based on the variation for compensation of the deviation.

The present disclosure addresses the technical problem of prediction of a preheat refractory temperature profile of a ladle furnace. Operational temperature of the ladle furnace, stability of sensors and placement make sensors not feasible. Computational Fluid Dynamics (CFD) simulations require large computation time and cannot be used for runtime applications in plants. The method and system of the present disclosure uses CFD modeling to carry out parametric study to generate data which is further processed to train an Artificial Neural Network (ANN) model that serves as a prediction model for predicting the preheat refractory temperature profile for at least a portion of the side refractory and at least a portion of the bottom refractory layer separately for which a new set of input data is obtained. The trained prediction model of the present disclosure provides a quick runtime prediction in plants.

The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

A method and device for blast furnace state monitoring based on multi-modes fusion. In the process of blast furnace state monitoring, the concept of sub-mode is introduced, and the method for pre-acquisition and multi-modes weighted fusion by sub-mode is proposed to monitor blast furnace state. After preprocessing the main parameter data of the blast furnace, several sub-modes of the blast furnace and their corresponding blast furnace state indication variable data are obtained by means of a mean shift clustering algorithm. Taking the real-time parameter data of the blast furnace as inputs, the Euclidean distance between the real-time parameter data of the blast furnace and each sub-mode is calculated, and the sub-modes are weighted and fused according to the distance, and the fusion result is the real-time blast furnace state, so as to realize the real-time state monitoring of the blast furnace in a smooth operation.

A servo motor controller comprises: a position command generation unit; a position detection unit; a position control unit that generates a velocity command for position control based on a position command and a position FB; a force command generation unit that generates a force command for instructing force to be applied to a subject by a driving target; a force detection unit; a force control unit that generates a velocity command for force control based on the force command and a force FB; a selection unit that selects one of the position control and the force control; and a storage unit that stores a force threshold. The selection unit selects one of the velocity commands for the position control and the force control of a smaller value. If the force FB is smaller than the force threshold, the selection unit selects the velocity command for the position control.

A method and device for blast furnace state monitoring based on multi-modes fusion. In the process of blast furnace state monitoring, the concept of sub-mode is introduced, and the method for pre-acquisition and multi-modes weighted fusion by sub-mode is proposed to monitor blast furnace state. After preprocessing the main parameter data of the blast furnace, several sub-modes of the blast furnace and their corresponding blast furnace state indication variable data are obtained by means of a mean shift clustering algorithm. Taking the real-time parameter data of the blast furnace as inputs, the Euclidean distance between the real-time parameter data of the blast furnace and each sub-mode is calculated, and the sub-modes are weighted and fused according to the distance, and the fusion result is the real-time blast furnace state, so as to realize the real-time state monitoring of the blast furnace in a smooth operation.

The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remainsin preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

The present disclosure addresses the technical problem of prediction of a preheat refractory temperature profile of a ladle furnace. Operational temperature of the ladle furnace, stability of sensors and placement make sensors not feasible. Computational Fluid Dynamics (CFD) simulations require large computation time and cannot be used for runtime applications in plants. The method and system of the present disclosure uses CFD modeling to carry out parametric study to generate data which is further processed to train an Artificial Neural Network (ANN) model that serves as a prediction model for predicting the preheat refractory temperature profile for at least a portion of the side refractory and at least a portion of the bottom refractory layer separately for which a new set of input data is obtained. The trained prediction model of the present disclosure provides a quick runtime prediction in plants.

A servo motor controller comprises: a position command generation unit; a position detection unit; a position control unit that generates a velocity command for position control based on a position command and a position FB; a force command generation unit that generates a force command for instructing force to be applied to a subject by a driving target; a force detection unit; a force control unit that generates a velocity command for force control based on the force command and a force FB; a selection unit that selects one of the position control and the force control; and a storage unit that stores a force threshold. The selection unit selects one of the velocity commands for the position control and the force control of a smaller value. If the force FB is smaller than the force threshold, the selection unit selects the velocity command for the position control.

A dispatch control method for a furnace process including the following steps is provided. Before a plurality of lots of wafers is loaded into a furnace, the characteristic variation value of each of the plurality of lots of wafers is calculated. The plurality of lots of wafers is ordered according to the size of the characteristic variation values. The plurality of lots of wafers is placed in the furnace in a descending order of the characteristic variation values corresponding to a plurality of locations in the furnace causing the characteristic variation values to change from smaller to larger.