Abstract: Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. Simulated experience data for the HVAC system is generated or received. The simulated experience data is used to initially train the RL model for HVAC control. The HVAC system operates within a building using the RL model and generates real experience data. A determination may be made to retrain the RL model. The real experience data is used to retrain the RL model. In some embodiments, both the simulated and real experience data are used to retrain the RL model. Experience data may be sampled according to various sampling functions. The RL model may be retrained multiple times over time. The RL model may be retrained less frequently over time as more real experience data is used to train the RL model.

Abstract: Disclosed is a method for providing control data for an additive manufacturing device. The method includes accessing computer-based model data of a section of the object to be manufactured, defining a first portion and an adjoining second portion within the object section, and producing a data model of a region of a layer of the object section. Within the region a first portion cross-section cuts through the first portion and a second portion cross-section cuts through the second portion. An energy input parameter in the data model has, on average, a different value in the first portion cross-section than in the second portion cross-section and the energy beam is moved over the boundary of the first and second portion. Control data corresponding to the data model is provided for the generation of a control data set for the additive manufacturing device.

Abstract: Various embodiments include methods and apparatuses to provide human safety and machine safety and operations. In one example, a distributed interlock system includes at least one master device coupled to a number of slave device. The slave devices receive signals from one or more tools and provide the signals to the master device. The master device evaluates the signals and prevents unsafe conditions prior to one or more command executions, related to the unsafe conditions, being transmitted to one or more of the slave devices. Other methods and systems are disclosed.

Abstract: Disclosed is an intelligent control system of spunlace production line, which includes a data acquiring module, which is used for acquiring and storing real-time production line data; the production line data includes cotton feeding roller value, real-time moisture value, real-time speed value and real-time gram weight value; the data process module is used for classify and controlling that production line data, and giving the adjustment opinions of the cotton feeding roller parameters; the parameter control module is used for verifying the parameter adjustment opinions and applying the opinions to the control system; the data acquiring module, the data processing module and the parameter control module are connected in sequence.

Abstract: A device and method select an optimal boiler combustion model from among a plurality of boiler combustion models for boiler combustion to update a memory in which the boiler combustion models are stored. The device includes a memory configured to store first boiler combustion models that are derived in advance and second boiler combustion models that are derived in advance; and a processor configured to select an optimal boiler combustion model from among the first and second boiler combustion models and to update the memory according to characteristics of the selected optimal boiler combustion model. The processor may further generate a third boiler combustion model for the combustion of the boiler to select the optimal boiler combustion model from among the first to third boiler combustion models and verification data for model verification through the latest data measured in the boiler and data on basic characteristics of the measured data.

Abstract: An automation system for monitoring a safety-critical process includes a platform, a fail-safe peripheral module, and a safe runtime environment. The platform executes user programs. The user programs include a first user program and a second user program, which together implement a safety function. The second user program is diversitary with respect to the first user program. The fail-safe peripheral module couples the user programs with the safety-critical process. The safe runtime environment is implemented on the platform independently of the user programs and provides the user programs with safe resources independent of the platform.

Abstract: In prior art grid systems, power-line control is done by substation based large systems that use high-voltage (HV) circuits to get injectable impedance waveforms that can create oscillations on the HV power lines. Intelligent impedance injection modules (IIMs) are currently being proposed for interactive power line control and line balancing. These IIMs distributed over the high-voltage lines or installed on mobile platforms and connected to the HV power lines locally generate and inject waveforms in an intelligent fashion to provide interactive response capability to commands from utility for power line control.

Abstract: Described herein are methods and manufacturing systems that select fasteners for pre-feeding and, in some examples, pre-feed the selected fasteners. These methods involve aggregating historical manufacturing data, comprising hole identifications and fastener grip lengths, previously selected for these hole identifications. A specific grip length and a corresponding fastener repeatability rating are then determined for each hole identification from this historical manufacturing data. For example, a specific grip length corresponds to the most frequently selected grip length for this hole identification. In some examples, the historical manufacturing data is analyzed using machine learning. The fastener repeatability rating is compared to an operating threshold, in some examples, to determine if the corresponding grip length should be selected for a particular hole location.

Abstract: A method for visually displaying a facility mimic diagram of a technical facility which has an operator station server and an operator station client, wherein the method includes transferring the facility mimic diagram from the operator station server to the operator station client, where the facility mimic diagram includes an alarm signal indicator, communicating a duration for visually displaying the indicator, visually displaying the facility mimic diagram with the alarm signal indicator, informing the operator station client about an emergence of a new alarm signal in the management system via a message, determining a value an actual display time has to visually display the alarm signal indicator, determining whether a display time for the visual display of the alarm signal indicator with the new alarm signal would overshoot the value of the duration, limiting the visual display of the alarm signal indicator when the available duration is overshot.

Abstract: A power tool element indicating system (100) comprises a detection device (102) in communication with a replaceable and/or adjustable element (104), such as a blade, a chain or other element, of a power tool (106), to determine that the replaceable and/or adjustable element of the power tool requires replacement or adjustment. The system comprises an indicator (108), such as a LED, or a display, or other indicator, in communication with the detection device to indicate to a user that the replaceable and/or adjustable element requires replacement or adjustment.

Abstract: Disclosed is a control device of a plant.

Abstract: Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data for a manufacturing process, wherein the expected output data defines an attribute of an output of the manufacturing process; accessing a plurality of machine learning models that model the manufacturing process; determining, using a first machine learning model, input data for the manufacturing process based on the expected output data for the manufacturing process, wherein the input data comprises a value for a first input and a value for a second input; combining the input data determined using the first machine learning model with input data determined using the second machine learning model to produce a set of inputs for the manufacturing process, wherein the set of inputs comprises candidate values for the first input and candidate values for the second input.

Abstract: A method can include obtaining a set of images of a set of occupants located in an interior environment. The interior environment can have a first temperature. The method can include identifying, based on the set of images, a set of occupant characteristics corresponding to the set of occupants. The method can include obtaining a second temperature of an external environment. The method can include generating, by comparing the set of occupant characteristics to the second temperature, a discrepancy factor. The method can include determining that the discrepancy factor exceeds a threshold. The method can include initiating, in response to the determining that the discrepancy factor exceeds the threshold, a modification of the first temperature.

Abstract: A synchronization control device (10) includes: a main axis command calculator (Cmm) that calculates a main axis command position based on time-series target position information; a main axis modeler (Mm) that calculates a predicted main axis feedback position by a dynamic characteristic model of a main axis servo control mechanism (20) by inputting the main axis command position, a main axis feedback position, and a predicted main axis command position after a predetermined time calculated based on the target position information; and a driven axis command calculator (Cms) that calculates a driven axis command position based on the predicted main axis feedback position. This configuration achieves synchronization control that improves the accuracy of synchronous drive of the driven axis.

Abstract: A control device of a machine tool receives a target machining, in accordance with which a workpiece should be machined by a tool of the machine tool. The control device also receives via a human-machine interface or via an interface to an external memory device a selection, a parameterization and/or a specification of a sequence of predefined rules, which define the manner in which the machining of the workpiece should be modified in the event of undesired vibrations during machining. When undesired vibrations do not occur, as determined from acquired sensor signals, machining is carried out in accordance with the target machining. When undesired vibrations occur, the machining is modified in accordance with the rules, wherein the control device selects the rules in accordance with the selection, parameterizes the rules in accordance with the parameterization and/or carries out the rules in accordance with the specified sequence.

Abstract: A method, device and apparatus for processing a control parameter, and a storage medium are provided. The method includes: acquiring first information input to a control system; acquiring second information output after the first information is processed by the control system; obtaining a first parameter characteristic for characterizing a system parameter, according to the first information and the second information; obtaining a second parameter characteristic for adjusting the control parameter, according to the first parameter characteristic and a corresponding relation between the system parameter and the control parameter; and adjusting the control parameter according to the second parameter characteristic. Thus, an adaptive adjustment of the control parameter based on the variation of the scene is realized, and the parameter adjustment efficiency is improved.

Abstract: The present invention relates to a method for generating control data for the secondary machining of a solid body (1), in particular wafer, which is modified by means of laser beams (10). The interior of said solid body (1) has multiple modifications (12), said modifications (12) having been produced by means of laser beams (10).

Abstract: The feedback control device takes information regarding a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object; comprising: a first control unit that takes information regarding the control deviation as input, and outputs a first control amount for the controlled object; a second control unit that takes information regarding the control deviation as input and outputs a second control amount for the controlled object, and in which a parameter for calculating the second control amount is determined by machine learning; an operation unit that operates the controlled object using the first control amount output from the first control unit and the second control amount output from the second control unit; and a sampling unit for thinning out at a predetermined period information regarding the control deviation input to the second control unit.

Abstract: A method and system for aggregate programming a computer numerical control (CNC) machine that includes (a) shifting a control point from an aggregate base point to a center tip of a tool being programmed, thereby enabling tool length and diameter adjustment along with C-axis aggregate rotation during a machine activity. The shifting includes (i) activating a tilted work plane (TWP); and (ii) shifting the control point to the tip of the tool using a tool length compensation. The system and method also include (b) implementing an activity on the CNC machine using the shifted control point and C-axis aggregate rotation. In some cases, activating the TWP comprises using a special machine control code such as g-code G68.2. In some cases, shifting the control point to the tip of the tool using a tool length compensation comprises using a special machine control code such as g-code G43.

Abstract: An occupancy tracking device configured to identify devices connected to an access point over a predetermined time period. The device is further configured to populate entries in a device log for the identified devices. The device is further configured to determine a predicted occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.