A method is provided for detecting and avoiding conflict along a current route of a robot. The method includes accessing or determining trajectories of the robot and a nearby moving object forward in time from their respective current positions, and detecting a conflict from a comparison of the trajectories. The method includes selecting a maneuver to avoid the conflict, and outputting an indication of the maneuver for use in at least one of guidance, navigation or control of the robot to avoid the conflict. Selection of the maneuver includes determining a plurality of angles that describe the conflict such as those at which the robot and moving object observe one another, and/or an angle between their trajectories, and evaluating the plurality of angles to select the maneuver.

A method for automating process development in a bioprocessing environment is provided. The method comprising: executing a first experiment run according to a set of parameters; retrieving a first real-time set of data of the experiment run while the experiment run is being executed; retrieving a second real-time set of data of an experiment run being executed in parallel, analysing the retrieved first real-time set of data and the second real-time set of data to determine an adjusted set of parameters; and, modifying, based on the analysis, the parameters upon which the experiment run is being executed during execution of the run such that the run continues to be executed according to the modified set of parameters. A system, computer program and computer readable medium are also provided.

A method for operating a power generating facility connected to a power distribution grid having an uncertain power generation condition includes predicting a probabilistic power flow forecast in a transmission line of the power distribution grid for a period of time, wherein the transmission line is electrically coupled to the power generating facility, predicting, using the probabilistic power flow forecast, a probability of congestion over the transmission line of the power distribution grid during the period of time, generating a mitigation plan, including a load adjustment on the transmission line, using the probability of congestion predicted over the transmission line and a thermal limit constraint of the transmission line, wherein the mitigation plan balances load adjustment and an overlimit line capacity on the transmission line, and controlling the power generating facility, using the mitigation plan, to achieve load modification and mitigate the probability of congestion predicted in the transmission line.

Apparatus and methods for recovering energy in a petroleum, petrochemical, or chemical plant as described. The invention relates to a recovered electric power measuring system comprising at least one processor; at least one memory storing computer-executable instructions; and at least one receiver configured to receive data from a sensor on an electrical powerline connected to a generator of a power-recovery turbine, the power-recovery turbine located in a petroleum, petrochemical, or chemical process zone wherein a portion of a first process stream flows through the power-recovery turbine and generates recovered electric power as direct current, the power-recovery turbine electrically connected to a single DC to AC inverter and the output of the DC to AC inverter electrically connected to a first substation.

A non-transitory computer-readable recording medium storing a control program for causing a computer to execute a process for controlling a system. The process includes, obtaining a result of a target that fluctuates in accordance with control performed by a system, calculating a weight for a control value in accordance with the result, a history of the control value input to the system and a comparison between the result and a predetermined range, calculating the control value based on the result and the weight, and inputting the calculated control value to the system to control the target.

A control system includes a vision system including an imaging device and a VAR monitoring system configured to determine a power adjustment phase of the VAR process based on the images from the vision system and a process parameter. The VAR monitoring system includes a vision analysis module configured to analyze the images from the vision system to detect a melt marker based on a remelt image process model, and a prediction module configured to predict an operational characteristic of the VAR process that is associated with the power adjustment relative to a melt marker location and a remelt prediction model. The VAR monitoring system is configured to initiate the power adjustment phase in response to the melt marker satisfying a predetermined melt marker condition, the operational characteristic of the VAR process satisfying a predetermined operational condition, or a combination thereof.

A method for diagnosing a technical system, an apparatus, and a computer program product are provided for carrying out a main component analysis of predefined values for n variables for describing a normal state of the technical system, where n≥2, and at least one main component of the n variables is ascertained. The at least one main component is predefined for describing the normal state of the technical system. Deviations of values for the n variables for describing a current state of the technical system are ascertained from the at least one predefined main component for describing the normal state, in order to infer a fault. A data carrier is also provided.

A heating, ventilation, and air conditioning (HVAC) control device configured to generate the machine learning model using the first set of weights and the second set of weights. The machine learning model is configured to output a probability that a user is present at the space based on an input that identifies a day of the week and a time of a day. The device is further configured to determine a probability that a user is present at the space for a predicted occupancy schedule using the machine learning model, to determine an occupancy status based on a determined probability that a user is present at the space, and to set a predicted occupancy status in the predicted occupancy schedule based on a determined occupancy status for each time entry. The device is further configured to output the predicted occupancy schedule.

Provided herein is a system for occupiable space automation using neural networks that delivers scalable and more intelligent occupiable space automation that can continuously learn from user actions and experiences and adapt to specific needs of each individual occupiable space. The occupiable space automation control system is built based on brain inspired multi-layer neural network with plastic connectivity between neurons. The occupiable space automation control system is configured to (a) adaptively predict previously learned activity patterns and (b) alert about potentially harmful or undesired activity patterns of the plurality of periphery devices based on response events of the plurality of artificial neurons and coupling strengths of the plurality of synapses. The occupiable space automation control system is configured to automatically operate the at least one controller based on the predicted activity pattern and/or provide user alerts based on a detected harmful activity pattern.

An acquisition unit (11) acquires an explanatory variable that is to be input to a model (37) configured to output an objective variable for the explanatory variable, a specification unit (12) associates a frequency at which an explanatory variable included in each of a plurality of areas, which are obtained by dividing an explanatory variable space, is acquired by the acquisition unit (11) with each of the plurality of areas, and specifies an area to which an explanatory variable included in learning data used to learn the model (37) belongs and in which a frequency of an explanatory variable acquired by the acquisition unit (11) is a predetermined value or less, and an update unit (14) updates the model (37) in such a manner that learning data including an explanatory variable belonging to an area specified by the specification unit (12) is forgotten.