A material synthesis apparatus may include a synthesis device configured to perform a synthesis of a material of a target product; a communication interface configured to receive a first synthesis method of the target product, the first synthesis method being calculated by an external apparatus using a previously trained synthesis prediction model; and a processor configured to: determine first commands for synthesizing the target product based on the first synthesis method, schedule an order in which the first commands are executed, and control the synthesis device based on the scheduled order.

MPC controller systems and methods are disclosed that may be applied to various different types of MPC controllers and methods for use in any desired or appropriate controller settings, such as physical systems with non-linearities that require careful modelling tactics, which are considered complex systems. Complex systems include fast systems which are those where solving the descriptive model of the system has the potential to surpass the closed loop sampling time required to control the system. Examples of complex fast systems are robot manipulators, quadracopters and injection speed of an injection molding process.

A system and method for providing spatiotemporal costmap inference for model predictive control that includes receiving dynamic based data and environment based data to determine observations and goal information associated with an ego agent and a traffic environment. The system and method also include training a neural network with the observations and goal information and determining an optimal path of the ego agent based on at least one spatiotemporal costmap. The system and method further include controlling the ego agent to autonomously operate based on the optimal path of the ego agent.

Provided is an apparatus including: a first acquisition unit acquiring an operation plan of a piece of equipment, and at least identification information of a parameter among target setting data used for learning of an operation model operating the piece of equipment, the target setting data including identification information of a parameter for which a target range is to be set among parameters relating to the piece of equipment and a target range set for the parameter; and a first learning processing unit performing, by using learning data including the identification information of the parameter and the operation plan acquired by the first acquisition unit, learning processing of a target setting model outputting at least one of the identification information or the target range of the parameter among the target setting data that should be used for learning of the operation model, in response to the operation plan being input.

A chiller fault prediction system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to receive chiller data for a plurality of chillers, the chiller data indicating performance of the plurality of chillers. The one or more processors are configured to generate, based on the received chiller data, a plurality of single chiller prediction models and a plurality of cluster chiller prediction models, the plurality of single chiller prediction models generated for each the plurality of chillers and the plurality of cluster chiller prediction models generated for chiller clusters of the plurality of chillers. The one or more processors are configured to label each of the plurality of single chiller prediction models and the plurality of cluster chiller prediction models as an accurately predicting chiller model or an inaccurately predicting chiller model based on a performance of each of the plurality of single chiller prediction models and a performance of each of the plurality of cluster chiller prediction models. The one or more processors are configured to predict a chiller fault with each of the plurality of single chiller prediction models labeled as the accurately predicting chiller models. The one or more processors are configured to predict a chiller fault for each of a plurality of assigned chillers assigned to one of a plurality of clusters labeled as the accurately predicting chiller model.

A system comprising a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body; and at least one computer hardware processor programmed to perform: applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output; and identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

A system is controlled using particle filter executed to estimate weights of a set of particles based on fitting of the particles into a measurement model, wherein a particle includes a motion model of the system having an uncertainty modeled as a Gaussian process over possible motion models of the system and a state of the system determined with the uncertainty of the motion model of the particle, wherein a distribution of the Gaussian process of the motion model of one particle is different from a distribution of the Gaussian process of the motion model of another particle. Each execution of the particle filter updates the state of the particle according to a control input to the system and the motion model of the particle with the uncertainty and determines particle weights by fitting the state of the particle in the measurement model subject to measurement noise.

A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to determine an average of a minimum half of sorted energy consumption values for a first time period and determine an average of a maximum half of sorted energy consumption values for a second time period. The instructions also cause the processor to determine a ratio of the average of the minimum half of sorted energy consumption values for the first time period to the average of the maximum half of sorted energy consumption values for the second time period, compare the calculated ratio to an adaptively tunable threshold value and activate a system responsive to the calculated ratio exceeding the adaptively tunable threshold value.

Methods and systems for utilizing co-simulation of a physical model and a self-adaptive predictive controller using hybrid automata are described. For example, there is disclosed a self-adaptive predictive control system to generate an initial patient model with initial patient model settings (k.sub.1, k.sub.2, . . . , k.sub.n) and execute a program that takes as input, the initial patient model settings as configuration parameters that specify at least a plurality of initial states. Such a system further receives new patient inputs and generates hybrid automata as a variation of the initial states by applying the new patient inputs to the initial patient model. The system further derives reachable states from the hybrid automata and outputs all reachable states computed. The system further detects changes and responsively generates a new patient predictive model with new parameter settings (k′.sub.1, k′.sub.2, . . . , k′.sub.n), iteratively repeating until a termination criterion is satisfied. Other related embodiments are disclosed.

Systems, devices, media, and methods are presented for controlling remote equipment in a network. A switch heater control system includes a weather modeling function. The system periodically obtains weather data according to a predetermined time interval. Based on the closest weather data set, the weather modeling function generates a hyperlocal forecast associated with each switch heater location. The system includes an active snowfall mode and a maintenance mode that controls heating based on an estimate of local snow depth, adjusted for wind conditions and passing trains. When the hyperlocal forecast indicates heating is required, the system calculates a melt duration, starts a timer, and transmits a start signal to the switch heater.