A system and method for modeling, controlling and analyzing electrical grids for use by control room operators and automatic control provides a multi-dimensional, multi-layer cellular computational network (CCN) comprising an information layer; a knowledge layer; a decision-making layer; and an action layer; wherein each said layer of said CCN represents one of a variable in an electric power system. Situational awareness/situational intelligence is provided therefrom so that the operators and grid control systems can make the correct decision and take informed actions under difficult circumstances to maintain a high degree of grid integrity and reliability by analyzing multiple variables within a volume of time and space to provide an understanding of their meaning and predict their states in the near future where these multiple variables can have different timescales.

A method and system for optimizing a human supervised cyber-physical system determines a state of the human operator based on data from multiple psycho physiological sensors, determines a state of each of multiple cyber-physical systems in the human supervised cyber-physical system based on data provided by the cyber-physical systems, and fuses the state of the human operator and the state of each of the plurality of cyber-physical systems into a single state of the human supervised cyber-physical system. The single state is then used to generate recommendations for optimizing a user interface and to generate high level control signals for the cyber-physical systems.

The energy storage system operating apparatus and method includes a pre-optimization processing unit configured to generate an operating schedule, which is for operating the energy storage system during a set period, for each predetermined section by reflecting electric power billing environment data in at least one of a consumer policy and operating characteristics of the energy storage system and configured to set an electric power reserve to prepare for a shortage of an electric power amount; and an operating control unit configured to detect, for each section, an error between a value measured as being actually consumed and a predicted value of the operating schedule generated by the pre-optimization processing unit and configured to selectively reflect the electric power reserve in a discharging amount corresponding to the operating schedule in a subsequent section according to the detected error to control an energy storage system operating unit.

Methods and systems are provided for controlling a temperature of a processor of a controller. In one embodiment, a method includes: collecting a first set of measurement data based on measurement parameters; processing the first set of measurement data associated with the processor using a predictor model to determine a temperature of the processor; and selectively controlling the temperature of the processor based on the determined temperature.

A method and system for wastewater treatment based on dissolved oxygen control by a fuzzy neural network, the method for wastewater treatment comprising the following steps: (1) measuring art inlet water flow rate, an ORP value in an anaerobic tank, a DO value in an aerobic tank, an inlet water COD value, and an actual outlet water COD value; (2) collecting the measured sample data and sending them via a computer to a COD fuzzy neural network predictive model, so as to establish an outlet water COD predicted value, (3) comparing the outlet COD predicted value with the outlet water COD set value, so as to obtain an error and an error change rate, and using them as two input variables to adjust a suitable dissolved oxygen concentration. Accordingly, the on-line prediction and real-time control of dissolved oxygen wastewater treatment are achieved. The accurate control of dissolved oxygen concentration by the present method for wastewater treatment can achieve a saving in energy consumption while ensuring stable running of the sewage treatment system, and the outlet water quality meets the national emission standards.

A system and method for unsupervised root cause analysis of machine failures. The method includes analyzing, via at least unsupervised machine learning, a plurality of sensory inputs that are proximate to a machine failure, wherein the output of the unsupervised machine learning includes at least one anomaly; identifying, based on the output at least one anomaly, at least one pattern; generating, based on the at least one pattern and the proximate sensory inputs, an attribution dataset, the attribution dataset including a plurality of the proximate sensory inputs leading to the machine failure; and generating, based on the attribution dataset, at least one analytic, wherein the at least one analytic includes at least one root cause anomaly representing a root cause of the machine failure.

A neural-Model Predictive Control (MPC) controller is described to control a dynamical system (i.e., “plant”). The MPC controller receives, in a base controller, a measurement of a current state of a plant and generates a control signal based on the measurement of the current state of the plant. A forward module receives the measurement of the current state of the plant and the control signal to generate a forward module prediction. A forward module corrector receives the measurement of the current state of the plant and the control signal from the base controller to generate an additive correction to the forward module prediction to generate a predicted plant state. Control sequences of length L of pairs of control signals and corresponding predicted plant states are generated until N.sub.s control sequences have been generated. A next plant control signal is generated based on the N.sub.s control sequences.

A building management system includes building equipment operable to affect a variable state or condition of a building and a control system configured to receive a user input indicating a model form. The model form includes a plurality of matrices having a plurality of elements defined in terms of a plurality of parameters. The control system is configured to parse the model form to generate a sequence of machine-executable steps for determining a value of each of the plurality of elements based on a set of potential parameter values, identify a system model by executing the sequence of machine-executable steps to generate a set of parameter values for the plurality of parameters, generate a graphical user interface that illustrates a fit between predictions of the identified system model and behavior of the variable state or condition of the building, and control the building equipment using the identified system model.

A mobility device that can accommodate speed sensitive steering, adaptive speed control, a wide weight range of users, an abrupt change in weight, traction control, active stabilization that can affect the acceleration range of the mobility device and minimize back falls, and enhanced redundancy that can affect the reliability and safety of the mobility device.

Identifying models of dynamic systems is described herein. One method for identifying a model of a dynamic system includes estimating a number of parameters for each of a number of models of the dynamic system, predicting an output using the estimated number of parameters for each of the number of models, calculating a rate of error of the predicted output for each of the number of models compared to an observed output, and identifying a best model among the number of models of the dynamic system based on the calculated rate of errors.