Abstract: A method selects one or more power sources in a hybrid electric vehicle (HEV) at a particular moment in time for a current route to optimize energy consumption for the HEV, wherein the HEV includes one or more electric engines (EC) and one or more internal combustion engines, by first determining, in an off-line processor, a regression model that predicts a terminal cost of a state along a route from features associated with the route and the vehicle, using a computed true costs-to-go of multiple states from multitude of real or imaginary routes. In an online processor in the HEV, truncated dynamic programming is performed using the regression model to estimate a terminal cost at an end of a truncated time horizon for a current route. Then, one or more of the power sources are selected for the one or more EC and the one or more ICE based on a minimal cost-to-go of a current state.

Abstract: A method selects one or more power sources in a hybrid electric vehicle (HEV) at a particular moment in time for a current route to optimize energy consumption for the HEV, wherein the HEV includes one or more electric engines (EC) and one or more internal combustion engines, by first determining, in an off-line processor, a regression model that predicts a terminal cost of a state along a route from features associated with the route and the vehicle, using a computed true costs-to-go of multiple states from multitude of real or imaginary routes. In an online processor in the HEV, truncated dynamic programming is performed using the regression model to estimate a terminal cost at an end of a truncated time horizon for a current route. Then, one or more of the power sources are selected for the one or more EC and the one or more ICE based on a minimal cost-to-go of a current state.

Abstract: A method and system personalizes a heating ventilation and air conditioning (HVAC) system for an occupant in an environment, by first obtaining biometric data of the occupant and measuring continuously environmental data in the environment as current conditions. An estimate of a comfort index of the occupant is adapted continuously based on the current conditions. Then, the HVAC system is controlled based on the estimate of the comfort index to personalize the HVAC system.

Abstract: An information system includes a prediction engine for predicting an action based on a set of driving state parameters, and a driving history, and a simulation engine for generating a hypothetical scenario by simulating one or a combination of at least one driving state parameter and at least part of the driving history, such that the prediction engine predicts the action for the hypothetical scenario.

Abstract: A method for controlling an operation of a machine according to a model of the machine having uncertainties determines a set of triples of data points representing the operation of the machine over a period of time. Each triple of data points in the set includes two subsequent states of the machine and corresponding control input causing a transition of the machine between the subsequent states. Next, the method determines a current model of the machine based on the set of triples of data points, such that the current model is within a feasible space of models of the machine corresponding to a polytope with vertices defined by base models representing extreme values of different parameters of the model.

Abstract: A method schedules elevator cars in a group elevator system in a building by first generating a set of probability distributions for arrivals of future passengers at any floor of the building, wherein the set of probability distributions are characterized by probabilistic variables that specify arrival information of the future passengers, wherein the arrival information includes a probability of service requests by the future passengers and a probability of possible times of the service requests. A schedule for the elevator cars is based on the set of probabilistic distribution. Then, the schedule is provided to a controller of the group elevator system to move the elevator cars according to the schedule.

Abstract: A method delivers information to satisfy current needs of a driver of a vehicle, by first acquiring data related to a current state of the vehicle. The current state and a predictive model are passed to a predictive procedure to determine choice probabilities for nodes in a choice tree (CT). Based on the choice probabilities, information related to the current needs to the driver are selected from a database storing commercial and non-commercial information. Then, the information is delivered to the driver, wherein the delivering is autonomous of the driver.

Abstract: A method determines a power flow of a power grid by optimizing an objective function representing an operation of the power grid using a spatial branch and bound (BB) framework for determining iteratively upper and lower bounds of the objective function. During the optimization, the lower bounds are determined using a semi-definite programming (SDP) relaxation of an optimal power flow (OPF) problem.

Abstract: A method operates a set of heating, ventilation and air-conditioning (HVAC) units by optimizing jointly operations of the set of HVAC units subject to constraints to determine times of switching each HVAC unit ON and OFF and by controlling each HVAC unit according to the corresponding times of switching. The operation of each HVAC unit is represented by a continuous function. The constraints include a complementarity constraint for each HVAC unit, such that the complementarity constraint for the HVAC unit defines a discontinuity of the operation of the HVAC unit at corresponding times of switching.

Abstract: Anomalies in real time series are detected by first determining a similarity matrix of pairwise similarities between pairs of normal time series data. A spectral clustering procedure is applied to the similarity matrix to partition variables representing dimensions of the time series data into mutually exclusive groups. A model of normal behavior is estimated for each group. Then, for the real time series data, an anomaly score is determined, using the model for each group, and the anomaly score is compared to a predetermined threshold to signal the anomaly.

Abstract: A method detects anomalies in time series data, by first learning a final set of exemplars by summarizing training time series data using a divide-and-conquer procedure. Then, for each window of testing time series data, a distance to a nearest exemplar in the final set of exemplars is determined, wherein the distance is an anomaly score. Finally, an anomaly is signaled when the anomaly score for a window is greater than a threshold.

Abstract: A method adapts a user interface of a vehicle navigation system. Based on an input vector representing a current state related to the vehicle, probabilities of actions are predicted to achieve a next state using a predictive model representing previous states. Then, a subset of the actions with highest probabilities that minimize a complexity of interacting with the vehicle navigation system are displayed in the vehicle.

Abstract: A method detects an anomaly in a discrete manufacturing process (DMP) by first acquiring signals from the DMP. Events are extracted from the signals as a sequence. Then, it is determined whether the sequence is inconsistent with a normal behavior as represented by a log-based ordering relationship table. If true, then an alarm is signaled.

Abstract: A power flow problem (OPF) in an electric power network is globally optimized using a branch and bound tree of nodes connected by edges. The BB initially includes at least a root node, and each node represents a feasible region of limits on voltages and powers. An upper bound on the OPF problem is solved for selected nodes using nonlinear programming, while a lower bound is solved using a convex relaxation. The lowest upper and lower bounds are updated using the current upper and lower bound. If a difference between the lowest upper and lowest lower bound is less than a threshold, then outputting the voltages and the powers for the electric power network as represented by the feasibility region for the selected node. Otherwise, the feasible region of the node is partitioned to replace the node. The process is repeated until the tree is empty.

Abstract: A method determines a power flow of a power grid by optimizing an objective function representing an operation of the power grid using a spatial branch and bound (BB) framework for determining iteratively upper and lower bounds of the objective function. During the optimization, the lower bounds are determined using a semi-definite programming (SDP) relaxation of an optimal power flow (OPF) problem.

Abstract: A method detects anomalies in time series data, wherein the time series data is multivariate, by partitioning time series training data into partitions. A representation for each partition in each time window is determined to form a model of the time series training data, wherein the model includes representations of distributions of the time series training data. The representations obtained from partitions of time series test data are compared to the model to obtain anomaly scores.

Abstract: An information system includes a prediction engine for predicting an action based on a set of driving state parameters, and a driving history, and a simulation engine for generating a hypothetical scenario by simulating one or a combination of at least one driving state parameter and at least part of the driving history, such that the prediction engine predicts the action for the hypothetical scenario.

Abstract: A method adapts a user interface of a vehicle navigation system. Based on an input vector representing a current state related to the vehicle, probabilities of actions are predicted to achieve a next state using a predictive model representing previous states. Then, a subset of the actions with highest probabilities that minimize a complexity of interacting with the vehicle navigation system are displayed in the vehicle.

Abstract: A method delivers information to satisfy current needs of a driver of a vehicle, by first acquiring data related to a current state of the vehicle. The current state and a predictive model are passed to a predictive procedure to determine choice probabilities for nodes in a choice tree (CT). Based on the choice probabilities, information related to the current needs to the driver are selected from a database storing commercial and non-commercial information. Then, the information is delivered to the driver, wherein the delivering is autonomous of the driver.

Abstract: A climate control system for a building includes air conditioning systems and ventilation (HVAC) systems. The system is controlled by determining sensible cooling and heating rates using temperature differences at inlets and outlets at evaporators, as well as moisture absorption, moisture desorption, ventilation influences, human activities, and condensation at the evaporator. Humidity differences and temperature differences are determined as driving forces for moisture absorption, desorption, and temperatures and humidities of interior fabrics. The differences are obtained from a thermal circuit model. A latent cooling rate is determined using the humidity differences. The model integrates a temperature model and a humidity model for jointly predicting temperatures and humidities for operating the HVAC systems using a mass transfer process.