A laundry treatment device includes a washing module configured to perform operation related to washing, a camera configured to capture an image of a tag attached to a laundry, and a processor configured to acquire laundry information of a plurality of laundries, to convert the acquired laundry information into encoding data, and to acquire values of laundry control variables corresponding to the converted encoding data based on a laundry course learning model learned using a plurality of reference data through a deep learning algorithm.

A measurement operation parameter adjustment apparatus that enables efficient measurement of the placement position of an object to be measured even in the case where there are variations in the placement positions, the sizes, and the product types of objects to be measured includes a machine learning device. The machine learning device observes measurement operation parameter data representing the measurement operation parameter of the measurement operation and measurement time data representing time taken to perform the measurement operation as a state variable representing a current environmental state and performs learning or decision-making using a learning model obtained by modeling adjustment of the measurement operation parameter based on the state variable.

A hierarchical resource management system for a building includes one or more processors. The processors implement a plurality of agents that each monitor sensed values, and generate operating scenarios based on the sensed values for corresponding resources. The processors also implement a coordinator that filters the operating scenarios to remove the operating scenarios that violate internal laws of the agents to form an aggregate validated set of operating scenarios. The processors further implement a supervisor that, responsive to receipt of target conditions for the zones and the aggregate validated set of operating scenarios from the coordinator, selects a combination of the operating scenarios from the aggregate validated set of operating scenarios that achieves target conditions and minimizes overall energy consumption by the resources such that some of the operating scenarios of the combination do not minimize energy consumption of the resources corresponding to the some of the operating scenarios.

Described herein are systems and methods for inverse reinforcement learning to leverage the benefits of model-based optimization method and model-free learning method. Embodiments of a framework combining human behavior model with model predictive control are presented. The framework takes advantage of feature identification capability of a neural network to determine the reward function of model predictive control. Furthermore, embodiments of the present approach are implemented to solve the practical autonomous driving longitudinal control problem with simultaneous preference on safe execution and passenger comfort.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

A site monitoring system may analyze information from sites to determine when a device, a sensor, a controller, or other structure associated with optically switchable devices has a problem. The system may, if appropriate, act on the problem. In certain embodiments, the system learns customer/user preferences and adapts its control logic to meet the customer's goals.

A control device for controlling a first variable physical parameter characterized based on a physical parameter application state includes a sensing unit and a processing unit. The sensing unit sensing a second variable physical parameter to generate a sense signal, wherein the second variable physical parameter is characterized based on a physical parameter application range represented by a measurement value application range. The processing unit is coupled to the sensing unit, obtains a measured value in response to the sense signal, and causes the first variable physical parameter to be in the physical parameter application state under a condition that the physical parameter application range which the second variable physical parameter is currently in is determined by checking a mathematical relation between the measured value and the measurement value application range.

There is proposed a model update device and method, and a process control system, which are capable of significantly reducing the labor, time, and monetary cost required for model update and are also easily applicable to a plant in which the existing model predictive control is introduced, without causing a loss of operating profit.

By converting a data format of operation data of a target process into a delay coordinate format and solving a regression problem including a regularization term for the operation data converted into the delay coordinate format, an update model reflecting secular change information of the target process is generated, and the model is replaced with the generated update model to be updated.

Techniques are described for implementing an automated control system to control operations of a target physical system, such as production of electrical power in an electrical grid. The techniques may include determining how much electrical power for each of multiple producers to supply for each of a series of time periods, such as to satisfy projected demand for that time period while maximizing one or more indicated goals, and initiating corresponding control actions. The techniques may further include repeatedly performing automated modifications to the control system's ongoing operations to improve the target system's functionality, by using reinforcement learning to iteratively optimize particles generated for a time period that represent different state information within the target system, to learn one or more possible solutions for satisfying projected electrical power load during that time period while best meeting the one or more defined goals.

Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated. Generating the optimal control configuration comprising the optimal control variable values associated with the continuous flow process is based on solving a deterministic convex optimization problem and a corresponding stochastic optimization problem.