A semiconductor device is provided. The semiconductor device includes a processing device that provides resource usage information including a utilization value; and a prediction information generating device that generates resource usage prediction information based on the resource usage information and provides the resource usage prediction information to the processing device. The prediction information generating device includes: an error calculator to calculate an error value between the utilization value and a predicted value included in the resource usage prediction information; a margin value calculator to receive the error value from the error calculator and calculate a margin value using the error value; an anchor value calculator to calculate an anchor value using the utilization value; and a predictor to output the predicted value using the anchor value and the margin value. The processing device controls resource allocation of the processing device based on the resource usage prediction information.

Deep learning models and other complex models provide accurate representations of complex industrial processes. However, these models often fail to satisfy properties needed for their use in closed loop systems such as Advanced Process Control. In particular, models need to satisfy gain-constraints. Methods and systems embodying the present invention create complex closed-loop compatible models. In one embodiment, a method creates a controller for an industrial process. The method includes accessing a model of an industrial process and receiving indication of at least one constraint. The method further includes constructing and solving an objective function based on at least one constraint and the model of the industrial process. The solution of the objective function defines a modified model of the industrial process that satisfies the received constraint and can be used to create a closed-loop controller to control the industrial process.

A device and a method for planning of maintenance work on a machine. Based on sensor data S, input data E are generated. The input data E are compared with model sensor data of different model data pattern and are checked regarding conformity. Each model data pattern is assigned a maintenance model in the model database. If a conformity of input data E with model sensor data M of a model data pattern has been determined, the assigned maintenance model can be selected for a maintenance of the machine. The maintenance model comprises at least one maintenance step and the resource required for it to be carried out. Subsequently, the availability of the required resource is checked and the resource is requested, if available. By such a device or method, a predicted maintenance can be performed based on empirical knowledge contained in the maintenance models of model database.

The invention relates to a container treatment machine for treating containers, in particular in the beverage-processing industry, medical technology, or the cosmetics industry, the container treatment machine comprising a control unit for controlling the function of the container treatment machine and at least one treatment unit for treating the containers; the container treatment machine is designed to treat the containers in exactly one way; the container treatment machine comprises at least one component which can output data relating to its operating state and/or the operating state of the container treatment machine to the control unit; and the control unit comprises a neural network which is configured and trained to use the data to determine whether a deviation of the operating state of the container treatment machine from a normal state is present and/or imminent.

A method includes identifying a schedule to operate a microwave enhanced air disinfection (MEAD) system and causing, based on the schedule, intermittent generation of microwave energy by a microwave generator of the MEAD system. A multi-component filter disposed in a housing of the MEAD system is configured to collect contaminants from airflow through the housing. At least a portion of the contaminants from the airflow is to be destroyed at least one of directly or indirectly via the microwave energy.

An apparatus for the production of solid dosage forms is presented, wherein the apparatus comprises a material processing chamber which is operable for manufacturing a product according to a pre-set product formation process path. The apparatus has at least one sensor for continuously monitoring formation of the product in the material processing chamber during the product formation process non-invasively in real time by sensing at least one product functional attribute value and a means for comparing each sensed product functional attribute value with a desirable product functional attribute value for that point on the product formation process path. A controller controls operation of the material processing chamber in response to the sensed product functional attribute value for maintaining the product on the product formation process path.

The present disclosure discloses an air conditioner controlling method and apparatus, as well as an air conditioner having the same. The method includes: determining that an outdoor heat exchanger of the air conditioner runs in an evaporator state; acquiring a state signal of a refrigerant of an indoor refrigerating unit; and adjusting an opening degree of an electronic expansion valve of an outdoor unit according to the state signal of the indoor refrigerating unit. This method may adjust the opening degree of the electronic expansion valve of the outdoor unit according to the state signal of the indoor refrigerating unit when the outdoor heat exchanger of the air conditioner runs in the evaporator state, thereby effectively improving the reliability of control, broadening the reliable operation range of the system, and reasonably distributing the refrigerant between the indoor refrigerating unit and the outdoor unit under a low temperature working condition.

Provided are a system and method for implementing control systems. One example includes configuring a processor to predict instability in control of a system by using multiple non-eigenvalue indices. Instability predictions may be communicated to an actuator of a device being controlled to regulate activity of the device. One example includes using transformation allergic indices (TAIs) as non-eigenvalue indices. One example includes using stability definite indices (SDIs) as novel introduced non-eigenvalue indices.

A system and method for managing operation of electrical devices includes a control module that monitors status of multiple sources of electrical power to one or more electrical devices and electrical usage of the one or more electrical devices that receive electricity from the source of electrical power. The operation of the one or more electrical devices is managed using a machine learning model that forecasts status of the at least one source of electrical power and generates operational rules for the one or more electrical devices from historical values of control parameters of the one or more electrical devices, the status of the source of electrical power, and the electrical usage of the one or more electrical devices. The system may optimize renewable energy utilization, power grid stabilization, cost of electrical power usage, and the like.

Methods and related systems are disclosed for determining a temperature of an indoor space with a plurality of onboard sensors of a device of a climate control system. In an embodiment, the method includes detecting raw temperatures with the plurality of sensors. In addition, the method includes determining a combined temperature offset for a first sensor of the plurality of sensors based on outputs of a plurality of models. The plurality of models are to determine a plurality of temperature offsets for the first sensor based on different airflow directions relative to the device. Further, the method includes adjusting the raw temperature detected by the first sensor with the combined temperature offset.