A rotating equipment system with in-line drive-sense circuit (DSC) electric power signal processing includes rotating equipment, in-line drive-sense circuits (DSCs), and one or more processing modules. The in-line DSCs receive input electrical power signals and generate motor drive signals for the rotating equipment. An in-line DSC receives an input electrical power signal, processes it to generate and output a motor drive signal to the rotating equipment via a single line and simultaneously senses the motor drive signal via the single line. Based on the sensing of the motor drive signal via the single line, the in-line DSC provides a digital signal to the one or more processing modules that receive and process the digital signal to determine information regarding one or more operational conditions of the rotating equipment, and based thereon, selectively facilitate one or more adaptation operations on the motor drive signal via the in-line DSC.

A self-configuring controller includes one or more processors and one or more non-transitory machine readable media storing instructions. When executed by the one or more processors, the instructions cause the one or more processors to receive an output signal from a controlled system or device representative of an operation of the controlled system or device in response to a first perturbed control input perturbed using a first dither signal, estimate a bandwidth of the controlled system or device based on the output signal and the first dither signal, perturb a second control input using a second dither signal based on the bandwidth of the controlled system or device to generate a second perturbed control input, and transmit the second perturbed control input to the controlled system or device.

Systems, methods, and computer-readable media are described for intelligent, real-time monitoring and managing of changes in oilfield equilibrium to optimize production of desired hydrocarbons and economic viability of the field. In some examples, a method can involve generating, based on a topology of a field of wells, a respective graph for the wells, each respective graph including computing devices coupled with one or more sensors and/or actuators. The method can involve collecting, via the computing devices, respective parameters associated with one or more computing devices, sensors, actuators, and/or models, and identifying a measured state associated with the computing devices, sensors, actuators, and/or models. Further, the method can involve automatically generating, based on the respective graph and respective parameters, a decision tree for the measured state, and determining, based on the decision tree, an automated adjustment for modifying production of hydrocarbons and/or an economic parameter of the hydrocarbon production.

A method of determining multimodal polyethylene quality comprising the steps of (a) providing a multimodal polyethylene resin sample; (b) determining, in any sequence, the following: that the multimodal polyethylene resin sample has a melt index within 30% of a target melt index; that the multimodal polyethylene resin sample has a density within 2.5% of a target density; that the multimodal polyethylene resin sample has a dynamic viscosity deviation (% MVD) from a target dynamic viscosity of less than about 100%; that the multimodal polyethylene resin sample has a weight average molecular weight (M.sub.w) deviation (% M.sub.wD) from a target M.sub.w of less than about 20%; and that the multimodal polyethylene resin sample has a gel permeation chromatography (GPC) curve profile deviation (% GPCD) from a target GPC curve profile of less than about 15%; and (c) responsive to step (b), designating the multimodal polyethylene resin sample as a high quality resin.

A sleep sensing system comprising a sensor to obtain real-time information about a user, a sleep state logic to determine the user's current sleep state based on the real-time information. The system further comprising a sleep stage selector to select an optimal next sleep state for the user, and a sound output system to output sounds to guide the user from the current sleep state to the optimal next sleep state.

A sleep sensing system comprising a sensor to obtain real-time information about a user, a sleep state logic to determine the user's current sleep state based on the real-time information. The system further comprising a sleep stage selector to select an optimal next sleep state for the user, and a sound output system to output sounds to guide the user from the current sleep state to the optimal next sleep state.

Embodiments of the invention control a device based on physiological measurements associated with a user. A determination is made that a user has manually adjusted a controlled device. A context associated with the user is identified in response to determining that the user has manually adjusted the controlled device. A change is detected in at least one physiological measurement associated with the user in response to the controlled device being manually adjusted. A target physiological measurement associated with at least one physiological measurement is modified based on the change that has been detected. The context that has been identified is associated with the target physiological measurement that has been modified.

A system and approach for verifying and validating a room condition and its behavior in a critical environment. The system and approach may be a room controller built on top of a Niagara™ framework or launched from a Niagara workbench, and leverages extensible of Niagara. The system and approach may be web-based and used to test and verify the room condition per preset conditions. The system may have steps or tabs. They may incorporate screens for a create/open task, select test zone, read flow, hood/booster, T-stat set-up or temperature lever set-up, visual checks, and a report. One may create a new task and edit any existing task on the controller. One may move from task to task in either direction or go directly to the report of a completed task.

A method of determining multimodal polyethylene quality comprising the steps of (a) providing a multimodal polyethylene resin sample; (b) determining, in any sequence, the following: that the multimodal polyethylene resin sample has a melt index within 30% of a target melt index; that the multimodal polyethylene resin sample has a density within 2.5% of a target density; that the multimodal polyethylene resin sample has a dynamic viscosity deviation (% MVD) from a target dynamic viscosity of less than about 100%; that the multimodal polyethylene resin sample has a weight average molecular weight (M.sub.w) deviation (% M.sub.wD) from a target M.sub.w of less than about 20%; and that the multimodal polyethylene resin sample has a gel permeation chromatography (GPC) curve profile deviation (% GPCD) from a target GPC curve profile of less than about 15%; and (c) responsive to step (b), designating the multimodal polyethylene resin sample as a high quality resin.

A priori geo-referenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.