Abstract: Through the plasma spraying technology and the cold spraying high-speed deposition technology, an evenly distributed protective coating is formed on the surface of a plasma etching chamber. The protective coating, having a double-layer composite structure, includes a metal+Y2O3 coating as a metal+Y2O3 transition layer deposited by plasma spraying as a lower layer of the double-layer composite structure, and a high-purity Y2O3 ceramic coating coated on the metal+Y2O3 transition layer as an upper layer of the double-layer composite structure, the metal+Y2O3 transition layer is configured to reduce the difference in expansion coefficient between the Y2O3 ceramic coating and the metal substrate, and enhance the bonding force between the Y2O3 ceramic coating and the metal substrate; the high-purity Y2O3 ceramic coating is formed by depositing Y2O3 ceramic powders on the metal+Y2O3 transition layer at high speed through cold spraying high-speed deposition.

Abstract: Through the plasma spraying technology and the cold spraying high-speed deposition technology, an evenly distributed protective coating is formed on the surface of a plasma etching chamber. The protective coating, having a double-layer composite structure, includes a metal+Y2O3 coating as a metal+Y2O3 transition layer deposited by plasma spraying as a lower layer of the double-layer composite structure, and a high-purity Y2O3 ceramic coating coated on the metal+Y2O3 transition layer as an upper layer of the double-layer composite structure, the metal+Y2O3 transition layer is configured to reduce the difference in expansion coefficient between the Y2O3 ceramic coating and the metal substrate, and enhance the bonding force between the Y2O3 ceramic coating and the metal substrate; the high-purity Y2O3 ceramic coating is formed by depositing Y2O3 ceramic powders on the metal+Y2O3 transition layer at high speed through cold spraying high-speed deposition.

Abstract: Techniques are described for implementing automated control systems that each control or otherwise manipulate, for a target system having one or more battery cells each having internal components that include one or more internal supercapacitor components in parallel with at least one battery component, usage operations for one of the internal components of one of the battery cells, with the usage operations for the internal components of a particular battery cell being synchronized or otherwise coordinated to protect the battery component(s) of the battery cell while satisfying other criteria (e.g., to increase battery cell life and/or reduce power dissipation). In at least some situations, the target system is an electric vehicle, and the automated control systems control the electric vehicle's battery cells to provide electrical power to the motor during acceleration and constant speed driving, and to store electrical power in the battery cells during braking or other deceleration.

Abstract: Techniques are described for implementing automated control systems to control operations of target physical systems including batteries, such as based at least in part on models of the batteries' dynamic behaviors that are generated by gathering and analyzing information about the batteries' operations under varying conditions. The techniques may include, for each of multiple charge levels of a battery and for each of multiple resistive loads, injecting multi-frequency microvolt pulses into the battery, and using sensors to collect time changes of the responses to these pulses. Information about the inputs and the responses is then analyzed and used to generate an incremental parametric state model representing the internal dynamics of the battery, which is further used to control additional ongoing battery operations (e.g., to control whether and how much power is supplied to and/or extracted from the battery in a current or future time period).

Abstract: An automated control system to control at least some operations of one or more target physical systems that each includes one or more batteries. The described techniques may include determining whether and how much power to supply for each of a series of time periods, and implementing the determined power amount for a time period by determining and setting one of multiple impedance level control values of an associated actuator component. Repeated automated operations of this type may include using parametric linear approximation to determine one of multiple enumerated control values that best satisfies one or more defined goals at a given time in light of current state information (e.g., current output from the battery, voltage from the battery, battery temperature, etc.), such as by repeatedly determining an improved distribution function over the control values, and propagating it over multiple future time periods.

Abstract: Techniques are described for implementing automated control systems for target battery systems based at least in part on battery state information gathered from active excitation of the batteries, such as to maximize battery life while performing other battery power use activities. The excitation of a target battery system may occur while it is in use, by repeatedly introducing small defined variations as input to the battery system while the battery system is otherwise used to supply or receive electricity.

Abstract: Techniques are described for implementing automated control systems that repeatedly perform automated modifications to control system actuator components' ongoing operations to improve functionality for target battery systems, such as to reduce power dissipation while performing other battery power use activities to maximize battery life. Controlling a battery's usage may include using a DC-to-DC amplifier, and the repeated automated modifications may include modifying the state of the DC-to-DC amplifier actuator to adjust a level of resistance and/or an amount of time during which power is supplied. The repeated automated modifications may be performed to repeatedly reduce the distance between the current battery performance and an idealized version of the battery performance (e.g., a version with no power dissipation).

Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine readable instructions that when executed by the processor, cause the processor to identify a plurality of tasks, identify a plurality of resources configured to execute the tasks, and decompose the plurality of tasks into multiple groups of tasks based on a plurality of rules applicable to the multiple groups of tasks. The instructions may also cause the processor to, for each group in the multiple groups of tasks, model the group of tasks and a subset of the plurality of resources as a respective resource allocation problem and assign a respective node of a plurality of nodes to solve the resource allocation problem.

Abstract: Techniques are described for implementing automated control systems that repeatedly perform automated modifications to control system actuator components' ongoing operations to improve functionality for electrical devices in target systems, such as to reduce power dissipation while using the electrical devices. The described techniques further include synchronizing a particular control system's state improvements with corresponding control system state improvements being performed for one or more other control systems that are each controlling one or more distinct electrical devices in the target system(s), so as to improve the collective control system functionality according to one or more criteria (e.g., to reduce power dissipation)—such synchronizing may include, for example, generating a mean field representation of the overall control system state for the target system(s) and using the mean field representation to improve the overall control system state.

Abstract: Methods and systems for transcribing a media file using a human intelligence task service and/or reinforcement learning are provided. The disclosed systems and methods provide opportunities for a segment of the input media file to be automatically re-analyzed, re-transcribed, and/or modified for re-transcription using a human intelligence task (HIT) service for verification and/or modification of the transcription results. The segment can also be reanalyzed, reconstructed, and re-transcribed using a reinforcement learning enabled transcription model.

Abstract: Techniques are described for implementing automated control systems for target battery systems based at least in part on battery state information gathered from active excitation of the batteries, such as to maximize battery life while performing other battery power use activities. The excitation of a target battery system may occur while it is in use, by repeatedly introducing small defined variations as input to the battery system while the battery system is otherwise used to supply or receive electricity. Corresponding small variations in output of the battery system from the excitation activities are then measured by hardware sensors, aggregated and analyzed to generate a current model of the internal state of the one or more batteries, and then used to assist in controlling further operations of the battery system, including in some cases to update a previously existing model of the battery system.

Abstract: Methods and systems for transcribing a media file using reinforcement learning are provided. In one aspect, the method includes: identifying a low confidence of accuracy portion from a transcription result of the media file; constructing a phoneme sequence that includes an audio segment corresponding to the identified low confidence of accuracy portion, based on at least on a reward function; creating a new audio waveform based at least on the constructed phoneme sequence; and generating a new transcription using a transcription engine based on the new audio waveform.

Abstract: Techniques are described for implementing automated control systems that repeatedly perform automated modifications to control system actuator components' ongoing operations to improve functionality for target battery systems, such as to reduce power dissipation while performing other battery power use activities to maximize battery life. Controlling a battery's usage may include using a DC-to-DC amplifier, and the repeated automated modifications may include modifying the state of the DC-to-DC amplifier actuator to adjust a level of resistance and/or an amount of time during which power is supplied. The repeated automated modifications may be performed to repeatedly reduce the distance between the current battery performance and an idealized version of the battery performance (e.g., a version with no power dissipation).

Abstract: A set of SKUs is divided into a plurality of different Mean Field clusters, and a tracker (or sensor) is identified for each cluster. Product decisions for each Mean Field cluster are generated based on the tracker (or sensor) and each Mean Field cluster is then deconstructed to obtain product decisions for individual SKUs in the Mean Field cluster.

Abstract: According to examples, an apparatus may include a processor that is to generate a plurality of candidate fulfillment plans regarding delivery of items over a network, in which each of the candidate fulfillment plans is generated using a respective decision variable of an array of values. The processor may also calculate an evaluation value for each of the candidate fulfillment plans, in which the evaluation value for a candidate fulfillment plan is a measure of a compliance of the candidate fulfillment plan with a plurality of factors pertaining to the delivery of the items. The processor may further output instructions regarding delivery of the items over the network according to the candidate fulfillment plan that corresponds to a maximized compliance with the plurality of factors among the calculated evaluation values to maximize compliance with the plurality of factors in the delivery of the items.

Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine readable instructions that when executed by the processor, cause the processor to identify a plurality of tasks, identify a plurality of resources configured to execute the tasks, and decompose the plurality of tasks into multiple groups of tasks based on a plurality of rules applicable to the multiple groups of tasks. The instructions may also cause the processor to, for each group in the multiple groups of tasks, model the group of tasks and a subset of the plurality of resources as a respective resource allocation problem and assign a respective node of a plurality of nodes to solve the resource allocation problem.

Abstract: Technologies are described to provide parameter estimation for a probabilistic forecaster in inventory management. A forecaster model may be generated based on observed delivery data, demand data, and a state of a delivery system managed by an inventory management service or an enterprise resource planning service. A probability of the state of the delivery system transitioning to a subsequent state of the delivery system may be determined based on an estimation of one or more parameters using a linear regression model. In some examples, the forecaster model may be derived from the discretized version of the linear Fokker-Planck equations using maximum log-likelihood estimate with optimization through a fast marching algorithm. In other examples, Lagrange multipliers may be used to determine initial constraints on the parameters. An optimal inventory level to be maintained may be computed based on the determined probability.

Abstract: A set of conditional rules (or transformations) that are effective for an article under analysis is identified. The set of rules is compressed into a single rule which is applied to a first quantity identifier that identifies a first quantity of the article, to obtain a second quantity. An order generation system generates an order based on the second quantity.

Abstract: A set of SKUs is divided into a plurality of different Mean Field clusters, and a tracker (or sensor) is identified for each cluster. Product decisions for each Mean Field cluster are generated based on the tracker (or sensor) and each Mean Field cluster is then deconstructed to obtain product decisions for individual SKUs in the Mean Field cluster.

Abstract: A method for preparing a grounding substrate for a semiconductor device, the method including: 1) polishing the surface of a matrix of a grounding substrate to remove a carbon layer therefrom, and washing the surface of the matrix with anhydrous ethanol; 2) providing a cold spray system including a spraying device, a spray chamber, and a special fixture disposed in the spray chamber; and disposing the matrix on the special fixture; 3) using the cold spray system to spray a compressed gas carrying aluminum powder on the surface of the matrix at the supersonic speed to form an aluminum coating, thus obtaining the grounding substrate; 4) disposing the grounding substrate in a heat treatment furnace, raising the temperature therein to between 100 and 500° C., and maintaining the temperature for between 1 and 5 hrs; and 5) wet polishing the grounding substrate.