Abstract: A system for controlling an operation of a machine for performing a task is disclosed. The system submits a sequence of control inputs to the machine and receives a feedback signal. The system further determines, at each control step, a current control input for controlling the machine based on the feedback signal including a current measurement of a current state of the system by applying a control policy transforming the current measurement into the current control input based on current values of control parameters in a set of control parameters of a feedback controller. Furthermore, the system may iteratively update a state of the feedback controller defined by the control parameters using a prediction model predicting values of the control parameters and a measurement model updating the predicted values to produce the current values of the control parameters that explain the sequence of measurements according to a performance objective.

Abstract: The present invention discloses a method for onset time detection of a time-domain acoustic emission signal based on histogram distance. The method comprises the following steps: acquiring an acoustic emission signal; dividing the signal into two intervals with a sliding point k as the demarcation point; obtaining the relative frequency histograms of two adjacent intervals; obtaining histogram distance of the relative frequency histograms of two adjacent intervals; moving the sliding point k to the next element to obtain two new intervals and generating new histograms of the two new intervals and calculating the histogram distance of two new intervals; searching for the point which gives the maximum value of the histogram distances, and the corresponding time to this point is regarded as the onset time.

Abstract: The present invention relates to a method for measuring the AC impedance of a battery in a composite power supply power system, comprising the following steps: determining the AC disturbance signal amplitude, operating the DC/DC voltage converter to generate the AC disturbance signal, collecting the output signals of the fuel cell and the lithium battery, calculating the output power of the fuel cell and the lithium battery; calculating the demand power of the load, and when the demand power is stable, calculating the impedance of the lithium battery and the fuel cell separately, otherwise, only the impedance of the fuel cell is calculated.

Abstract: The invention relates to a computer-implemented method for predicting a modeled state of health of an electrical energy store having at least one electrochemical unit, in particular a battery cell, having the following steps: providing a data-based state of health model trained to assign a modeled state of health to the electrochemical energy store on the basis of characteristics of operating variables of the energy store; providing time characteristics of the operating variables that characterize operation of the electrical energy store; and determining a present or predicted modeled state of health on the basis of the generated characteristics of the operating variables using the state of health model, wherein data gaps in the time characteristics of the operating variables owing to a phase of inactivity are completed based on a characteristic of a temperature of the energy store that is derived from at least one provided ambient condition.

Abstract: An information processing device according to an embodiment estimates a probability distribution of a maximum value or a minimum value of time-series target sensor data in a designated estimation period after a base time. The information processing device includes one or more hardware processors that: calculate a reference feature amount representing a feature of related sensor data at a reference time before the base time based on a preset feature amount calculation model; estimate a reference distribution parameter in a reference period based on a preset parameter estimation model; update the feature amount calculation model and the parameter estimation model; calculate an estimation feature amount representing the feature of related sensor data at the base time based on the updated feature amount calculation model; and estimate an estimation distribution parameter based on the updated parameter estimation model.

Abstract: The present disclosure provides methods and systems for the characterization of a potential microtexture region (MTR) of a sample, component, or the like. The methods may include determining a threshold width of spatial correlation coefficient and/or a threshold spatial correlation coefficient slope for an actual MTR, characterizing a potential MTR as an actual MTR or a defect, characterizing an actual MTR as an acceptable MTR or not, and/or characterizing various components with potential MTRs as defective or not. The characterization may include calculating a width of spatial correlation coefficient and/or a spatial correlation coefficient slope of the potential MTR and comparing the width of spatial correlation coefficient to a threshold width of spatial correlation coefficient and/or comparing the spatial correlation coefficient slope to a threshold spatial correlation coefficient slope for the potential MTR to be characterized as an actual MTR or a defect (crack).

Abstract: A wafer is prepared, and a thickness shape of the prepared wafer at each position in a radial direction is measured for each of a predetermined number of angles into which 360 degrees of a circumference around the center of the wafer are divided. The thickness shape obtained by a measuring machine for each angle is approximated with a sixth or higher order polynomial, and a function of the wafer thickness at the position in the radial direction is created. The thickness shape outputted by the measuring machine and a thickness shape outputted by the function are compared with each other, and an error on the entire surface of the wafer is confirmed to be not greater than a predetermined error. After the confirmation, information of the function for each angle is attached to the wafer as data representing the wafer shape and supplied to a user.

Abstract: Approaches, techniques, and mechanisms are disclosed for assessing battery states of batteries. According to one embodiment, raw sensor data are collected from a battery module. The battery module includes multiple battery cells. Input battery features are extracted from the raw sensor data collected from the battery module. The input battery features are used to update node states of a GNN. The GNN include multiple GNN nodes each of which representing a respective battery cell in the multiple battery cells. Estimation of one or more battery state of health (SoH) indicators is generated based at least in part on individual output states of individual GNN nodes in the multiple GNN nodes. The individual output states of individual GNN nodes in the multiple GNN nodes are determined based at least in part on the updated node states of the GNN.

Abstract: An assembled battery monitoring device in the present disclosure includes: an excitation signal processor generates a excitation signal by processing an in-phase signal of an orthogonal reference signal generated by a signal generator; a current exciter generates an excitation current based on the excitation signal according to voltage signals, and energizes a battery cell; and an impedance measurer measures an AC impedance of the battery cell based on the excitation current measured by the current measurer and a voltage of the battery cell measured by the voltage measurer.

Abstract: A method for estimating a status of a system includes performing independent measurements on the system, associating a value P(?) with a Monte Carlo calculated probability measure P(?), which depends on (1) a preset threshold Nu of unfavorable events, and (2) a total number Ñtot(?) of the independent measurements for collecting the preset threshold Nu, setting a threshold level ? requiring that a critical value ?c of a power related metric ? makes a value P(?c) of the probability measure P to be substantially equal to the threshold level ?, comparing the total number Ñtot(?) to a ratio of (1) a product of a control parameter ? the preset threshold Nu, and (2) the threshold level ?, and interrupting the independent measurements if Ñtot(?) is larger than the ratio, or increasing an output of the system if Ñtot(?) is smaller than or equal to the ratio.

Abstract: A test arrangement and a method for controlling a measurement apparatus are provided. The measurement apparatus, in particular the triggering of the measurement apparatus is controlled by a state machine. If a desired state for triggering the measurement apparatus is not entered within a predetermined time period, the conditions for triggering the measurement apparatus can be adapted. For example, a required state for triggering can be changed or an automated triggering can be initiated.

Abstract: There is presented a system and method for detecting mobile device fault conditions, including detecting fault conditions by software operating on the mobile device. In one embodiment, the present invention provides for systems and methods for detecting that a mobile device has a cracked screen, and reporting the status of the screen, working or not, so that appropriate action may be taken by a third party. In one embodiment, the data obtained by testing of the mobile device is encrypted to prevent tampering or spoofing by the user of the mobile device, and is suitably decrypted by the recipient or software running within a server.

Abstract: Test equipment for a battery management system is provided. A battery-parameter recognition module measures a standard battery to obtain the first correction input, and uses the capacity test formula and the relaxation time test formula to perform a first charge and discharge test on the battery to be tested to obtain first battery parameter. A real-time simulation module determines the battery model and the simulated battery state based on the first battery parameter and the dynamic load. Each simulator of a physical signal simulation module provides a battery physical signal indicating the battery model. A connector provides the battery physical signal to the battery management controller under test. The battery management controller under test provides a stimulated battery state based on the battery physical signal. Master equipment compares the simulated battery state with an estimated battery state to determine whether the battery management controller under test is normal.

Abstract: A method can include receiving a digital operational plan that specifies computational tasks for seismic workflows, that specifies computational resources and that specifies execution information; dispatching instructions that provision the computational resources for one of the computational tasks for one of the seismic workflows; issuing a request for the execution information; receiving the requested execution information during execution of the one of the computational tasks using the provisioned computational resources; and, based on the received execution information indicating that the execution of the one of the computational tasks deviates from the digital operational plan, dispatching at least one additional instruction that provisions at least one additional computational resource for the one of the computational tasks for the one of the seismic workflows.

Abstract: A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.

Abstract: According to an aspect, a battery system for controlling a charging current includes a temperature sensor configured to measure a temperature of a first battery, a state-of-charge (SoC) calculation engine configured to calculate an SoC value of the first battery, and a charge current controller configured to determine an adjusted current according to a charge profile based on the temperature and the SoC value, where the charge current controller is configured to transmit current data that indicates the adjusted current to a device.

Abstract: A method includes, for each floating-point layer in a set of floating-point layers: calculating a set of input activations and a set of output activations of the floating-point layer; converting the floating-point layer to a low-bit-width layer; calculating a set of low-bit-width output activations based on the set of input activations; and calculating a per-layer deviation statistic of the low-bit-width layer. The method also includes ordering the set of low-bit-width layers based on the per-layer deviation statistic of each low-bit-width layer.

Abstract: A determination apparatus (10) includes: a criteria value acquisition unit (11) that acquires a criteria value which is a performance value of a storage battery at a criteria time; a target value acquisition unit (12) that acquires a target value which is the performance value of the storage battery at a determination time; a usage method data acquisition unit (13) that acquires usage method data indicating a usage method of the storage battery from the criteria time to the determination time; a predicted range computing unit (14) that computes a predicted range of the performance value at the determination time on the basis of the criteria value, the usage method data, and a time elapsed from the criteria time to the determination time; and a determination unit (15) that determines that the storage battery is not to be compensated if the target value is within the predicted.

Abstract: A method of detecting abnormality may include the following steps. A normal-value range of a parameter for a target object is determined based on historical values of the parameter in a preset time period or at a preset time point. Whether the target object is abnormal is determined based on the normal-value range and the value of the parameter for the target object in the preset time period or at the preset time point within a current time cycle. Further, another normal-value range may be determined based on historical deviation values for the target object in historical time periods or at historical time points before the preset time period or the preset time point. Whether the target object is abnormal is determined based on either of the two normal-value ranges.

Abstract: This disclosure provides a method and an apparatus for determining available energy of a battery, a battery management system, and a storage medium, and relates to the field of battery technologies. The method includes: obtaining a DOD interval corresponding to a current SOC interval of the battery, and a cycle quantity and a cycle temperature that correspond to the DOD interval; obtaining recoverable energy information of the battery based on the DOD interval, the cycle quantity, and the cycle temperature; and determining current remaining available energy of the battery based on the recoverable energy information. For a battery with a recoverable attenuation capacity, the method, the apparatus, the battery management system, and the storage medium in this disclosure could help improve accuracy in estimating remaining available energy of the battery, thereby improving battery reliability, prolonging a service life of the battery, and enhancing user experience.