Abstract: A battery management apparatus is configured to diagnose a state of a battery cell based on a capacity-voltage differential profile. Specifically, the battery management apparatus diagnoses a state of the battery cell according to a change pattern of a peak associated with a positive electrode reaction area and a peak associated with an available lithium loss in the capacity-voltage differential profile. The state of the battery cell may be diagnosed in real time even while the battery cell is in operation. In addition, it is possible to independently diagnose whether the positive electrode reaction area of the battery cell is reduced and whether available lithium is lost.

Abstract: Systems and methods for providing insights about a machine learning model are provided. The method includes, using training data to train the machine learning model to learn patterns to determine whether data associated with an event provides an indication that the event belongs to a certain class from among a plurality of classes, evaluating one or more features of the machine learning model to produce a data set pairing observed scores S and a set of predictive input variables Vi, and constructing at least one data-driven estimator based on an explanatory statistic, the estimator being represented in a computationally efficient form and packaged with the machine learning model and utilized to provide a definition of explainability for a score generated by the machine learning model.

Abstract: Provided is a battery system, including: at least two batteries, each battery comprising a battery management system; a plurality of sensors for sensing battery operation data; at least one external connection port; wherein: at least two battery management systems of the at least two batteries are coupled to the at least one external connection port; and at least two battery management systems of the at least two batteries store machine-executable instructions, that when executed, cause the at least one battery management system to communicate battery operation data to at least one external load.

Abstract: A battery system includes a plurality of battery modules and a control apparatus. The battery includes a battery group that includes two or more battery modules including battery units and battery monitoring units. The battery monitoring units of the battery modules being connected to each other via a communication line. The battery modules perform wired communication via the communication line. The battery group further includes a wireless receiving unit and a wireless transmitting unit. The wireless receiving unit wirelessly receives a command signal transmitted from the control apparatus. The wireless transmitting unit wirelessly transmits, to the control apparatus, a data signal including module identification information differing for each battery module. In the battery group, the wireless receiving unit is provided in a single battery module of the battery modules and the wireless transmitting unit is provided in a single battery module of the battery modules.

Abstract: A method for predicting time-to-failure and remaining-life of an electrical power system asset includes analyzing characteristics of the power system asset over a specified time interval; based on the analysis, associating the power system asset with a pool of similar power system assets having similar historical performance characteristics; and based on the association, calculating time-to-failure and remaining-life probability factors for the power system asset. A computer program product is also provided for carrying out the method, and the method may further include a mechanism whereby the computer program learns ways to modify and enhance the analysis of the performance characteristics to provide for higher confidence levels in time-to-failure and remaining-life probability calculations.

Abstract: An angle sensor generates an angle detection value based on a first and a second detection signal. A correction apparatus performs correction processing for generating a first corrected detection signal by adding a first correction value to the first detection signal and generating a second corrected detection signal by adding a second correction value to the second detection signal. When an angle to be detected varies with a period T and if no correction processing is performed, the angle detection value contains an Nth-order angle error component varying with a period of T/N. Each of the first and second detection signals contains an (N+1)th-order signal error component. The order of the first and second correction values is N?1.

Abstract: The embodiments of the present disclosure provide a method and an Internet of Things (IoT) system for determining a gas meter measurement failure of a smart gas, the method including: by the smart gas data center, obtaining, based on the smart gas sensing network platform, gas flow information of pipelines of each level from at least one flow monitoring device, the at least one flow monitoring device being configured in the smart gas object platform; by the indoor smart gas device management sub-platform, determining a candidate area based on the gas flow information; determining a target gas meter based on gas meter reading information of the candidate area, and then determining a maintenance plan for the target gas meter; and sending the maintenance plan to the smart gas data center, and sending the maintenance plan to the smart gas user platform based on the smart gas service platform.

Abstract: Techniques to match a signature in seismic data with a seismic attribute space. A method includes automatically selecting a first plurality of seismic attributes corresponding to seismic data as first selected seismic attributes, combining the first selected seismic attributes into a first realization of attributes, performing a first cluster analysis on the first realization of attributes to generate a first clustered volume, selecting a region of interest (ROI) in the seismic data, projecting the ROI onto the first clustered volume to generate a first signature, determining a first level of correlation between the ROI and the first signature, and determining whether the first level of correlation between the ROI and the first signature exceeds a predetermined threshold and outputting a first correlation volume corresponding to the first signature when the first level of correlation between the ROI and the first signature exceeds the predetermined threshold.

Abstract: A battery system includes a lithium ion battery configured to couple to an electrical system, and a battery management system configured to electrically couple to the lithium ion battery and to control one or more recharge parameters of the lithium ion battery. The battery management system is programmed with an electrochemical model, and the battery management system is configured to monitor parameters of the lithium ion battery, and to control the one or more recharge parameters of the lithium ion battery based on the electrochemical model and the one or more monitored parameters. The electrochemical model determines lithium plating reaction kinetics at an anode of the lithium ion battery, determines a quantity of plated lithium at the anode of the lithium ion battery, or both, and indicates a relationship between the one or more monitored parameters and the lithium plating reaction kinetics, the quantity of plated lithium, or both.

Abstract: A diagnostic device (100) includes: an acquisition unit (101) that acquires first charging information including a first detected value detected by an external charger (200) during charging of a battery mounted in a vehicle (10) and second charging information including a second detected value detected by the vehicle; and a deterioration estimation unit (105) that estimates a deterioration degree that indicates a degree of deterioration of the battery on the basis of the first charging information and the second charging information.

Abstract: A fixing member includes a substrate having an endless shape; and an elastic layer on an outer peripheral surface of the substrate, the elastic layer containing a silicone rubber and fillers dispersed in the silicone rubber, a content of the fillers with respect to the elastic layer being 35 vol % or more and 50 vol % or less. The fillers includes at least a first filler and a second filler. The first filler is at least one selected from the group consisting of: magnesium oxide; and zinc oxide. The second filler is at least one selected from the group consisting of metal silicon and silicon carbide. A proportion of a sum of the first filler and the second filler to a total amount of the fillers in the elastic layer is 90 vol % or more. Further, the average of 6 sets of representative coefficient A is 1.4 or more.

Abstract: A computer-implemented method of diagnosing one or more SoXs, such as State-of-Charge (SoC), State-of-Health (SoH), State-of-Energy (SoE), State-of-Power (SoP), State-of-Function (SoF) and State-of-Safety (SoS), of at least one battery, comprises a SoX diagnostics loop, which includes a given battery model, and a model update loop, which is configured to update the battery model, the method comprising: receiving, by the SoX diagnostics loop from at least one sensor, at least one measured battery parameter of the battery, determining, by the SoX diagnostics loop, at least one SoX descriptor of the battery using at least one of the following: at least one SoX parameter, at least one comparison of the at least one SoX parameter: to its previous value, to the at least one another SoX parameter, to the at least one threshold value, wherein the at least one SoX parameter is one of the following: the at least one measured battery parameter, at least one simulated battery parameter provided by the battery model, t

Abstract: A storage battery monitoring apparatus includes: monitoring units attached to storage batteries connected in series and/or in parallel; and a management unit capable of wireless communication connection with the monitoring units. By using identification information of a specific monitoring unit included in a message broadcasted from the management unit to the monitoring units, communication between the management unit and the specific monitoring unit is established.

Abstract: In one embodiment, a sensor device includes a sensor configured to obtain sensor data and a communication interface configured to communicate with a set of secondary sensor devices. The set of secondary sensor devices comprise a set of secondary sensors and a set of secondary processing devices. The set of secondary sensors obtain additional sensor data. The set of secondary processing devices process the additional sensor data based on a set of secondary machine learning models, and generate activation data based on the additional sensor data. The sensor device also includes a processing device. The processing device is also configured to receive the activation data from the set of secondary sensor devices and cause the sensor to obtain first sensor data based on the activation data. The processing device is further configured to generate one or more inferences based on the first sensor data and a machine learning model.

Abstract: The present invention is a method to determine an azimuthal orientation of a borehole seismometer sensor performed by a computing device using a control server having a database and an arithmetic function, the computing device performing a method to determine the azimuthal orientation of a borehole seismometer sensor using long-period surface waves in microseisms, including step S100 in which a data collection unit 100 collects continuous waveform data recorded by a borehole seismometer and a reference seismometer; step S200 in which a frequency band setting unit 200 sets a frequency band to be analyzed in the collected continuous waveform data; step S300 in which a filtering unit 300 performs bandpass filtering on the frequency band to be analyzed; step S400 in which a waveform dividing unit 400 divides seismic waveform into waveform segments with preset time units; step S500 in which a phase shift unit 500 shifts the phase of the divided vertical component waveforms by 90°; step S600 in which a waveform cal

Abstract: Embodiments of this application provide a battery management system (BMS) wake-up method, a BMS wake-up apparatus, and a storage medium, and pertains to the field of battery safety technologies. In this application, before a BMS hibernates, a wake-up threshold corresponding to first sampling information is determined based on an obtained extremum of the first sampling information of a battery cell. After the BMS hibernates, a sampling chip collects second sampling information of the battery cell when the BMS is hibernating, and transmits a wake-up signal to the BMS when it is determined that the second sampling information of the battery cell reaches the wake-up threshold. In this way, the BMS can be woken up in a timely manner by the wake-up signal, and therefore the BMS can find battery safety problems in a timely manner.

Abstract: A method, apparatus, and computer program product are provided to determine sensor orientation. In the context of a method, the orientation of a sensor is predicted based on a predetermined dataset. The method also includes receiving information regarding a location of the sensor and information from which a gravity direction of the sensor is derivable. The method further includes determining a relative orientation of the sensor in relation to a frame of reference and the gravity direction of the sensor. The method still further includes comparing the orientation of the sensor that was predicted and the relative orientation of the sensor to determine a prediction accuracy. The method finally includes updating the predetermined dataset based on the prediction accuracy of the sensor orientation.

Abstract: Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes receiving a sensor signal from an analyte sensor. A plurality of risk factors associated with end of life symptoms of analyte sensors is evaluated. The risk factors include a downward drift in sensor sensitivity over time, an amount of non-symmetrical, nonstationary noise and a duration of noise. An end of life status of the analyte sensor is determined based at least in part on the evaluating. An output related to the end of life status of the analyte sensor is provided.

Abstract: The present invention relates to miniature biosensor technology which can be directly embedded into medical device technology to create a new category of multifunctional smart medical devices. The resulting data from these smart medical devices results in wireless communication networks and standardized referenceable databases, which are used in the creation of best practice guidelines, clinical decision support tools, personalized medicine applications, and comparative technology assessment.

Abstract: An optical scanning device and an electronic imaging apparatus are provided. The optical scanning device includes a light source, a first optical unit, an optical deflector, and an imaging optical system for guiding a light beam deflected by the optical deflector to a scanned surface for imaging. The imaging optical system includes an F-? lens satisfying fc/fs?0.6, X1?X1c>0, and X2?X2c>0, where fs is a focal length of the F-? lens, fc is an f? coefficient of the F-? lens; X1 is a distance between a projection of one incident point on the main optical axis and the scanning origin, X2 is a distance between a projection of one exit point on the main optical axis and the scanning origin, X1c is a distance between the central incident point and the scanning origin, and X2c is a distance between the central exit point and the scanning origin.