Abstract: The present invention provides a machine abnormality marking and abnormality prediction system connected with a factory host and including a parameter streaming unit connected with the machines, an abnormality reporting unit, a prediction analysis unit, and a neural network classifier. The parameter streaming unit and the abnormal reporting unit collect data of each machine in the factory, and the collected data are compared and analyzed with historical records by the prediction analysis unit. The generated parameter values can be continuously compared with the collected data to predict the state of each machine in the factory, and provide an early warning of possible abnormality or need of maintenance, so that the personnel in the factory can arrange production line maintenance or capacity adjustment in advance or adjust the machine of the factory production line, to avoid occasional shutdown and reduce factory losses.

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment. The generative AI model may be trained to predict root causes of a plurality of first problems corresponding to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request for servicing building equipment. The method may include predicting, by the one or more processors using the generative AI model, a root cause of a second problem corresponding to the second service request based on characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model.

Abstract: A method for discriminating vertical distribution models of organic carbons is provided, the method includes: obtaining a concentration of organic carbons in a surface layer of the ocean water area, depths of water of the ocean water area and depths of mixed layers of the ocean water area; determining different vertical distribution models of organic carbons in the ocean water area according to the concentration of the organic carbons in the surface layer and the depths of water of the ocean water area; calculating ratios of the depths of water of the ocean water area to the depths of the mixed layers of the ocean water area; and discriminating the vertical distribution models according to the ratios. According to this method, the accuracy of estimation of the stock of organic carbons in the ocean water area can be improved greatly.

Abstract: A processor-implemented method for simultaneously tracking one or more objects includes receiving, via a dynamical system with a set of sensors, a first set of unlabeled measurements from one or more objects. Each of the measurements is a function of time. A set of candidate tracks is determined for the one or more objects. Probabilities of each of the first set of unlabeled measurements being assigned to each of the set of candidate tracks are computed. A track from the set of candidate tracks is determined for each of the one or more objects based on a joint probability distribution of track attributes and the probabilistic assignment of each of the first set of unlabeled measurements to each of the set of candidate tracks.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery performance prediction. One of the methods includes actions of receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property of at least two battery tests. Each battery test includes applying pulses on the battery cell during a battery cycle. The battery test data is provided as input to a machine learning system to predict battery cell performance. The machine learning system includes a machine learning model that has been trained using training data includes test data of battery cells that reached respective end of life (EOL) cycles. In response, a prediction result for the battery cell is automatically generated by the machine learning model. The prediction result indicates an EOL cycle of the battery cell. An action is taken based on the prediction result.

Abstract: An arrangement includes a supply unit of an industrial control system and a superordinate computing unit assigned to the supply unit. The supply unit includes an electronic assembly which includes an electronic component. The supply unit is configured to calculate a first temperature of the electronic component using a first thermal model. The superordinate computing unit is configured to calculate a second temperature of the electronic component using a second thermal model when the supply unit is without current, with the supply unit providing the superordinate computing unit with relevant data for a calculation of the second thermal model. The supply unit and the superordinate computing unit interact in such a way that at least one of the first temperature and the second temperature of the electronic component is calculated.

Abstract: An abnormality determination apparatus includes a processing unit, a communication unit configured to communicate with a plurality of mobile bodies and a storage unit configured to store power reception history information received from each of the mobile body. The power reception history information includes a power receiving location of the received power received by the power receiving device, and a power transmission efficiency calculated based on the received power, or the received power. The processing unit is configured to extract information of the power receiving location where the power transmission efficiency is less than a predetermined value from the power reception history information of the plurality of the mobile bodies stored in the storage unit, and is configured to determine an abnormality of the power transmission device based on the information of the extracted power receiving location.

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: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery defect identification. One of the methods includes receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property in a battery test during at least one portion of a battery cycle. The battery test includes applying one or more pulses on the battery cell. The battery test data of the battery cell is provided as input to a machine learning model running on the computing system to predict whether the battery cell will experience catastrophic fade. The machine learning model has been trained using training data including battery test data of battery cells that experienced catastrophic fade. A prediction result for the battery cell is automatically generated by the machine learning model. An action is taken based on the prediction result for the battery cell.

Abstract: Disclosed are methods and systems for predicting relationships between points in automated environments by retrieving a plurality of data points from a plurality of data sources associated with an automated environment, wherein the plurality of data points comprises timeseries data structure; detecting a plurality of events based on the plurality of data points, wherein an event of the plurality of data points is indicative of an anomaly reading of one or more states associated with the plurality of data sources; generating a correlation matrix of events across the plurality of data points; suppressing non-physical relation factors in the correlation matrix by normalization to generate a normalized matrix; and clustering the data points based on the normalized matrix, wherein the clustered data points represent the data points belonging to a same data source.

Abstract: An example operation includes one or more of detecting a movement of an object in a vehicle when the vehicle is in motion, and determining that the object is an inanimate object, based on a vertical and horizontal distance of the detected movement.

Abstract: An apparatus for alarm information determination includes: an input unit; a processing unit; and an output unit. The input unit provides the processing unit with historical process control data, the process control data including a plurality of data signals, a plurality of alarm data, and data relating to an event of interest. The processing unit determines a plurality of correlation scores for the plurality of data signals paired with the plurality of alarm data, a correlation score being determined for a data signal paired with an alarm data, a high correlation score indicating a higher degree of correlation than a low correlation score. The processing unit identifies at least one first alarm data from the plurality of alarm data, the identification including utilization of the data relating to the event of interest.

Abstract: A method comprising, by at least one processing unit, obtaining data collected by one or more sensors of at least one marine vessel, said data being representative of one or more situations encountered by the marine vessel during its voyage, prioritizing data according to at least one relevance criterion, wherein when the marine vessel is located in a zone in which at least one remote data communication link meets a criterion, transmitting at least some of the data using the at least one remote data communication link, wherein data are transmitted according to priority determined for the data, thereby facilitating transmission of relevant data for the purpose of training one or more machine learning algorithms (e.g. deep learning algorithms) providing output based on these data.

Abstract: Systems and methods for autonomous vehicle testing are provided. In one example embodiment, a computer-implemented method includes obtaining, by a computing system, data indicative of a test of an autonomous vehicle computing system. The method can include determining, by the computing system, one or more autonomous vehicle capabilities that are tested by the test. The method includes determining, by the computing system, a testing scenario that corresponds to the test. The testing scenario can generated at least in part using real-world data. The method includes associating, by the computing system, the data indicative of the test with data indicative of the one or more autonomous vehicle capabilities that are tested by the test and data indicative of the testing scenario. The method includes storing such associated data in in an accessible memory.

Abstract: A method for predicting a day-ahead wind power of wind farms, comprising: constructing a raw data set based on a correlation between the to-be-predicted daily wind power, the numerical weather forecast meteorological feature and a historical daily wind power; obtaining a clustered data set and performing k-means clustering, obtaining a raw data set with cluster labels, and generating massive labeled scenes based on robust auxiliary classifier generative adversarial networks; determining the cluster label category of the to-be-predicted day based on the known historical daily wind power and numerical weather forecast meteorological feature, and screening out multiple scenes with high similarity to the to-be-predicted daily wind power based on the cluster label category; and obtaining the prediction results of the to-be-predicted daily wind power at a plurality of set times based on an average value, an upper limit value and a lower limit value of the to-be-predicted daily wind power.

Abstract: The present disclosure relates to a battery diagnosis system including a processor configured to: receive high-voltage battery information of a parked car; measure impedance of a battery through the received battery information; and determine, based on a comparison between a normal battery impedance and an abnormal battery impedance, that a battery having an abnormal battery impedance is an abnormal battery.

Abstract: A computer-implemented method for analyzing data obtained for a chemical and/or biological process comprises: obtaining a result of statistical data analysis on the data obtained with respect to the chemical and/or biological process; calculating, for values of process parameters obtained at groups of time points during batch processes of the chemical and/or biological process, a ratio of a correlation value to a confidence value of the correlation value, the correlation value indicating a correlation between the values of the process parameter and at a process output value; calculating, for process parameters, an average of absolute values of the ratios calculated for the values of the process parameter obtained at different groups of time points during the batch processes; excluding the values of one of the process parameters having a smallest average; and iterating, until at least one specified condition is met.

Abstract: Liquid flow detection is described herein. One method for liquid flow detection includes heating a zone of liquid in a closed system, measuring changes in liquid temperature of the zone, and detecting a liquid flow pattern based on the measured changes in liquid temperature of the zone.

Abstract: A method of detection of a recurrent feature of interest within a signal including: obtaining evidence, based on a signal, the evidence including a probability density function for each of a plurality of parameters for parameterizing the signal, including at least one probability density function for a parameter, of the plurality of parameters, that positions a feature of interest within signal data of the signal; parameterizing a portion of the signal data from the signal based upon a hypothesis that a point of interest in the signal data is a position of the feature of interest; determining a posterior probability of the hypothesis being true given the portion of the signal data by combining a prior probability of the hypothesis and a conditional probability of observing the portion of the signal data given the hypothesis.

Abstract: The subject matter of this specification can be implemented in, among other things, a method, system, and/or device to receive current metrology data for an operation on a current sample in a fabrication process. The metrology data includes a current value for a parameter at each of one or more locations on the current sample. The method further includes determining a current rate of change of the parameter value for each of the one or more locations. The current rate of change is associated with the current sample. The method further includes identifying one or more violating locations each having an associated current rate of change of the parameter value that is greater than an associated reference rate of change of the parameter value, and identifying an instance of abnormality of the fabrication process based on the one or more violating locations.