Abstract: A liquefaction evaluation model generation device includes a data acquisition unit configured to acquire training data in which learning vibration data indicating a physical quantity associated with vibrations observed in the ground is defined as a question and learning liquefaction data indicating the degree of liquefaction occurring in the ground where the vibrations associated with the learning vibration data have been observed is defined as an answer, and a machine learning execution unit configured to execute machine learning using the training data and generate a liquefaction evaluation model that is a machine learning model.

Abstract: Operating settings for a benchmarking operation may be determined, based at least in part, on one or more benchmarking task parameters. The operating settings may be used to establish a set of worker resources to send requests against a target until a stop condition is valid. Data may be collected from the target and used to train one or more machine learning systems in order to provide improved operating setting recommendations for subsequent benchmarking tasks.

Abstract: A non-transitory computer-readable medium storing a program code including an image generation model, which when executed, causes a processor to input input data including sampling data of some of a plurality of semiconductor dies of a wafer to a generator network of the image generation model and output a wafer map indicating the plurality of semiconductor dies, and to input the wafer map output from the generator network to a discriminator network of the image generation model and discriminate the wafer map.

Abstract: A system for monitoring potential failure in a machine or a component thereof, the system including: at least one optical sensor configured to be fixed on or in vicinity of the machine or the component thereof, at least one processor in communication with the sensor, the processor being executable to: receive signals from the at least one optical sensor, obtain data associated with characteristics of at least one mode of failure of the machine or the component thereof, identify at least one change in the received signals, for an identified change in the received signals, apply the at least one identified change to an algorithm configured to analyze the identified change in the received signals and to classify whether the identified change in the received signals is associated with a mode of failure of the machine or the component thereof, thereby labeling the identified change as a fault, based, at least in part, on the obtained data, and for an identified change is classified as being associated with a mode

Abstract: The present disclosure is directed to providing a battery diagnosing system and method, which updates a diagnosis table of a battery diagnosing apparatus by communication between a central server and the battery diagnosing apparatus, and stores battery state information and a diagnosis code as a backup. According to an aspect of the present disclosure, since the diagnosis table stored in the battery diagnosing apparatus may be updated to a latest state, the battery state may be diagnosed more accurately. In addition, according to an aspect of the present disclosure, since the central server determines whether the diagnosis code generated by the battery diagnosing apparatus is misdiagnosed, the battery state may be diagnosed more accurately and reliably.

Abstract: According to an embodiment, a diagnosis method of a battery is provided. In the diagnosis method, a similarity to a measurement result of a voltage of the battery is calculated in regard to each of a plurality of voltage arithmetic data calculated by setting an internal state in mutually different states in a voltage model of the battery. In the diagnosis method, a deterioration state of the battery is determined based on the similarity calculated in regard to each of the voltage arithmetic data.

Abstract: The present invention relates to a method for inspection of a multilayer electrode sheet for a battery cell, comprising at least the following steps: joining together at least two functional layers; connecting the functional layers to form an electrode-separator assembly; detecting at least part of a surface of the electrode-separator assembly by means of a detection device for generating a measurement result; evaluating the generated measurement result and generating an evaluation result; and outputting the evaluation result.

Abstract: Some implementations of the present disclosure provide a method that include: accessing a set of seismic traces from a grid of locations inside an geo-exploration area, each seismic trace records seismic reflections from underneath the geo-exploration area at a location of the grid; accessing an input indicating a surface in the set of seismic traces; extracting a plurality of wavelets from the set of seismic traces, each wavelet covering an adjustable length around the surface; determining a reference wavelet for each wavelet of a corresponding adjustable length; and quantifying a quality of the surface based on correlating the plurality of wavelets with each reference wavelet of the corresponding adjustable length.

Abstract: A brain-computer interface (BCI) includes a multichannel stimulator and a decoder. The multichannel stimulator is operatively connected to deliver stimulation pulses to a functional electrical stimulation (FES) device to control delivery of FES to an anatomical region. The decoder is operatively connected to receive at least one neural signal from at least one electrode operatively connected with a motor cortex. The decoder controls the multichannel stimulator based on the received at least one neural signal. The decoder comprises a computer programmed to process the received at least one neural signal using a deep neural network. The decoder may include a long short-term memory (LSTM) layer outputting to a convolutional layer in turn outputting to at least one fully connected neural network layer. The decoder may be updated by unsupervised updating. The decoder may be extended to include additional functions by transfer learning.

Abstract: According to an embodiment, a monitoring apparatus configured to generate time-series predicted data based on time-series measured data and a prediction model that generates predicted data including one or more predicted values predicted to be output from one or more sensors; and generate, for a first sensor among the one or more sensors, a displayed image including a measured value graph representing a temporal change in a measured value included in the time-series measured data in a second period after a first period, a predicted value graph representing a temporal change in a predicted value included in time-series predicted data in the second period, past distribution information representing a distribution of a measured value in the first period, and measurement distribution information representing a distribution of the measured value included in the time-series measured data in the second period.

Abstract: A method for operating a central processing unit which is communicatively connected to a plurality of devices having electrical energy stores, includes determining a data-based state of health model which is trained to assign a state of health to an operating feature point which characterizes operation of a corresponding electrical energy store of the electrical energy stores of the plurality of devices and results from a plurality of operating features, determining operating feature points for the electrical energy stores of the plurality of devices, and selecting at least one of the operating feature points based on state uncertainties of the states of health of all operating feature points of the corresponding electrical energy store, such that the at least one operating feature point has a highest relevance for improving the state uncertainties of the operating feature points determined.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating weather forecasting modules from historical weather data, including unstructured and structured data. The weather forecasting modules each comprise at least one model, such as a neural network, that generates a forecast from input weather data. Each module may be focused on a particular region, weather variable, and or forecasting time period. Unstructured data may be analyzed by the system to generate weather-related variables which are combined with structured weather data to generate a combined weather feature set that is input to the model. Alternatively, unstructured data may be input directly in the module, with or without preprocessing. The weather forecasts can then be used to forecast various power-generation related variables or events, including power generation, electrical load, etc.

Abstract: A deterioration state estimation system of a secondary battery includes: a state variable measurement unit which measures a state variable including an output current and an output voltage of the secondary battery in operation at each prescribed timing; a preprocessing unit which outputs input data calculated on the basis of the measured state variable; and a deterioration state estimation unit which estimates a deterioration state of the secondary battery in operation through a trained deterioration state model using the input data, wherein the preprocessing unit includes a state variable processing unit which sets a unit amount of charge/discharge and a most recent section closest to a current time and one or more previous sections before the most recent section as a desired section based on the unit amount of charge/discharge used for calculating the input data, calculates, as section data, an amount of voltage change based on the state variable for each set section, includes the calculated section data in

Abstract: Systems and methods include a computer-implemented method includes concurrently outputting, by a computing device to a display of the computing device, a graphical time-domain interpretation of seismic data, a graphical velocity model related to the seismic data, and a graphical depth-domain interpretation of the seismic data. The method may further include identifying, by the computing device, a first alteration to one of the time-domain interpretation, the velocity model, and the depth-domain interpretation. The method may further include identifying, by the computing device based on the first alteration, a second alteration to another of the time-domain interpretation, the velocity model, and the depth-domain interpretation. The method may further include updating, by the computing device based on the first alteration and the second alteration, at least two of the graphical time-domain interpretation, the graphical velocity model, and the graphical depth-domain interpretation.

Abstract: An illustrative embodiment includes a method for detecting whether a given item within input data is anomalous. The method includes: extracting activations at respective nodes of an autoencoder trained on the data, the activations comprising activations at the respective nodes for the given item within the data and for one or more other items within the data; calculating p-values corresponding to the respective nodes, wherein the p-value for a given node represents a proportion of the activations at the given node for the other items which are greater than the activations at the given node for the given item; determining at least one score at least in part by scanning for a subset of the respective nodes which maximizes a statistical scoring function applied to the corresponding p-values; and providing a visualization of at least the given item within the input data to a human user.

Abstract: A method of calibration is described that simplifies the measurement of battery impedance conducted in-situ while determining battery state-of-health. A single shunt measurement with a known Sum of Sines (SOS) current, at the desired frequency spread and known root mean squared (RMS) current is used to create a calibration archive. A calibration selected from this archive is used to calibrate an impedance measurement made on the battery.

Abstract: A computer-implemented method that uses a preprogrammed neural network and a trained neural network computer program product to predict and then compared borehole and formation sigmas, when using a pulse neutron source and at least three dual-function radiation detectors. These dual-function radiation detectors are used for detecting both neutrons and gamma rays and further pre-programmed to distinguish between neutrons and gamma rays by using pulse shape discrimination techniques. The trained neural network computer program product can be used on above-surface systems, as well as below surface systems like borehole assemblies in logging-while-drilling systems.

Abstract: Performance information associated with a previous repetition of an exercise movement is received. Performance of one or more upcoming repetitions is predicted, based at least in part on the performance information associated with the previous repetition of the exercise movement. A failure classification of whether the one or more upcoming repetitions is associated with an occurrence of physical failure is performed, based at least in part on the predicted performance of the one or more upcoming repetitions of the exercise movement. A number of repetitions in reserve is determined, based at least in part on the failure classification.

Abstract: A battery degradation evaluation system includes a memory and a processor. The processor is configured to acquire state quantities of a battery mounted at a vehicle, derive probabilities, and evaluate degradation of the battery based on the derivation results. The probabilities are a short-term degradation probability of the battery degrading in a pre-specified short period, a medium-term degradation probability of the battery degrading in a period that is longer than the short period, and a long-term degradation probability of the battery degrading in a period that is longer than the medium period. When a number of the state quantities is smaller, the processor sets a higher weighting for a combined degradation probability for the short period and medium period, which is calculated from the long-term degradation probability. When the number of state quantities is larger, the processor sets a higher weighting for the short-term degradation probability.

Abstract: A process for training and sharing generic functional modules across multiple diverse (architecture, task) pairs for solving multiple diverse problems is described. The process is based on decomposing the general multi-task learning problem into several fine-grained and equally-sized subproblems, or pseudo-tasks. Training a set of (architecture, task) pairs then corresponds to solving a set of related pseudo-tasks, whose relationships can be exploited by shared functional modules. An efficient search algorithm is introduced for optimizing the mapping between pseudo-tasks and the modules that solve them, while simultaneously training the modules themselves.