Abstract: Provided is a method for simulating electricity of a wafer chip. The method includes: a database is constructed, the database including spectroscopic data of a semiconductor structure of the wafer chip obtained from a target key process, actual electrical data of the wafer chip, and a correspondence between the spectroscopic data and the actual electrical data; the target key process is performed on a target wafer chip to obtain the spectroscopic data of the semiconductor structure of the target wafer chip obtained from the target key process, the spectroscopic data being target spectroscopic data; the electrical data of the target wafer chip is simulated based on the obtained target spectroscopic data and the database, the electrical data being target electrical data.

Abstract: A method can include accessing data generated during field operations; generating a graph that includes vertices and edges using at least a portion of the data, where the edges represent relationships between vertices; and generating a query result using the graph responsive to receipt of a query.

Abstract: An illustrative optical measurement system includes a light source configured to emit light directed at a target, an array of photodetectors configured to detect photons of the light after the light is scattered by the target, and a processing unit. The processing unit is configured to measure a noise level of a photodetector included in the array of photodetectors and determine that the noise level meets a predetermined threshold. The processing unit is further configured to prevent, based on the determining that the noise level meets the predetermined threshold, an output of the photodetector from being used in generating a histogram based on a temporal distribution of photons detected by the array of photodetectors.

Abstract: A system for performing automated inspection of a sewer line using machine learning. The system comprises a inspection data database storing sewer inspection data including images and metadata associated therewith; an inspection upload module receiving and uploading the sewer inspection data to the inspection data database; and an inspection module receiving the sewer inspection data from the inspection data database and generating therefrom identification data including characteristics of the sewer line identified and categorized.

Abstract: Some embodiments of the invention provide methods for performing root cause analysis for non-deterministic anomalies in a datacenter. For instance, the method of some embodiments identifies a root cause for degradation in performance of one or more components in a network of the datacenter. This method collects and generates resource consumption data regarding resources consumed by a set of components in this network. The method performs a first analysis on the collected and/or generated data to identify an instance in time when one or more components, while still operational, are possibly suffering from performance degradation. The method then performs a second analysis on the collected and/or generated data associated with the identified time instance to identify a root cause of a performance degradation of at least one component in the network.

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: A building security system of a building including one or more memory devices configured to store instructions causing one or more processors to receive door lock data collected from one or more sensors for one or more doors within the building indicating operation of the one or more doors, select a period for which the door lock data was collected, select a first door from the one or more doors of the building for which data is collected, and identify segments of the data indicating activity of the door. The instructions also apply a model to segments of the data indicating activity of the first door, compute a probability metric for each segment of the data, compute a combined probability score for the first door based on the computed probability metrics, and classify the first door as normally functioning or malfunctioning by comparing the combined probability score to a threshold.

Abstract: An embodiment provides a method, including: obtaining, from a multi-sensor pipe inspection robot that traverses through the interior of a pipe, two or more sets of condition assessment data for the interior of the pipe collected during a single pass through the interior of the pipe; the two or more sets of condition assessment data comprising a first data type obtained using a first sensor type and a second data type obtained using a second sensor type; combining, using a processor, two or more image processing techniques to adjust imaging of a pipe feature; and forming, using the processor, an image of the interior of the pipe using the two or more image processing techniques. Other embodiments are described and claimed.

Abstract: An electronic device, for each media content item of a plurality of media content items, receives a respective score for each a first set of objectives and one or more other objectives and generates a respective score between a user and the media content item. The generating includes applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for a second set of objectives, wherein the second set of objectives includes (i) a resulting objective corresponding to the first set of objectives and having the first combined score and (ii) the one or more other objectives. The electronic device streams media content to the user selected based on the respective scores between the user and the media content items.

Abstract: A method and system for training a fall detection classifier using subject-specific movement data. A subject sets a preferred non-fall detection rate. Movement data responsive to a subject's movements during everyday activities are obtained over a predetermined data collection period. For each detected event in the movement data, values for one or more parameters that may (together or individually) indicate a fall are obtained. The obtained values are used to generate a subject-specific non-fall detection rate function. This non-fall detection rate function is used to derive a threshold value, in reference to the subject-set preferred non-fall detection rate, to distinguish fall events from non-fall events.

Abstract: The present disclosure provides a method and apparatus for processing astronomical survey data. The method comprises: a data distribution step: delivering a spectral data file to be processed to a distributed file system, dividing the spectral data file into a plurality of data blocks, and distributing the data blocks to computing nodes; a data computing step: performing a spectral classification of spectra to be measured in the data blocks, with a template matching method; the data computing step comprises: reading template spectra, and extracting template universal operators; reading the spectra to be measured; performing spectral matching, and computing Chi-square error values for flow rate values of the template spectra multiplied by the polynomial and flow rate values of the spectra to be measured; and finding corresponding minimum Chi-square error and corresponding redshift for each of the template spectra, so as to determine classifications of the spectra to be measured.

Abstract: To perform a prognostic health analysis for an asset, a plurality of independent stochastic simulations are performed using transition probabilities of a discrete Markov Chain model. A prognostic asset health state evolution is computed over a time horizon from the plurality of independent stochastic simulations. An output is generated based on the computed prognostic asset health state evolution.

Abstract: Disclosed herein are computer-implemented methods and systems for forecasting security ratings for an entity. The methods and systems can include generating a plurality of simulated instantiations of a security scenario for the entity, in which the security scenario characterized by a plurality of security events associated with at least one event type. The methods and systems can further include determining a security rating for each instantiation of the plurality of instantiations; and generating a forecast cone based on the determined security ratings for the plurality of instantiations. In some examples, for each event type of the at least one event type, the methods and systems can include determining a rate, duration, and/or temporal placement of the security events associated with the event type over a forecasting period.

Abstract: A computer-implemented method for simulation of an integrated circuit for yield analysis includes: a) for plurality of variables, generating initial sampling sets by sampling from provided distributions related to physical properties of circuits; b) selecting at least one sample from each initial set randomly and combining into initial simulation set; c) running initial simulation of operation of circuit, applying initial simulation set, the operation having passing/failing criterion; d) if fails: storing samples of initial set into initial sampling distributions for each variable; e) repeating steps b)-d) until sufficient failures obtained; f) building importance sampling distribution based on each initial sampling distribution, the importance distribution having lower, center, upper portions; g) generating secondary simulation set by drawing samples from importance sampling distribution for each variable; h) simulating circuit by applying the secondary set; i) repeating steps g)-h); j) mapping resulting yie

Abstract: Systems and methods for building predictive and prescriptive analytics of wind turbines generate a historical operational dataset by loading historical operational SCADA data of one or more wind turbines. Each sensor measurement is associated with an engineering tag and at least one component of a wind turbine. The system creates one or more performance indicators corresponding to one or more sensor measurements, and applies at least one data clustering algorithm onto the dataset to identify and label normal operation data clusters. The system builds a normal operation model using normal operational data clusters with Efficiency of Wind-To-Power (EWTP) and defines a statistical confidence range around the normal operation model as criterion for monitoring wind turbine performance. As real-time SCADA data is received by the system, the system can detect an anomalous event, and issue an alert notification and prescriptive early-action recommendations to a user, such as a turbine operator, technician or manager.

Abstract: A case data set storage unit includes a plurality of case data sets including difference data representing, a variation from measurement data taken at an initial state to measurement data taken at a plurality of points of time for the same sample under the same measurement condition. An aging degradation estimation unit uses these case data sets to output difference data representing an estimate value of a variation from the measurement data taken at the initial state to measurement data taken, by a measurement device, at or after a first elapsed time, based on difference data representing a variation from the measurement data taken at the initial state to measurement data taken at a plurality of points of time that precede the first elapsed time. An aging degradation correction unit corrects the measurement data taken at or after the first elapsed time, based on the output difference data.

Abstract: Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault.

Abstract: The present disclosure provides a method and apparatus for processing astronomical survey data. The method comprises: a data distribution step: delivering a spectral data file to be processed to a distributed file system, dividing the spectral data file into a plurality of data blocks, and distributing the data blocks to computing nodes; a data computing step: performing a spectral classification of spectra to be measured in the data blocks, with a template matching method; the data computing step comprises: reading template spectra, and extracting template universal operators; reading the spectra to be measured; performing spectral matching, and computing Chi-square error values for flow rate values of the template spectra multiplied by the polynomial and flow rate values of the spectra to be measured; and finding corresponding minimum Chi-square error and corresponding redshift for each of the template spectra, so as to determine classifications of the spectra to be measured.

Abstract: A target trajectory generation device generates and outputs target trajectories each including a target position and a target speed of a vehicle. A first target trajectory is intended to perform at least one of steering, acceleration, and deceleration of the vehicle. A second target trajectory is intended to decelerate and stop the vehicle. When a malfunctioning device does not exist, a vehicle traveling control device executes vehicle traveling control based on the first target trajectory. When the malfunctioning device exists, the vehicle traveling control device stops the vehicle by executing the vehicle traveling control based on the second target trajectory output before the malfunction occurs, or based on the second target trajectory output from the target trajectory generation device other than the malfunctioning device.

Abstract: Described herein are software and systems for analyzing videos and/or images. Software and systems described herein are configured in different embodiments to carry out different types of analyses. For example, in some embodiments, software and systems described herein are configured to locate an object of interest within a video and/or image.

Abstract: Methods of detecting and/or identifying an artificial target within an image are provided. These methods comprise: applying to a region of the image a primary classification algorithm for performing a feature extraction of the image region, the primary classification algorithm being based on a spectral profile defined by one or more spectral signatures with one or more features in at least part of the infrared spectrum; obtaining a relation between the extracted features of the image region and the spectral profile; verifying whether a level of confidence of the obtained relation between the extracted features and the spectral profile is higher than a first predetermined confirmation level; and, in case of positive (or true) result of said verification, determining that the image region corresponds to artificial target to be detected, thereby obtaining a confirmed artificial target. Systems and computer programs are also provided that are suitable for performing said methods.

Abstract: A system and method for utilizing automatically generating related subject matter areas to create footer links for a subject matter page of a tutor matching website is disclosed and claimed. In particular, a document embedding algorithm such as word2vec, doc2vec or GloVe can be trained using on a particular subject using pertinent material such as, for example, textbooks, learned papers, and transcripts of lectures. Once trained, the document embedding algorithm can be used to generate a list of related subjects that can be used to automatically build footer links for a particular subject matter page. The related footer links can improve the positioning of the tutor matching website with common Internet search engines. The same document embedding algorithm can be used to identify previously answered questions that may assist a user posing a question to the tutor matching website.

Abstract: Example methods and systems are provided for network diagnosis. One example method may comprise: detecting an egress packet and determining whether each of multiple network issues is detected for the egress packet or a datapath between a first virtualized computing instance and a second virtualized computing instance. The method may also comprise: generating network diagnosis code information specifying whether each of the multiple network issues is detected or not detected; generating an encapsulated packet by encapsulating the egress packet with an outer header that specifies the network diagnosis code information; and sending the encapsulated packet towards the second virtualized computing instance to cause a second computer system to perform one or more remediation actions based on the network diagnosis code information.

Abstract: A method is used in issuing alerts for storage volumes using machine learning. A machine learning system analyzes Input/Output (I/O) data of a storage volume in a data storage system. The machine learning system is trained with sample I/O data patterns associated with the storage volume. Based on the I/O data, the machine learning system identifies atypical behavior associated with I/O data patterns of the I/O data. The method then issues an alert.

Abstract: The value for equipment to be replaced can be maximized by determining a threshold cutoff value for a failure prediction indicator and a window size for obtaining the threshold cutoff value for a piece of oil or gas drilling or recovery equipment; applying the threshold cutoff value and the window size to an equipment failure prediction model; and deriving a recall value and an average hour-loss value from the equipment failure prediction model. Predictive maintenance for the piece of oil or gas drilling or recovery equipment may be performed based on the recall value and the average hour-loss value to perform predictive maintenance for a piece of equipment in an oil or gas recovery operation.

Abstract: An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

Abstract: A system and method are disclosed for bottom-up modeling and prediction of asset anomalies. In one embodiment, the system receives first sensor data from a first sensor, the first sensor associated with a first asset; smooths the first sensor data; determines a first stage within sensor data, the sensor data including the first sensor data; determines a first set of anomalies within the first stage; generates a first asset state space associated with the first asset; trains a base model describing a first group of one or more assets, the first group of one or more assets including the first asset; and trains, using the base model, a final model particular to the first asset.

Abstract: A demand prediction method using a computer includes calculating an error between a prediction value of an amount of a past demand and a measurement value of the amount of the past demand, the prediction value of the amount of the past demand being calculated by inputting a measurement value of a past explanatory variable to a prediction model that is constructed in accordance with a measurement value of an amount of a demand at a predetermined location and an explanatory variable that serves as an external factor that affects an increase or a decrease in the amount of the demand at the predetermined location, determining whether the calculated error is an abnormal value, acquiring a new explanatory variable if the error is determined to be the abnormal value, updating the prediction model in accordance with the acquired new explanatory variable, and newly predicting an amount of a future demand using the updated prediction model.

Abstract: Techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters are described. An exemplary method includes receiving a request to determine a search space for at least one hyperparameter of a machine learning algorithm; determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets; and tuning the machine learning algorithm using the determined optimal hyperparameter values for the at least one hyperparameter of the machine learning algorithm to generate a machine learning model.

Abstract: The serving of advertisements with (e.g., on) video documents may be improved in a number of ways. For example, a system may (a) accept information defining at least one ad spot associated with at least one instance of an video document, (b) accept offers to have advertisements served in the ad spot(s), and (c) arbitrate among competing advertisements, using at least the offers, to determine at least one advertisement to be served in that ad spot(s). As another example, a system may (a) accept relevance information for an advertisement, (b) determine at least one video document using the accepted relevance information, (c) present information about the video document(s) to an advertiser associated with the advertisement, and (d) accept, from the advertiser, an offer to have its advertisement served with at least one of the video document(s) accepted.

Abstract: A flash memory system may include a flash memory and a circuit for decoding a result of a read operation on the flash memory using a first codeword. The circuit may be configured to generate an estimated codeword based on a result of hard decoding the first codeword and a result of hard decoding a second codeword. The circuit may be further configured to generate soft information based on the hard decoding result of the first codeword and the estimated codeword. The circuit may be further configured to decode the result of the read operation on the flash memory using the soft information.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to estimate an audience population.

Abstract: Systems and methods for unsupervised training and evaluation of anomaly detection models are described. In some embodiments, an unsupervised process comprises generating an approximation of a data distribution for a training dataset including varying values for a metric of a computing resource. The process further determines, based on the size of the training dataset, a first quantile probability and a second quantile probability that represent an interval for covering a prescribed proportion of values for the metric within a prescribed confidence level. The process further trains a lower limit of the anomaly detection model using a first quantile that represents the first quantile probability in the approximation of the data distribution and an upper limit using a second quantile that represents the second quantile probability in the approximation. The trained upper and lower limits may be used to monitor input data for anomalous behavior and, if detected, trigger responsive action(s).

Abstract: Facilitation of an explanation about an object to be analyzed is realized with high accuracy and with efficiency. A data analysis apparatus is disclosed which uses a first neural network configured with an input layer, an output layer, and two or more intermediate layers provided between the input layer and the output layer. Each performs a calculation by giving data from a layer of a previous stage and a first learning parameter to a first activation function and outputs a calculation result to a layer of a subsequent stage. The data analysis apparatus includes a conversion section; a reallocation section; and an importance calculation section.

Abstract: A differential pressure monitoring system includes a differential pressure monitoring device and at least one client device. The differential pressure monitoring device includes a water pressure sensor that detects a water pressure at an inlet of the dry pipe valve, a valve air pressure sensor that detects an air pressure at an outlet of the dry pipe valve, and a control circuit that computes a ratio of the water pressure and the air pressure, predicts whether a valve tripping event is expected to occur based on the computed ratio, and in response to predicting that the valve tripping event is expected to occur, provides a prediction that the valve tripping event is expected to occur for remedial action. The system includes at least one client device that receives the prediction from the control circuit and presents display data regarding the prediction.

Abstract: Technology described in this document can be embodied in a method that includes generating, using one or more processing devices of a vehicle operating in an environment, a discretized representation of the environment, including a plurality of cells. The cells are occupied by particles representing at least one of: an object, or a free space in the environment. Sensor data indicative of a state of at least one particle is received, and an update to a time-varying particle density function associated with a location of the particle in the dynamic occupancy grid is calculated from the sensor data and one or more models. The method further includes generating a prediction of occupancy of at least one cell in the discretized representation based on the updated particle density function, and operating the vehicle, using a controller circuit of the vehicle, based at least in part on the prediction.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to calculate long-term effects of marketing campaigns.

Abstract: A method is used in issuing alerts for storage volumes using machine learning. A machine learning system analyzes Input/Output (I/O) data of a storage volume in a data storage system. The machine learning system is trained with sample I/O data patterns associated with the storage volume. Based on the I/O data, the machine learning system identifies atypical behavior associated with I/O data patterns of the I/O data. The method then issues an alert.

Abstract: Methods, systems and computer program products for determining an accurate flow rate based on limited observations are provided. Aspects include receiving a limited sample of flow rate data associated with a test site of a test river. Aspects also include determining topographical characteristics of the test site. Aspects also include determining a plurality of similar tested sites and generating a test site rating curve based on a combination of the limited sample of flow rate data and sufficient flow rate data associated with one or more of the plurality of similar tested sites. Aspects also include obtaining a stage reading associated with the test site from a sensor disposed at the test site. Aspects also include determining a flow rate of the test site based on the stage reading and the test site rating curve.

Abstract: Devices, systems, and methods are provided for classifying detected objects as static or dynamic. A device may determine first light detection and ranging (LIDAR) data associated with a convex hull of an object at a first time, and determine second LIDAR data associated with the convex hull at a second time after the first time. The device may generate, based on the first LIDAR data and the second LIDAR data, a vector including values of features associated with the first convex hull and the second convex hull. The device may determine, based on the vector, a probability that the object is static. The device may operate a machine based on the probability that the object is static.

Abstract: A method for detecting a fall by a user wearing a fall detector, including: detecting a trigger event identifying the time location of a possible fall event in user data; extracting motion features from motion data and physiological features from physiological data from within a time window around the identified time location; and determining whether the detected trigger event is a fall by the user by inputting the at least one of the motion features and at least one of the physiological features into a classifier.

Abstract: Processor systems to estimate audience sizes and impression counts for different frequency intervals are disclosed. An example processor system includes a memory management unit (MMU) to assign requests from computing devices indicative of accesses to media to a first block of memory and to assign user-identified impression data corresponding to user-identified impressions to a second block of memory. The processor system including an arithmetic logic unit (ALU) to determine multipliers relating a first probability distribution for the user-identified impressions to a second probability distribution for census impressions. The multipliers based on probability constraints defined by weighted probabilities associated with the second probability distribution, where different ones of the probabilities weighted based on estimated populations for corresponding ones of the different demographics.

Abstract: A substation asset management method can include the steps of: determining whether to compensate a reliability model by element of the substation by comparing reliability of a reference reliability model by substation type with reliability depending on health index by element thereof generated based on state data and real-time monitoring data by element of the substation; compensating the reference reliability model by substation type and generating a unique reliability model by element of the substation as a result of the determination; evaluating system reliability and economic feasibility by maintenance scenario based on a reference system reliability model for each candidate element subject to maintenance among the elements of the substation; and drawing a maintenance scenario of the elements of the substation by using a fitness function of maintaining the substation based on the result drawn through the evaluation of the system reliability and the economic feasibility.

Abstract: Techniques for predicting failure mode specific reliability characteristics of tangible equipment using parametric probability models are disclosed. In some example embodiments, a computer system receives a model training configuration entered via a user interface, trains a failure curve model for a selected failure mode of a selected equipment model based on the model training configuration at a time indicated by training schedule data, generating, and generates analytical data for the selected failure mode of the selected equipment model using the trained failure curve model. The failure mode corresponds to a specific way in which the equipment model is capable of failing. In some example embodiments, the training of the failure curve model comprises determining a shape parameter and a scale parameter for the failure curve model based on a fitting of failure event data to a continuous probability distribution, and storing the parameters for use in generating the analytical data.

Abstract: Provided herein are systems and methods for providing digital guidance in an underlying computer application. In one exemplary implementation, a method includes recording, in a computing device, steps of digital guidance content as the steps are created by a content author. The exemplary method also includes automatically segmenting, in the computing device, the digital guidance content as it is being created such that the digital guidance content is only associated with segments of the underlying computer application where the content is relevant. The exemplary method further includes making the digital guidance content available for playback to an end user on a computing device only when the end user is in a segment of the underlying computer application that is relevant to the digital guidance content.

Abstract: This patent specification relates to systems and methods that use networks of standardized time series and models in a generic and extensible platform. More particularly, this patent specification relates to standardizing time series and models, using standardized time series and models in a network of time series (NOTS), and creating networks of time series in a NOTS platform. In addition, this patent specification relates to use of nodes that can be arranged in any user defined order, and each node is evaluated to return a time array that may be used to populate a time series. A layering framework may be used to define how each node is evaluated.

Abstract: An artificial intelligence (AI) based system for detecting defects in infrastructure uses an image recognizer and image data. A set of annotated training and validation data is generated to train and validate the image recognizer. The image data is annotated with classification data such as defect type and severity of the defect. Once trained and validated, the image recognizer can analyze inspection images to identify detects therein and generate an output report including the identification and classification of the defect, and remediation recommendations.

Abstract: Seismic data of a subterranean region is received by data processing apparatus. The seismic data includes multiple seismic data points. For each seismic data point, gradients are computed based on the received seismic data and a dip angle is computed based on the gradients for the each seismic data point. The dip angle is smoothed using anisotropic diffusion.

Abstract: The present disclosure provides an apparatus for complementing automotive radar, which comprises a camera for obtaining an image of a field of view, a radar sensor for transmitting a radar signal and detecting a radar signal reflected by an object, and a controller for obtaining first information on a target from the image, setting a monitoring range of the radar sensor according to the obtained first information, obtaining second information on the target based on a radar signal detected in the monitoring range, and detecting malfunction of the radar by determining whether or not the first information matches the second information, and further provides a method thereof. According to the present disclosure, it is possible to quickly detect malfunction of the radar sensor.

Abstract: The present disclosure relates to a signal processing apparatus and system. In calibration, a sensor section is used to acquire a time of day when a stereo camera image capturing a target is acquired and a time of day when a radar image capturing the target is acquired. The target has a radar reflector and a marker. The image acquisition times of day are acquired while at the same time changing a distance between the sensor section and the target so as to find an amount of time-of-day discrepancy, a difference in time of day between the stereo camera image and the radar image at the same distance. At the time of object detection, the radar image acquired first is buffered and then output together with the stereo camera image acquired by the time-of-day discrepancy later, thus combining the stereo camera image and the radar image.