A computing device (2002) accesses a machine learning model (2050) trained on training data (2032) of first bonding operations (1308, 2040A) (e.g., a ball and/or stitch bond). The first bonding operations comprise operations to bond a first set of wires (1504) to a first set of surfaces (1506, 1508). The machine learning model is trained by supervised learning. The device receives input data (2070) indicating process data (2074) generated from measurements of second bonding operations (2040B). The second bonding operations comprise operations to bond a second set of wires to a second set of surfaces. The device weights the input data according to the machine learning model. The device generates an anomaly predictor (2052) indicating a risk for an anomaly occurrence in the second bonding operations based on weighting the input data according to the machine learning model. The device outputs the anomaly predictor to control the second bonding operations.
Tops of geological layers can be automatically identified using machine-learning techniques as described herein. In one example, a system can receive well log records associated with wellbores drilled through geological layers. The system can generate well clusters by applying a clustering process to the well log records. The system can then obtain a respective set of training data associated with a well cluster, train a machine-learning model based on the respective set of training data, select a target well-log record associated with a target wellbore of the well cluster, and provide the target well-log record as input to the trained machine-learning model. Based on an output from the trained machine-learning model, the system can determine the geological tops of the geological layers in a region surrounding the target wellbore. The system may then transmit an electronic signal indicating the geological tops of the geological layers associated with the target wellbore.
An apparatus includes processor (s) to: generate a set of candidate n-grams based on probability distributions from an acoustic model for candidate graphemes of a next word most likely spoken following at least one preceding word spoken within speech audio; provide the set of candidate n-grams to multiple devices; provide, to each node device, an indication of which candidate n-grams are to be searched for within the n-gram corpus by each node device to enable searches for multiple candidate n-grams to be performed, independently and at least partially in parallel, across the node devices; receive, from each node device, an indication of a probability of occurrence of at least one candidate n-gram within the speech audio; based on the received probabilities of occurrence, identify the next word most likely spoken within the speech audio; and add the next word most likely spoken to a transcript of the speech audio.
An apparatus includes a processor to: instantiate collection threads, data buffers of a queue, and aggregation threads: within each collection thread, assemble a row group from a subset of the multiple rows, reorganize the data values row-wise to columnar organization, and store the row group within a data buffer of the queue; operate the buffer queue as a FIFO buffer; within each aggregation thread, retrieve multiple row groups from multiple data buffers of the queue, assemble a data set part from the multiple row groups, transmit, to storage device(s) via a network, the data set part; and in response to each instance of retrieval of a row group from a data buffer of the buffer queue for use within an aggregation thread, analyze a level of availability of at least storage space within the node device to determine whether to dynamically adjust the quantity of data buffers of the buffer queue.
An apparatus includes a processor component of a first node device caused to receive data block encryption data and an indication of size of an encrypted data block distributed to the first node device for decryption, and in response to the data set being of encryptd data: receive an indication of the quantity of sub-blocks within the encrypted data block, and a hashed identifier for each data sub-block; use the data block encryption data to decrypt the encrypted data block to regenerate data set portions from the data sub-blocks; analyze the hashed identifier of each data sub-block to determine whether all data set portions are distributed to the first node device for processing; and in response to a determination that at least one data set portion is to be distributed to a second node device for processing, transmit the at least one data set portion to the second node device.
An apparatus including a processor to receive search criteria including a data value for a search within a data field; in response to the receipt of the query instructions, and for each data cell within a super cell, perform the specified search by comparing the data value to ranges of values indicated in a corresponding cell index to determine whether the data cell includes a data record meeting the search criteria, and in response to a determination that the data cell includes such a data record, use a unique values index in the cell index to search the data records of the data cell to identify one or more data records meeting the search criteria; and in response to identifying at least one data record meeting the search criteria, provide an indication that at least the data cell includes at least one data record meeting the search criteria.
A first computing device manages a cluster of event stream processing (ESP) engines (ESPEs). A local ESP model is created based on information read from a manager configuration file that includes first connection information to connect to the second computing device and second connection information to connect the third computing device. An ESPE is instantiated on the first computing device based on the created local ESP model. The event block object is received from the second computing device in a first source window of the instantiated ESPE. A remote ESP model is deployed to a remote third computing device. The manager configuration file includes an indicator of the remote ESP model. The third computing device to receive the processed event block object is selected. The processed event block object is published to a second source window defined by the remote ESP model deployed to the third computing device.
Machines can be controlled using advanced control systems. Such control systems may use an automated version of singular spectrum analysis to control a machine. For example, a control system can perform singular spectrum analysis on a time series by: generating a trajectory matrix from the time series, performing singular value decomposition on the trajectory matrix to determine elementary matrices and corresponding eigenvalues, and automatically categorizing the elementary matrices into groups. The elementary matrices can be automatically categorized into the groups by: generating a matrix of w-correlation values based on the eigenvalues, categorizing the w-correlation values into a predefined number of w-correlation sets, and forming the groups based on the predefined number of w-correlation sets. The control system can then determine component time-series based on the groups, and generate a predictive forecast using the component time-series. The control system can use the predictive forecast to control operation of the machine.
A computing device resolves a prioritized list of Internet protocol (IP) address to domain names. Each request of a plurality of requests is added to a request list using a priority value. A lookup request packet is created from a first request selected from the request list and then removed from the request list. The lookup request packet is sent to a third computing device, and includes an IP address for which to resolve the domain name. A response is received from the third computing device that includes the IP address and the domain name of the IP address. The IP address is added to keystore data in association with the domain name. When the request list includes a next request, the next request is selected from the request list, and processing continues with creating the lookup request packet with the next request.
An apparatus includes a processor component caused to: retrieve metadata of organization of data within a data set, and map data of organization of data blocks within a data file; receive indications of which node devices are available to perform a processing task with a data set portion; and in response to the data set including partitioned data, compare the quantities of available node devices and of the node devices last involved in storing the data set. In response to a match, for cacti map data map entry: retrieve a hashed identifier for a data sub-block, and a size for each of the data sub-blocks within the corresponding data block; divide the hashed identifier by the quantity of available node devices; compare the modulo value to a designation assigned to each of the available node devices; and provide a pointer to the available node device assigned the matching designation.
A computer system computes a score for a received data exchange and, in accordance with a neural network and input variables determined by received current exchange and history data, the computed score indicates a condition suitable for a denial. A set of attribution scores are computed using an Alternating Decision Tree model in response to a computed score that is greater than a predetermined score threshold value for the denial. The computed score is provided to an assessment unit and, if the computed score indicates a condition suitable for the denial and if attribution scores are computed, then a predetermined number of input variable categories from a rank-ordered list of input variable categories is also provided to the assessment unit of the computer system.
Apparatuses, systems and methods are disclosed for tolerating fault in a communications grid. Specifically, various techniques and systems are provided for detecting a fault or failure by a node in a network of computer nodes in a communications grid, adjusting the grid to avoid grid failure, and taking action based on the failure. In an example, a system may include receiving grid status information at a backup control node, the grid status information including a project status, storing the grid status information within the backup control node, receiving a failure communication including an indication that a primary control node has failed, designating the backup control node as a new primary control node, receiving updated grid status information based on the indication that the primary control node has failed, and transmitting a set of instructions based on the updated grid status information.
In a computing device supporting a failover in an event stream processing (ESP) system, an event block object is received. A first status of the computing device as active or standby is determined. When the first status is active, a second status of the computing device as newly active or not newly active is determined. Newly active is determined when the computing device is switched from a standby to an active status. When the second status is newly active, a last published event block object identifier that uniquely identifies a last published event block object is determined. A next event block object is selected from a non-transitory computer-readable medium accessible by the computing device. The next event block object has an event block object identifier that is greater than the determined last published event block object identifier. The selected next event block object is published to an out-messaging network device.
A computing device configured to determine when an alarm is triggered for a drilling operation is provided. Measured drilling data that includes a value measured for an input variable during a previous connection event of a drilling operation is received. A predicted value for a fluid flow back measure is determined by executing a predictive model with the measured drilling data as an input. The predictive model is determined using previous drilling data that includes a plurality of values measured for the input variable during a second drilling operation. The second drilling operation is a previous drilling operation at a different geographic wellbore location than the drilling operation. A fluid flow back measurement datum determined from sensor data is compared to the determined predicted value for the fluid flow back measure. An alarm is triggered on the drilling operation based on the comparison.
A method of determining an optimal value for a control of a drilling operation is provided. Drilling data from a drilling operation is received. The drilling data includes a plurality of values measured for each of a plurality of drilling control variables during the drilling operation. An objective function model is determined using the received drilling data. The objective function model maximizes a rate of penetration for the drilling operation. Measured drilling data is received that includes current drilling data values for a different drilling operation. An optimal value for a control of the different drilling operation is determined by executing the determined objective function model with the measured drilling data that includes the current drilling data values for the different drilling operation as an input. The determined optimal value for the control of the different drilling operation is output.
E21B 44/00 - Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
SYSTEMS AND METHODS FOR GENERATING A CROSS-PRODUCT MATRIX IN A SINGLE PASS THROUGH DATA USING SINGLE PASS LEVELIZATION
Systems and methods are provided for a data processing system having multiple executable threads that is configured to generate a cross-product matrix in a single pass through data to be analyzed. An example system comprises memory for receiving the data to be analyzed, a processor having a plurality of executable threads for executing code to analyze data, and software code for generating a cross-product matrix in a single pass through data to be analyzed. The software code includes threaded variable levelization code for generating a plurality of thread specific binary trees for a plurality of classification variables, variable tree merge code for combining a plurality of the thread-specific trees into a plurality of overall trees for the plurality of classification variables, effect levelization code for generating a plurality of sub-matrices of the cross-product matrix using the plurality of the overall trees for the plurality of classification variables, and cross-product matrix generation code for generating the cross- product matrix by storing and ordering the elements of the sub-matrices in contiguous memory space.
Systems and methods are provided for a grid computing system that performs analytical calculations on data stored in a distributed database system. A grid-enabled software component at a control node is configured to invoke database management software (DBMS) at the control node to cause the DBMS at a plurality of the worker nodes to make available data to the grid- enabled software component local to its node; instruct the grid-enabled software components at the plurality of worker nodes to perform an analytical calculation on the received data and to send the results of the data analysis to the grid-enabled software component at the control node; and assemble the results of the data analysis performed by the grid-enabled software components at the plurality of worker nodes.
Systems and methods are provided for generating multiple system state projections for one or more scenarios using a grid computing environment. A central coordinator software component executes on a root data processor and provides commands and data to a plurality of node coordinator software components. A node coordinator software component manages threads which execute on its associated node data processor and which perform a set of matrix operations. Stochastic simulations use results of the matrix operations to generate multiple state projections. Additional processing can be performed by the grid computing environment based upon the generated state projections, such as to develop risk information for users.