Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to determining time series model stability and robustness in refreshment. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a computation component that can employ weighted model evaluation to compute stability of time series pipelines over respective holdout datasets and a determination component that can select, based on the computed pipeline stabilities, a most stable time series pipeline.

Abstract: In an approach for post-modeling data visualization and analysis, a processor presents a first visualization of a training dataset in a first plot. Responsive to receiving a selection of a data group of the training dataset to analyze, a processor identifies three or fewer key model features of the data group of the training dataset. A processor ascertains a representative record of each key model feature of the three or fewer key model features using a Local Interpretable Model-Agnostic Explanation technique. A processor presents a second visualization of the three or fewer key model features and the representative record of each key model feature in a second plot.

Abstract: Determining whether synthetic data is sufficient for utilization in connection with one or more machine learning models. The computing device accesses a protected batch of data associated with a machine learning model. The computing device accesses a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data. The computing device accesses one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value. The computing device performs a machine learning function utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold.

Abstract: The present disclosure provides a copper alloy material, a preparation method therefor and use thereof, and belong to the technical field of additive manufacturing. The copper alloy material provided by the present disclosure includes the following components by mass percentage: 2.0-7.0% of Cr, 1.0-5.0% of Nb, 0.1-2.0% of Ag, 0.1-0.7% of Zr, 0.02-0.3% of RE, and the balance of Cu. The RE includes the following components by mass percentage: 88-93% of La, 6-9% of Ce, 1.5-1.9% of Pr, and Nd less than or equal to 0.3%, and a sum of mass is 100%. By means of the synergistic effect among RE, Cr, Nb, Ag, Zr and Cu in the present disclosure, thermal conductivity, high-temperature creep property, high-temperature strength, and high-temperature fatigue of the copper alloy material are effectively improved, and the problem of poor high-temperature mechanical properties of a copper alloy material in the prior art is solved.

Abstract: A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

Abstract: An embodiment configures a plurality of parameters, the parameters being usable to generate artificial data from original data, the configuring adjusting a level of privacy in the artificial data. An embodiment fits a distribution type to a variable of the original data. An embodiment adjusts, using a desired level of privacy and the distribution type, a level of noise, wherein the level of noise corresponds to the desired level of privacy. An embodiment generates, using the distribution type and the level of noise, the artificial data, the artificial data achieving the desired level of privacy by including noise data corresponding to the level of noise.

Abstract: A double-mirror focusing inside-beam powder-feeding laser-cladding device includes: first and second reflection beam splitters, a focusing lens, a vertical piping powder feeding device, and a collimated lens group. The first reflection beam splitter is disposed at a side of the collimated lens group; the second reflection beam splitter is disposed outside the first reflection beam splitter; the focusing lens is disposed at a side of the second reflection beam splitter facing away the collimated lens group; and the vertical piping powder feeding device is disposed at a side of the first reflection beam splitter facing away the collimated lens group. When the device is in use, a collimated beam is formed through the collimated lens group, and then reflected by the first and second reflection beam splitters to form an annular laser beam that can be focused by the focusing lens to form a light spot.

Abstract: An in-situ robot material-reducing processing method and system for a hydraulic turbine top cover are provided. The method includes: S1, obtaining frequency response data of an end of a cutter controlled by a robot by performing an impact hammer test; S2, obtaining a modal parameter of the end of the cutter controlled by the robot by using a modal analysis software; S3, obtaining a damping matrix [C] and a stiffness matrix [K] by using a free vibration equation of a damped system; S4, establishing a dynamic model of a three-degree-of-freedom robot processing system; S5, obtaining a milling force coefficient of the cutter by performing a calibration experiment; S6, solving a dynamic equation; S7, drawing a lobe diagram of flutter stability of a milling process performed by the robot; and S8, obtaining stable milling process parameters according to the lobe diagram of flutter stability.

Abstract: A device for repairing a top cover of a hydro turbine by adding or removing materials includes a rotating support base, a connecting arm, a movable frame, and a machining assembly. The rotating support base includes a base and a rotating seat disposed on the base, a driving assembly is disposed on the base, and an output shaft of the driving assembly is drivably connected to the rotating seat. An end of the connecting arm is connected to the rotating seat, and the other end of the connecting arm is connected to the movable frame. A lifting plate is disposed on the movable frame, a sliding table assembly and a measuring system are disposed on an upper side of the lifting plate. A swivel seat is disposed on an upper side of the sliding table assembly, and the machining assembly is detachably mounted on the swivel seat.

Abstract: Since defect areas in collected hydro turbine defect images account for a small proportion and there is a significant color difference between surrounding background and defects, which creates too much redundant information and greatly affects detection speed, a method for detecting defects in a hydro turbine top cover based on an improved YOLOv8 model involves first cropping collected defect images to obtain cropped images, then expanding the cropped images by image enhancement techniques such as image flipping and image blurring to obtain a training dataset. A defect detection network based on YOLOv8-CBAM is constructed and trained to generate a defect detection model for detecting the defects in the hydro turbine top cover. The method achieves high precision in defect detection for the hydro turbine top cover.

Abstract: Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.

Abstract: A content management platform automatically identifies content items that are presented during videoconferences. The platform is configured to access a video file that contains a plurality of frames of a recording of a videoconference, where the recording of the videoconference contains a record of screen sharing by one or more participants in the videoconference. The platform can classify one or more of the frames as content-containing frames. For each of these content-containing frames, the platform generates a vector representation of an image of at least a portion of the respective frame. An image of a respective frame can then be matched to a selected content item from a content repository, based on a degree of similarity between the image of the frame and the content item. The platform can then store a representation that links the video file to the selected content item.

Abstract: A computer-implemented method for generating an artificial data set is provided. Aspects include obtaining an input data set, calculating an association between the plurality of categorical variables of the input data set, and creating, based on the association, a plurality of clusters of categorical variables. Aspects also include identifying a key variable for each of the plurality of clusters of categorical variables, creating a key cluster for each of the plurality of clusters, and creating a cluster contingency table for each of the clusters. Aspects further include generating, based on the cluster contingency table for each of the plurality of clusters and for the key cluster, a data set for each of the plurality of clusters and the key cluster and generating the artificial data set based on a combination of the data set for each of the plurality of clusters and the key cluster.

Abstract: An in-situ robot programming method for a hydraulic turbine top cover based on binocular structured light vision is provided, which relates to the field of robot vision-guided intelligent processing technologies. The method performs hand-eye calibration on a binocular structured light camera and a robot to construct a coordinate conversion relationship thereof, collects an annular to-be-processed area of the hydraulic turbine top cover by the binocular structured light camera for joining, and obtains a trajectory and a trajectory plan through point cloud processing algorithms, to thereby achieve a rapid programming for the hydraulic turbine top cover based on guidance of binocular structured light vision. The method adapts to changes in sizes of the hydraulic turbine top cover and take into account real-time performance and robustness, so that impacts of uncertainty factors on the system are greatly weakened, and the method has characteristics of intelligence and high degree of systematization.

Abstract: In an approach for optimizing abnormal point detection, a processor receives a set of data, wherein the set of data is partially labeled time series data; determines a data block size for the set of data; splits the set of data into data blocks based on the data block size; computes trait measurements for traits for each data block; assigns a tag to each data block, wherein the tag is selected from the group consisting of a normal tag, an abnormality tag, and an unknown tag; uses the respective data blocks with either the normal tag or the abnormality tag as training data; updates the training data with artificial abnormalities; trains a detection model with the updated training data; and utilizes the trained detection model to predict whether the respective data blocks with the unknown tag have an abnormality or no abnormality.

Abstract: Methods, systems, and devices for techniques for firmware enhancement in memory devices are described. A memory system may include a volatile memory device and a non-volatile memory device, which may store a node address mapping. A host system in communication with the memory system may transmit a command instructing the memory system to transfer at least a portion of the node address mapping from the non-volatile memory device to the volatile memory device. The memory system may transmit a response to the command to the host system indicating a status associated with transferring the portion of the node address mapping.

Abstract: A time series anomaly detection method, system, and computer program product that processes time series data includes absorbing profiles of the time series data and anomaly types of a model as features, optimizing biased ranks to create optimized ranks through merging initial ranks with new ranks generated by real anomalies, and auto-suggesting the optimized ranks for saving a predetermined amount of data operation.

Abstract: An embodiment identifies, by a post-modeling category merging engine, a plurality of valid pairs associated with a categorical predictor, the plurality of valid pairs representing potential mergers of categories associated with a categorical predictor of a predictive model. The embodiment tests, by the post-modeling category merging engine, a merge strategy for the plurality of valid pairs to determine a merger that minimizes a loss in accuracy of the predictive model. The embodiment merges, by the post-modeling category merging engine based on the testing, a valid pair in the plurality of valid pairs to form a hybrid category.

Abstract: A horizontal parallax multiview display employs a plurality of slanted multibeam columns to scatter out of a light guide a plurality of directional light beams having principal angular directions corresponding to different view directions of the horizontal parallax multiview display. The plurality of directional light beams is modulated using an array of light valves to provide a multiview image having horizontal parallax. Further, the horizontal parallax multiview display employs a light control film having a slanted light control axis aligned with slanted multibeam columns of the slanted multibeam column plurality. The light control film is configured to control a view angle of the multiview image in a direction orthogonal to the horizontal parallax. The slanted multibeam columns may provide the multiview displays with a balanced resolution.

Abstract: A three-dimensional sorting robot-based data processing method and a three-dimensional sorting robot and system are provided. According to the method, a three-dimensional sorting robot is provided with a processor and a sorting assembly. The data processing method comprises: an obtaining step: obtaining the identity information of a target sorting tack in real time, the identity information comprising structure data of the sorting tack and/or position information of basket units in the rack; a binding step: binding the identity information of the target sorting rack with the information of a three-dimensional sorting robot, the binding relationship being associated with a target order executed by the three-dimensional sorting robot; and a sorting step: under a sorting task of the target order, sorting, by the sorting assembly according to the identity information of the rack by means of the processor, goods to be sorted.