309 results about "Automated method" patented technology

Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of whether there is a hazard at the 3D location corresponding to each initial transformer query, the shape of the hazard, and / or its class. These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations (e.g., obstacle avoidance, lane keeping, lane changing, merging, splitting, etc.). Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected static scene points, navigable space boundaries, or detected hazard objects. Accordingly, hazards such as road debris and other obstacles may be detected and ground truth data may be generated for a variety of sensing tasks.

The invention provides a gastric cancer TNM staging automation method and system based on a large language model. The method comprises the steps that a medical data set related to gastric cancer of a patient is acquired; preprocessing the medical data set; inputting the preprocessed medical data set into a trained large language model; extracting examination conclusions in the preprocessed medical data set, splicing all diagnosis conclusions into a long text for input, and inserting special separators between different sources for distinguishing; performing feature extraction on the long text input to generate a feature vector; probability inference is carried out after suspicious remote transfer labeling in the feature vectors; carrying out conflict statement resolution and credibility judgment on the feature vectors, and then carrying out TNM stage classification; and outputting the TNM staging result of the patient. The method has the beneficial effects that full-process automatic processing from medical data set acquisition, preprocessing and feature extraction to final TNM stage classification is realized.

The invention discloses a cross-channel marketing automation method and system based on real-time behavior triggering. The method comprises the following steps: capturing a user behavior event flow in real time through a front-end acquisition unit; performing real-time mode matching on the event stream by utilizing a complex event processing engine, and generating a marketing trigger signal when a preset rule is matched; dynamically selecting an optimal channel for the user based on a real-time scoring model in response to the signal; the marketing content is sent to the selected channel through the unified execution module; and finally, tracking user feedback and updating model parameters and user preferences in real time to form closed-loop optimization. The corresponding system comprises a front-end data acquisition module, a streaming event processing module, a real-time intelligent decision-making module, a multi-channel execution module and a closed-loop optimization module which are used for implementing the steps. According to the method, millisecond-level intention perception, cost and experience optimal channel intelligent decision and data-driven adaptive optimization closed loop are realized, and the problems of delayed response, extensive channel and lack of self-adaption of traditional marketing are solved.

Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of one or more features of each point (e.g., refined height, range, driving condition, etc.) on a sampled surface (e.g., the road). These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations. Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected ground surface models, detected surface features, detected weather and / or surface condition labels, and / or detected per-point artifact labels. Accordingly, surface features such as ground surface heights along a predicted trajectory may be detected and ground truth data may be generated for a variety of sensing tasks.

An automated method is provided for comparing a source database and a target database that are intended to be kept in synchronization with each other. An application processes transactions that are posted to the source database, and a synchronization process processes transactions that are posted to the target database. Change log data is chunked from a change log associated with the source database to create first chunks of change log data. Change log data is also chunked from a change log associated with the target database to create second chunks of change log data. A comparison engine compares representations of the first chunks of change log data with representations of the second chunks of change log data, wherein the second chunks of change log data correspond to the same respective first chunks of change log data that were made to the source database and kept in synchronization with the target database via the synchronization process. The source database and the target database are presumed to be in synchronization with each other via the synchronization process when the comparison indicates that the representations of the first chunks of change log data match the representations of the second chunks of change log data. The source database and the target database are presumed not to be in synchronization with each other when the comparison indicates that the representations of the first chunks of change log data do not match the representations of the second chunks of change log data. An action is performed when the comparison indicates that the representations of the first chunks of change log data do not match the representations of the second chunks of change log data. The action may be a corrective action that resynchronizes the target database with the source database, or that suspends or stops the application from processing transactions.

An automated method and system for modeling intrusion detection on an Internet of Things network preprocess received raw data packets. The raw data packets are converted to a packet capture file, which is converted to a comma separated value (CSV) file. The CSV file is filtered to create normal and abnormal CSV files. An attack is identified in the abnormal CSV file. Features associated with the attack are labeled and classified as an attack type. The abnormal and normal CSV files are merged. Redundant data is removed from the merged file, which is added to a concatenated file with other merged files. Features are extracted from the concatenated file and a machine language (ML) model is built using the extracted features. ML classifiers are determined for each merged file for the ML model to build an ensemble ML model. A dynamic ensemble of ML classifiers detects evolving attack types.

The invention provides an Android application task automation method based on a user interaction description diagram, and belongs to the technical field of automation. Comprising the steps that a first stage comprises user interaction description graph generation and aims at obtaining a user interaction description graph of an application; and outputting the user interaction description diagram of the application by taking the Android project as input. And the second stage comprises task execution path planning and adaptive interactive operation generation. According to the method, static program analysis is combined with the semantic reasoning ability of the LLM, the structured user interaction description graph is automatically constructed from the source code of the target application, the user interface elements, the interaction logic and the state jump relation are covered, and the problems of operation blindness and the like caused by lack of application prior knowledge before task execution of the LLM are solved. An initial operation instruction sequence can be generated in combination with user task requirements; through an adaptive instruction re-planning mechanism, state fault tolerance and path optimization in a task execution process are realized, and the task completion rate in a complex scene is remarkably improved.

The invention discloses an automatic tightening method for vehicle screws, and relates to the technical field of automatic control, and the method comprises the steps: obtaining the dynamic information of a target vehicle and the initial position information of all target screw holes; based on the dynamic information, the mobile platform is controlled to perform synchronous following motion, so that the mobile platform and the target vehicle keep a preset relative position relationship; based on the initial position information, an execution mechanism installed on the mobile platform is controlled to position a preset target screw hole, the actual position of the preset target screw hole is obtained, and the preset target screw hole is determined from all the target screw holes based on a preset screening rule; determining target positions of all target screw holes based on the actual position and initial position information of the preset target screw holes; and the execution mechanism is controlled to move to target positions corresponding to all the target screw holes, and all the target screws are tightened.

A computer-implemented method and system standardize code patterns within enterprise software environments. A code modernization system utilizes a Large Language Model (LLM) and a specialized prompt library. The library includes pattern recognition prompts to guide the LLM in identifying specific code patterns within selected software code, potentially using enterprise-specific context. It also includes standardized solution prompts to guide the LLM in generating replacement code conforming to predefined enterprise standards for the identified patterns. The system orchestrates communication, transmitting code and relevant prompts to the LLM and receiving identified patterns and subsequently the generated standardized replacement code. This automated approach facilitates improved code quality, consistency, maintainability, and can support code translation efforts within the enterprise.

The invention discloses a method for automatically constructing a pathological image data set and training cell nucleus detection and classification based on a space transcriptome technology, and belongs to the field of image processing and artificial intelligence auxiliary pathological diagnosis. According to the method, a spatial transcriptome public data set is obtained, and a data set containing image blocks, weak supervision / semi-supervision labels and cell nucleus boundary information is automatically constructed through preprocessing, deconvolution cell type annotation and cell nucleus instance segmentation, so that the dependence on manual annotation is reduced. Furthermore, a detection and classification model is designed, a multi-scale deformable attention encoder and a decoupled detection and classification decoder are adopted, a limited deformable cross attention mechanism is introduced into the classification decoder, KL divergence classification loss is combined, and instance-level cell nucleus categories are learned from region-level proportion labels. According to the method, end-to-end automation is realized, the cell nucleus detection and classification precision and efficiency are improved, and a high-quality pre-training model basis is provided for downstream pathological analysis.

Intent-based automation that discovers automatable tasks and / or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI) / machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI / ML models are used to determine which parts of the flows are similar and / or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

The invention relates to the technical field of road construction automation and intelligent control, discloses an intelligent sensing and control road laying automation method and system, and aims to solve the problem of unstable construction quality caused by discontinuous sensing data, poor equipment collaboration and lack of closed-loop feedback in the prior art. The method comprises the steps that operation parameters and material state data of paving and compacting equipment are collected in real time through a multi-mode sensing module; performing space-time alignment and fusion on the multi-source heterogeneous data, and constructing a high-precision three-dimensional digital construction model; and on the basis of a model prediction control algorithm, combining the laying quality prediction model and multi-objective optimization to generate optimal equipment control parameters. According to the scheme, intelligent sensing, self-adaptive decision making and accurate control in the construction process are achieved, the pavement flatness, the compaction uniformity and the construction efficiency are remarkably improved, manual dependence and resource waste are reduced, and the construction safety and the engineering quality traceability are enhanced.

The invention relates to an automatic filling rubber sheath preforming method and device for rare refractory metal powder. The device comprises a vacuum feeding mechanism, a weighing and metering mechanism, a vibration filling mechanism, an automatic plugging mechanism, a sheath transposition mechanism, a manipulator loading and unloading mechanism and a control system. According to the system, efficient, uniform and sealed filling of tantalum powder is achieved through vacuum material suction, weighing control, filling and compaction at the same time, automatic plugging and sealing and bag sleeve replacement through linkage of a mechanical arm. The control system coordinates sequential operation of all links on the basis of signals of multiple sensors, the powder amount range of a single bag is 6-17 kg, and the control system is matched with rubber bags of multiple specifications such as phi 60 * 1000 mm and the like. According to the method, the powder loading density consistency, the sealing reliability and the production line rhythm are improved, the labor intensity and the dust pollution are remarkably reduced, and the method is suitable for the field of metallurgy preforming of tantalum, alloy powder of tantalum and other metals.

The invention discloses a circuit board testing and quality screening automation method and system, and aims to solve the problems of low efficiency, insufficient precision and imperfect quality control of traditional circuit board testing. Precise positioning of the circuit board is achieved through the high-precision X / Y / Z three-dimensional positioning mechanical arm, parallel testing is conducted in combination with equipment such as a multi-channel oscilloscope, a signal source and a spectrum analyzer, and the testing efficiency is improved. Temperature and humidity control testing is carried out through the environment simulation bin, the thermal imager scans temperature distribution of the circuit board, a high-precision instrument is used for detecting signal integrity and analyzing electromagnetic compatibility, and the performance of the high-speed circuit board is ensured. A machine learning algorithm predicts potential faults, a test process is optimized, an automatic sorting system is adopted to separate qualified and unqualified circuit boards, and high consistency and high reliability of product quality are ensured. Through the automatic and intelligent testing process, the production efficiency, the quality control capability and the testing precision are greatly improved, and the labor cost and the production risk are reduced.

A real-time automated method to diagnose and / or detect stroke and engage the patient, care-takers, emergency medical system and stroke neurologists in the management of this condition includes the steps of continuously measuring natural limb activity, conveying the measurements to a cloud based real-time data processing system, identifying patient specific alert conditions, and determining solutions for acting upon needs of the patient. The system by which the method is implemented includes at least one body worn sensor continuously measuring natural limb activity and a patient worn data transmission device conveying the measurements to a cloud based real-time data processing system that identifies patient specific alert conditions and determines solutions for acting upon needs of the patient. In an example solution, motion data that reflects upper limb movements of a user is received from one or more sensors, specific changes in user movement are determined by estimating several quantitative signal features, and the results are input into a machine learning model to detect if the user's movements reflect a change due to the occurrence of a stroke. The quantitative features and the machine learning model determine the degree of motor deficit induced by a stroke as reflected by changes in time-series measures of signal magnitude, variability, complexity, and interrelation. The solution operates in two distinct modes, one by continuously monitoring subject activity and the second by evaluating short duration data segments when the subject is performing prescribed movement tasks. In both modes the solution detects if the user has suffered a stroke and estimates a motor deficit score to determine the severity of the stroke.

An automated method and computer program product are provided for performing multi-channel attribution for conversion events associated with a plurality of consumers. Each consumer has an associated consumer identifier. Each conversion event is associated with a brand or product that has corresponding media advertising electronically delivered on a plurality of different media-based delivery channels to the plurality of consumers via a plurality of media devices associated with respective consumers. Media advertising exposure is electronically detected for each conversion event. Attribution for each of the delivery channels is then electronically determined.

The invention provides an automatic detection method and system for in-situ purity data of a radiopharmaceutical separation device, and relates to the technical field of data processing.The method comprises the steps that in-situ purity data is divided into a plurality of turning fragments, the anchoring offset relation of all nodes relative to a front anchor point and a rear anchor point is identified, and an anchoring sequence is constructed; calculating a response difference value and a time compression amount of adjacent nodes in each anchoring sequence, identifying a structure offset degree of a local signal in a turning segment, obtaining a bit segment extrusion amount, carrying out proportional reflection trimming on each node in the anchoring sequence, constructing a reflection node sequence, extracting a tail end reflection node and a starting reflection node of the adjacent turning segment, and obtaining a reflection node sequence; and identifying the evolution trend of the overall purity change to obtain the purity migration amount, and detecting the in-situ purity data to obtain in-situ purity detection data. According to the invention, timely and accurate purity monitoring can be realized in the separation device.

The invention relates to the technical field of software engineering automation of digital data processing, and discloses a game development automation method based on a multi-agent system. The method is used for solving the problem that a machine-readable cross-product dependence model and consistency verification are lacked in a traditional method. According to the method, based on a task list freezing project context, a constraint packet and a threshold aperture, and based on a demand account book and registration demands, constraints, codes, configuration, resources and other entry abstracts, version information and strong and weak reference relationships; the multiple agents only submit change packets in a temporary storage area by writing tokens and occupying fragmentation write-in gating, and an influence range report binding verification list is generated for each change packet; boundary compliance, reference integrity, compilation verification, determinacy operation verification and acceptance assertion verification are sequentially carried out on the temporary storage area snapshots, and when conflicts occur, rollback or confinement repair is triggered to output traceable and acceptance deliverables based on reproducible evidence judgment.

Globally our environment comprises structures built to perform a meet different requirements including residential, commercial, retail, recreational and service infrastructure. Whilst millions of tons of construction materials are deployed annually the quality control procedures in many instances have not changed to reflect today's demands. Accordingly, it would be beneficial to provide construction companies, engineering companies, infrastructure owners, regulators, etc. with means to automated testing / characterization of construction materials during at least one of its manufacture, deployment in construction and subsequent infrastructure life. It would be further beneficial for such automated methods to exploit self-contained data acquisition / logging modules allowing them to be employed with ease at the different points in the life cycle of a construction material and / or construction project.

Systems and methods are disclosed for automated method parameter configuration for differential mobility separations. As non-limiting examples, various aspects of this disclosure provide receiving a sample in an open port interface; transferring the sample to an ionization source; ionizing the transferred sample; introducing the ionized sample into a mass spectrometer; mass analyzing the ionized sample to produce an initial mass analysis result; determining a peak width of the initial mass analysis result; and determining a dwell time for subsequent measurements based on the determined peak width, a pre-defined number of data points across subsequent mass analysis peak widths, and a number of different analytes to be assessed for the sample. The sample may be diluted and transferred to the ionization source by a sample introduction apparatus selected from a group including an acoustic droplet ejector (ADE), a pneumatic ejector, a piezoelectric ejector, and a hydraulic ejector.

The invention discloses an automatic advertisement screenshot and container automatic expansion method and system in a virtual desktop environment. A virtual display capable of exporting a DMA-BUF is created by deploying a Virtual Display Driver in a kernel of a host machine, and after Chrome is driven by Puppeteer to complete hardware acceleration rendering in a container, zero-copy desktop-level screenshot is carried out on a shared memory frame buffer by utilizing desktop-script-native; and meanwhile, a Kubernetes CRD unified database task and a declarative API are adopted, a Redis Stream second-level index and a predictive HPA algorithm are combined to realize advanced delta t capacity expansion of Pod, and the P99 queuing delay is ensured to be less than 2s. According to the method, single-node concurrency is improved by 3.2 times without extra hardware cost, CPU occupation is reduced by 45%, WebGL / Flash advertisement fidelity is 100%, and the method is suitable for cloud advertisement verification, brand security and programmed creative monitoring scenes.

The invention belongs to the technical field of aerospace equipment manufacturing, and discloses a full-automatic crimping method for a VPX connector. Comprising the steps of material automatic matching, product flexible assembly, pressure curve fitting and intelligent visual detection. The materials subjected to automatic material matching are assembled through product flexible assembly, and pressure detection, pressure fitting comparison and pressure feedback adjustment are conducted in real time according to pressure curve fitting in the assembling process; and after assembly is finished, finished product inspection is performed through intelligent visual inspection. VPX connector crimping is carried out in an intelligent and automatic mode, crimping work of VPX connector plugs / sockets on PCBs of various shapes and specifications is realized, the production efficiency and the production quality are greatly improved, the cost is reduced, and the production reliability is guaranteed.

The invention provides an automatic front-end rendering defect repairing method and related equipment, and the method comprises the steps: determining a key event in a front-end application, and determining at least one burying point assembly corresponding to the key event; performing point burying on the point burying component, and obtaining a point burying log generated by the point burying component when the key event occurs; based on the point burying log, detecting whether a preset problem and a corresponding problem type exist in a front-end page, and when the problem is detected, generating problem information containing the problem type and corresponding path information; based on the path information, obtaining a code file corresponding to the question type; aggregating the problem information, the burying point log and the code file, performing analysis and reasoning through a preset large language model, retrieving a code problem based on the problem type, and generating an optimization code; and deploying the optimization code to a preset environment. According to the invention, the automatic repair efficiency of the front-end rendering defect is greatly improved.

The deburring system comprises a mechanical arm, a control assembly, a pneumatic grinding device, an air compressor, a machining table, a processor and an image collector, the mechanical arm clamps the pneumatic grinding device, and the image collector is installed on the side, close to the machining table, of the pneumatic grinding device; the pneumatic polishing device is provided with an air inlet and an air outlet, the air compressor is connected with the air inlet, and the control assembly is in signal connection with the mechanical arm, the air compressor, the processor and the image collector. A closed-loop intelligent deburring system integrating real-time image recognition, high-precision mechanical arm control, pneumatic grinding and multi-sensor fusion is constructed, so that the automation degree and the intelligent level of deburring machining are improved; the problems that traditional manual grinding depends on experience and is low in efficiency, and a traditional automatic method is poor in adaptability and prone to damage parts, so that the machining quality is unstable are effectively solved.

A computer-implemented method includes presenting a user interface to a user, upon receiving an input from the user, presenting a beginning user interface to the user, receiving customer information through the user interface, providing directions to the user for gathering information and photos about one or more spaces for a construction project, providing prompts to the user and receiving information including at least a type of construction project, sending the photos and information about the construction project to a fine-tuned large language model (LLM), using the fine-tuned LLM to select relevant elements from the photos and information, and produce one or more data objects, receiving the data objects from the LLM and converting the data objects to one or more construction estimates, the construction estimate including at least a labor estimate, and presenting the construction estimate through the user interface to the user.

Embodiments presented provide for processing of sonic images. In specific embodiments, an automated method for separation of upper and lower reflector signals is presented.

The disclosure relates to an automated method comprising the steps: - Determining (S3) a modified object (51, 521, 522, 53, 54, 55) using a modification parameter (2) and at least part of structural parameters (1), - calculating (S4) a variation parameter (4), the variation parameter (4) describing a difference in a physical impact (16, 56) between: o a physical realisation (R5) of an environment comprising the modified object (51, 521, 522, 53, 54, 55) and o a physical realisation (R1) of the environment comprising an initial object (11, 121, 122, 13, 14, 15), - generating (S5) a modified visualization (V5), the modified visualization (V5) visualizing the environment comprising the modified object (51, 521, 522, 53, 54, 55) and visualizing the variation parameter (4). A corresponding computer program product and a corresponding autonomous system for executing such method are disclosed as well.

The invention relates to the field of seismology, signal processing and artificial intelligence, namely to automated methods and systems for early detection of earthquakes based on the analysis of seismic data, and can be used to recognize and classify longitudinal waves (P-waves) preceding the main seismic shocks, using deep learning and computer vision methods. The aim of the present invention is to create an automated early earthquake detection system using the classification of seismic signal spectrograms generated by the complex Morlet wave CWT using the YOLO deep neural network architecture. The technical result is an increase in the accuracy and speed of early earthquake detection by analyzing the time-frequency characteristics of seismic signals and automatically localizing P-wave signatures using a neural network model. The device includes a seismic sensor, an analog-to-digital converter, and a microprocessor implementing time-frequency analysis and neural network detection algorithms. The seismic signal is recorded in real time, digitized, segmented into time intervals, and subjected to preliminary digital processing, including noise filtering and amplitude normalization. Each time interval is converted into a time-frequency representation using a continuous wavelet transform, generating a two-dimensional distribution of signal energy over time and frequency. Based on the obtained data, a spectrogram is generated and fed to the YOLO neural network detection model, which is capable of automatically detecting and localizing longitudinal P-wave signatures. Based on the neural network analysis, a determination is made regarding the presence of a P-wave and its arrival time is determined. If a predetermined threshold is exceeded, an early warning signal is generated. The system provides for data accumulation and the possibility of subsequent retraining of the neural network model.

The current document is directed to methods and systems that provide an emotional compass that reflects the emotional states of human beings, dynamics of the emotional states over time periods, emotional synchronization between humans and between humans and automated interactive systems, and the degree to which the emotional state of one human is influenced or affected by the emotional state of another human or by an automated interactive system. The emotional compass can be used for continuous or intermittent emotional orientation by both automated systems and human analysts. The emotional awareness provided by the emotional compass facilitates continuous or intermittent strategy selection for achieving various different types of emotion-related results.

This application belongs to the field of nondestructive testing technology, specifically relating to an automated method and device for wet magnetic particle inspection and identification of weld areas in drill pipes, aiming to solve the problem of insufficient automation in weld area inspection and identification of drill pipes. This application includes: acquiring magnetic trace images of the surface of the weld area of ​​the drill pipe; processing the magnetic trace images based on a preset image analysis model to identify abnormal regions in the magnetic trace images; comparing the abnormal regions with pre-established defect feature information to determine whether the abnormal regions are defective regions; if the abnormal regions are determined to be defective regions, obtaining the spatial coordinates of the defective regions in the drill pipe cylindrical surface coordinate system based on the position of the defective regions in the image coordinate system; and controlling the rotation of the drill pipe based on the spatial coordinates to position the defective regions at a marking station for automatic marking. This ensures the stability and reliability of the inspection quality, effectively avoids missed detections and false judgments, and solves the problem of insufficient automation in existing equipment.