1079 results about "External source" patented technology

An example operation may include one or more of establishing a network connection between a computing system and one or more external sources over a computer network, receiving a request from a user via a software application on a user device, collecting data about the user from the one or more external sources via the established network connection, executing a machine learning model on the collected data about the user to determine a goal of the user, and displaying an image of the goal via a user interface of the software application.

An apparatus for carbon emission optimization using machine-learning, apparatus including a processor and a memory containing instructions configuring the processor to receive an integrated logistics data collection, determine a projected carbon emission as a function of the integrated logistics data collection, generate a transportation plan as a function of the integrated logistics data collection and the projected carbon emission, continuously receive a current logistics datum from an external source, and iteratively modify the transportation plan based on the current logistics datum.

A digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (Al) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

A system for improving data integrity through blockchain-based smart contracts, consisting of: a data input module configured to receive transaction data from at least one external source; a hashing unit coupled with the data input module and configured to generate a cryptographic hash of the transaction data; a smart contract execution engine that is operationally connected to the hashing unit and configured to execute predefined conditions associated with the transaction data, wherein the smart contract execution engine is configured to trigger automated processes based on compliance with these predefined conditions; a blockchain node interface in communication with a blockchain network, configured to validate and record the hashed transaction data, with the blockchain node interface transmitting validated data entries to be appended to a decentralized ledger; a verification module configured to check the integrity and authenticity of the attached data within the blockchain network; and A data retrieval unit with an interface to the blockchain network, configured to allow authorized users to access and retrieve validated transaction data from the decentralized ledger, with permissions governed by the terms of the smart contract.

An orchestration component implemented within a cybersecurity system and operating in concert with a cybersecurity appliance to enhance cyber threat detection or a response to a cyber threat detected by the cybersecurity appliance is described. The orchestration component comprises a first landscape analysis module, a data score and an action severely configured to operate with a first large language model to (i) analyze threat landscape data received from one or more external sources and (ii) identify threat technique data associated with one or more cyber threats included within the threat landscape data. The orchestration component further comprises a data store adapted to maintain the threat technique data identified by the threat landscape analysis module; and an action severity module is configured to adjust a sensitivity of a cyber threat detection engine of the cybersecurity appliance in monitoring for the one or more cyber threats represented by the threat technique data.

This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.

A large language model (LLM) is used to broaden a search for supporting material for a media project. The LLM is provided with contextual material from the media project and optional grounding material and generates a list of types of supporting material. The list is provided to a machine-learning-based encoder, which encodes the list items into embedding space vectors. A body of source material, which may multimodal, is also encoded into the embedding space. A search engine is used to locate items of source material having embedded space vectors closest to vectors corresponding to the list of types of supporting material. Located items are provided to a media editing application as suggested supporting material. The body of source material may include live external sources. A small language model fine-tuned with a training data set generated by a LLM may be used instead of the LLM.

Disclosed herein are apparatus, system, method, and computer-readable medium aspects for identifying and preventing digital skimming attacks using a machine learning model. A threat management system may crawl one or more external sources in order to obtain training data for one or more machine learning models. A plurality of different models may be used to conduct different analyses with respect to an application under test. For example, a first model may identify malicious code. A second model may detect the presence of threat protection code which may protect against skimming attacks. Depending on whether an application under test is free from malicious code and / or includes threat protection code, a threat management system may determine whether close may be promoted to a production or live environment. The threat management system may also use a third model to generate security protocol code for a developer based on learned best practices.

The invention discloses a complex scene multi-source heterogeneous data acquisition and monitoring diagnosis method and device, and the method comprises the steps: collecting multi-source heterogeneous data of an object to be monitored and diagnosed, and carrying out the data cleaning, data preprocessing, feature extraction and fusion of the multi-source heterogeneous data, so as to obtain a feature data set; and constructing and training a causal decoupling representation learning model based on input and output driving by using the feature data set and the data after data cleaning, so as to extract decoupling representation of a to-be-monitored diagnosis object, deduce a causal relationship between variables, and obtain a to-be-monitored diagnosis object. Wherein the causal decoupling characterization learning model comprises an external source control conversion network and a double-view evolution modeling and causal inference network, and the trained causal decoupling characterization learning model is used as a heterogeneous signal monitoring and diagnosis model and is used for monitoring the state of an object to be monitored and diagnosed and diagnosing the root cause of a fault of the object to be monitored and diagnosed. According to the invention, data acquisition, state monitoring and fault reason diagnosis can be integrally realized, the cost is saved, and the application prospect is wide.

A redundant brake unit for a motor vehicle comprises: an inlet port configured to receive fluid under pressure from an external source; an outlet port connected to a supply passage and configured for fluid connection to a wheel brake; a reservoir port connected to a return passage and configured for fluid connection to a fluid reservoir; a boost valve configured to selectively control fluid communication between the inlet port and the supply passage; and a pressure supply unit including a pump element configured to pump fluid between the return passage and the supply passage. The pump element has an inlet directly connected to the reservoir port with no actuated valves therebetween. A normally-closed valve and a throttle valve are disposed in a series configuration between the supply passage and the return passage, with the throttle valve being operable to control a fluid pressure in the supply passage.

The invention discloses a non-aligned spatio-temporal data driven multi-sensor fusion method and system, and the method comprises the steps: building a matching measurement function through collecting radar point cloud density and visual image texture, and constructing a synchronous gradient field through employing a space depth change parameter and a visual motion contour, and determining a data synchronization standard; detecting a time sequence abnormal accumulation area based on a synchronization standard, and identifying an abnormal starting point and propagation time delay through a backtracking track to form an asynchronous sensor coordinate table; iMU angle increment and sensor response delay are collected, and sampling imbalance information is generated through time domain matching of an attitude switching signal and a data collection inflection point; performing association mapping on the asynchronous coordinate table and the sampling imbalance information to generate a feature offset, determining a matching core area, and creating a hierarchical calibration sequence; performing internal and external source difference identification analysis on the calibration sequence to obtain an offset transfer speed to form an offset coupling factor; and a fusion enhancement region is determined based on the offset coupling factor, and high-precision multi-source data fusion is realized through cascade analysis and gain coefficient coding.

The invention discloses a supply chain risk analysis method and system based on artificial intelligence big data, and relates to the technical field of supply chain risk analysis, and the method comprises the following steps: obtaining all data related to commodities and suppliers from an internal system and an external source of an enterprise, and providing comprehensive data support for subsequent analysis and evaluation; based on comprehensive data, feature engineering is carried out, key features capable of reflecting the dependency relationship between commodities and suppliers are extracted, and core features capable of reflecting the dependency relationship between commodities and suppliers are extracted from big data. According to the invention, by integrating an internal system and external data and utilizing feature engineering and a machine learning model, the dependency relationship between commodities and suppliers is quantified intelligently and changes are monitored in real time. Therefore, a purchaser can take effective measures before the potential risk outbreaks, the supply interruption risk is reduced, and the continuity of a production line is guaranteed. For a long time, countermeasures are optimized, a continuous risk management mechanism is established, and the toughness and stability of the supply chain are improved.

A fluid control device includes an inlet configured to be placed directly or indirectly in fluid communication with a bodily fluid source and an outlet configured to be placed in fluid communication with a fluid collection device. The fluid control device has a first state in which a negative pressure differential produced from an external source such as the fluid collection device is applied to the fluid control device to draw an initial volume of bodily fluid from the bodily fluid source, through the inlet, and into a sequestration portion of the fluid control device. The fluid control device has a second state in which (1) the sequestration portion sequesters the initial volume, and (2) the negative pressure differential draws a subsequent volume of bodily fluid, being substantially free of contaminants, from the bodily fluid source, through the fluid control device, and into the fluid collection device.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

Systems and methods for improving the accuracy of information obtained using a large language model. In one embodiment, this involves augmenting the capabilities of a graph generated from unstructured data with information from an external source using Retrieval Augmented Generation (RAG). In one embodiment, expert knowledge is used to review clustering and cluster summarizations derived from the results of a search over the graph data and information prior to application of RAG to generate additional information to augment the search results.

Systems, apparatus, and methods for calibration of satellite communication antennas are disclosed. An example aerial vehicle includes an antenna; an attitude sensor; machine-readable instructions; and programmable circuitry to at least one of instantiate or execute the machine readable instructions to at least identify a first angular position of the antenna associated with a peak power of a signal received by the antenna when the aerial vehicle is at a first heading; identify a second angular position of the antenna associated with a peak power of a signal received by the antenna when the aerial vehicle is at a second heading, the second heading different than the first heading; determine an offset between the attitude sensor and the antenna based on the first angular position and the second angular position; and cause a pointing angle of the antenna relative to an external source to be adjusted based on the offset.

The present disclosure generally relates to user interfaces related to sources of video data. User interfaces enable users to configure and interact with the sources of video data, such as for displaying a live video feed and a recorded video from an external source of video data. In some embodiments, a device provides user interfaces for displaying video from a video source and controlling external devices related to the source.

A method for training a model for discovering objects in an input image sequence, the model includes an encoder; an attention module configured to transform the first feature vector into a plurality of feature vectors, called slots; a decoder; the learning of the attention maps being monitored by a set of binary masks for discovering mobile objects produced by an external source, called pseudo-labels; the pseudo-labels being filtered by means of the following steps of: determining an attention map of the foreground of the image; computing a confidence score from the average of the values of the attention map of the foreground of the image at the positions of each mobile object present in a pseudo-label; filtering the mobile objects of the pseudo-labels for which the confidence score is below a predefined threshold.

Disclosed are various embodiments for leveraging large language models (LLMs) and retrieval-augmented generation (RAG) for data standardization of clinical AI data. A query comprising a target dataset that needs to be standardized and a dataset dictionary can be obtained. Augmented data can be extracted from an external source. A prompt including the query and the augmented data can be generated and applied to a large language model configured to output a response to the query. The response can include the target dataset standardized according to a standard format.

The invention discloses a geomagnetic diurnal variation data forecasting method, system, equipment, medium and product, and relates to the technical field of geomagnetic navigation, and the method comprises the steps: collecting and preprocessing global geomagnetic station data, carrying out the fast Fourier transform of geomagnetic field diurnal variation data, solving to obtain a trigonometric function coefficient, and carrying out the calculation of the trigonometric function coefficient; according to the method, geomagnetic daily variation data of any region in the world are obtained through geomagnetic daily variation, then an association Legendre coefficient is obtained through solving, an internal source field Gaussian coefficient and an external source field Gaussian coefficient are obtained through solving according to the Gaussian theory, finally daily variation model forward modeling can be carried out according to the internal source field Gaussian coefficient and the external source field Gaussian coefficient, and geomagnetic daily variation data of any region in the world are output. According to the method, coefficient solution and model forward modeling related to spherical harmonic analysis are utilized, the problems that in the prior art, the control range of a virtual terrestrial magnetism daily variation station is limited, and data are prone to being polluted are solved, and the forecasting precision of terrestrial magnetism daily variation data is improved.

The invention relates to a medical term semantic analysis and evaluation method based on a large language model. The method comprises the following steps: inputting a to-be-evaluated medical term pair into a computer system; the computer system retrieves definitions of the medical term pairs and the shortest semantic path of the definitions and the shortest semantic path of the definitions and the shortest semantic path from the UMLS, and supplemental definitions and external semantic paths are extracted from other authoritative external sources for terms missing information in the UMLS; constructing a unified prompt which contains the definition, the semantic path and a score of a generation set range and is based on a knowledge interpretation instruction, inputting the unified prompt into a large language model evaluation module, and generating a semantic similarity / correlation score and natural language interpretation; on the basis of the unified prompt, using supplementary information to verify whether explanation is compliant, has no information misdescription and has no illusion, allowing output only when the explanation, the information misdescription and the illusion are all satisfied, and otherwise, iteratively optimizing the prompt and regenerating; and outputting a structured result containing the semantic similarity / correlation score, the natural language interpretation, the knowledge reference and the supervision log.

A ML-based method and system for automatically generating responses to inputs using a multi-tier technique, is disclosed. Initially, first inputs are obtained from electronic devices of first users. An information is retrieved from external sources based on first inputs, using RAG engine. The responses are generated based on optimized information, using ML model. The ML-based method determines whether queries of first users within first inputs are resolved through the optimized responses. Issues are categorized using ML-based issue classification engine when the queries are not resolved. Dynamic forms are generated with fields based on categorization of issues, using a dynamic workflow engine. Second inputs are obtained and processed to determine whether queries are resolved using a rule-based logic engine. The first users are adapted to interact with second users to resolve the issues through communication channels. The responses are provided as output upon resolving the issues, to first users.

The invention relates to the field of mass spectrometry, and describes a mass spectrometer based on multi-electrode coordination of a Jooden well. A multi-port ion source configured for multi-port input of electrons of ionization of gas molecules in a well and ions in an ion source outside the well, the ports being autonomously usable, comprising two external electrodes and four internal electrodes configured to excite periodic movement of ions in the well along an axis of symmetry and to detect signals caused by ions of secondary potential oscillation in the well, wherein ions are introduced into the trap from an external source with a slot, and ion movement is excited by applying an alternating voltage or the like to the electrodes. Or using a slot to introduce ions from an external source into a well in an external electrode, applying a potential to an internal electrode to create an electric field, the size of which is in a quadratic relationship with the coordinates, the electrodes having a specific equipotential surface geometry. Pulsed potentials can be applied to capture ions, multiple external ion sources can be equipped, and if the ion sources are not combined, each can be provided with a separate port. The mass spectrometer is uniquely designed in the aspects of ion input, excitation, detection, ion source configuration and the like.

The disclosure relates to a portable electric nail filing system, the system including a control box for supplying power and control to an electric nail file handpiece, the control box including a rechargeable battery and a processor. Also, a charging base is provided for engaging the control box, the charging base operable to recharge the rechargeable battery of the control box while the control box is engaged with the charging base, the charging base also including a rechargeable battery to be charged from an external source such as an AC outlet.

The invention discloses a distributed consistency control method suitable for a multi-unmanned trolley system, and designs a control strategy based on a preset time disturbance observer for the problem that communication topology dynamic change and external disturbance interference are difficult to consider at the same time. In system modeling, each trolley is modeled as a second-order nonlinear system, and external disturbance is generated by a heterogeneous external source system. In the design of the controller, a disturbance observer with a preset time convergence characteristic is introduced, and strict convergence of disturbance estimation errors can be realized in finite time. Control input is fused with three parts of neighbor state difference feedback, leader tracking and disturbance compensation, and it is ensured that the system can still achieve a consistent target in a complex dynamic environment. By constructing a combined Lyapunov function and combining a Markov jump system theory, it is proved that a system state error and a disturbance estimation error converge to zero after a preset time. A simulation result verifies the effectiveness and stability of the method under communication switching and non-ideal disturbance interference.

The invention relates to a navigation deception signal detection method and device, equipment, a storage medium and a program product. The method comprises the following steps: determining a visual navigation satellite corresponding to a communication satellite by taking a geometric center of a beam coverage area of a directional beam generated by the communication satellite as a reference point; generating an enhanced navigation message according to the time service information of the communication satellite, the beam coverage area, the visual navigation satellite and the navigation message corresponding to the visual navigation satellite; and sending the enhanced navigation message to a user terminal in the beam coverage area, and detecting a deception signal by the user terminal according to the enhanced navigation message. According to the method, an inter-satellite exchange navigation message is included into a space section for processing, and a communication satellite sends an enhanced message signal to a user terminal in a beam coverage area, so that the user terminal performs external source verification on a local clock according to the enhanced message signal and compares the enhanced message signal with a local demodulation message, and therefore, under the condition of not depending on a ground private network, the user terminal can perform external source verification on the local clock. Therefore, the detection of the deception signal is realized, and the navigation deception resistance of the user terminal is improved.

An auxiliary hydraulic system including an accumulator, a control valve, and a hydraulic connector. The accumulator connects to a primary hydraulic system including a hydraulic pump, a brake circuit, and a steering circuit. The control valve selectively allows or prevents flow from an accumulator to at least one of the brake circuit and the steering circuit. The hydraulic connector selectively connects an external source to the accumulator and the control valve.

Containerized orchestration of secure socket layer virtual private network benchmarking is disclosed. A test portal can accommodate developing test event information (TEI) based on test input information. Test input information can be used to generate event images, e.g., containers, that can be readily scaled, mutated, etc., via a containerization environment instance. One or more event image can be groups into a pod. An event image can be a new container, a previously used container, or a permutation of a container. Event image(s) can be retrieved from external sources, e.g., a library, a commercial vendor of event images, etc. TEI can be based on a pod(s) and communicated to a device-under-test (DUT). DUT performance can be measured, and results can be accessed by an entity, e.g., a test engineer, a results analysis engine, etc. Results can be employed to direct subsequent DUT testing.

A torque command calculation unit calculates a torque command value for a motor based on a required braking force commanded from an external source. A relationship between a motor torque and braking forces generated in the electric brakes has a hysteresis characteristic. When the torque increases, the braking force increases along a positive efficiency line, and when the torque decreases, the braking force decreases along an inverse efficiency line. A specific controller calculates a torque command value to bring an actual load closer to a load command value or to bring an actual position closer to a position command value. A control adjuster adjusts a parameter of a control calculation of the specific controller, or a parameter of a control calculation on an input side or on output side of the specific controller during increase operation, during decrease operation, or during transition between the increase operation and the decrease operation.

The invention discloses an additive preparation method and application of an aluminum-steel composite material. The additive preparation method comprises the following steps: S1, carrying out laser preheating treatment on an aluminum alloy raw material to obtain an aluminum alloy; and S2, aluminum alloy material increase is conducted on the base material steel plate through a friction stir material increase technology, and the aluminum-steel composite material is obtained. According to the method, solid-phase additive materials are combined with various external source auxiliary process technologies, a laser heat source is introduced in the stirring friction additive material process to preheat the aluminum alloy raw materials, the material softening degree can be well improved, the metal fluidity in the additive material process is improved, heat input needed in the additive material process is reduced, and cracks, air holes and other defects are effectively avoided.