727 results about "Analyser" patented technology

The invention relates to the technical field of civil engineering monitoring, and discloses a structure health monitoring system based on civil engineering. A high-density sensing network of the system collects strain field distribution, vibration spectrum and environmental corrosion parameters of a structure through a distributed multi-mode sensor array; performing clock drift compensation and space coordinate normalization on the asynchronous sampling data by a space-time alignment engine to generate an original feature tensor of a unified space-time reference; the semantic modeling unit is combined with a design drawing and a material parameter library, the original feature tensor is mapped to a component semantic space, and hierarchical structure features with topological marks are output; the adaptive fusion core executes dynamic weight distribution, eliminates sensor conflict data and generates an anti-interference fusion diagnosis index; the edge computing node operates a lightweight damage detection model according to the fusion diagnosis index, and outputs a local component health state level; and the cloud collaborative analyzer aggregates multiple edge node results and predicts the overall residual life of the structure in combination with historical degradation data.

The invention discloses a medical report generation method, a model training method, equipment and a medium, and the model training method comprises the steps: constructing a medical report generation model framework which comprises a global semantic collaborative multi-modal enhancement module, a visual encoder, a text encoder, a medical insight analyzer and an LLM decoder; wherein the global semantic collaborative multi-modal enhancement module respectively enhances a medical image and a medical report by utilizing a selected image enhancement strategy and a text enhancement strategy, and the medical insight analyzer comprises a fine-grained structure learning device and a global context guide learning device which are connected in sequence so as to enhance the cross-modal alignment capability; and performing intelligent collaborative optimization by taking a strategy set formed by an image enhancement strategy and a text enhancement strategy and architecture configuration parameters of the medical insight analyzer as optimization targets to obtain an optimal medical report generation model. The medical report generation performance can be effectively improved.

The invention relates to the technical field of angle-resolved photoelectron spectroscopy experimental instruments, and discloses an angle-resolved multi-electron coincidence measurement system and method based on an event-type delay line detector, and the system comprises an angle-resolved energy analyzer; a detection surface of the event type delay line detector is divided into at least two independent areas, and the event type delay line detector is used for receiving photoelectrons in parallel and independently outputting event signals; the external trigger and synchronization module is used for generating a unified trigger period to establish a global time reference; the time digital reading module is used for receiving the event signal and generating a timestamp; and the data acquisition and processing module is used for carrying out association judgment and selection based on the timestamps in the same triggering period. The dead time is reduced through multi-region parallel reading, unified time base calibration is achieved through time base line calibration and delay compensation, multi-electron coincidence measurement can be achieved under the condition that the angular resolution capability is kept, and the method is suitable for pulse or continuous light sources.

A system and computer-implemented method facilitate expansion of knowledge. The system allows for characterization of natural language documents and of search queries to locate those documents. A natural language processing (NLP) analyzer finds subject-verb-object (SVO) triplets in received text and assigns initial hierarchical classifications to word components of the SVO triplets. An SVO analyzer generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens. The tokens represent respective hierarchical classifications of the at least one hierarchical classification selected. The token stream produced may represent a natural language (NL) document to be stored to facilitate matching the NL document to a subsequently independently specified query. Alternatively, the token stream produced may represent a query and the token stream is used for generating a response to the query.

The invention discloses an ANTLR4-based multi-manufacturer network equipment configuration analysis method and system, and the method comprises the steps: carrying out the preprocessing of a configuration file; based on a machine learning algorithm of multi-dimensional feature analysis, automatic identification of configuration file manufacturer types is realized; based on the identification result of the manufacturer type, loading an ANTLR4 grammar parser corresponding to the manufacturer type; a lexical analyzer in ANTLR4 realizes lexical analysis on the configuration file through lexical rule definition, a Token classification system and an optimized analysis algorithm; a syntactic analyzer in ANTLR4 constructs an abstract syntax tree according to a lexical analysis result and a syntactic rule defined based on a context-independent grammar; traversing the abstract syntax tree, and extracting key configuration information; and converting the specific configuration format of each manufacturer into a unified standard model. According to the method and the system, network equipment configuration files of different manufacturers can be automatically identified and analyzed, and key configuration information is extracted and converted into a unified model.

Systems and methods for interfacing with data profilers using a machine learning model. In some aspects, the system receives a data profiler configured to create and interface with a plurality of data profiles. The system trains a profile query model to generate responses to user queries relating to data profiles. The system receives a user query concerning data profiles associated with the data profiler. Using an interpretation model, the system pre-processes the user query and data profile attributes to determine an activation pattern for the profile query model. The system uses the profile query model to process the user query and the data profile attributes of the data profiler and generate a preliminary response. The system post-processes the preliminary response to generate a verified response by applying a corrective program. The system then transmits the verified response in a conversational program related to the user query.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

Conditions are identified in a telecommunications network based on data collected from the telecommunications network. The data comprises time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and raw error logs collected alongside the time series telemetry data for the telecommunications systems. Outputs from a time series insight generator and a sentiment analyzer are combined to generate an output report indicative of anomalous metrics in the telecommunications network.

A hypergraph workload manager in a server is configured for failure tolerant and explainable state machine driven hypergraph execution. The hypergraph executor comprises a query optimizer, a hypergraph enlister, a pipeline analyzer, and a state machine generator. The query optimizer translates a user query into a query operator graph. The hypergraph enlister enlists the query operator graph into a hypergraph containing a set of query operator graphs representative of already submitted user queries. The enlistment is configured to join query operator graphs where it makes sense to optimize query executions. Updates to the hypergraph based on the enlistment results in a set of disconnected graphs. The pipeline analyzer performs an analysis of all operators of all queries in the hypergraph to find an optimal sequencing of execution. The state machine generator is configured to generate a hierarchical state machine for all operators of a disconnected graph of the hypergraph.

In order to allow an analyzer to be calibrated or a sampling channel to be purged without removing a sampling probe from a sampling location, provided are a sampling channel through which an exhaust gas from an internal combustion engine flows, an analyzer that is connected to the sampling channel and analyzes the exhaust gas, a diluter provided upstream of the analyzer in the sampling channel, a recirculated channel that branches from a branch point set between the analyzer and the diluter in the sampling channel and joins the diluter, a pump that is provided in the recirculated channel and guides part of the exhaust gas from the branch point to the diluter, an additional channel that is connected to the recirculated channel or the sampling channel and through which a calibration gas or a purge gas flows, and an opening and closing mechanism provided in the additional channel.

The invention discloses an automobile industry code quality automatic compliance method based on a large language model. The method comprises the following steps: calling a static code analyzer to generate a violation report; the method comprises the following steps: intelligently classifying violation into Tier1, Tier2 and Tier3 based on a multi-feature analysis algorithm of a rule document; for Tier1 violation, performing three times of retry iteration repair by using a large language model, automatically detecting compilation errors and performing rollback; for Tier2 violation, multiple technical schemes are extracted by a large language model for a user to select; a multi-LLM antagonistic cooperation mechanism is adopted for Tier3 violation, automatic restoration of complex violation is achieved through a Generator generation restoration scheme, a Disscriminator review challenge scheme and antagonistic iteration of Arbiter arbitration conflicts, and an optimal scheme is selected through a weighted voting algorithm when conflicts occur. The method obviously reduces the manual intervention rate, improves the repairing quality, and is low in cost and high in universality.

A computer hardware system includes a static analyzer, a load estimator, and a hardware processor configured to perform the following executable operations. Using the static analyzer and for each egress API call site respectively associated with an ingress API handler of a microservice, a weight is generated and is included within a static analysis of the microservice also generated by the static analyzer. Using the load estimator and for each of the egress API call sites, a load is determined based upon the weight for the egress API call site, a number of times, over a predetermined period of time, a particular ingress API handler associated with the egress API call site is called, and a cost of a call to the egress API call site. Based upon the load, the microservice is modified.

The invention relates to the technical field of ion characterization and preparation, in particular to a high-throughput preparation mass spectrum device and method giving consideration to high-resolution characterization. The device comprises an ion source, an ion transmission cavity, a quadrupole mass screening cavity, an ion dissociation cavity, an ion deflection cavity, an ion deceleration and deposition cavity and a high-resolution mass spectrum characterization cavity which are sequentially arranged, wherein the high-resolution mass spectrum characterization cavity is formed in one side of the ion deflection cavity in the vertical direction; soft ionization is combined with a quadrupole analyzer to screen ions, the ions are dissociated through a dissociation cavity, an ion deflection cavity is used for selecting a high-resolution characterization or high-throughput preparation function, a high-resolution mass spectrum characterization cavity is used for achieving high-resolution characterization of screened and fragmented ions, and an ion deceleration and deposition cavity is used for high-throughput preparation of the ions. According to the method, perfect compatibility of high-throughput preparation and high-resolution mass spectrum characterization is realized through ion deflection design, and the method has a wide application prospect in the fields of efficient preparation of biological macromolecules, synthetic macromolecules and the like and the like.

Embodiments facilitate software analysis by machine learning (ML) models, through extensible software analysis architecture (ESAA) or software analysis work allocation (SAWA). Pluggable ESAA ML modules include a vetted prompt which is actionable for software analysis, with a vetting certification. Some ML modules contain computational cost information such as a token count or model round trip time. Tools are tailored to ML analyzers to control background execution, availability offerings, and results displays. SAWA determines how well a software analyzer meets a prompt's software analysis requirements, and an ML planning model generates an analysis plan that balances software analysis workloads among ML analyzers and non-ML analyzers. ML analyzers are favored for summarization, task decomposition, task scheduling, and source code change review, while non-ML analyzers are otherwise favored. Non-ML analyzers gather control flow, data flow, internal structure, and similar context which is then supplied to an ML analyzer.

A system includes a hardware processor and a memory storing a cross-language voice similarity analyzer (analyzer). The hardware processor executes the analyzer to generate an embedding vector representation of an audio sample of a human voice in a feature space including existing embedding vectors corresponding respectively to different reference voices, decompose the embedding vector representation to identify a linear or non-linear combination of vocal component vectors corresponding to the human voice, each vocal component vector representing a respective predetermined voice characteristic descriptor, increase the dimensionality of the linear or non-linear combination of the vocal component vectors to match the dimensionality of the embedding vector representation to provide a reconstructed embedding vector representation of the human voice, and identify, by comparing the reconstructed embedding vector representation with one or more of the existing embedding vectors, one of the reference voices as a match for the audio sample of the human voice.

The invention discloses an intelligent analysis system for the running state of a 3C vehicle-mounted contact network, and belongs to the technical field of crossing of intelligent operation and maintenance of rail transit and industrial artificial intelligence. And the system associates the multi-source heterogeneous observation data to a specific equipment unit through an equipment centralized data binding module. And the multi-modal feature extraction and state management unit processes the data and maintains a multi-dimensional state vector of the equipment by using the Kalman filtering updating unit. And the physical-data hybrid decision maker fuses the data driving rule and the simplified physical model to output a diagnosis result. The topology analyzer performs global verification based on a mechanical transfer rule. The system further comprises a long-term health state prediction and feedback module, early failure risks are predicted through a hidden Markov model, Kalman filtering process noise is dynamically fed back and adjusted, and cooperation of long-term prediction and short-term estimation is achieved. According to the method, the technical problems of multi-source data splitting, lack of physical basis in diagnosis and incapability of predictive maintenance are solved.

The invention relates to the field of computer systems based on specific calculation models, and discloses an intelligent water meter data driving leakage identification early warning system based on a neural network model, comprising a learnable decoupling module, parameters of which are determined by a collaborative adversarial training rule, and which is used for decomposing data into a baseline data stream and a transient data stream; the system is provided with a baseline channel and a transient channel in parallel; according to the system and the method, a behavior mode monitor, a transient mode analyzer and an abnormal arbitration module are arranged, through information cooperation of the three modules, accurate identification of different leakage states and normal mode drifting is achieved, through the architecture, the fundamental problem that a calculation model is shielded by a high-amplitude normal water use signal is solved, and the system and the method have the advantages of being high in practicability and high in practicability. According to the method, high sensitivity of the system to tiny baseline leakage is recovered, and meanwhile, active adaptation to user habit changes and effective compensation for a high-frequency leakage blind area are achieved through cross-channel information collaboration.

A method of performing liquid chromatography (LC) with an LC system including an LC column and an analyzer includes delivering a transport liquid to an open port interface (OPI) of a sample receiving circuit, wherein the sample receiving circuit includes a sample transfer chamber. A sample is ejected from a sample holder into the OPI with a non-contact ejector. The transport liquid and the sample are received in the sample transfer chamber. The sample transfer chamber is decoupled from the sample receiving circuit. A solvent is delivered to the analysis circuit. The sample is pushed from the sample transfer chamber with the solvent. The sample and the solvent is passed through the LC column to produce an eluent. The eluent is analyzed with the analyzer.

A monitoring entity for an AI / ML model used in a wireless communication system is configured to receive model data associated with the model and to execute an evaluation of the model data with respect to a reference model to obtain an evaluation result. The monitoring entity has a plurality of analyser modules, wherein each analyser module is configured for evaluating an associated set of parameters derived from the model data with respect to an evaluated property to determine an associated monitoring metric associated with the analyser module, the associated monitoring metric indicating a performance of the model with respect to the associated set of parameters. The monitoring entity is adapted to request, from a different network entity, additional information relating to the model and to use the additional information relating to the model for obtaining the plurality of associated monitoring metrics.

The invention discloses an intelligent agent-based office network and server network intelligent isolation system and method, a storage medium and computer equipment, and the method comprises the steps that a business process analyzer determines a core business application and a data flow according to an enterprise business process, and determines business demand information according to the core business application and the data flow; the security policy engine configures a basic isolation rule of the intelligent isolation gateway according to the service demand information and the initial security policy; each sensing agent monitors network flow data and system behavior data of a deployment position; the analysis agent analyzes the network flow data and the system behavior data to generate a first risk identification result; the security policy engine receives the first risk identification result and generates a dynamic security policy according to the first risk identification result; the execution agent adjusts isolation parameters of the intelligent isolation gateway based on the dynamic security policy; and the intelligent isolation gateway isolates and controls communication data between the office network and the server network based on the basic isolation rule and the adjusted isolation parameters.

A system and method are disclosed for identifying malicious activity on a target device based on behavior analysis of the target device. The system includes a behavioral analyzer run on a virtual machine connected to the target device. The virtual machine collects system events and parameters from the target device and run a script, independent of the target device, to detect a threat. The script is a set of instructions executed to analyze behavior of an object by processing and correlating the events. The script includes a rule structure which stores signatures and expressions of the known malwares. By correlating the selected event parameters with known malware parameters, it is determined whether the event imposes a threat or not. A finite state machine is used for the state transition table.

Systems, apparatuses, and methods for implementing a real-time Open Radio Access Network (O-RAN) fronthaul analyzer and graphical user interface (FHA / GUI) are provided. In one example, a method describes receiving an O-RAN fronthaul (OFH) packet traffic flow; parsing packet headers of the OFH packet traffic flow; identifying and tracking active flows in the OFH packet traffic flow using the parsing; and presenting a visual representation of the active flows using the tracking of active flows. In some examples, the real-time O-RAN FHA / GUI may be used for testing an OFH link for compliance with the O-RAN standards.

The invention provides a dynamic scheduling method and system for a redundant executor based on a foreground theory and a Q learning evolutionary game method. The method comprises the following steps: constructing a strategy space, a profit structure and strategy evolution dynamics of the executor and a reward and punishment mechanism of an enhanced analyzer-regulator; according to the dynamic scheduling method, an executor simulates interaction under a tripartite game framework, iterative loop is carried out, and a strategy space of a mimicry defense system is output as a redundant executor after the loop is ended, the simulation interaction process comprises the following steps: the enhanced analyzer-regulator monitors and obtains objective income of each execution body in real time, calculates subjective perception utility of each execution body according to an income structure of the execution body, and then adjusts the subjective perception utility of the mimicry defense system according to a reward and punishment mechanism of the enhanced analyzer-regulator. And each execution body dynamically updates the strategy space of each execution body according to the adjusted subjective perception utility and strategy evolution.

The invention relates to the technical field of big data translation systems, and discloses a language translation system and method based on big data. The method comprises the steps of collecting multi-language historical data, and obtaining a source language text data set, a target language text data set and a translation evaluation data set which are collected within a preset time range; then, executing stability evaluation on the three data sets to obtain a language use stability factor, a translation consistency stability factor and an evaluation reliability stability factor; then, the stability factors serve as key indexes, dense exploration is executed in a translation parameter configuration domain, and priority translation configuration is determined; and finally, translating the input source language text according to the priority translation configuration and a predetermined translation task set to generate a serialized translation data set, analyzing the set by adopting a translation result analyzer, and outputting a final translation result.

The embodiments provide a system and method for interpreting predictions from machine learning systems using natural language. The system includes a prediction module and an explanation module. The prediction module includes a machine learning model to make predictions for quantities such as recommended coverage amounts for insurance policies. The explanation module includes an impact analyzer that calculates impact values, which represent the degree of influence that each attribute has on predicted values. The explanation module also includes a natural language processing system for transforming generating natural language explanations indicating how the user attributes have influenced the predicted value.

Systems / techniques are provided for facilitating mass analyzer calibration via reinforcement learning. In various embodiments, a system can predict, via execution of one or more reinforcement learning neural networks on present-time state data of a mass analyzer of a scientific instrument, what adjustments to one or more operational parameters of the mass analyzer would cause the mass analyzer to approach a calibrated state, wherein the one or more operational parameters include an electrode voltage of the mass analyzer or a timing control of the mass analyzer. In various aspects, the system can modify the one or more operational parameters based on the adjustments, thereby causing the mass analyzer to approach the calibrated state.

The utility model relates to the technical field of water quality analyzers, and discloses an anodic dissolution water quality analyzer device, which solves the problems in the background technology, and comprises a water quality analyzer body, the water quality analyzer body is connected with a pretreatment assembly, the pretreatment assembly comprises a primary filter cabin, and the primary filter cabin is connected with a secondary filter cabin. The primary filtering cabin is connected with the acidification neutralization cabin through a first conveying pipe, the acidification neutralization cabin is connected with the ultraviolet digestion cabin through a second conveying pipe, the ultraviolet digestion cabin is connected with the water quality analyzer body through a third conveying pipe, and pump bodies are arranged on the first conveying pipe, the second conveying pipe and the third conveying pipe; the device has the effects that full-automatic detection of'sample feeding-result obtaining 'can be realized, the pretreatment is standardized, and manual operation errors are avoided.

A system for determining at least one biomechanical property of a reproductive cellular structure includes a radiation source for generating illuminating radiation, at least one optic for directing the illuminating radiation onto at least a portion of the reproductive cellular structure, a detector for detecting at least a portion of radiation scattered from the reproductive cellular structure, a spectrometer for generating a frequency spectrum of the detected scattered radiation, and an analyzer. The analyzer is configured to identify at least one Brillouin frequency shift in the frequency spectrum for the at least a portion of the reproductive cellular structure, and determine viability of the reproductive cellular structure based on the at least one Brillouin frequency shift.

Techniques are described for providing a threat analysis platform capable of automating actions performed to analyze security-related threats affecting IT environments. Users or applications can submit objects (e.g., URLs, files, etc.) for analysis by the threat analysis platform. Once submitted, the threat analysis platform routes the objects to dedicated engines that can perform static and dynamic analysis processes to determine a likelihood that an object is associated with malicious activity such as phishing attacks, malware, or other types of security threats. The automated actions performed by the threat analysis platform can include, for example, navigating to submitted URLs and recording activity related to accessing the corresponding resource, analyzing files and documents by extracting text and metadata, extracting and emulating execution of embedded macro source code, performing optical character recognition (OCR) and other types of image analysis, submitting objects to third-party security services for analysis, among many other possible actions.