892 results about "Unsupervised learning" patented technology

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention belongs to the technical field of artificial intelligence, and discloses a computer network fault detection system based on an artificial intelligence technology, which comprises a data acquisition module, a feature extraction and preprocessing module and a fault positioning and repairing module. According to the invention, the detection link is accurate and comprehensive, the data acquisition module performs multi-source fusion to obtain rich materials, the feature extraction and preprocessing module generates multi-dimensional vectors to capture complex features, and multi-level fault identification performs comprehensive troubleshooting and reduces misjudgment; the diagnosis process is intelligent and efficient, the hybrid fault diagnosis model fuses supervised and unsupervised learning, processes known faults and detects unknown anomalies, and supervised learning branches are optimized to improve performance; the repair work is timely and reliable, the automatic repair scheme generation method has strategy library matching, dynamic adjustment and rollback mechanisms, quick response and flexible repair can be achieved, and the subsequent diagnosis accuracy is improved through repair verification multi-dimensional evaluation and closed-loop feedback.

Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.

The invention discloses a cross-modal remote sensing target detection method and system based on spatial consistency constraint and deep feature alignment, belongs to the field of computer vision and remote sensing science and technology and the technical field of machine learning and deep learning, and solves the problem that a conventional cross-modal method does not fully consider feature hierarchy difference. According to the invention, an improved teacher-student network model is constructed and comprises a student branch network, a teacher branch network and an optimization module for performing pseudo-label optimization, non-monitoring learning and supervised learning on the student branch network and the teacher branch network; training the improved teacher-student network model by adopting the target domain data set and the source domain data set to obtain a trained improved teacher-student network model; and carrying out cross-modal remote sensing target detection on a to-be-detected target domain image by adopting the trained improved teacher-student network model. The method is used for cross-modal remote sensing target detection.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention discloses a vibration signal anomaly detection method based on unsupervised learning, which relates to the technical field of vibration anomaly detection, and comprises the following steps: collecting vibration signals of electromechanical equipment during normal operation and synchronously recording working condition information; the collected vibration signals are preprocessed; setting a plurality of window segmentation lengths, and enabling each segment of vibration signal to generate a multi-stage sub-sequence; extracting time-frequency domain features of the vibration signals in the subsequences; the time-frequency domain features form feature vectors in a feature matrix splicing mode, feature standardization processing is carried out on the feature vectors, the feature vectors are fused with real-time working condition feature vectors obtained through working condition information, and a fused feature matrix is generated; and constructing an OCSVM model, and carrying out vibration anomaly detection on the electromechanical equipment by utilizing fusion feature matrix training. The method has the advantages that robustness and abnormal interpretability of single-class data are enhanced, the misjudgment rate is reduced through the dynamic confidence interval algorithm and probability distribution modeling, and the defect of insufficient model generalization is overcome through cross-modal feature fusion and working condition correlation modeling.

The invention provides a drilling risk determination method and device. Before specific implementation, a graph auto-encoder is introduced and used, and a preset detection model which is based on physical constraints and has a good application effect is obtained through unsupervised learning and training. In specific implementation, current target logging data of a target well and a logging data set of a current time period can be firstly obtained; determining a current working condition according to the target logging data; according to the current working condition, the logging data set of the current time period and a preset geological-engineering pre-drilling evaluation profile, a target dynamic threshold value which aims at a current target well and is based on working condition constraints and considers the change condition of data processed in the current time period before graph self-coding is determined; processing the target logging data by using a preset detection model to obtain a corresponding target reconstruction error; and detecting whether a drilling risk exists according to the target reconstruction error and the target dynamic threshold. Therefore, the drilling risk can be accurately detected and identified, and the false alarm rate is reduced.

The invention discloses a multi-source federal cross-domain and source-domain enhanced millimeter wave action recognition method and system, and the method comprises the steps: generating a micro-Doppler spectrogram through a millimeter wave radar, and extracting the motion features of human body actions through a signal processing module; in the federal multi-source domain adaptation module, dynamically evaluating and fusing knowledge of a plurality of source domains by adopting a voting-based pseudo-tag method and a weighted knowledge aggregation mechanism, and optimizing the generalization ability of a target model; and through a generalization gap optimization method, the performance of the source domain model is improved, and the robustness of the system in different environments is ensured. Through combination of a federated learning framework and a multi-source domain adaptation technology, unsupervised learning under the condition that a target domain has no annotated data is realized, only a single set of millimeter wave equipment is needed, a millimeter wave communication protocol is compatible, and the method has the characteristics of privacy protection, unsupervised learning, multi-source knowledge fusion and strong generalization ability. The method is suitable for application scenes of smart home, health monitoring, man-machine interaction and the like, and has wide practical application value and research prospect.

The present invention discloses a radar automatic recognition method and device for low-altitude small targets. The method first performs radar data preprocessing; then uses a deep neural network to extract the features of the processed data, and then respectively uses supervised learning and unsupervised learning to perform binary classification and anomaly detection; finally, determines whether it is a drone, a bird or other low-altitude floating objects according to the results of binary classification and anomaly detection. This method solves the problem of identifying low-altitude floating objects, has a very wide range of applications, strong generalization ability, less computation, lower cost, and higher recognition accuracy.

The invention discloses a box-type transformer substation monitoring method based on unsupervised learning, and the method comprises the following steps: S1, collecting multi-source sensing data in the operation process of a box-type transformer, and generating a standardized input data set; s2, selecting a data sample in a normal operation state, constructing an unsupervised learning model, and obtaining a feature representation set; s3, inputting data, extracting current operation state characteristics, and recognizing a voiceprint abnormal state by combining outlier detection; s4, performing window sliding and statistical analysis on the time sequence data, and outputting a trend abnormal interval and an abnormal index type; s5, constructing a variable association graph structure, and identifying potential abnormal variables and propagation paths; s6, integrating the various types of abnormal information and the feature representation set, and constructing a voiceprint map library; and S7, integrating and processing the abnormal result and the voiceprint map database, and executing visual display and intelligent early warning. According to the invention, box transformer substation abnormity monitoring and early warning based on unsupervised learning are realized, and the fault identification accuracy and response efficiency are improved.

The invention relates to a power system network security threat monitoring and early warning method and device, power equipment, a computer readable storage medium and a program product, and belongs to the field of power system network security. The method comprises the following steps: acquiring historical data and real-time data; obtaining first network feature data based on historical data, performing unsupervised learning training on the initial detection model, and taking abnormal data as second network feature data; marking the second network feature data as third network feature data; marking the first network feature data to obtain fourth network feature data to perform self-supervised learning training on the model, and obtaining first network attack features to construct a network attack feature library; performing semi-supervised learning training on the model based on the first network feature data and the third network feature data to obtain a detection model, and updating a network attack feature library; and detecting real-time data based on the detection model and the network attack feature library. The method can improve the accuracy of power system network security threat monitoring and early warning.

The invention is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention discloses a nonlinear aerodynamic damping estimation method based on LSTM, and the method comprises the following steps: 1, building a nonlinear state space model of a structure based on structural response, including a state equation, an observation equation and a relation between nonlinear aerodynamic damping and structural vibration amplitude; 2, performing updating and covariance prediction on response data by using unscented Kalman filtering; 3, correcting the Kalman gain in real time by using a long short-term memory network; 4, performing state updating and covariance updating based on the corrected Kalman gain; 5, training the long-short-term memory network through an unsupervised learning mode, optimizing the filtering performance, and defining a mean square error of a posterior observation predicted value and a real observation value as a loss function; and 6, calculating the nonlinear aerodynamic damping according to the estimated nonlinear aerodynamic damping parameters. The invention further discloses a nonlinear aerodynamic damping estimation system based on the LSTM and a storage medium.

The invention discloses an intelligent prospecting prediction method based on a three-in-one metallogenic prediction theory, and relates to the technical field of prospecting prediction, and the method comprises the steps: determining various geological elements formed by a regional control ore deposit, obtaining data related to the elements formed by the regional control ore deposit, and storing the data in a database; comprising geological survey, engineering exploration, geophysics, geochemistry and remote sensing data; performing data preprocessing on the geological element data and dividing the geological element data into a plurality of categories; unsupervised learning is carried out on the geological data according to a deep learning algorithm, and the geological structure of the mining area is described in detail; in the areas with relatively high reconstruction errors, selecting the areas with relatively high geochemical anomaly and geophysical anomaly indication variables or reconstruction error values, comparing the selected areas with known typical ore deposits to determine the correlation of space structures, and extracting ore-forming favorable anomaly feature information from the selected areas; and the extracted abnormal information is matched with various geological elements to delineate the target region, so that the accuracy and efficiency of prospecting prediction are improved.

The invention discloses an intelligent operation and maintenance alarm generation method based on unsupervised learning, and the method comprises the steps: constructing a dynamic topological graph which represents all service nodes in a system and the mutual relation of the service nodes based on operation and maintenance data, and generating a node state vector which represents the current state of each service node for each service node, the method comprises the following steps: inputting a dynamic topological graph structure and a node state vector into a pre-trained time-space diagram neural network model, calculating an abnormal score of each service node, and when the abnormal score exceeds a dynamically determined alarm threshold value, generating an operation and maintenance alarm, and furthermore, improving the reliability of the operation and maintenance alarm. A root cause node and a fault propagation path are determined based on time priority and anomaly severity, anomaly score distribution change is monitored through KL divergence, and incremental online learning is carried out; according to the method, the limitation problems of high supervision dependence, neglect of space-time topology, fixed threshold value and the like in the prior art are solved, the root cause positioning precision, robustness and generalization capability of alarm are improved, the false alarm rate and the missing report rate are reduced, and the operation and maintenance efficiency and the real-time performance are improved.

The embodiment of the invention relates to the technical field of image processing, in particular to a garbage incinerator flame combustion situation recognition method based on unsupervised learning. According to the method, the problem of lack of labeling data in flame combustion situation recognition of the garbage incinerator is effectively solved through an unsupervised learning framework; a mutual information maximization objective function driving model is utilized to autonomously excavate essential feature association in a flame image, and a corresponding relation between a combustion state and a visual feature can be established without depending on manual labeling; and hierarchical description of the combustion state is realized through dual-label output of main clustering and super clustering, so that the macroscopic working condition classification capability is reserved, and the microscopic dynamic change characteristics are captured. Visibly, according to the embodiment of the invention, the adaptability of the model to a complex combustion scene is enhanced while the data annotation cost is reduced, and refined monitoring and recognition of the combustion state of the incinerator can be realized.

An AI and machine learning-based system for automating employee management and work information processing within an organizational structure, the system comprising: a central processing module configured to aggregate and pre-process data from multiple sources, including attendance records, performance logs, task management systems, and communication channels, with the central processing module normalizing and filtering the data to ensure consistency and accuracy in real-time analysis; a machine learning-based analysis unit operatively connected to the central processing module, the analysis unit comprising a natural language processing (NLP) sub-module, a sentiment analysis sub-module, and a pattern recognition sub-module, and configured to extract, analyze, and interpret both structured and unstructured data for insights into employee behavior and performance assessment, and further configured to adapt and refine models based on continuous data inputs from the central processing module; a predictive task scheduling component comprising a reinforcement learning-based model that leverages employee skill profiles, historical task completion rates, and workload patterns to dynamically distribute tasks, with the predictive task scheduling component further configured to self-optimise based on real-time feedback regarding task completion efficiency, priority changes, and schedule adjustments within the organization; a performance tracking unit configured to receive inputs from the central processing module and the machine learning-based analytics unit, wherein the performance tracking unit continuously monitors employee performance, challenges, and areas for improvement and stores these insights in individualized, encrypted employee profiles that can be accessed in real time, thus supporting data-driven performance reviews and improvement plans; an input / output interface for user interaction, the interface providing managers with interactive access to review employee metrics, task assignments, and performance feedback, and enabling employees to securely view individual performance metrics, feedback, and task details, the interface further being configured with user-level access controls based on historiographical organizational roles; a secure data storage component configured to securely store employee data, task logs, and performance metrics, where the data storage component supports both local and cloud-based storage solutions, uses encryption protocols for secure data retention, and provides access control mechanisms for authorized retrieval of stored data; a communication interface module operatively connected to the central processing module and configured with multi-protocol communication capabilities, including Wi-Fi, Ethernet, and Bluetooth, wherein the communication interface module facilitates real-time data synchronization between remote and local devices and is integrated with an AI-based anomaly detection system that flags irregularities or potential security threats in data transmissions; and an adaptive learning module operatively connected to the machine learning-based analytics unit and configured to continuously retrain machine learning models based on real-time data inputs, where the adaptive learning module uses reinforcement learning and unsupervised learning techniques to refine task recommendations, adjust performance metrics, and optimize task assignment rules based on the evolving needs of the organization.

The invention provides a fly ash composite material goaf filling body interface quality intelligent evaluation method, and belongs to the technical field of mining engineering and artificial intelligence detection crossing. The method comprises the steps that firstly, filling body interface quality characteristic data are collected and comprise interface sound wave signals, stress strain, coal ash composite material physical parameters and environment working condition data; secondly, constructing a multi-physical field data completion model, performing unsupervised learning on the acquired sound wave, stress, temperature and moisture content data, and generating completion data of global spatial distribution; secondly, constructing a multi-field fusion interface quality index prediction model, and inputting multi-source data into the model to obtain an interface quality index; and finally, combining the quality index to realize interface defect mode classification and grade evaluation, and generating a targeted maintenance strategy. The invention provides an intelligent evaluation method which fuses multi-source data and gives consideration to real-time performance and comprehensiveness, so as to solve the industrial pain points of interface quality evaluation lag, low precision, large destructiveness and the like.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

The invention provides a cross-modal image and text corpus association analysis system, and relates to the field of image and text analysis. Comprising a data collection and preprocessing module, a feature extraction module, a cross-modal association learning module and a model training and optimization module. The data collection and preprocessing module collects image and text data from multiple sources and preprocesses the image and text data; the feature extraction module extracts image and text features by using CNN and NLP models; the cross-modal association learning module enhances the semantic consistency of image and text features through feature alignment, weighted summation, an attention mechanism and a cross-modal interaction unit; the model training and optimizing module adopts an unsupervised learning method to train a model and uses an optimization algorithm to adjust parameters; according to the system, through an innovative cross-modal association learning mechanism and an advanced deep learning model, the accuracy and reliability of cross-modal image and text corpus association analysis are effectively improved.

The invention provides a method and device for directly carrying out HPV prediction by utilizing a cervical cell pathology image, electronic equipment, a storage medium and a program, and relates to the technical field of medical image processing, and the method comprises the following steps: S10, data acquisition: obtaining a same-period cervical cell pathology full-slide image of a detected person which has been subjected to cervical tissue pathology analysis and a conclusion of which is evaluated as ASCUS; and S20, lesion area positioning: determining an abnormal cell area in the cervical cell pathological full-slide image by using the trained target detection model. Compared with the prior art, the method has the following beneficial effects: firstly, suspicious cells are screened out through a target detection method, and an existing method in a tissue pathology all-slide image is migrated into a cell pathology image; secondly, due to application of unsupervised learning and an attention mechanism, the model can automatically learn and emphasize the most important features for HPV detection in the image; and thirdly, the HPV infection state of the ASCUS patient is directly and accurately predicted from the cervical image.

A robotic surgical system network includes a plurality of robotic surgical systems. Each system includes robotic arms, sensors, a surgeon console, and a control system with an integrated AI module. A network interface is associated with robotic surgical system and provides secure data communication. A central or distributed data repository securely stores surgical data aggregated from the robotic surgical systems. The surgical data includes at least one of procedural data, sensor readings, imaging data, AI decision logs, surgical outcomes, or user interaction data. A training module utilizes aggregated surgical data to train or update AI models for the robotic surgical systems using unsupervised learning, transfer learning, or federated learning techniques. A cybersecurity module implements security measures for data transmission and system access, the measures comprising at least one of encryption, multi-factor authentication, or real-time threat detection.

The invention discloses a network attack detection method fusing momentum contrast learning and Transform, and the method comprises the steps: collecting a large amount of untagged data of an industrial system network, carrying out the unsupervised learning through employing the momentum contrast learning and fusing the Transform, extracting the high-dimensional and long-sequence features in the data, constructing a feature embedding space with high discrimination, and carrying out the recognition of a network attack through employing the momentum contrast learning and the Transform. The method comprises the following steps of: performing dimension reduction on high-dimensional features by using a principal component analysis method, taking the features subjected to dimension reduction as input of a BDSCAN density clustering algorithm, performing clustering classification, optimizing parameters in the DBSCAN algorithm through a grey wolf optimization algorithm, outputting optimal algorithm parameters through iteration, and ending a program until a detection performance requirement is met. The method can be better suitable for a scene with high-dimensional and complex data, is suitable for a condition that the data has no label, improves the overall network detection accuracy and robustness, and enhances the network detection practicability and adaptability.

The invention relates to the technical field of intelligent diagnosis, and discloses a micro-grid fault diagnosis method and system based on data driving and unsupervised learning. The method comprises the following steps: collecting current, voltage, temperature and power data of a micro-grid and constructing a time sequence matrix; inputting a time sequence prediction network and a time sequence reconstruction network, and performing parallel processing to obtain a prediction error and a reconstruction error; carrying out weighted fusion on the two errors and constructing a two-dimensional error space to judge normal fluctuation and fault abnormity; and extracting a state variable to generate a dynamic threshold to judge a fault. The false alarm rate and the missing report rate of fault diagnosis are reduced.

The invention relates to the technical field of power utilization monitoring, and discloses a photovoltaic user power utilization abnormity monitoring method and system based on artificial intelligence. The photovoltaic user electricity consumption abnormity monitoring system based on artificial intelligence comprises a data acquisition module which is used for acquiring photovoltaic power generation data, electricity consumption data and environment data of a user; the data preprocessing and feature engineering module is used for cleaning, aligning and normalizing the original data acquired by the data acquisition module and constructing a feature data set for model training and reasoning; and the artificial intelligence analysis engine module comprises an unsupervised learning unit, a supervised learning unit and a deep learning unit. According to the invention, the false alarm rate and the missing report rate can be effectively reduced, the accurate diagnosis of the abnormal type can be realized, and the intelligent and accurate operation and maintenance requirements of power grid enterprises on the power utilization monitoring of photovoltaic users are met.

The invention is applicable to the technical field of target range target measurement, and provides a post-event target range multi-target trajectory measurement method and system, a storage medium and a processor. Firstly, combined intersection calculation of targets at all moments is completed through azimuth pitch angles of the targets relative to multiple observation stations, three-dimensional position coordinates of the multiple targets at all moments are obtained, dimension raising operation of data to be separated is achieved, and due to the fact that one-dimensional data is additionally introduced, intersection of the target data is greatly reduced, and the trajectory separation difficulty is lowered; meanwhile, an unsupervised learning method, namely a clustering algorithm, is introduced, the algorithm can group the data without any guidance, and the efficiency is extremely high. The afterward target range multi-target trajectory measurement system adopting the method also has the above effects.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

The technology relates to machine responses to anomalies detected using machine learning based anomaly detection. In particular, to receiving evaluations of production events, prepared using activity models constructed on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, to responding to detected anomalies in near real-time streams of security-related events of tenants, the anomalies detected by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. An anomaly score received for a production event is determined based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.

The invention discloses a communication radiation source target confrontation decision-making method based on unsupervised learning. The method comprises the following steps of obtaining an IQ signal of a communication signal and performing signal preprocessing; training a contrast predictive coding CPC model based on the IQ data after signal preprocessing, and extracting IQ data features; inputting the IQ data features into a ResNet network for closed set individual classification, and outputting closed set scores; correcting the closed set score based on an OpenMax algorithm to obtain an open set score, and identifying a radiation source individual according to the open set score; constructing a target adversarial decision model based on a GBDT algorithm, extracting individual features of a communication radiation source and interference adversarial parameter features, and constructing a data set to train the target adversarial decision model; and inputting individual characteristic parameters of the communication radiation source to the trained target adversarial decision model, and outputting interference adversarial parameters. According to the method, the identification accuracy of the communication signals is improved, and the robustness in the face of signals of unknown categories is enhanced.

The invention provides a personal health abnormal behavior monitoring system fused with unsupervised learning, and relates to the technical field of data processing, and the system is used for carrying out the clustering processing of historical behavior data, dividing the historical behavior data into a plurality of behavior tags, extracting the feature vector of each behavior tag, building an individual behavior physical model according to the feature vector, and carrying out the analysis of the individual behavior physical model. Inputting the target feature data into the individual behavior physical model, calculating the deviation degree of the recent behavior of the user, monitoring and recording the deviation degree to obtain an observation label group, judging whether the observation label group meets the absorption condition of the individual behavior physical model or not, if not, judging that the behavior label is an abnormal behavior, and if not, judging that the behavior label is an abnormal behavior. And according to the deviation degree and the duration of the abnormal behavior, calculating a behavior inhibition factor, and according to the behavior inhibition factor, dynamically adjusting the individual behavior physical model to limit the abnormal behavior from being identified as a normal behavior. According to the invention, the system can be prevented from mistakenly considering periodic abnormal behaviors as normal behaviors.