99 results about "Finite-state machine" patented technology

The application discloses an industrial control system protocol fuzzing test method based on state conversion, and particularly, the communication flow of an upper computer and a PLC is analyzed, a protocol finite state machine of a communication interaction process is constructed, and the PLC is subjected to the fuzzing test by using test cases violating the finite state machine in a test process, so that the purpose of triggering unknown vulnerabilities of the PLC is finally achieved. The application uses the protocol finite state machine to guide the state conversion path of the fuzzing test process. The method for obtaining the test cases violating the original protocol finite state machine and then performing the fuzzing test on the equipment can efficiently find vulnerabilities. The confirmation method for the state conversion leading to the vulnerabilities can obtain the shortest state conversion path leading to the vulnerabilities.

The application discloses a model training process-oriented computing power chip hardware Trojan design method, and relates to the field of integrated circuit security.The method is designed in view of the computing architecture of a GPU, and contains a flip-flop, a finite state machine and a load circuit.The flip-flop identifies the typical characteristics of deep learning training by monitoring the arithmetic instruction intensity and the Warp concurrent activity of the GPU runtime; when the characteristics meet a preset threshold, the finite state machine is triggered, so that the hardware Trojan enters an activated state.The load circuit has two types of designs: the first type is a gradient flipping circuit which interferes with the gradient update direction by modifying the sign bit output by a floating point calculation unit, so that the model cannot converge; the second type is a redundant calculation circuit which consumes computing resources by inserting invalid floating point operations, thereby prolonging the training time.The application designs and triggers the hardware Trojan by utilizing the architecture characteristics of the GPU, has high concealment, and can effectively destroy the accuracy and efficiency of a deep learning training task.

Example embodiments of the present disclosure relate to cell health metrics management. According to an example embodiment, a device may be configured to receive cell information associated with a cell in a telecommunication network and determine cell health metrics of the cell based thereon. The cell information may include a current state of a finite state machine (FSM) of the cell. The device may also be configured to record the cell health metrics in a local cache and then synchronize a table with the cell health metrics recorded in the local cache based on determining that the predetermined time period has elapsed. Further, the device may also be configured to transmit the table to a northbound component of the telecommunication network.

This application relates to the field of smart medical technology and discloses a method and system for monitoring medication adherence in patients during ovulation induction cycles. This invention significantly improves the accuracy and reliability of ovulation induction medication adherence assessment in complex home environments by deeply integrating the real physical boundaries of the human body, the mechanical rigidity constraints of instruments, and the fluid dynamics laws of porous media into a visual monitoring framework. Specifically, this application utilizes dynamic pixel physical mapping and biomechanical filtering technology to effectively eliminate the interference of lens distortion and physiological tremors, reconstructing a pure kinematic sequence. By using a finite state machine with dual boundaries of force application and limit, it accurately identifies and coherently processes the mid-process pauses caused by patients' fear of pain, completely solving the defect of traditional algorithms that are prone to logical breaks.

This invention provides an adaptive control method, device, equipment, and storage medium for a conference system. The invention collects the age and identification confidence levels of attendees at each conference terminal's location, and calculates the weighted average age of the entire meeting by combining role weights. A sliding time window is then used for filtering to obtain a stable average age. Based on a finite state machine model, the current mode is determined by comparing the stable average age with a preset age threshold and the duration for which the threshold is continuously met. The modes include at least a normal mode and an elderly mode. When the elderly mode is determined to be entered, UI adjustment instructions and display driver parameter adjustment instructions are generated and sent to all conference terminals, enabling each terminal to uniformly switch the UI and display parameters. This method achieves deep linkage between the interface and display parameters and ensures consistency in multi-terminal switching, thereby providing an intelligent, stable, and age-friendly paperless conference experience.

The present application relates to a kind of network security CTF competition multi-class cheating identification and trace analysis method, belong to network security technical field.The present application first monitors and records log to the process of player competition answering question;Then use finite state machine to analyze the logic abnormality of player of answering question step, based on normal distribution to judge the time abnormality of answering question step of player, and according to flag submission time, propose player correlation degree calculation method, find suspicious high similarity player, analyze and trace back the source of player cheating;Finally, collect and comprehensively judge suspicious player Writeup, answering question log under new and old environment, output cheating identification result and analysis report.The method effectively identifies the cheating behaviors in CTF competition, such as proxy participation, multiple accounts, plagiarism of other players' flags or solution ideas, and locates suspicious similar players.

The application relates to a kind of multi-robot scheduling control methods based on roundabout movement and deadlock prevention, comprising: constructing annular buffer and dynamic circulation path between picking station and storage area, and modeling finite state machine for dynamic behavior of robot, to obtain the finite state machine model corresponding to different robots;Based on the finite state machine model of robot, combined with resource allocation constraints and preset operation constraints, the multi-robot scheduling strategy is output by executing roundabout movement scheduling algorithm, to control the working state of each robot accordingly.Compared with the prior art, the application can dynamically guide the robot to move in order along the circulation path between the picking station and the storage area, and can actively prevent deadlock, effectively solving the limitations of traditional scheduling strategy in deadlock prevention, resource utilization and efficiency improvement.

A method includes detecting an outage in a wireless communications network, acquiring specifications documents describing communication protocols between a plurality of network elements and a plurality of user equipment of the network, translating, using a generative artificial intelligence technique, the specifications documents into a first FSM that models expected events and corresponding actions from perspectives of the plurality of network elements and the plurality of user equipment, translating, using the generative artificial intelligence technique, a plurality of event logs of the network into a second FSM that models observed behaviors of the plurality of network elements and the plurality of user equipment, detecting, based on a comparison of the second FSM to the first FSM, a behavior of the observed behaviors that deviates from the communication protocols, and identifying a network element of the plurality of network elements that is associated with the behavior as a contributor to the outage.

The present invention relates to a computer implemented decoding method that includes decoding a number of run-length encoded mask strings to obtain decoded mask bits for each of a number of scan channels. The method also includes applying the decoded mask bits to automatic test pattern generator (ATPG) scan response data of each scan channel to obtain a multiple input signature register (MISR) signature. Additionally the present invention also relates to an apparatus configured to carry out the method. The apparatus being implemented either with at least one memory and at least one processor or with a plurality of finite state machines and a plurality of logical AND gates.

Systems and methods are disclosed that monitor for supply noise from a power source using a voltage noise monitor (VNM). For instance, the VNM may include voltage sense circuitry comprising a controllable resistor that is controlled using threshold information. The resistance of the controllable resistor may be changed based on closing and / or opening one or more switches associated with step resistors using the bits from the threshold information. Furthermore, the VNM may include digital circuitry that comprises a hold finite state machine and a sticky hold counter. Using the digital circuitry, the VNM may be configured to hold a noise detection event for a plurality of clock cycles. In addition, the VNM may perform a calibration process based on setting two voltages for the power source to obtain two codes, and determining a transfer function based on the two voltages and the two codes.

This invention discloses a method for autonomous operation of humanoid robots based on ROS. It employs a decision-making and control framework that fuses hierarchical finite state machines with YOLOv8 vision to achieve continuous autonomous execution of multiple tasks in complex unstructured environments. The method includes: system initialization and standardized processing of multimodal perception data; target 3D localization fused with YOLOv8 and coordinate transformation; task scheduling and state transitions using hierarchical finite state machines; segmented navigation and motion planning of joint trajectories; and ROS command issuance and full-process closed-loop control. This invention addresses the rigidity of traditional script-based control by fusing hierarchical state machines with visual servoing. Combined with a full-process anomaly handling mechanism and multi-target scene processing rules, it can dynamically adapt to changes in complex unstructured environments, effectively avoiding problems such as state freezes and erroneous grasping.

This invention relates to a six-DOF microgravity quality assessment method based on mission phase-driven approaches, belonging to the field of spacecraft control and ground simulation. It constructs a multi-physics fusion six-DOF microgravity quality index, divides the on-orbit service mission phase using a finite state machine and matches dynamic weights and assessment thresholds, combines digital twins to achieve virtual-real consistency verification, employs rapid independent component analysis and extended state observers to complete disturbance decoupling and contribution rate quantification, and uses a graph neural network long short-term memory network model and Shapley sum interpretation algorithm to achieve pre-experiment performance prediction and in-experiment anomaly diagnosis. This invention addresses the technical problems of existing microgravity ground simulation assessments, such as the disconnect between static thresholds and on-orbit mission requirements, the inability of a single index to cover multi-DOF coupling characteristics, the lack of virtual-real consistency verification, the inability to quantify and attribute disturbance sources, over-reliance on expert experience, and post-event offline assessment.

A buck-boost converter includes a converter, for converting an input voltage into an output voltage, the converter including a plurality of switches and an inductor, and a controller for controlling the plurality of switches. The operation of the controller is driven by a finite state machine configured to receive as input state change signals and to provide as output state signals for driving the controller. The state change signals are generated by a comparator based on a comparison of a replica signal and an error signal, wherein the error signal is computed on the basis of a signal representative of a difference between a characteristic of the converter and a predetermined reference signal, and wherein the replica signal is representative of the current flowing through the inductor.

The application discloses a kind of university student project education simulation method and system based on multi-agent.It includes: obtaining project creation request, creating project directory and initializing session manager and version controller, session manager maintains finite state machine, the state space of finite state machine includes three stages of system initialization, collaborative construction period and game defense period;In the collaborative construction period stage, each step is executed in turn, after completion, automatically promote state and carry out persistence, after completing review modification and auxiliary writing, enter the game defense period;In the game defense period stage, three experts parallel review and conflict detection, proposal revision based on expert opinion and final report chapter generation and cross-platform file export step are executed in turn, after proposal revision and audit pass, final report adopts the chapter independent generation strategy, and the PDF format file of Markdown and cross-platform adaptation is output.

The application discloses a task intelligent agent implementation method based on a finite state machine and deep reinforcement learning, which is applied to a two-party confrontation process, and the two parties each include a preset number of formations, each formation has a task to perform, and the two-party confrontation process is as follows: the task to perform is divided into a preset number of execution stages, and a conversion condition between the execution stages is constructed; the execution stage is taken as a state of a finite state machine, and the finite state machine is executed according to the conversion condition between the execution stages; an intelligent agent is set for each execution stage, and an action space of each intelligent agent is configured; each intelligent agent is trained based on the finite state machine and the action space; the task to perform is implemented in stages by using the finite state machine technology, scientific process organization and stage behavior execution can be performed on a complex execution task containing multiple action sequences, the rationality, simplicity and expansibility of the task execution process are ensured, and the reusability of the module is improved.

The application discloses a path tracking control method for a man-machine co-driving type intelligent vehicle, and steps are as follows: a vehicle path tracking error model is established, and a hierarchical control architecture is constructed, the hierarchical control architecture comprising an upper controller and a lower controller; the upper controller is configured to: in a human driving mode, generating a first front wheel steering angle through a two-point preview driver model, and in an automatic driving mode, generating a second front wheel steering angle through an automatic driving controller fusing a radial basis function neural network, a heuristic reinforcement adjustment mechanism and a non-singular fast terminal sliding mode control; the lower controller is configured to: driving a finite state machine to switch the driving mode based on a driving risk assessment index, and performing weighted fusion on the first front wheel steering angle and the second front wheel steering angle in a driving authority transition stage to output a final front wheel steering angle control instruction; the application solves the contradiction between path tracking precision and chattering, switching safety and comfort, and is suitable for man-machine collaborative control of an intelligent vehicle under multiple working conditions.

The application belongs to the technical field of broadcast television network, and is a DVB full-signal source fusion intelligent network management system, which comprises a three-dimensional integrated no-reference evaluation system of a protocol layer, a content layer and a device layer, and comprises the following steps: S1: performing multi-dimensional no-reference intelligent perception of multicast signal quality, deeply analyzing the protocol layer and monitoring core indexes, and simultaneously analyzing audio and video content based on a single stream to identify implicit faults; S2: performing DCM device full-life-cycle white-box monitoring, collecting device hardware states and service information through deep fusion of a standard protocol and a management information base, and realizing intelligent correlation analysis of device layer alarms and signal layer alarms; S3: performing intelligent closed-loop linkage control, constructing an alarm strategy engine, and designing a safe switching control process based on a finite state machine; and S4: deploying a distributed micro-service architecture, splitting the system into multiple micro-services and decoupling through a message queue, and realizing dynamic balanced scheduling and fault migration of tasks.

The application discloses a kind of computing power-power collaborative scheduling method based on dynamic barrier option model, including the following steps: step S1. computing power task preliminary screening and gradient product pricing;Step S2. establish and utilize dynamic barrier option model, calculate instantaneous expected net income difference;Step S3. double finite state machine smooth delivery: FSM-B is executed based on instantaneous expected net income difference and the state transition control of time sequence physical constraint parameter.This application discloses the traditional hardware depreciation concept, focuses on quantifying the consistency repair cost in distributed computing and exponential default fine, so that computing power center can accurately calculate profit and loss balance point when participating in peak shaving, to avoid inverted loss.The application introduces dynamic barrier option model, and complex algorithm network interaction is converted into scientific financial decision, and the risk premium generated by computing power task over time can be accurately described by the dynamically floating barrier point, to maximize the arbitrage space of data center.

This invention relates to the field of tobacco procurement information technology, and discloses an intelligent processing system for weighing information and a weighing and sealing device for tobacco leaf pre-inspection. The system includes data acquisition, processing, interaction, and execution control modules. The data acquisition module performs time-series alignment of continuous weight data and identity data. The data processing module uses a peak-valley range algorithm to determine stability and lock the net weight, while simultaneously performing tare deduction and compliance verification. The data interaction module, based on visual anchor point positioning technology, establishes a non-intrusive transmission channel with external business systems, automatically corrects control coordinates, and simulates data entry. The execution control module, based on a finite state machine, drives seal generation and data entry when verification is successful, and triggers an audible and visual alarm and forcibly blocks the entry signal and sealing command when verification fails. This invention solves the problems of difficult integration with heterogeneous systems and error-prone manual data entry, achieving automated and accurate acquisition of weighing data and closed-loop quality control.

Systems and methods for configuring hardware resources of an electronic device based on operating characteristics of the electronic device are provided. In one embodiment, a processor may configure the hardware resources of the electronic device that equally weighs decisions made by a machine learning (ML) engine and a finite state machine (FSM) engine. In another embodiment, the processor may configure the hardware resources of the electronic device that gives greater weight to decisions made by the ML engine compared to decisions made by the FSM engine. In yet another embodiment, the processor may configure the hardware resources of the electronic device that gives greater weight to decisions made by the FSM engine compared to decisions made by the ML engine.

This invention belongs to the field of intelligent industrial load management technology, specifically relating to a load identification and control method and system based on edge AI and online self-learning. It includes the following steps: collecting multi-source heterogeneous raw load data through an edge intelligent terminal deployed on the user side; inputting the load feature data into a lightweight deep learning model deployed on the edge intelligent terminal; initiating an online self-learning process based on the confidence level; generating and executing an optimal control strategy for specific electrical equipment based on a preset finite state machine and multi-objective optimization model, according to the identification results and received power grid control instructions; and uploading the model parameters and control effect data locally on the edge intelligent terminal to a cloud master station for global model aggregation and updating. This invention achieves millisecond-level real-time accurate load identification by deploying a lightweight CNN-LSTM hybrid model on the edge.

The application provides a protocol finite state machine extraction method and system, which comprises the following steps: preprocessing an original protocol document to generate a structured text block and a vector knowledge base; performing entity recognition on the structured text block to obtain a target entity list; performing skeleton extraction on the structured text block based on a first large language model and the target entity list to generate an initial global protocol finite state machine skeleton; performing iterative structure correction on the initial global protocol finite state machine skeleton, and if the corrected initial global protocol finite state machine skeleton meets the structure constraint, regarding it as a target global protocol finite state machine skeleton; performing iterative filling of details on the target global protocol finite state machine skeleton based on the target entity list, the structured text block and the vector knowledge base to obtain an initial protocol finite state machine; and performing format conversion on the initial protocol finite state machine to obtain a target protocol finite state machine. The application can improve the accuracy and reliability of the generated model, and overcome the problems of low model processing efficiency and information dispersion.

The application discloses an automatic auditing method for two-dimensional drawings of electric drive assembly parts, and relates to the field of vehicle data auditing.The method comprises the following steps: T1, constructing a training data set: extracting electric drive assembly part drawing samples and labeling, constructing an image-text pair, an instruction fine-tuning set and a corresponding basic model training data set; T2, demand structure analysis: converting the natural language or fuzzy auditing demand of a user into a structured instruction through a large language model, calculating an image quality score and a text density score; T3, workflow scheme matching: converting the structured instruction into a feature weight vector, matching the feature weight vector with a feature preference matrix of a preset workflow scheme, and selecting an optimal execution scheme; and T4, finite state machine scheduling execution: defining core states and flow logic, scheduling the workflow scheme execution, monitoring the running state and processing exceptions.

The application discloses a state self-driven multi-agent crop growth report generation method and system, first, a multi-agent cooperative communication mechanism is constructed, a unified event-driven model and a communication pipeline are built, information routing and state synchronization between heterogeneous agents such as planning, querying and data analysis are realized, and asynchronous cooperation and data intercommunication of a multi-agent network under complex logic are guaranteed. Secondly, a state self-driven agent architecture is realized, that is, a self-driven core based on a finite state machine is implanted into the agent, so that each node can autonomously complete state flow, action decision and local task execution according to real-time context environment and task feedback mechanism. Finally, automatic generation of a structured document is realized, special report agents are used to logically aggregate and template rendering of multi-dimensional analysis data and knowledge fragments output by the cooperative network, and finally, a structured analysis report with standard layout and rigorous content is output.

A method of quantum probability estimation of an event in a stochastic process is provided. The stochastic process is modelled based on a finite state-machine model. A classical predictive model for the event is modelled based on the stochastic process and includes a set of states and a set of state transition matrices associated with the finite state-machine model. The method includes: generating a circuit description of a quantum state-preparation operator based on a past observation sequence of the stochastic process and the set of state transition matrices of the classical predictive model; generating a circuit description of a quantum search operator based on the circuit description of the quantum state-preparation operator and an event indication function, whereby the event indication function is configured to indicate whether the event is to occur with respect to the future sequence of the stochastic process; generating a plurality of circuit descriptions for a plurality of measurement quantum circuits, respectively, for compiling the plurality of measurement quantum circuits on a quantum computer, whereby each circuit description of the measurement quantum circuit is generated based on the circuit description of the quantum state-preparation operator and the circuit description of the quantum search operator; for each of the plurality of circuit descriptions of the plurality of measurement quantum circuits: compiling the circuit description of the measurement quantum circuit on the quantum computer to form the measurement quantum circuit thereon; and executing the measurement quantum circuit formed on the quantum computer to obtain a measurement result associated with the measurement quantum circuit that indicates a number of times that the measurement quantum circuit produces an indication that the event is to occur; and estimating a probability of the event based on the measurement results obtained. There is also provided a corresponding system for quantum probability estimation of an event.

This invention discloses a flexible cable-driven upper limb exoskeleton device and its control method for over-the-shoulder harvesting in agriculture. It employs a back-mounted drive and Bowden cable distal transmission architecture, and introduces a bidirectional cable chain mechanism to adaptively compensate for the instantaneous rotational center drift of the shoulder joint, eliminating human-machine kinematic interference. The perception layer uses a dual-node pure IMU scheme for the upper and lower arms, combined with a lightweight LSTM network and a majority-voting smoothing strategy to achieve low-latency, highly robust four-state action intention recognition. The control layer constructs a finite state machine scheduling architecture, mapping intention adaptive gravity compensation, position impedance, and zero-force following modes. Implicit state alignment and torque cross-gradient achieve non-disruptive switching between multiple modes. The underlying servo integrates fuzzy PID online tuning to suppress transmission nonlinearity. This invention significantly reduces the activation load on the deltoid and biceps muscles under lightweight design, making it suitable for long-duration over-the-shoulder harvesting operations in orchards.

The application discloses a helium mass spectrum leak rate determination method fusing confidence quantification and finite state machine control and relates to the technical field of vacuum leak detection. The method comprises the following steps: S1, obtaining multi-dimensional features from a leak signal subjected to multi-frequency band filtering processing; S2, if a baseline signal and environmental interference features collected in the absence of helium gas meet corresponding preset conditions, respectively, determining a preset threshold value as an initial dynamic threshold value; S3, determining the weight of each feature in the multi-dimensional features by using an analytic hierarchy process and calculating the sub-confidence of each feature in the multi-dimensional features; S4, calculating the comprehensive confidence of the multi-dimensional features, and if the comprehensive confidence is not lower than a preset value, determining the helium mass spectrum leak rate by using the leak signal and the initial dynamic threshold value; and S5, dynamically updating the initial dynamic threshold value based on the comprehensive confidence, and repeatedly executing steps S1-S5 to continuously determine the helium mass spectrum leak rate. The method improves the determination reliability, the weak leak signal capturing capacity and the like.

A method of testing using a memory testing device connected to a memory device is disclosed, including receiving a test command. When the test command is a finite state machine (FSM) operation command, the memory device is tested according to the FSM operation command, and an operation is performed depending on a pass / fail result to output a result. However, when the test command is a direct access command, an automatic operation test of input data is performed in a test area according to received address information, and a test result is output, which can include output data with failure information or an automatic operation.