42 results about "Explicit model" patented technology

A Software Fault Management (SFM) system for managing software faults in a managed mobile telecommunications network. The SFM system includes an Intelligent Management Information Base (I-MIB) comprising a Management Information Base (MIB) and a Knowledge Base (KB) having a functional model of the managed network and a trouble report/known faults (TR/KF) case base. The SFM system also includes an intelligent multi-agent portion having a plurality of agents which process the software faults utilizing the functional model from the I-MIB, case-based information, and other management information. The I-MIB and the intelligent multi-agent portion are compliant with Telecomunications Management Network (TMN) principles and framework. Fault management is both proactive and reactive. The SFM system is made independent of technology-specific implementations by representing the underlying switch design knowledge in a modular and changeable form which is then interpreted by the intelligent multi-agent portion. A clear separation is maintained between the generic procedural inference mechanisms and agents, and the specific and explicit models of the different network elements of a mobile telecommunications network.

Methods for creating and using space-time surrogate models of subsurface regions, such as subsurface regions containing at least one hydrocarbon formation. The created surrogate models are explicit models that may be created from implicit models, such as computationally intensive full-physics models. The space-time surrogate models are parametric with respect to preselected variables, such as space, state, and/or design variables, while also indicating responsiveness of the preselected variables with respect to time. In some embodiments, the space-time surrogate model may be parametric with respect to preselected variables as well as to time. Methods for updating and evolving models of subsurface regions are also disclosed.

An intelligent control system based on an explicit model of cognitive development (Table 1) performs high-level functions. It comprises up to O hierarchically stacked neural networks, N_m, . . . , N_m+(O−1), where m denotes the stage/order tasks performed in the first neural network, N_m, and O denotes the highest stage/order tasks performed in the highest-level neural network. The type of processing actions performed in a network, N_m, corresponds to the complexity for stage/order m. Thus N₁ performs tasks at the level corresponding to stage/order 1. N₅ processes information at the level corresponding to stage/order 5. Stacked neural networks begin and end at any stage/order, but information must be processed by each stage in ascending order sequence. Stages/orders cannot be skipped. Each neural network in a stack may use different architectures, interconnections, algorithms, and training methods, depending on the stage/order of the neural network and the type of intelligent control system implemented.

The invention discloses an attitude control type direct lateral force and aerodynamic force composite missile attitude control method based on mixed forecasting control, belongs to the field of aircraft control, and solves the problem that a nonlinear characteristic and a control input mixing characteristic of a model cannot be simultaneously solved by an existing attitude control method. The attitude control type direct lateral force and aerodynamic force composite missile attitude control method is characterized by comprising the following steps: constructing a complete direct lateral force and aerodynamic force composite missile attitude control model and a direct lateral force model, and converting a nonlinear kinetic model into a piecewise affine model by analyzing an aerodynamic characteristic; constructing a composite control missile mixing logic dynamic model by considering the mixing characteristic of a control input according to the equivalence property of the piecewise affine model and the mixing logic dynamic model; designing an explicit model forecasting control rule based on the mixing logic dynamic model so as to determine an aerodynamic steering engine control rule and an attitude control engine starting rule. The method disclosed by the invention is suitable for being used in the field of aircraft missile control.

Large numbers of engine parameters result in a need for significant processing power to calculate the engine control values using algorithms in real-time. The disclosure provides an engine control system having a data library comprising an explicit model predictive control derived set of output data values. The data library comprises a subset of output data values in respect of each combination of input data values. The subset of output data values may be configured to control one or more aspects of engine performance.

The invention discloses an explicit model forecast control-based flight path control method of an unmanned surface vehicle. The explicit model forecast control-based flight path control method comprises the steps of firstly, building a control model of the unmanned surface vehicle, and acquiring position gesture and reference flight path point information of the unmanned surface vehicle; secondly,calculating expected flight speed and heading direction in real time according to a path planning algorithm, and simplifying ship flight path control to heading direction control; and finally, obtaining a rotational speed and a rudder angle of a propeller by a display model forecast control algorithm for the unmanned surface vehicle to achieve ship flight path control. The explicit model forecastcontrol algorithm is applied to the field of flight path control of the unmanned surface vehicle, the complicated on-line optimization process is converted to simple table lookup process by introducing a multi-parameter secondary planning theory, the on-line calculation complexity is reduced, and the real-time performance of a flight path control system is improved.

The invention discloses a video action recognition method and device and machine equipment. The method comprises the following steps: acquiring video data for action recognition; when the video data is subjected to feature extraction of each layer of the network in the neural network, enabling the extracted spatial domain features on the video image to enter a bilinear layer through the network structure of the neural network; performing bilinear correlation operation on the adjacent frames of video images according to the spatial domain characteristics to obtain the time-spatial domain characteristics of each frame of video image in the video data; classifying actions in the video through the time-space domain features to obtain an action recognition result of the video data. For featureextraction of each layer of the network in the neural network, parameters and calculation complexity in bilinear correlation operation on a bilinear layer are controlled, and then time-space domain features are extracted under the condition of control complexity, so that explicit modeling on a time domain relationship is realized, and the performance of action recognition is effectively improved.

A method for controlling a system using an explicit model predictive control (EMPC) evaluates, with respect to a state of the system, each inequality in a set of inequalities defining a set of regions of a state space of the system to produce a set of Boolean results. At least some of the inequalities are evaluated concurrently, and a size of the set of Boolean results equals a size of the set of inequalities. The method determines a region including the state by applying a Boolean function to elements of the set of Boolean results corresponding to inequalities forming boundaries of the region and determines a control for the system based on the state and a gain associated with the region. At least some Boolean functions are applied to corresponding elements concurrently after all elements in the set of Boolean results are evaluated.

Various technologies described herein pertain to automatic in-situ calibration and registration of a depth sensor and a robotic arm, where the depth sensor and the robotic arm operate in a workspace. The robotic arm can include an end effector. A non-parametric technique for registration between the depth sensor and the robotic arm can be implemented. The registration technique can utilize a sparse sampling of the workspace (e.g., collected during calibration or recalibration). A point cloud can be formed over calibration points and interpolation can be performed within the point cloud to map coordinates in a sensor coordinate frame to coordinates in an arm coordinate frame. Such technique can automatically incorporate intrinsic sensor parameters into transformations between the depth sensor and the robotic arm. Accordingly, an explicit model of intrinsics or biases of the depth sensor need not be utilized.

The invention provides a doubly-fed fan fault ride-through method and system based on explicit model predictive control. The defect of fixed coefficients of traditional demagnetization control is overcome; demagnetization current can be flexibly adjusted according to a fault condition to give full play to the capacity of a rotor side converter; on the basis of the traditional demagnetization control, redundant measurement links are not needed, and the method and the system can be suitable for various fault types and fault depths, suitable for online optimization control, and can meet the transient response requirements of the doubly-fed fan in the power grid fault period.

Systems, methods, apparatuses and program products configured to provide information technology (IT) system combination strategies are described. An explicit model of business to IT dependency is described and utilized by the systems and methods described to expose trade-offs in different combination alternatives, thus assisting in making proactive choices regarding business combination of disparate IT systems and resources.

The invention discloses a transformer hot spot temperature rise prediction method for comparing optical fiber temperature measurement, which comprises the following steps: obtaining the measured hot spot temperature, top layer oil temperature, ambient temperature and load current data of an oil-immersed transformer equipped with optical fiber temperature measurement equipment; obtaining the measured hot spot temperature, ambient temperature and load current data of an oil-immersed transformer installed with optical fiber temperature measurement equipment; Setting up the basic frame of the hotspot temperature rise prediction model; dividing The measured data into training set and prediction set after being processed. Adopting Genetic Programming (GA) to model the data of training set, andsetting up an explicit model for predicting the temperature rise of hot spots. Inputting the data of prediction set into the prediction model to predict the temperature rise of transformer hot spots.The invention has the beneficial effects that an explicit hot spot temperature rise prediction model is finally established, and the mechanism of the hot spot temperature rise dynamic change with theload can be intuitively disclosed; With strong generalization performance, it can realize batch prediction and on-line monitoring of hot spot temperature rise of oil-immersed transformers with the same capacity in power system.

The invention provides a multi-scale approximate explicit model predictive control method for the mechanical vibration of a high-speed elevator. The method comprises the steps that 1) an elevator mechanical vibration system is modeled to acquire the performance index function of the elevator mechanical vibration system, and the function is an object to be approximated; 2) a piecewise linear interpolation method is used to initially acquire an approximate control law; 3) adaptive hierarchical function approximation is carried out to transform the form of the approximate control law; 4) a centroid function is introduced to acquire the approximate control law based on the centroid function by using centroid interpolation; and 5) multi-scale approximate explicit model predictive control of thehigh-speed elevator mechanical vibration system is carried out.

The invention relates to an unmanned helicopter control optimization method based on a particle swarm algorithm, and belongs to the field of unmanned helicopter explicit model tracking control. The method comprises the following steps: determining an unmanned helicopter explicit model control system to obtain an integral constant matrix G4 and a gain diagonal matrix R; g4 and R being used as parameters in the particle swarm to be optimized through a particle swarm algorithm, and outputting a controller. According to the method, the particle swarm optimization algorithm is introduced into the explicit model control method, parameters in the unmanned helicopter controller are optimized, the control quality of the unmanned helicopter is improved, and the robustness of a controlled object is improved. The obtained controller can enable the performance of the controlled object to be optimal in a constraint range, the design is simpler, and the use is more flexible. The method also solves the disadvantage that the selection of the integral constant matrix G4 and the gain diagonal matrix R in the traditional explicit model tracking control method depends on expert experience through a trial and error method, so that the G4 and the R can be constructed more conveniently and the optimal controller can be obtained.

An explicit model predictive control method based on a connected graph for a three-degree-of-freedom helicopter comprises the following steps: step 1) modeling the three-degree-of-freedom helicopter to a model predictive control problem (MPC), and transforming the model predictive control problem (MPC) into a multi-parameter programming (MP-QP) problem, that is, a problem to be solved in offline calculation; step 2) introducing a critical domain, an effective constraint set and a concept of the connected graph; step 3) initializing a solving algorithm of the connected graph to obtain an initial critical domain and an optimal effective set; 4) judging feasibility of an effective candidate set; step 5) calculating on the feasible effective candidate set to obtain a critical domain and a control law; step 6) using the connected graph to generate a new candidate set and repeating the steps 4) - 6) until all solutions are generated; step 7) conducting the explicit model predictive control based on the connected graph for the three-degree-of-freedom helicopter system. The explicit model predictive control method based on connected graph proposed by the invention improves rate of off-linecalculation while ensuring good control performance of the system.

The invention discloses a multi-scale approximate explicit model predictive control method for a three-degree-of-freedom helicopter. The multi-scale approximate explicit model predictive control method comprises the following steps that step (1), modeling is carried out on the three-degree-of-freedom helicopter to obtain a parameter optimization problem, namely an object to be approximated below;step (2), a piecewise linear insertion method is carried out to initially obtain an approximate control law; step (3), self-adaptive separation function approximation is carried out, and the form of the approximate control law is transformed; step (4), a barycenter function is introduced, and barycenter interpolation is utilized to obtain an approximate control law based on the barycenter function; and step (5), multi-scale approximate explicit model predictive control over a three-degree-of-freedom helicopter system is carried out. According to the multi-scale approximate explicit model predictive control method, the real-time performance of the three-degree-of-freedom helicopter control system is improved, the control complexity is reduced, the storage capacity requirement of a controller is reduced, the online calculation time is saved, and a good control effect is achieved.

A multi-scale approximate explicit model predictive control method for disk drive systems comprises the following steps: 1) modeling the disk drive system to obtain a parameter optimization problem; 2) performing a piecewise linear insertion method to obtain an approximate control law; 3) converting the form of the approximate control law into an adaptive layered function approximation; 4) introducing a center of gravity function to obtain an approximate control law base on the center of gravity function by using the center of gravity interpolation; 5) performing multi-scale approximate explicit model predictive control of the disk drive system. The invention improves the real-time performance of the disk drive control system, reduces the control complexity, reduces the requirement of thestorage capacity of the controller, saves the on-line calculation time, and has good control effect.

A multi-scale approximate explicit model predictive control method for a brushless DC motor comprises the following steps: (1) modeling a brushless DC motor system to obtain a parameter optimization problem, that is, an object to be approximated below; 2) piecewise linear insertion method to obtain an approximate control law; 3) convert that form of the approximate control law into an adaptive layered function approximation; 4) introducing a barycenter function to obtain an approximate control law based on that barycenter function by use the barycenter interpolation value; and 5) multi-scale approximate explicit model predictive control of the motor control system. The invention improves the real-time property of the motor control, reduces the requirement of the memory capacity of the control, saves the on-line calculation time, and has a good control effect.

The invention discloses an implementation method for explicit model predictive control of a permanent magnet synchronous motor. The method comprises the steps of: constructing a controller for explicit model predictive control of the permanent magnet synchronous motor, processing and generating an optimal control sequence, and extracting an optimal control quantity from the optimal control sequence and applying the optimal control quantity to a control system of the permanent magnet synchronous motor; calculating an optimal control quantity and a closed-loop transfer equation of a control system under a typical working condition according to an optimal control sequence generated by a controller, taking a solution of the closed-loop transfer equation as a closed-loop pole, drawing a closed-loop pole trajectory diagram, determining an ideal band, and configuring the closed-loop pole to the ideal band so as to obtain each optimal weight coefficient; and substituting into a value function solution to carry out optimal control. According to the method, the problems that a control system is difficult to consider multiple control performances and the stability under different working conditions is difficult to guarantee are solved, the steps are simplified, the excellent control performance is obtained, and meanwhile, the method has very high universality and practicability.

In the invention, a search result diversified training method based on the generative adversarial network is realized through a method in the field of artificial intelligence. The method comprises thefollowing steps: after giving out a query word, defining a corresponding candidate document set, and a sampler, a generator and a determiner unit which are sequentially arranged for a logic path; setting diversified scoring functions in the determiner and the generator, and carrying out training through a positive feedback process; introducing a generative adversarial network, and combining an explicit model and an implicit model through the generative adversarial network, wherein the final generator can generate better diversified document sequences through the means.