52 results about "Discrete dynamics" patented technology

Segmenting the prostate boundary is essential in determining the dose plan needed for a successful bracytherapy procedure—an effective and commonly used treatment for prostate cancer. However, manual segmentation is time consuming and can introduce inter and intra-operator variability. This present invention describes an algorithm for segmenting the prostate from two dimensional ultrasound (2D US) images, which can be full-automatic, with some assumptions of image acquisition. Segmentation begins with the user assuming the center of the prostate to be at the center of the image for the fully-automatic version. The image is then filtered to identify prostate edge candidates. The next step removes most of the false edges and keeps as many true edges as possible. Then, domain knowledge is used to remove any prostate boundary candidates that are probably false edge pixels. The image is then scanned along radial lines and only the first-detected boundary candidates are kept the final step includes the removal of some remaining false edge pixels by fitting a polynomial to the image points and removing the point with the maximum distance from the fit. The resulting candidate edges form an initial model that is then deformed using the Discrete Dynamic Contour (DDC) model to obtain a closed contour of the prostate boundary.

A fast semi-automatic prostate contouring method is provided using model-based initialization and an efficient Discrete Dynamic Contour (DDC) for boundary refinement. The user initiates the process of the preferred embodiment by identifying four (4) points on the prostate boundary, thereby scaling and shaping a prostate model, and then the final prostate contour is refined with a DDC. The method of the present invention has particular application during the pre-implant planning phase of a brachytherapy procedure. However, this method also has uses in any phase of dose planning in the brachytherapy procedure or any other therapy approach.

The invention discloses a meteorological threat assessment method based on a discrete dynamic Bayesian network. The method comprises the following steps: collecting an observed weather type, intensity information and UAV (Unmanned Aerial Vehicle) position and attitude information; performing a quantization treatment according to a divided quantization level, and establishing an observation evidence list; using expert knowledge or experience to establish a conditional probability transfer matrix between states, and determining a state transfer matrix between time slices; establishing a discrete dynamic Bayesian network model between a meteorological threat level, a meteorological factor and the UAV; and using a Hidden Markov Model reasoning algorithm to calculate the final meteorological threat level. The meteorological threat assessment method based on the discrete dynamic Bayesian network provided by the invention realizes the organic combination of a continuous observation value and the discrete dynamic Bayesian network, and reasons out the probability distribution of a meteorological threat degree in combination with the HMM (Hidden Markov Model) reasoning algorithm, so that the effectiveness, the practicability and the accuracy of meteorological assessment can be greatly improved.

A golf ball including a plurality of continuous dynamic focus patterns between 0.5 and 8 millimeters wide on the exterior surface of the golf ball, such that each continuous dynamic focus patterns circumscribes the golf ball and the center of each said continuous dynamic focus pattern coincides with the center of said golf ball. A golf ball including at least four discrete dynamic focus patterns between 0.5 and 8 millimeters across on the exterior surface of the golf ball. A golf ball including both a plurality of continuous dynamic focus patterns and at least four discrete dynamic focus patterns on the exterior surface of the golf ball.

The invention relates to a motion curve discrete dynamic planning method, which relates to the technical field of motion control. The motion curve discrete dynamic planning method is based on a discrete mathematical model of object motion, adopts a dynamic forwarding algorithm, calculates a speed value satisfying maximum deceleration of a subsequent position distance at first according to currentmotion information (position, speed), then calculates acceleration forwardly in an inverse mode, planning a curve by utilizing accelerated motion, decelerated motion and constant velocity motion, realizes trapezoidal accelerated and decelerated motion of an object, can ensure that a number of planned routes is an integral multiple of a discrete period and a speed is zero when a position destination is reached, and can plan the curve in real time according to varied target positions, so as to improve the motion control precision and efficiency.

The invention discloses an IP network congestion link positioning method. In the method, aiming at dynamic characteristics of IP network routing, a variable structure discrete dynamic Bayesian network model is established according to detection results of path performance and a topology structure obtained by multiple end-to-end detection, and is simplified according to first-order Markovian hypothesis and homogeneity hypothesis, and an IP network congestion link is deduced based on the simplified model, so that the deduction of the IP network congestion link under dynamic routing is realized. By performing imitation experiments and simulation experiments on different types and scales of IP networks and using a traditional CLINK algorithm and the method provided by the invention to deduce the congestion link in the same scene respectively, the experiment proves that compared with the CLINK algorithm, the method provided by the invention is higher in deduction accuracy and robustness.

The invention discloses a PIO-based round orbital space vehicle formation reconfiguration type anti-collision path planning method, and belongs to the technical field of space vehicle attitude control. According to the algorithm, firstly, a spaceflight short-distance relative motion linear C-W equation is used for dynamics modeling; then, a space vehicle formation discrete dynamic equation is usedfor calculating the total fuel consumption of the space vehicle formation; an infinite series method is used for calculating the collision probability between the space vehicles; a corresponding fitness function is built through an exterior penalty function method; then, a self-adaptive geomagnetic factor coefficient is introduced; the original global search and local search capability of the deterrent algorithm is balanced; the flight optimization path considering the fuel consumption and avoiding collision is finally obtained. The algorithm is simple; the formation fuel consumption and collision avoidance factors between the space vehicles are considered under the condition of given initial and final space vehicle formation; the optimization of the space vehicle formation flight path isrealized.

A topological categorization method, based on inclusive intervals, provides a general method of analyzing escape topologies for discrete dynamic systems, in complex and higher dimensions, including the calculation of both potential for complex and hypercomplex and field lines for complex iterations.

The invention discloses a junction temperature calibration method based on a mathematical filtering algorithm, and the junction temperature calibration method belongs to the field of electronic device tests. The traditional semiconductor device junction temperature measurement methods include an electrical method, an infrared method and the like. Due to the existence of theoretical errors and noise in the measurement process, the traditional methods cannot measure junction temperature of a semiconductor device accurately. The junction temperature calibration method regards a semiconductor device as a temporally discrete dynamic system with single input and double outputs, wherein the input is a thermal power matrix x<k-1><+> at a previous moment, and the double outputs are respectively junction temperature T<k><+> and a thermal power matrix x<k><+> at the moment. The junction temperature of the semiconductor device is effectively calibrated in real time through continuous recursion operation. The junction temperature calibration method is an algorithm for carrying out optimal estimation of system states by utilizing a linear system state equation and observing system outputs through system input. The algorithm can estimate states of the dynamic system from a series of data with noise under the condition that measured variance is known, and obtains data closer to a true value.

The invention provides an acceleration estimation method. The method comprises the following steps of converting motion of an object into a SISO linear discrete dynamic system so as to acquire a system mathematics model of the object; carrying out recursive least squares processing on the system mathematics model of the object so as to acquire a recursive least squares model; according to a sampling period, collecting a speed of the object so as to acquire a speed variation of the object in the sampling period; according to the sampling period and the speed variation, processing the recursive least squares model so as to acquire a real-time estimation value of a system parameter; and according to the real-time estimation value of the system parameter, outputting a real-time estimation value of an accelerated speed of the object. The invention also provides an acceleration estimation apparatus, a locomotive motion control method and a locomotive. By using the acceleration estimation method and the apparatus thereof, and the locomotive motion control method and the locomotive provided in embodiments of the invention, through estimation of the system parameter, the real-time estimation value of the accelerated speed is acquired and can be used to guarantee continuous approximation to a real acceleration signal; and real-time performance is high and the methods, the apparatus and the locomotive can directly take part in control.

The invention discloses a hydropower station set load distribution method for a power grid multi-stage power transmission section. The hydropower station set load distribution method comprises the following steps of dividing sets with different hydropower station fed-in grid output limits into different virtual hydropower stations, according to discrete dynamic characteristics of the sets of the virtual hydropower stations, obtaining an economic operation summary table based on different working heads and unit combinations of each virtual hydropower station; using a bicubic B-spline interpolation method to construct a dynamic characteristic three-dimensional surface of each virtual hydropower station; by taking the minimum water consumption rate of the hydropower station as a goal, establishing a real-time load distribution model of the virtual hydropower station; via using a differential evolution algorithm in combination with the dynamic characteristic three-dimensional surface, solving the real-time load distribution model to obtain a load distributed by each virtual hydropower station; and obtaining optimal load distribution and flow consumption of each set of the virtual hydropower station based on the load and the economic operation summary table. The hydropower station set load distribution method is advantageous in that, the load distribution process of a hydropower station is changed from a discrete domain to a continuous domain, calculating precision is improved, and real-time load distribution of the hydropower station sets for the multi-stage power transmission section is achieved.

An acoustic imaging apparatus and method that achieves desired delays with coded signals. Linear, curved linear and sector scanning is provided in 1-D arrays and planar, curved planar and sector scanning is provided in 2-D arrays. Composite and non-linear implementations are presented. Dynamic and discrete dynamic focusing is disclosed for the relevant arrays. The 2-D array makes possible 3-D imaging.

The invention discloses a compressed sensing mine target positioning method based on discrete dynamic mesh selection for mainly the problem of reducing the influence of electromagnetic interference onthe positioning accuracy in a mine environment, so that the positioning accuracy is significantly improved. The method comprises the following steps: in an offline state, sampling signal strength indiscrete regions to obtain a feature fingerprint database; and transmitting signals in an online state, performing signal strength sampling on the target at a reference location to obtain a measurement value vector, in a first positioning phase, constructing a measurement matrix via a reference node location, importing the measurement matrix into the fingerprint database from a server, constructing a sparse base, and performing dynamic selection on divided grid areas via a reconstruction algorithm under the condition that the measurement matrix and the sparse base met irrelevant conditions toobtain several optimal grid locations, in a second positioning phase, obtaining corresponding coordinates of the several grid areas obtained based on compressed sensing positioning via the fingerprintdatabase, selecting a corresponding weighting function, and accurately calculating a final location of the target by using a grid centroid method.

The invention provides a method for acquiring the dynamic traffic volume of the whole road sections of a road network. The method describes a dynamic traffic network based on a cell transmission model, and uses a path time-varying traffic volume as a combined link to establish, based on the variational inequality theory, a discrete dynamic O-D inversion model and a dynamic traffic assignment combination model that satisfies the dynamically-predicted user optimal constraint, so as to calculate the time-varying dynamic full O-D demand and the whole road network dynamic traffic volume according to the real-time traffic volume of some road sections of the road network. The method balances the dynamic total deviation of the dynamic O-D inversion model and the path travel time of the dynamic traffic assignment model, realizes the comprehensive optimization of the two models, eliminates the solution of an intermediate variable, reduces a calculation error, and improves calculation efficiencyand precision.

A control unit has at least one control element, in particular with at least one integrating and/or differentiating transfer characteristic which is constructed as a temporally discrete dynamic fuzzy logic control element. A temporally discrete dynamic fuzzy logic control element of this kind is for example a so-called fuzzy logic automaton, which has processing states. It is advantageous that the control unit of the invention can be constructed with control elements that each have systematically dynamic fuzzy logic properties, and in which nonlinearities can be introduced in a targeted way for a desired control performance.

The invention discloses a DET and RELAP5 coupled dynamic characteristic analysis method based on a universal auxiliary variable method, which comprises the following steps of: constructing a DET simulation model of a discrete dynamic event tree, and modifying an input file TRIP card in a manner of increasing universal auxiliary TRIP variables according to a DET simulation object state transition type; the universal auxiliary TRIP variable is a variable type or a logic type; the simulation time step length is controlled by setting the simulation time of the RELAP5 program, and an output result file of each simulation step length of the RELAP5 program is analyzed; According to the DET simulation object state transition type, backtracking and executing the RELAP5 program. The method has the advantages that the setting process and method of the DET state transition object TRIP in the RELAP5 program input file needed by DET and RELAP5 program coupling are simplified, the modeling complexity is reduced, and the modeling efficiency is improved.

The invention discloses a Petri network based method for stabilizing load side voltage under network attack, which includes steps of step S1, confirming a continuous state variable and a discrete state variable of a description system according to a dynamic behavior and a mixing behavior of the load change in an electric system; step 2, applying the petri network to perform mathematical description on the discrete dynamic behavior of a loaded voltage-regulating transformer, and building the petri network model for describing the discrete dynamic behavior; step 3, performing mathematical description on the continuous behavior of the load change, describing the continuous state by the differential equation and describing the restriction condition by a transition condition; step 4, model simulation and analysis. The method proves that the loaded transformer plays a positive role in voltage regulation in the power distribution system and is also benefit to solve the big disturbance of themixing system.

The invention discloses a fuzzy gain scheduling prediction control-based control method for a boiler-turbine coordination system. The control method is an intelligent control method organically combining a nonlinear modeling method, a fuzzy logic reasoning theory and a predictive control technology. Firstly, a multivariable nonlinear discrete dynamic model of the local working condition of the turbine-boiler coordination system is established based on a state space nonlinear modeling method; state variables reflecting dynamic characteristics of the turbine-boiler coordination system are selected as fuzzy scheduling variables, a global multivariable nonlinear discrete fuzzy model of the turbine-boiler coordination system is reasoned according to fuzzy logic; a predictive control technologyis applied on the basis of the model to predict the multi-step output of the model, and control action constraints are considered, and a rolling optimization strategy is implemented. According to themethod, the load can be rapidly tracked in a full working condition range, the constraint optimization problem of a control effect can be solved, the good robustness is achieved while the global optimum of a control system is ensured. The method has important significance in operation optimization of a thermal power generating unit for deep peak regulation.

The invention discloses a DDET and RELAP5 coupled dynamic characteristic analysis method based on an auxiliary variable method. The method comprises the following steps: constructing a DET simulation model of a discrete dynamic event tree, and modifying a TRIP card of an RELAP5 program input file according to a DET simulation object state transition type in a manner of adding auxiliary TRIP variables; the simulation time of the RELAP5 program is set, the step length of the simulation time of the RELAP5 program is controlled, and an output result file of each simulation step length of the RELAP5 program is analyzed; According to the DET simulation object state transition type, backtracking and executing the RELAP5 program. The method has the advantages that the setting process and method of the DET state transition object TRIP in the RELAP5 program input file needed by DET and RELAP5 program coupling are simplified, the modeling complexity is reduced, and the analysis efficiency is improved.

The invention discloses a data classification method and device based on a discrete dynamic mechanism, and the device is based on the data classification method based on the discrete dynamic mechanism, and the method comprises the steps: receiving a data sample set, and carrying out the data preprocessing; constructing a dynamic neural network model, wherein each layer of the dynamic neural network model comprises a plurality of dynamic modules established by neurons, the dynamic module in each layer is connected with a certain dynamic module in the next layer, and a full connection network isformed between features extracted by the dynamic module in the last layer and data types; Using the standardized data sample set to train a dynamic neural network model to obtain a data classification model; And receiving to-be-classified data, and carrying out data classification according to the data classification model.

The invention discloses a model-free self-adaptive prediction control method for a propulsion motor based on a neural network observer. The model-free self-adaptive prediction control method is used for solving the problems of unmodeled dynamics and load disturbance of a pod propulsion motor of a semi-submerged ship under complex sea conditions. The method comprises the following steps: establishing a pod propulsion motor discrete dynamics model; according to the discrete dynamics model, establishing a dynamic linearization data model and a multi-step prediction equation of the system; estimating a nonlinear disturbance term by using a neural network observer, and feeding back an estimation result to a control law for disturbance compensation; and determining a control scheme according to the propulsion motor model-free adaptive prediction control method based on the neural network observer. The control performance and robustness of the pod propulsion motor control system are effectively improved by introducing the input information at the future moment.