116 results about "Deterministic system" patented technology

Collision detection and estimation from a monocular visual sensor is an important enabling technology for safe navigation of small or micro air vehicles in near earth flight. In this paper, we introduce a new approach called expansion segmentation, which simultaneously detects “collision danger regions” of significant positive divergence in inertial aided video, and estimates maximum likelihood time to collision (TTC) in a correspondenceless framework within the danger regions. This approach was motivated from a literature review which showed that existing approaches make strong assumptions about scene structure or camera motion, or pose collision detection without determining obstacle boundaries, both of which limit the operational envelope of a deployable system. Expansion segmentation is based on a new formulation of 6-DOF inertial aided TTC estimation, and a new derivation of a first order TTC uncertainty model due to subpixel quantization error and epipolar geometry uncertainty. Proof of concept results are shown in a custom designed urban flight simulator and on operational flight data from a small air vehicle.

An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.

The invention discloses a simulation method of texture evolvement of a heat affected zone in a welding process, relating to a fine texture simulation method of a welding connector and aiming at solving the problems that the quantification of the present fine texture evolvement of a weld joint is mainly based on an experiential or half experiential deterministic model or analysis calculation, and only the texture content is calculated but the state, the size and the distribution of the texture cannot be reflected dynamically. The method comprises the following steps: step 1, calculating the temperature fields of the heat affected zone; step 2, calculating the crystalline grain growth process of crystalline grains at different positions above a beta phase zone according to the distribution of the temperature fields; and step 3, calculating continuous cooling solid state phase change according to the cooling velocity and the beta phase crystalline grain distribution calculation result obtained through the temperature field calculation. The simulation method is used for the fine texture simulation of the welding connector.

The invention discloses a robust environment economic scheduling method considering multi-microgrid energy interaction. The method comprises the following steps: in the background of supporting the rapid development of an active distribution network by microgrids, the energy interaction among multiple microgrids is thoroughly considered; a renewable energy generation model and a cost model in themicrogrid are built; an environment economic scheduling model considering multi-microgrid energy interaction is built; the uncertainty of renewable energy and load is considered, a robust environmenteconomic scheduling model considering multi-microgrid energy interaction is built, a Latin hypercube method is adopted for sampling, and the robust environment economic scheduling model is converted to a robust certainty model; and a multi-objective chemotaxis algorithm is adopted to solve the above robust certainty model, and the Pareto optimal solution is found out. The uncertainty of renewableenergy and load prediction is thoroughly considered, the energy interaction among multiple microgrids is considered, the calculation result is closer to the actual situation, the rationality is strong, and reliable basis is provided for the economic operation of the power system.

A method for processing a sound signal y in which redundancy, consisting mainly of almost repetitions of signal profiles, is detected and correlations between the signal profiles are determined within segments of the sound signal. Correlated signal components are allocated to a power component and uncorrelated signal components to a noise component of the sound signal. The correlations between the signal profiles are determined by methods of nonlinear noise reduction in deterministic systems in reconstructed vector spaces based on the time domain.

The invention discloses an active distribution network robust active and reactive power coordination optimization method based on time series scene analysis, which comprises the following steps: establishing an active distribution network active and reactive power coordination optimization deterministic model; The uncertain factors are analyzed and the scene is generated, and then the clustering method is used to cluster the similar scenes. A two-stage robust optimization model is established, and the original problem is transformed into a single objective function model which only contains the main problem. A two-stage robust optimization model is used to control the active distribution network. The invention considers the uncertain factors caused by the output power of the distributed generation and the load fluctuation in the active distribution network, characterizes the system uncertainties through an uncertain set mode, and improves the stability and reliability of the system operation. Through ARMA model combined with K-Means clustering technology reduces the computational complexity of the problem by reducing the size of the scene.

The invention provides a power resource management method for opportunistic array radar multi-target tracking in a clutter environment. The power resource management method comprises the steps of measuring measurement origin uncertainty caused by clutters by means of an information reduction factor, expressing RCS of targets by using random variables, establishing a random chance-constraint processing model, relaxing the random chance-constraint processing model by adopting a conditional value at risk, converting the random chance-constraint processing model into a certainty model which is easy to solve, further solving the certainty model by adopting a Lagrangian multiplier method, regarding a Bayesian Cramer Rao Lower Bound as a measurement standard for power distribution so as to solvethe optimal power distribution at a next moment, and adopting a probability data association filter for tracking the targets in the clutter environment. The power resource management method considersthe target tracking clutter environment, adopts the information reduction factor for measuring magnitude of clutter influence, further considers a target information uncertainty caused by the target RCS, introduces the random chance-constraint processing model, comprehensively considers the relationship between resources and tracking precision, and makes the model closer to reality.

A system includes a deterministic system, and a controller electrically coupled to the deterministic system via a link, wherein the controller comprises a transaction scheduling mechanism that allows data responses from the deterministic system, corresponding to requests issued from the controller, to be returned out of order.

A system for time-stamping a data packet associated with a data network allows software applications, running on non-deterministic systems, to determine the precise time that packets were transmitted to or received from a physical network interface. The system includes a network media interface device for transferring a data packet to or from a network medium, and a time source for generating and maintaining a time code. The system further includes a time-stamping circuit for sampling a time code from the time source when the interface device transfers the data packet to or from the network medium, and for associating the time code with the receive data packet so as to form a time-stamped packet.

The invention provides an engineering structure optimization design method used in an uncertain environment, and relates to the technical field of engineering structure reliability optimization. According to the method, an uncertain engineering structure optimization design problem is defined first, an engineering structure and uncertain information in the use environment are described as random variables, interval variables or combinations of the random variables and the interval variables according to existing samples, corresponding optimization models are established according to different types of uncertain variables, reliability indexes in the optimization models are calculated on the basis of the point collocation random/interval propagation analysis method, and finally different optimization solvers are selected according to actual problems for outer layer optimization. Reliability indexes of an inner layer failure function are obtained through efficient uncertain propagation analysis, and formation of the nested optimization problem is avoided; hybrid uncertainty models including random parameters and interval parameters in the same problem are considered, and the method has a practical engineering significance in interval parameter optimization problem under random process stimulation.

Flight management systems and control methods for meeting a required time of arrival (RTA) with reduced fuel burn. The method can account for probabilistic wind forecast uncertainty in RTA calculations by reformulating the speed and thrust profile problem as a multi-stage stochastic program, using a wind forecast uncertainty model to generate scenario sets for the fuel optimization problem. The method can iteratively calculate a fuel-efficient advised air speed for achieving an RTA over a flight path with an arbitrary number of recourse points.

Methods for creating wind forecast uncertainty models applicable to a variety of routes through a given airspace, and for use with the flight management systems and control methods. An example wind forecast uncertainty model can be position-specific, data-driven and based on a Markov chain representing error values between historical wind speed data and forecasted wind speed data long a planned flight route or between an origin-destination pair.

The invention provides a method for establishing a wind-driven generator power output random model capable of reflecting wind speed variation characteristics. The method comprises the following steps of establishing a wind speed model in consideration of the wind speed variation characteristics; establishing a wind turbine generator output power model; establishing a wind power plant output power model. According to the method, the wind-driven generator power output random model is established based on a sequential hour determinacy model, a semi-invariant method and a Cornish-Fisher stage number, and can reflect the regularity of power output of a wind-driven generator along with seasonal variations and day and night alternation as well as the fluctuations and the indeterminacy of the power output of the wind-driven generator under the influence of factors such as weather, so that the power output of the wind-driven generator is more consistent with an actual condition.

The invention discloses an indoor pseudo-satellite signal multipath microscopic parameter analysis system based on a statistical model. A software system comprises an indoor structured scene modelingmodule, which is used for carrying out three-dimensional scene modeling with an office area as a target research environment, and is provided with a pseudo-satellite launcher and a pseudo-satellite data collection point; a multipath signal tracking module for carrying out multi-path signal tracking by adopting an image method; a multipath signal extraction module used for extracting multipath signals based on time and power judgment; and a data derivation and statistics analysis module used for matching a deterministic model obtained by inductive statistics with a theoretical model, and verifying the correctness of a multipath microcosmic parameter theoretical model suitable for a pseudo-satellite signal system. Based on the system disclosed by the invention, channel characteristics of pseudo-satellite multipath of a multipath delay extended model, a multipath power delay model and the like in indoor environments can be revealed, and the design of a pseudo-satellite receiver and a simulator is facilitated.

The invention relates to an energy-storage scheduling method and device for an intelligent power grid, and the method comprises the steps: obtaining a power generator uncertainty model, a load uncertainty model and an electric car charging uncertainty model in the intelligent power grid, wherein the intelligent power grid comprises a wind power generator, an energy storage apparatus, and an electric car charging station; carrying out the stochastic load flow calculation of the intelligent power grid through employing a two-point estimation method based on a fourth-order Gram-Charlier series expansion equation, carrying out the random sampling of a stochastic load flow calculation result, and obtaining an expected load flow distribution; determining a constraint condition according to the expected load flow distribution, solving a pre-built target function through employing a particle swarm optimization algorithm based on a segmented inertia decreasing weight, and obtaining an optimal energy storage scheme meeting the constraint condition; and carrying out the scheduling of the energy storage device according to the optimal energy storage scheme. The method can effectively inhibit the uncertainty of the intelligent power grid, and enables the intelligent power grid to operate safely and stably.

The invention discloses a second-order cone programming method for power-off grid load recovery with consideration of recovery quantity uncertainty. On the basis of improvement of a load recovery robustness optimization model with consideration of uncertainty, a robust model with a mixed integer second-order cone mode is established and solving is carried out to obtain a load recovery robust plan. The method comprises: 1, establishing a power-off grid load recovery certainty model; 2, on the basis of an information gap decision-making theory, establishing a robustness optimization model with consideration of load recovery uncertainty; 3, with a second-order cone relaxation method, carrying out convex optimization relaxation processing on a non-linear power flow equation in the optimization model; 4, carrying out linear processing on the rest of nonlinear constraints; and 5, on the basis of a direct-current power flow model, invoking CPLEX at two stages to solve the optimization model, thereby obtaining a load recovery plane. With the method disclosed by the invention, a load recovery plan can be obtained rapidly and accurately; load fluctuation within a certain range can be carried; and security of the grid recovery process can be guaranteed. The method has the certain theoretical value and engineering value.

The invention relates to a static voltage stability risk evaluation method of a power grid and particularly relates to a static voltage stability risk evaluation method of the power grid based on load uncertainty modeling, aiming at solving the problem of an existing static voltage stability risk evaluation method that an evaluation result is inaccurate. The static voltage stability risk evaluation method of the power grid based on the load uncertainty modeling is realized by the following steps: (1) establishing a load uncertainty model; (2) establishing a trend operation model; and (3) establishing a risk evaluation index, and recognizing a high-operation-risk region in the power grid by applying the risk evaluation index. The static voltage stability risk evaluation method of the power grid based on the load uncertainty modeling is applicable to static voltage stability risk evaluation of the power grid.

The invention discloses a power grid load recovery robustness optimization method based on an information gap decision theory. A determinacy load recovery optimization model is converted into a robustness optimization model, and an artificial bee colony algorithm is adopted for carrying out solving, so a load recovery scheme related to indeterminacy is acquired. The method comprises steps of 1, establishing a determinacy load recovery optimization model in a power grid recovery process; 2, adopting the artificial bee colony algorithm to solve a determinacy model to obtain an optimal solution of the determinacy load recovery optimization model; 3, according to an optimal solution of an original model, determining the acceptable smallest recovery quantity of the load recovery; 4, based on the information gap decision theory, establishing a robustness optimization model considering indeterminacy of the load recovery; and 5, adopting the information gap decision theory to solve the robustness model so as to obtain a load recovery scheme capable of achieving an expected recovery target. According to the invention, load fluctuations in a certain range can be borne by the obtained load recovery scheme; safety of a power grid recovery process can be ensured; and the method has certain theoretical and engineering value.

The invention discloses an adaptive fault-tolerant attitude control method in an initial state stage of a satellite. The method comprises steps: in an initial state control stage after successful satellite and rocket separation, a satellite rotational inertia uncertainty model during a solar panel unfolding process is built; a satellite attitude dynamic model is built; a preset performance function is used to build an attitude nonlinear mapping model; and based on the built satellite rotational inertia uncertainty model during the solar panel unfolding process, the satellite attitude dynamic model and the attitude nonlinear mapping model, a backstepping method is used to design an adaptive fault-tolerant controller. Through the method, the rotational inertia uncertainty of the satellite inthe initial state control stage and the attitude stability problem in the case of actuator failure and under effects of an external disturbance torque can be solved, the fault-tolerant ability and the robustness of the attitude control system are ensured, and the attitude convergence speed, overshoot and convergence errors are ensured to meet the preset requirements.

The invention relates to a transmission system planning method using distributionlly robust optimization, and provides a transmission system planning method using distributionlly robust optimization. A transmission network planning scheme which meets the requirements of safe operation of a transmission system under any possible probability distribution realization scenario of wind power and minimizes the investment cost is selected. According to the technical scheme of the invention, the method comprises the following steps: (1) a distributionlly robust chance-constrained optimization model of transmission system planning is established; (2) random variables in the distributionlly robust chance-constrained optimization model are eliminated by making use of the complementary features of S-lemma and the matrix Schur, and the distributionlly robust chance-constrained optimization model is converted into a deterministic model containing a matrix inequality; and (3) the model obtained in step (2) is solved by a genetic algorithm based on linear matrix inequality optimization, and the final transmission system planning scheme is obtained according to the requirements of power system operation. The transmission system planning method using distributionlly robust optimization of the invention is mainly applicable to power grid planning and construction.

The invention relates to an optimal configuration method for a micro grid, which comprises the following steps: S1, acquiring the topological structure, basic parameters and power generation data of amicro grid; S2, constructing the actual unbalanced power between the output of renewable energy and the load; S3, establishing a water/wind/diesel micro grid double-layer optimal configuration model,wherein the model includes inner and outer optimization models, the outer layer is a multi-objective optimized micro power configuration model, and the inner layer is a micro grid operation model considering uncertainty; S4, transforming a constraint with random variables into a deterministic model; S5, combining an elitist strategy-based non-dominated sorting genetic algorithm and a mixed integer linear programming (ILP) algorithm to solve the double-layer optimal configuration model; and S6, selecting a most satisfactory configuration scheme from a Pareto solution set by using a TOPSIS method. The method of the invention not only improves the robustness of the micro grid, but also ensures the reliability and economy of the configuration scheme and provides technical support for the micro grid planning work.

A variety of adaptable electronic duobinary generating filters to be used in communication systems are provided, each filter generating an adaptable electronic duobinay signal which is optimized for system impairments. According to one exemplary implementation, an adaptable electronic duobinary generating filter comprises an adaptable delay-and-add circuit, having an adaptable electronic delay element having a delay αT: 1/T being the bit rate of the binary data input into the adaptable delay-and-add circuit, and a being an adaptation parameter which can be optimized depending on the system impairments. In one optional implementation, the adaptable electronic delay element can be programmably adaptable to optimize against deterministic system impairments. In another optional implementation, the adaptable electronic delay element can be dynamically adaptable to optimize against dynamically varying system impairments. Additionally, in one embodiment, an adaptable electronic duobinary drive circuitry based on the adaptable electronic duobinary generating filter can drive an adaptable optical duobinary transmitter in a fiber-optic communication system to produce an adaptable optical duobinary signal, where the adaptation parameter α is optimized to mitigate certain deleterious fiber-optic transmission system impairments, such as distortions due to narrow optical filtering. Corresponding optical duobinary systems and methods are provided. Similarly, the adaptable electronic duobinary generating filter can be used to form an adaptable electronic duobinary transmitter for an electronic duobinary communication system, to optimize the electronic duobinary signal generated.

The invention relates to a motion segmentation method based on deterministic fitting. According to the method, a deterministic model fitting method is introduced to obtain a stable and reliable motionsegmentation result. The motion segmentation method mainly comprises the following steps: S1, preparing a data set; s2, performing super-pixel segmentation on each frame of the input video; s3, performing model fitting on every two continuous frames in the video; s4, calculating the similarity between the feature point matching pairs according to the obtained residual error information; s5, accumulating the similarity matrix to generate an affine matrix; and S6, sparse clustering is carried out according to the affine matrix to obtain a segmentation result, and motion segmentation is completed. According to the method, the overall segmentation error rate is effectively reduced; in addition, a reliable and stable segmentation result can be obtained, and scientific development and progressare promoted.

Fixing a problem is usually greatly aided if the problem is reproducible. To ensure reproducibility of a multiprocessor system, the following aspects are proposed: a deterministic system start state, a single system clock, phase alignment of clocks in the system, system-wide synchronization events, reproducible execution of system components, deterministic chip interfaces, zero-impact communication with the system, precise stop of the system and a scan of the system state.

The invention relates to a low frequency oscillation identification method for a power system based on an O3KID algorithm. The method embeds an observer in the stochastic model of the power system, and uses the basic equation of the O3KID algorithm and the least squares method to estimate the Markov parameters and residuals of the observer, and transforms the stochastic system identification of the power system into an identification problem of the deterministic system. The introduced device is equivalent to the Kalman filter. The Hankel matrixes are respectively constructed by using the output of the observer and the residual time series. The orthogonal projection and singular value decomposition methods of the deterministic system are used to effectively identify the reduced-order modelof the power system, and accurately extract the frequency, damping ratio and vibration mode parameter information of the dominant mode of the low-frequency oscillation. The method provided by the invention is suitable for the low-frequency oscillation mode analysis of the power system for the WAMS synchronous measurement environment excitation signal and the transient ring-down signal, and the IEEE-39 node system simulation and the US Eastern Power Grid WAMS measured data analysis verify the effectiveness of the method.