56 results about "Random systems" patented technology

A plurality of resources, typically service operatives, are allocated to a plurality of tasks by a method in which initial information relating to the tasks to be allocated and the resources available to perform the tasks is provided. An initial series of schedules is first generated allocating resources to the tasks, and then modifying the individual schedule of at least one resource in response to updated information. Changes to individual schedules may be made in response to such updated information independently of the schedule generation. The initial, series of schedules may be generated in a two-stage process in which a rule-based system allocates tasks selected as being difficult to allocate (e.g., because they are linked to other tasks). then a stochastic (non-systematic) search system compiles the rest of the schedule. Periodically, the stochastic system may be interrupted to allow a further rule-based system to analyze the schedules created thus far, and fix the best ones in the schedule, so that the stochastic system can then concentrate on improving the remaining schedules. In order to allow the system to handle rapid changes in the requirements for tasks and the resources, on a scale faster than the time required to generate the schedules, a schedule modification system is arranged to make changes in the short term in between schedule updates delivered by the schedule generation system.

A device for measuring a mechanical property of a tissue includes a probe configured to perturb the tissue with movement relative to a surface of the tissue, an actuator coupled to the probe to move the probe, a detector configured to measure a response of the tissue to the perturbation, and a controller coupled to the actuator and the detector. The controller drives the actuator using a stochastic sequence and determines the mechanical property of the tissue using the measured response received from the detector. The probe can be coupled to the tissue surface. The device can include a reference surface configured to contact the tissue surface. The probe may include a set of interchangeable heads, the set including a head for lateral movement of the probe and a head for perpendicular movement of the probe. The perturbation can include extension of the tissue with the probe or sliding the probe across the tissue surface and may also include indentation of the tissue with the probe. In some embodiments, the actuator includes a Lorentz force linear actuator. The mechanical property may be determined using non-linear stochastic system identification. The mechanical property may be indicative of, for example, tissue compliance and tissue elasticity. The device can further include a handle for manual application of the probe to the surface of the tissue and may include an accelerometer detecting an orientation of the probe. The device can be used to test skin tissue of an animal, plant tissue, such as fruit and vegetables, or any other biological tissue.

A method and apparatus for improved modeling of random or stochastic systems such as financial markets and instruments. The Black-Scholes model for the price of a risky asset is replaced by an improved model which uses a fractal activity time process instead of a conventional time parameter. The new model is incorporated into an algorithm for analyzing empirical data in order to determine whether an observed random process exhibits multifractal or single fractal behavior. If the algorithm finds multifractal or single fractal behavior, the new model can be used, thereby providing improved accuracy over the Black-Scholes model. The algorithm can be implemented as an interactive procedure using a standard computer hardware and software platform operating under the control of suitable software for implementing the computing steps of the disclosed algorithm.

The invention discloses a Lie group filtering-based initial alignment method for strapdown inertial navigation. A strapdown algorithm is described by adopting Lie group and Lie algebra, so that the problem of singular value and a normalized error in a quaternion solving process can be effectively avoided. A new system model is established by integral computation and geographical position information of inertial elements. Recursive estimation is directly performed on an SO(3) group by adopting a Lie group filter, so that the problem of nonlinearity of a quaternion model can be effectively solved, and rapid and accurate initial alignment can be realized. An attitude change of a carrier under swaying interference is reflected in real time, so that the initial alignment can still be rapidly and accurately realized independently of an error model of a system under a swaying condition, and the initial alignment under a swaying pedestal can be finished without fine alignment on the basis of rough alignment. The method is simple in computation and high in adaptability, can be used for a complex random system, and has broad application prospect in practical engineering, the alignment accuracy is ensured, and meanwhile, the alignment speed is increased.

The invention discloses a method for evaluating the control performance of a PID (Proportion Integration Differentiation) loop based on subspace identification. The method comprises the following steps of: effectively segmenting a controller output signal u and a process output signal y according to the loop opening and closing states of a PID loop controller and the upper and lower limit constraints of the controller output signal u; solving a random system model by using a subspace identification algorithm; calculating a moment performance index and a performance index sequence at the reach of steady-state time according to the sampling time of the controller output signal u and the process output signal y, set PID loop steady-state time and desired time; and detecting oscillation in the loop according to a model obtained by using space identification and a self-correlation function of the process output signal y. The random system model is established by using the subspace identification, so that setting of a system order is avoided. By adopting the method, loop related information including 95 percent of steady-state time, 95 percent of dynamic time, overshoot and a peak moment is given by making full use of the model obtained by the identification. The performance index is calculated by using reference time set by a user, and is accordant with the practical situation of the PID loop.

An apparatus and method for estimation of a system having state variables representing the state of the system comprising predicting the estimate of the state variables along with the uncertainties in the state variables; observing a measurement of at least one state variable corrupted with some amount of error; updating the estimates of the state variables using a weighted average, with more weight being given to estimates with higher certainty; and providing an estimator input to update the estimates of the state variables, the estimator input operating to provide asymptotic convergence of the mean estimation error in all of the state variables in the presence of persistent excitation or disturbance that is not asymptotically decaying to zero.

A device for measuring a mechanical property of a tissue includes a probe configured to perturb the tissue with movement relative to a surface of the tissue, an actuator coupled to the probe to move the probe, a detector configured to measure a response of the tissue to the perturbation, and a controller coupled to the actuator and the detector. The controller drives the actuator using a stochastic sequence and determines the mechanical property of the tissue using the measured response received from the detector. The probe can be coupled to the tissue surface. The device can include a reference surface configured to contact the tissue surface. The probe may include a set of interchangeable heads, the set including a head for lateral movement of the probe and a head for perpendicular movement of the probe. The perturbation can include extension of the tissue with the probe or sliding the probe across the tissue surface and may also include indentation of the tissue with the probe. In some embodiments, the actuator includes a Lorentz force linear actuator. The mechanical property may be determined using non-linear stochastic system identification. The mechanical property may be indicative of, for example, tissue compliance and tissue elasticity. The device can further include a handle for manual application of the probe to the surface of the tissue and may include an accelerometer detecting an orientation of the probe. The device can be used to test skin tissue of an animal, plant tissue, such as fruit and vegetables, or any other biological tissue.

The invention relates to a particle filter resampling method which is applied to a nonlinear stochastic system state. The invention relates to a filtering method, in particular to a particle filter resampling method for estimating the nonlinear stochastic system state. The invention aims at solving the problem that the filtering precision of the state is poorer as the observation and likelihood function of the system state has double-humped characteristic when the nonlinear stochastic system state is filtered. In the resampling method, the similarity degree of the observation vector of the particles and the observation vector of the system state is utilized, so as to regulate the weight of the particles that are involved in the resampling. The invention is applied to the state filtering problem of the common nonlinear stochastic system, when the observation and likelihood function of the system state has the double-humped characteristic, the method can improve the filtering precision of a particle filter to the nonlinear stochastic system state.

The invention relates to a multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis. Sample data of a variety of sensors in one device is standardized, and optimally classified through fuzzy clustering. Then, classification information is fused based on a D-S evidence theory to get a credibility value describing the state of the device. Thus, a novel fault diagnosis method is obtained. The D-S evidence theory, a fuzzy algorithm and a clustering analysis method are combined efficiently and reasonably, and the advantages thereof are integrated. For a complex power plant operation system, the diagnosis result is more accurate and efficient. The algorithms are highly coherent and correlated. Comprehensive diagnosis based on multi-sensor data is faster, and the result obtained is more accurate. The method, which is of strong applicability, is applicable to all kinds of complex, coupling and random systems, and can also be used in thermal, nuclear and other power plant systems.

The invention discloses a networked control method for a nonlinear two-time-scale system (NTTSS) with random packet loss, and belongs to the technical field of networked control of a complex system. By fusing fuzzy logic, a singular perturbation technique and a random system theory, the method comprehensively considers the nonlinear two-time-scale coexistence characteristic of a controlled system and the random packet loss problem caused by network communication under a unified model framework, a discrete fuzzy singular perturbation model of the networked NTTSS is established, and a random fuzzy state feedback robust control method is disclosed. The network control method has the advantages that the problem that system unstability or steady-state errors caused by a fast variable of the complex two-time-scale system cannot be eliminated in an existing network control method is solved, and the networked control performance of NTTSSs is greatly improved.

The invention belongs to the technical field of hypersonic gliding aircraft reentry trajectory planning guidance, and discloses an intelligent robust reentry guidance method and system for a hypersonic aircraft, and the method comprises the steps: carrying out the modeling based on an uncertainty system; determining the uncertainty and the type of the uncertainty in the gliding reentry process of the hypersonic aircraft, and determining the distribution form and the distribution interval of uncertainty parameters; carrying out uncertainty quantitative analysis, and establishing a robust dynamic trajectory optimization model containing random system statistical moment characteristics; establishing a robust trajectory optimization numerical value sample set; designing a deep neural network model architecture, carrying out model training, and verifying the effectiveness of the model; and loading a deep neural network model, and carrying out intelligent real-time updating on a robust trajectory planning guidance instruction. According to the method and system, the robust trajectory design time can be shortened, the active defense capability of a guidance instruction to complex uncertainty is enhanced, and the design burden of an aircraft guidance control system is reduced.

The invention provides a vehicle dynamic scheduling method applying opportunity constraint planning. The method includes the steps of adopting a Fisher ordered clustering analysis algorithm to divideall-day time into a plurality of sub-periods with equal departure intervals, establishing a model according to random system constraint conditions of passenger waiting time and vehicle capacity, checking the random system constraint conditions, calculating a target value in the model, and solving an optimal value based on a stochastic simulation genetic algorithm, thereby determining a multi-time-period real-time scheduling scheme. According to the opportunity constraint-based real-time scheduling model, an optimization objective function and constraint conditions of scheduling decisions underthe condition that uncertain events such as passenger flow randomness, accidents and congestion occur are solved, the optimal solution of the decisions is determined by adopting a genetic algorithm and random simulation, and the problems that an analytical solution of a traditional optimization algorithm such as a quasi-Newton method is difficult to express and is liable to fall into a local optimal solution are solved.

The invention discloses a modeling method of a formation stochastic control system for missile autonomous formation with multiplicative noise, and belongs to the technical field of aircraft formationnavigation, guidance and control. The modeling method takes the formation stochastic control system for missile autonomous formation as a research object, and the obtains a stochastic system model forformation control by performing analysis and modeling on the formation stochastic interference and introducing stochastic factors into a formation model. For the control problem of the formation stochastic model for missile autonomous formation, the modeling method uses an estimator to estimate the system state, and uses a stochastic robust analysis and design method (SRAD) to optimize the estimator and controller of the formation stochastic model, so that the parameters of the controller can meet the requirements of stability and performance.

The invention discloses a limited and short time control method of a double-random-jumping system, and relates to multiple-probability description of a multiple-random-jumping system and transient state performance control of a random system within a limited and short period. Firstly, the probability random distribution feature of the double-random-jumping system is described through a Gaussian probability-density function; secondly, by broadening the condition that a system Lyapunov energy function strictly and gradually decreases at a sampling moment, a novel limited and short time stability definition in each mode in a double-random-jumping process is made; thirdly, for an energy bounded disturbance design controller of the system, the process track of a closed-loop system is made to move within a certain range of a balance point in a limited mode, and therefore the requirement for gradual stability is abandoned. According to the limited and short time control method of the double-random-jumping system, for a short-time work system required by actual production requirements, the double-random-jumping phenomenon is taken into account, the requirements for a system energy function are broadened, and an idea is provided for lowering the conservativeness of a common gradual and stable project from the aspect of time.

The invention discloses a design method of a tracking controller of a random system with multiplicative noise and time delay. A discrete linear system model with an observation function is used for describing a random system which is changed due to sudden environmental disturbance, equipment faults and the like and has multiplicative noise and input time delay, and an augmented system model related to a reference track is established by using an augmented matrix method. Under the condition of a limited time domain, an optimal tracking controller design scheme related to states and reference tracks is provided based on a new maximum value principle, a differential Riccati equation and a Lyapunov theory. In addition, under the condition of infinite time domain, an optimal tracking controllerdesign method capable of enabling the system to be stable is provided according to the Lyapunov stability theory and the algebraic Riccati equation, so that the stability of the system is ensured, and good tracking performance is provided.

The invention discloses a method for establishing a three-stage network system for the traditional Chinese medicine (TCM) integrated rehabilitation of the stroke. A characteristic TCM integrated rehabilitation solution is provided. According to the method, the improved Lushi massage is performed, and the mental recovery of a patient falls behind the limb recovery of the patient. The traditional research focuses more on the mental recovery and rehabilitation, wherein the cognitive therapy and other mental consultation methods are performed to realize the mutual promotion effect of the mental recovery and the limb recovery. No three-stage rehabilitation network system-based TCM integrated rehabilitation solution is provided during previous studies. However, although some TCM rehabilitation solutions are provided in previous studies, no mental consultation therapy is provided. Therefore, less attention is paid on the psychotherapy. The invention compares the constructing feasibility of two three-stage rehabilitation network systems for the first time so as to obtain a network system that is more applicable to the clinical promotion. Therefore, the method is more scientific compared with methods provided based on conventional random systems.

The invention discloses a random saturation control method of a water affair water supply system. The method comprises the following specific implementation steps of: the step 1, establishing a statespace model of an urban water supply network; the step 2, proposing a system matrix representation method; the step 3, considering a modal transition probability, and solving the description problem of the random system; the step 4, constructing a saturation state space model of the water affair system; and the step 5, designing a control law of the water quantity in the water supply tank of the water supply system under saturation limitation. By means of data acquisition, model establishment, saturation control, controller design and the like, the design method of the random saturation controller of the urban water supply system is determined, the water quantity and water valve saturation problem of the water supply system can be effectively controlled by utilizing the method, safe operation of the water supply system is guaranteed, and the method has the good performance indexes.

The invention relates to a nonlinear random model prediction control method based on multi-step state feedback. The method comprises the steps of studying nonlinear system characteristics with randominterference; establishing a system random interference model by using a statistical method; establishing a nonlinear discrete system random model with probability constraints, proposing a random optimization control problem for the model by combining a design method of a control target in traditional predictive control, and then solving a bounded model mismatch problem between the nonlinear modeland a linear nominal model by applying a robust tube invariant set thought. By designing a multi-step state feedback control law, optimizing statistical performance indexes in an infinite time domain, constructing a multi-step probability Tube invariant set with higher degree of freedom and ensuring probability constraint recursion, the future random state of the system is controlled, and the problems of traceability, economy and stability of the random system under random interference are effectively solved.

The invention discloses a single-connecting-rod manipulator fuzzy control algorithm based on a stochastic system under output limitation and input time lag. The single-connecting-rod manipulator fuzzycontrol algorithm is composed of a fuzzy self-adaptive controller, a visual sensor, a mechanical arm control armature and a tail end dexterous hand. According to the single-connecting-rod manipulatorfuzzy control algorithm, a fuzzy logic system is utilized, the fuzzy adaptive controller is designed for a manipulator system model with random disturbance and high nonlinearity, and a correspondingadaptive algorithm is designed, so that a reference signal and an operation target can still be accurately tracked under the conditions that the input delay and the output signal of the controller arelimited and various torque random disturbances occur during network control.

The invention discloses a hidden moving target defense strategy generation method and device, and the method comprises the steps: collecting system data, and carrying out the load flow calculation according to the system data, and obtaining the current operation state of a system; according to the installation data of the D-FACTS equipment, selecting a system line on which the equipment is installed; judging whether the equipment meets a condition for generating a hidden moving target defense strategy or not, and if so, taking the equipment as a premise for generating the hidden moving target defense strategy; on the premise of ensuring that the power flow of the system is kept unchanged, randomly changing the node state of the system according to the use scheme of the equipment to obtain a random system state, and calculating the susceptance action quantity of a system transmission line; and generating a hidden moving target defense strategy according to the random system state and the susceptance action amount. According to the method, the hidden moving target defense strategy executed by the system is hidden for attackers, so that the concealment of the system can be effectively improved, and the risk that the system is attacked can be reduced.

The invention belongs to the technical field of event trigger fault detection, and discloses a time-varying random system event trigger fault detection method based on coordinate transformation, and the method comprises the steps that: superposed unknown interference is removed from one subsystem by employing a coordinate transformation method; for each subsystem, an optimal event trigger filter is constructed by utilizing an unbiased and minimum variance optimal criterion; when any system breaks down, a residual error will exceed a preset threshold value, current measurement information willbe immediately sent to a remote filter, and a diagnosis decision triggered by an event does not need to be input. According to the invention, a coordinate transformation method is utilized to realizethe characteristic that the fault detection residual error is only sensitive to system faults and has robustness to additive unknown interference; a considered system can be converted into two subsystems, and interference is removed from one subsystem; and further the optimal gain of each filter is derived, and the optimal gain is applied to an optimization criterion of unbiased and minimum mean square estimation errors.

The invention discloses a food sampling method, which comprises the following steps of: after a sampling task is received, firstly obtaining sampled regional information, sampled unit information and sampling personnel information, and inputting the regional information, the sampled unit information and the sampling personnel information into a double random system so as to randomly distribute sampled units and sampling personnel according to the regional information; after the distributed sampling personnel arrive at the destination, sampling samples of corresponding links, and collecting video information in the sampling process through a visual monitoring system; after sampling is finished, photos, images, sampling lists, notices, work discipline feedback lists and sample purchase cost certificates in the sampling process are uploaded to an electronic evidence management system. According to the method, sampling personnel and sampled units can be randomly distributed, sampling video information can be collected through a visual monitoring system during sampling, evidence information can be stored through an electronic evidence management system after sampling is finished, and sampling information storage difficulty is low and accuracy is high.

A simulation system obtains multiple random number vectors and a parameter vector and calculates realized values of the behavior of a stochastic system corresponding to each obtained random number vector. Based on each obtained random number vector, on the obtained parameter vector, and on a weight function, the system calculates the weight of each obtained random number vector regarding each of the parameters in the obtained parameter vector, and calculates an evaluation value of the behavior of the stochastic system corresponding to each obtained random number vector. Based on the calculated behavior evaluation value corresponding to each obtained random number vector and on the weight of each obtained random number vector regarding the parameter selected from the obtained parameter vector, the system calculates the sensitivity of an expected value for the behavior evaluation value of the stochastic system with regard to the selected parameter.

The invention relates to a random system filter containing unknown input and non-Gaussian measurement noise. The method comprises the following steps: firstly, for a discrete time linear stochastic system containing unknown input / interference, completing prediction and estimation of a state based on a state equation; secondly, constructing an index function based on a Huber function instead of a traditional mean square error criterion when interference estimation and state filtering are solved in allusion to the facts that measurement noise of an actual system usually contains more outliers and probability distribution of the noise often has peak, fat tail and other strong non-Gaussian characteristics; thirdly, calculating interference estimation and state filtering estimation based on animmobile point iteration method through minimizing an index function; and finally, carrying out recursion on the state prediction, the interference estimation and the state filtering according to time, and giving a design flow of the filter. The method can be popularized and applied to the fields of integrated navigation, target tracking, signal processing and the like, and solves the problem of high-precision state estimation of an actual system.

The invention relates to a method for improving performance indexes of a complex networked random system. The method comprises the following steps: determining a first function relationship A between a performance parameter set and system stability according to a control model of the complex networked random system; acquiring a second function relationship B between the performance parameter set and the system life; establishing a system stability and system life relation model R; optimizing the noise frequency g by taking the noise frequency g as a decision variable to obtain a first optimization model Rg; and comparing the volatility t with a standard volatility to determine whether the first optimization model Rg needs to be adjusted, and if so, determining to return the system stability and system life relation model R according to a comparison result or adjusting the system stability and system life relation model R by taking the volatility t as a decision variable to obtain a second optimization model Rt. Therefore, the random system model can be optimized and adjusted for multiple times through the noise frequency and the fluctuation ratio on the premise of ensuring the network stability so as to improve the performance index of the random system.

The invention discloses a nonlinear three-step filtering method for the Mars power descent stage, which comprises the following steps: 1, building a kinetics equation and a measurement equation of the discrete type nonlinear three-step method; 2, presetting the initial value; 3, performing state quantity filtering; 4, performing error filtering of a kinetics system; 5, error filtering of an unknown measurement system of a measurement system; 6, updating the correlation coefficient, and correcting the state estimation and the kinetics error estimation; 7, returning to the step 3 to go ahead till equaling to the time T corresponding to the deadline of the Mars power descent stage, namely, the landing time of the Mars lander, so as to complete the nonlinear three-step filtering method of the Mars power descent stage. The method fully considers the estimation problems about position and speed of a detector of a nonlinear non-Gaussian stochastic system under the condition of errors of the kinetics system and errors of the unknown measurement system in the measurement system during Mars actual power descent, and efficiently ensures the position and speed estimation of the detector of the Mars power descent stage.

The invention discloses a method for examining the fairness of a random selection system. The method comprises the steps of simulation modeling, clustering analysis and result examination, wherein firstly, a simulation model is established after a result generated by the selection system is processed through simulation modeling; secondly, clustering analysis is conducted on the established simulation model, and a clustering result is formed; finally, poisson distribution comparison is conducted on the clustering result, and the fairness of the selection system is examined. Meanwhile, a distributed type storage system Hadoop and a distributed calculation mode Map / Reduce are adopted, the calculation amount is reduced, and the examination speed is greatly increased; poisson distribution which is generally followed by standard random systems is introduced to the method to be used as a comparison basis, accuracy and reliability are achieved, and the fairness of the random selection system is effectively examined.