247 results about "Order reduction" patented technology

Model-order reduction techniques are described for RLC circuits modeling the VLSI layouts. A structured model order reduction is developed to preserve the block-level sparsity, hierarchy and latency. In addition, a structured and parameterized model order reduction is developed to generate macromodels for design optimizations of VLSI layouts. The applications are thermal via allocation under the dynamic thermal integrity and via stapling to simultaneously optimize thermal and power integrity.

Improved approaches to flexibly implementing graphic equalizers on media players are disclosed. These approaches provide dynamic order reduction of a multi-band graphic equalizer so that equalizer effects can be timely performed with only limited computational resources. In one embodiment, a media player receives a media item and associated equalizer settings for a multi-band graphic equalizer. The media player can then automatically (i.e., without user action) approximate the multi-band graphic equalizer with the equalizer settings for the media item using a fewer number of filters. Fewer filters means order reduction, and thus reduction in computational requirements. After the multi-band graphic equalizer is approximated, the media player can present the media item to its user in accordance with the reduced complexity, approximated equalizer.

Apparatus for projecting a pattern includes a first diffractive optical element (DOE) configured to diffract an input beam so as to generate a first diffraction pattern on a first region of a surface, the first diffraction pattern including a zero order beam. A second DOE is configured to diffract the zero order beam so as to generate a second diffraction pattern on a second region of the surface such that the first and the second regions together at least partially cover the surface.

A methodology for process modeling and control and the software system implementation of this methodology, which includes a rigorous, nonlinear process simulation model, the generation of appropriate linear models derived from the rigorous model, and an adaptive, linear model predictive controller (MPC) that utilizes the derived linear models. A state space, multivariable, model predictive controller (MPC) is the preferred choice for the MPC since the nonlinear simulation model is analytically translated into a set of linear state equations and thus simplifies the translation of the linearized simulation equations to the modeling format required by the controller. Various other MPC modeling forms such as transfer functions, impulse response coefficients, and step response coefficients may also be used. The methodology is very general in that any model predictive controller using one of the above modeling forms can be used as the controller. The methodology also includes various modules that improve reliability and performance. For example, there is a data pretreatment module used to pre-process the plant measurements for gross error detection. A data reconciliation and parameter estimation module is then used to correct for instrumentation errors and to adjust model parameters based on current operating conditions. The full-order state space model can be reduced by the order reduction module to obtain fewer states for the controller model. Automated MPC tuning is also provided to improve control performance.

The invention discloses a method for analyzing wing structure aero-elasticity stability based on aerodynamic force uncertain order reduction. The method is based on unsteady aerodynamic force model order reduction method based on CFD technology; uncertainty of numerical calculation and aerodynamic parameters during aerodynamic force identification process is taken into comprehensive consideration and is uniformly quantified as an uncertain bounded interval noise sequence in an identification model; uncertain order reduction of the aerodynamic force model is realized by means of interval set membership identification algorithm; unsteady aerodynamic uncertain order reduction model based on CFD technology is established; mathematical model of uncertain aero-elasticity system of state space form is established coupled with structural motion equation; an effective method for predicting system robustness stability boundary from the prospective of section state matrix characteristic values. The aero-elasticity system uncertainty modeling thinking and stability boundary prediction technology provided by the invention gives consideration to calculation efficiency, analysis precise and system robustness.

Provided is an automated paperless order selection system of the type wherein an electronic order picking unit moves horizontally along a rail directly to a product storage location, thereby decreasing the distance traveled by the order picker and improving the accuracy of the order picking process, such order picking unit having an incremental encoder means for determining its exact location on the rail, and having a wireless radio frequency data communication (RF/DC) capability, and preferably including a printer, bar code scanner as well as audit and inventory report features.

The invention provides a satellite in-orbit micro-vibration simulation modeling correction method, which comprises the following steps of: analyzing satellite configuration to separately model a satellite body structure and an on-satellite rotating part; according to ground test data and a finite element principle, carrying out modeling of a satellite body structure finite element model; according to attitude control and ground micro-vibration test data of other on-satellite rotating parts, establishing an on-satellite rotating part simulation model; according to a ground test state, establishing a ground simulation model, deleting ground constraints and establishing an in-orbit micro-vibration simulation model; according to the ground test data, a moving part, a control mode of the moving part and a modal value order reduction method, correcting the in-orbit micro-vibration simulation model; carrying out micro-vibration simulation analysis on the ground test state and utilizing the ground micro-vibration test data to carry out model correction; and deleting the corrected model ground constraint conditions to complete modeling. According to the invention, micro-vibration interference of a flywheel to a satellite platform can be effectively reduced; and technical support is provided for development of a high-accuracy high-resolution high-stability extra-static platform.

The invention discloses a low-order recurrence least mean square error estimation for an MIMO-OPDM channel, which relates to the wireless transmission technical field. After a pilot frequency is used to insert in to obtain a recurrence least square estimation of a time-varying channel fading, the channel fading is decomposed into a signal subspace and a noise subspace by adopting a subspace tracking method of being capable of tracking singular values and singular vectors under the non-stationary complicated noise, then an order-reduction is made according to the quantity of main singular values to obtain an auto-correlation matrix of the channel fading, and a least mean square error estimation with higher precision is obtained through the recurrence. The invention has the characteristics of having computation complexity of decreasing algorithm, higher estimation accuracy as well as good robustness and applicability, and being capable of providing channel estimation and self-adaptive equalization proposals of systems such as third generation (3G) cell mobile communication, beyond third generation (B3G) cell mobile communication, fourth generation (4G) cell mobile communication and digital TVs, wireless local area networks (WLAN), wireless wide area networks (WWAN) and so on, with an important theoretical evidence and a concrete realization method and so on.

The invention discloses a free-hand sketch offline identification and reshaping method. The method comprises the following steps of: preprocessing an input image; converting a discrete and disordered point set in each connected domain into an ordered point sequence for compression; fitting a plurality of straight lines of the point sequence by using a dynamic programming algorithm, and determining the number of optimum fitted straight lines to obtain the strokes, represented by the straight lines, of each connected domain; analyzing a stroke result obtained by fitting the plurality of straight lines; if the number of the fitted straight lines is greater than the maximum edge number of a shape identified by a system, performing order reduction on the strokes, classifying the strokes, and calculating distance between the strokes; and selecting the nearer strokes for combination and analysis, and performing verification according to the geometrical characteristic to determine the shape formed by combination of the input strokes. The method is higher in identification rate, and the shape to be identified has scale invariance and rotation invariance; and the algorithm supports a multi-stroke form for identifying limited shapes, and the problems in the identification completely based on the geometrical characteristic are solved.

The invention discloses a linear frequency modulation signal parameter evaluation method based on LVD (Lv's distribution). A linear frequency modulation signal has energy impulse characteristic in an LVD domain, so the relative inhibition of cross items is realized, and the parameter values of the center frequency and frequency modulation slope of the linear frequency modulation signal are accurately evaluated. The method comprises the following specific steps of 1, collecting signals; 2, judging if pulse noise is contained or not; 3, carrying out order-reduction preprocessing; 4, extracting a self-correlation feature; 5, converting phase dimensions; 6, extracting an LVD spectrum feature; 7, searching an LVD spectrum peak. The method overcomes the defect that in the prior art, cross item inhibition and resolution enhancement cannot be combined, a decoupling self-correlation signal is converted into the independently distributed LVD spectrum, the signal parameter evaluation precision in complicated noise environment is improved, and a new path is provided for the design of a future signal phase feature extracting technology.

The invention relates to an operation instrument for optical tweezers. The operation instrument comprises a laser source, a convex lens, a plane mirror, a two-tone spectroscope, optical wedges, a focusing objective lens, a sample table, a sample pool, an illuminating light source and a CCD camera. Light beams are emitted by the laser source, expanded and shaped by a light beam coupler, and then are converged and irradiated by the plane first mirror, the two-tone spectroscope, the two deflection optical wedges and the focusing objective lens into the sample pool to capture target particles, the first optical wedge and the second optical wedge are driven by a motor to deflect around the Y axis and the X axis respectively in an orthogonal mode, the deflection angle can be measured through an angle sensor, the deflection of the light beams is controlled, and the target particles are moved; the particle capturing and moving process can be visually acquired through the CCD camera and a displayer. When the particles move in the plane parallel to the XOY plane, the sample pool does not need to move, namely, the particles are not affected by external vibration in the moving process; in addition, due to the fact that the two orthogonal deflection optical wedges can achieve the light beam deflection hundredfold-order reduction ratio, the deflection directions of the light beams can be accurately controlled, and the light beams can capture the particles accurately and carry the particles to move firmly.

The invention discloses an underwater robot control method based on reinforcement learning and a control method of tracking with an underwater robot, belonging to the technical field of underwater robot control. According to the methods provided by the invention, a control center gives desired trajectory information of the underwater robot, and sends the information to the underwater robot; according to a probability density function of uncertain parameters in an underwater robot model, sampling points are respectively selected for the uncertain parameters, and order reduction is executed foran original dynamical model based on the sampling points; the underwater robot performs interaction with surrounding environment so as to learn environment information, a one step cost function is calculated at different states and value updating is executed, weight of a value function corresponding to a control strategy is solved through a least square method, improvement of the control strategyis executed through a gradient descent method, loop iteration is executed for two processes of value updating and strategy improvement until convergence is realized, and consequently, an optimal control strategy of current position tracking desired trajectory is obtained; and steps above are repeated for obtaining the optimal control strategy for the rest desired trajectory tracking, and finally,a tracking task is completed.

The invention discloses a nonlinear large-attack-angle aerodynamic force order reduction model applicable to stall flutter. Firstly, a sine multi-level signal is designed as a nonlinear aerodynamic force recognition signal, and the amplitude of the designed signal needs to meet a large amplitude motion requirement. Secondly, according to a circular neural network model based on deep learning, nonlinear aerodynamic force recognition is carried out, and a CFD method is adopted for calculating a training output signal; according to the circular neural network model based on deep learning, a nonlinear aerodynamic force model of a dynamic time domain when wings vibrate with large amplitude is created, system response under a testing signal is calculated through utilization of CFD, and comparison with a recognition result of the network model is carried out to verify the performance of the model. The design cost of an aircraft is saved, the design efficiency of stall flutter is improved, thenonlinear aerodynamic force order reduction model is developed, and therefore stall flutter can be fast predicted.

A method and verification of estimating crosstalk noise in coupled RLC interconnects with distributed line in nanometer integrated circuits is provided. In this invention, nanometer VLSI interconnects are modeled as distributed RLC coupled trees. The efficiency and the accuracy of moment computation of distributed lines can be shown that outperform those of lumped ones. The inductive crosstalk noise waveform can be accurately estimated in an efficient manner using the linear time moment computation technique in conjunction with the projection-based order reduction method. Recursive formulas of moment computations for coupled RC trees are derived with considering both self inductances and mutual inductances. Also, analytical formulas of voltage moments at each node will be derived explicitly. These formulas can be efficiently implemented for crosstalk estimations.

The invention discloses a fractional order PFC method of an industrial heating furnace system. A traditional PID control method and an integer order PFC method have poor control effects on a kind of fractional order system. The control method of the invention comprises: firstly employing an Oustaloup approximation method to approximate a fractional order system into an integer order system; building a prediction output model based on an Oustaloup approximation model; expanding an integer order PFC method to a fractional order PFC method; introducing a fractional order calculus operator into an object function; and furthermore designing a fractional order prediction function controller based on the prediction model and selected performance indexes. The fractional order PFC method can be well applied to the real process objects described by a fractional order model, omit the order reduction step of the integer order PFC method to control a high order system model, meanwhile increase the flexibility of adjusting controller parameters, and obtain good control performances.

The invention provides an unsteady aerodynamic force order reduction method based on generalized aerodynamic force. The method comprises steps of firstly, converting generalized aerodynamic force under modal coordinates into unsteady aerodynamic force distributed on all nodes of a structural finite element model under physical coordinates through coordinate transformation; secondly, performing order reduction on the distributed unsteady aerodynamic force through curved surface spline interpolation, equivalently concentrating the unsteady aerodynamic force to a limited loading point so as to obtain a frequency domain aerodynamic force order reduction model, and finally fitting the frequency domain order reduction aerodynamic force model to a time domain by using a minimum state method. According to the method, the precision of the reduced-order aerodynamic model is improved on the basis of reducing the order of the reduced-order aerodynamic model as much as possible, so that the difficulty of designing an excitation force control system in a ground flutter simulation test is reduced, and the reduced-order method can be used for transonic flutter analysis by means of a CFD transonicvelocity unsteady aerodynamic calculation method.

The invention discloses a deflation modeling method of a micro-electronic-mechanical-system (MEMS) which contains a plurality of input ports. A three-dimension solid model of the structure is established according to the parameters of structure design, modal analysis is carried out by using a finite element method, the quality and the rigidity matrix of the structure is extracted from analysis results, and a second order dynamics action equation of the structure is converted into a first order state equation after being established; an input matrix B is uniformly splitted into k blocks by rows to obtain the state equation of the structure after splitting; deflation is carried out to the application subblock of the (i)th sub-system to obtain the macro model state equation of the (i)th sub-system; the integral macro model of the original system can be obtained after the macro models of all the sub-systems are superposed according to output. The method speeds up the MEMS system level modeling and simulation based on the macro models, thus improving the design efficiency of the MEMS.

The invention discloses an unmanned vehicle motion planning method oriented to planning control joint optimization. The method comprises the steps: obtaining a global linearized model through the recognition of an original kinetic model of a vehicle, and obtaining a simplified model through the order reduction of the global linearized model. During planning, a simplified kinetic model is considered and the non-convex obstacle constraint is also convex to consider the existing obstacle constraint, and the planning module plans the trajectory, the speed and the like by combining the simplified model and a rolling time domain ADP method, so that the planning efficiency is improved, the consistency of planning and control is ensured, and the motion result of the vehicle effectively meets the anticipated requirements.

The invention improves HOA sound field representation compression. The HOA representation is analysed for the presence of dominant sound sources and their directions are estimated. Then the HOA representation is decomposed into a number of dominant directional signals and a residual component. This residual component is transformed into the discrete spatial domain in order to obtain general plane wave functions at uniform sampling directions, which are predicted from the dominant directional signals. Finally, the prediction error is transformed back to the HOA domain and represents the residual ambient HOA component for which an order reduction is performed, followed by perceptual encoding of the dominant directional signals and the residual component.

The invention discloses a system and method for calculating the time lag stability upper limit based on the generalized eigenvalue and belongs to the technical field of analysis of the stability of an electric power system. The system for calculating the time lag stability upper limit based on the generalized eigenvalue comprises a data acquisition module, a time lag system processing module, a time lag upper limit solving module and a result output module, wherein the data acquisition module, the time lag system processing module, the time lag upper limit solving module and the result output module are connected in sequence. The method for calculating the time lag stability upper limit based on the generalized eigenvalue comprises the steps that network structural parameters, the frequency of an electric generator and the power angle of the electric generator are collected, wherein the network structural parameters, the frequency of the electric generator and the power angle of the electric generator are needed for establishing a time lag system state equation; the time lag system state equation is established, order reduction is conducted on a parameter matrix in the time lag system state equation, and a time lag system state equation with the order reduced is obtained; a time lag stability criterion based on an improved free weight matrix is generated; the time lag stability upper limit is solved according to the time lag stability criterion. According to the system and method for calculating the time lag stability upper limit based on the generalized eigenvalue, the conservation of time lag stability upper limit calculation can be effectively reduced, and the correctness and effectiveness are high.