65 results about "Linear optimization problem" patented technology

Many perceptual split-band coding systems that use analysis and synthesis filters assume the quantization noise introduced by quantizing split-band signals is substantially the same as the noise that results in the output signal obtained by applying the synthesis filters to the quantized split-band signals. In general, this assumption is not true because the synthesis filters modify or spread the quantization noise. A theoretical framework for deriving an optimum bit allocation that accounts for synthesis-filter noise spreading and the overlap-add process is disclosed. In concept, the problem of finding an optimal bit allocation can be expressed as a linear optimization problem in a multidimensional coordinate space. Simplified processes derived from this theoretical framework are disclosed that can obtain near-optimal solutions using modest computational resources.

A method of routing data from a source node to a destination node in a multi-hop network of nodes interconnected by links comprises: (a) determining that a link-flow vector satisfies one or more necessary scheduling conditions for achievability, wherein the link-flow vector represents a set of flows to be routed on one or more links from the source node to the destination node; (b) generating a scheduling multi-graph for the network, wherein the scheduling multi-graph comprises a graph having at least one pair of nodes with multiple edges therebetween; (c) deriving one or more sufficient scheduling conditions for achievability of the link-flow vector by edge-coloring the scheduling multi-graph; (d) solving a linear optimization problem over the one or more necessary scheduling conditions to obtain an upper bound on the achievability of the link-flow vector; (e) generating, based on the scheduling multi-graph, a solution comprising a set of routes and an associated schedule for achieving the link-flow vector, the solution being a lower bound on the achievability of the link-flow vector; and (f) implementing a routing method using the set of routes and the associated schedule to route the link-flow vector from the source node to the destination node. At least one node v of the network is adapted to receive transmissions from a specified plurality Ω(v) of other nodes, and at least one of the scheduling conditions depends on Ω(v).

An aero-engine online optimization and multivariable control design method based on model predictive control achieves the control and online optimization of multiple variables of an aero-engine undera constraint and according to requirements of thrust and speed. A control system consists of two parts. The first part is a prediction model acquisition layer. Based on the actual working state of each control cycle and the external environmental parameters of the aero-engine, an engine small deviation linear model near different steady-state points is continuously established, and model parameters are provided for a model predictive controller. The second part is a control law decision layer. A closed-loop structure is formed by the model predictive controller and an external output feedback.The model predictive controller, based on an engine model in the current state, a control command and relevant constraint limits, determines the output of the controller at the next moment by solvinga linear optimization problem. The external output feedback introduces the aero-engine actual output into the decision on future controlled quantity of the controller to compensate for the influenceof model mismatch and external disturbances.

The invention discloses a real-time tracking method of a multi-channel kernelized correlation filter. The method includes the following steps: a training step of conducting ridge regression on a previous frame of object information to acquire a filtering template; a detection step of detecting the current frame of image with the acquired filtering template and outputting a filtering response; an updating step of real-time updating the filtering template and the appearance of an object. According to the invention, the method uses kernel function to fuse multi-channel characteristics, overcomes the selection limit of the multi-channel characteristics, and transforms the problem of linear optimization of ridge regression to the problem of non-linear optimization of a higher space through the kernel function, such that the filtering template with excellent robustness is constructed, the speed of a tracking machine is greatly increased, and tracking requirements of real world can be met.

The invention relates to an automatic generating method for a day-ahead plan power flow in a power grid and belongs to the technical field of electric power system scheduling automatization. According to the invention, problems generated by the day-ahead plan power flow are resolved into active subproblems and reactive subproblems which are solved step by step; the subproblems are converted into constraint non-linear optimization problems which are solved by an interior point algorithm; the active subproblems are adopted to preliminarily eliminate mismatching quantity between a system electric generating set power generation plan and bus load projection data and coordinate the inconsistency between the electric generating set power generation plan and a communication section power transmission plan; and the reactive subproblems and active readjustment procedures are adopted to further eliminate the mismatching quantity between a system generating power and a load power, and an active power transmitted by the communication section is controlled according to the plan, and as a result, a reasonable and majorizing day-ahead reactive voltage plan is obtained. According to the invention, a credible day-ahead plan AC power flow can be effectively provided for a day-ahead plan safe checking module. Therefore, the method has favorable reliability and practicability.

A system and method for estimating a geolocation of a non-cooperative target using any reasonable target location estimate. Collectors may acquire actual signal measurements including a direction of arrival (DOA), a target range, a time difference of arrival (TDOA), a range rate, a range sum, and/or a frequency difference of arrival (FDOA). A processing device may receive the actual signal measurements and navigational data regarding the collectors. Then, the processing device may calculate an estimated target location as a solution to a nonlinear optimization problem where an objective function to be minimized is a weighted sum-of-squares of differences between the actual signal measurements and calculated values corresponding to signal measurements that theoretically should be produced for a particular target location. The algorithm used to solve this problem may be a globally convergent algorithm, such as a Levenberg-Marquardt algorithm.

The invention relates to a track profile irregularity amplitude estimation method employing optimal belief rules based inference. According to the method, the mapping relationship between parametric variable input and yield output is modeled by a belief rule base. A corresponding change relationship between vibration frequency domain characteristic data of different measurement points and a track profile irregularity amplitude is described by building the belief rule base. By a sequence linear programming method, an initial belief rules based (BRB) model is optimized through limited historical data; and the effects on the model caused by subjective factors are reduced. According to the sequence linear programming (SLP) method, a nonlinear optimization problem of an original model is converted into a step-by-step linear optimization problem; and various parameters of the optimization model can be relatively simply and rapidly calculated, so that the track profile irregularity amplitude can be accurately and rapidly estimated through belief inference under the condition of given vibration frequency domain characteristic. According to the track profile irregularity amplitude estimation method, the estimation accuracy and the calculation efficiency of the model are improved; and the method has the advantage of being relatively efficient on a track profile irregularity system which needs to be monitored in real time.

The invention provides an energy optimization scheduling method. The method comprises the following steps: constructing an optimization scheduling model of an energy system from power generation cost,environmental cost, standby cost and demand side response; optimizing the scheduling model to establish a corresponding target function, wherein constraint conditions of the target function comprisea system energy balance constraint, a comprehensive energy output constraint, an energy storage battery constraint, a refrigerator constraint, a demand side load constraint and a transferable load system balance capacity constraint; and optimizing and solving the target function of the energy system according to a dragonfly algorithm, and outputting an optimal solution meeting an iteration termination condition to obtain an optimal scheme of energy system scheduling. According to the invention, the transferable load in the electrical load, the thermal load and the cold load of the demand sidein the comprehensive energy system is fully considered. The objective function is used as an optimization objective, the dragonfly algorithm is used for carrying out multi-objective optimization solution on the objective function, the optimal scheme of energy system scheduling is obtained, and the problems of multi-objective and nonlinear optimization in energy system optimization scheduling are solved.

The invention discloses a generalized-point-set matching method based on distances from points to lines and the method is capable of realizing rapid and precise matching of point sets. Generalized point sets (p1, p2...pm) and (q1, q2...qn) are extracted respectively from a reference image and a target image, wherein the point sets are discrete and have directivity; when points corresponding to the generalized points sets are searched for, according to an index serial number and searching radius of a reference point, an index serial number of a target point in an adjacent domain of the reference point is determined for rapid query of a target point set; and at the same time, a variable-adjacent-domain nearest-point-searching method is combined and an angle threshold and a distance threshold are adopted to establish rapidly a corresponding relation between directed points; and then according to a point-line distance and point-point distance equivalent transformation method, transformation of a non-linear optimization problem and a linear optimization problem is realized so that a least square method is used to obtain a matching parameter and thus rapid and precise matching is realized and calculation efficiency is improved.

The invention discloses a MIMO radar waveform optimization method, used to improve parameter estimation performance of a MIMO radar system under a condition that target prior information exists in a clutter scene. Implement method thereof comprises: firstly, establishing a MIMO radar signal model, and deriving Cramer-Rao bound (CRB) of an unknown parameter based on the model; based on a Trace-Opt criterion, minimizing trace of the CRB, establishing a waveform optimization model; and based on a diagonal loading (DL) method, relaxing a nonlinear optimization problem to a semi-definite programming (SDP) problem, to obtain efficient solution of the optimization problem, so as to improve parameter estimation performance of the system. Compared with an uncorrelated transmitted waveform and a non-prior scene, the method can obviously improve system parameter estimation performance.

The invention belongs to the signal processing field, and relates to an MIMO-OFDM (Orthogonal Frequency Division Multiplexing) radar waveform design method for improving STAP detection performance. Under the constraint of a constant modulus, and based on an output SINR maximization criterion, the invention introduces the optimization problem of maximizing detection performance through designing emission waveforms. In order to solve a complex nonlinear optimization problem, the invention converts the nonlinear optimization problem into a semi-definite programming problem for obtaining an efficient solution based on a DL method, so as to maximize an output SINR, and thereby maximize system detection performance. The simulation result shows that compared with non-relevant emission waveforms, the emission waveforms obtained by the method can substantially increase an output SINR, thereby improving system detection performance. The method can obtain obviously improved detection performance compared with utilizing non-relevant waveforms.

Systems and methods for allowing analog Ising machines to be able to run Integer Linear Programming (“ILP”) problems, i.e. a compilation method for setting the state of the physical memory units, flexible to be adapted to each specific device. The method describes how variables and numeric parameters which specify the problem can be hard-coded (embedded and physically represented) in the hardware circuitry of the device in a deterministic way, with a pre-determined bound on the number of required physical spins to be used in the Ising device.

The invention discloses a hyperspectral unmixing compressive sensing method based on three-dimensional total variation sparse prior. The hyperspectral unmixing compressive sensing method is used for solving the technical problem that an existing hyperspectral image compressive sensing algorithm in combination with spectrum unmixing is low in precision. According to the technical scheme, a random observation matrix is adopted for extracting a small number of samples from original data as compression data. In the reconstruction process, according to an unmixing compressive sensing model, appropriate spectrums are selected from a spectrum library as an end member matrix in the model, then the three-dimensional total variation sparse prior of an abundance value matrix is introduced, and the abundance value matrix is accurately solved through solving a limited linear optimization problem. Finally, a linear mixing model is used for reconstructing the original data. When the compression ratio of urban data shot through a HYICE satellite is 1:20, the normalize mean squared error (NMSE) is smaller than 0.09, when the compression ratio is 1:10,the NMSE is smaller than 0.08, and compared with an existing compressive sensing algorithm, precision is promoted by more than 10%.

The invention relates to a multi-target generating set combination model based on security domain target and constraint and a solving method. By aiming at a problem of conventional generating set combination methods of possibility of inability of satisfying active static state security domain constraint, an improved generating set combination method is provided, and a multi-target optimization model having minimum generating costs and maximum security domain measure indexes is established. By introducing an intermediate variable, the model is in accordance with mixed integer linear optimization problem standard type. By using an expansion epsilon constraint method and optimization software CPLEX, a multi-target problem is solved, and a Pareto optimal solution set is acquired. Economic performance and safety performance are coordinated, and the multi-target generating set combination model is more suitable for actual power grid demands.

The invention discloses a vehicle-mounted application-oriented model prediction control rapid solving method. The method comprises the following steps of building a high-fidelity vehicle model; establishing a vehicle model for describing yawing motion and lateral motion of the vehicle; establishing a reference model, and generating reference values of the yaw velocity and the side slip angle according to the current vehicle speed and the front wheel rotation angle of the vehicle; describing a model prediction control problem as a typical nonlinear optimization problem according to the established vehicle model and the control requirement; for the nonlinear optimization problem, based on a Poincare minimum principle and a Nelder-Mead algorithm, performing rapid solving of the nonlinear optimization problem; and according to the optimal control input calculated by the model prediction controller, respectively calculating additional torques of the four tires , and distributing the additional torques to the four hub motors. According to the method, on the premise that the solving precision is guaranteed, the solving speed can be greatly increased, and the real-time performance of the controller is improved.

The present invention is a system and method of a feasible point method, such as a canonical coordinates method, for solving non linear optimization problems. The method goes from a point to another point along a curve of a defined nonlinear surface. An objective function is determined from the plurality of points. Each point is given a value determined from the objective function. The objective function value is maximized to improve computational efficiency of a non linear optimization procedure.

A nonlinear optimization method and a storage medium, wherein the method includes the steps of establishing a linearization model according to the nonlinear optimization problem; solving the linearization model through a first-order iterative algorithm, continuously reducing the error between the nonlinear optimization problem and the linearization model, and finally finding Feasible solutions of nonlinear problems under a certain target value; through the second-order iterative algorithm, the target value of the nonlinear problem is continuously updated to find the feasible solution of the problem with the maximum target value, that is, the solution of the nonlinear optimization problem. Robust, efficient, actuarial computation for solving large-scale nonlinear problems.

The invention discloses an optimal scheduling method for a distributed new energy power distribution network, and aims to solve the problem in optimal scheduling of a power distribution network containing a distributed new energy power supply under the maximum power supply capacity. The method comprises: establishing a power grid dispatching double-layer model considering the output uncertainty ofthe distributed new energy; converting the model with the lowest output cost of the lower-layer power distribution network into a linear constraint condition by adopting a KKT condition; converting the double-layer optimization model into a single-layer linear optimization problem; increasing the load capacity in a variable step size manner until the maximum load capacity is reached; and finally,obtaining a power distribution network optimization scheduling scheme considering the uncertainty of the distributed new energy output under the maximum load capacity. According to the method, the lowest power supply cost of the power distribution network and the worst new energy output uncertainty in the power market environment are considered, so that the power grid optimal scheduling scheme when the power supply capacity of the power distribution network is maximal is obtained, the calculation process is simplified, the calculation efficiency is greatly improved, and the method has guidingsignificance in practical application.

The invention discloses a multi-objective optimization method and system for a client-side multi-energy system, and the method comprises the steps: obtaining the attribute information of energy equipment in the client-side multi-energy system, and obtaining the prediction information of a client electrical load, a cooling and heating load and renewable energy output in the multi-energy system; constructing a decision variable and a constraint condition of an optimization problem according to the obtained attribute information and prediction information; based on the decision variable and the constraint condition, constructing each single target function considering economy, energy saving performance and environmental protection performance; and setting a decision maker preference weight for each single objective function, then constructing a decision multi-objective optimization function, and performing solving to obtain a final optimal solution. According to the method, a linear weighted sum method is improved, so that the importance degree of each component can be truly reflected; meanwhile, the method considers three objectives of economy, carbon emission and energy conservation, comprises a decision maker preference input module, a source-network-load-storage multi-energy optimization model and a linear optimization problem processing module, and can meet the selection requirements of different decision makers for optimization objectives.

A provided optical-phase-distribution measuring method, by which optical phase distribution is identified at high speed and with high accuracy from information on light-intensity distribution without using a special measuring device, comprises steps: for inputting light to be measured to optical systems, respectively, modulating the intensity and the phase, detecting the output light to be measured with CCD, and measuring the intensity distribution of detected light to be measured as an image with an optical-phase-distribution measuring system provided with the two different optical systems; for setting an observation equation, based on the intensity distribution and on the optical characteristics of the optical systems; for setting a phase-distribution identification inverse-problem from the observation equation, and formulating the set phase-distribution identification inverse-problem as a first nonlinear optimization problem in which complex amplitude representing the light to be measured is assumed to be a design variable; for converting the first nonlinear optimization problem to a second nonlinear optimization problem, in which expansion coefficients in a series expansion are assumed to be design variables, by series expansion of the phase distribution of the light to be measured; and for identifying the phase distribution of the light to be measured by solving the second nonlinear optimization problem.

A control system for controlling an operation of a system with continuous- time nonlinear dynamics subject to constraints including equality and inequality constraints on state and control variables of the system, including an estimator to estimate a current state of the system using measurements of the operation of the system and a controller to iteratively solve, at each control time step, an approximation of a constrained nonlinear optimization problem to produce a control solution, wherein the approximation includes a linearization of the nonlinear dynamics of the system discretized by time intervals in the control horizon and represented using an approximation of the constraint Jacobian matrix for each time interval of the control horizon. The iterative solution procedure is based ona block- wise update formula for the approximation of the constraint Jacobian matrix and the intermediate condensing matrices using an evaluation of one or combination of the discretized dynamics of the system and at least one directional derivative of the discretized dynamics of the system. Each block in the constraint Jacobian matrix and in the intermediate condensing matrices represents one time interval in the prediction horizon and can be updated independently, based on a block- wise rank-one update formula without any iterative solution procedure and without any matrix-matrix multiplications or matrix factorizations.