82 results about "Slack variable" patented technology

The invention discloses a modeling and optimized dispatching method of an electrical series-parallel system on the basis of an energy center. The modeling and optimized dispatching method comprises the following steps: firstly, establishing an electrical network, a natural gas network and an energy hub model, and coupling the electrical network with the natural gas network through an energy hub to form the electrical series-parallel system; then, taking total energy cost as a target function, and considering various constraint conditions to establish an optimized dispatching mathematic model of the electrical series-parallel system; and carrying out solving by a primal-dual interior-point method, importing a slack variable and a barrier parameter in sequence in a solving process so as to change the model into a model which only contains an equality constraint, then, importing a Lagrange multiplier to obtain a Lagrange function, and solving a non-linear equation set formed by a KKT (Karush-Kuhn-Tucker) condition of the Lagrange function by a Newton method. A constructed example simulation result indicates that an optimization effect on the electrical series-parallel system by the modeling and optimized dispatching method is superior to an independent optimization effect.

An optimization and management system using a global linearization approach and mixed integer linear programming to perform the dispatch and to schedule a river system for ten days forward while subject to real world, hourly physical, biological, environmental, and recreational constraints. The optimizer system uses a combination of slack variables and stitching methods to approximate a highly nonlinear optimization problem, thereby generating realtime generation schedules to promote to the management authority.

A system and method for providing at least one transmit precoder includes transforming at least one of a weighted sum-rate and max-min rate objective into two or more sub-problems by introducing at least one slack variable. The two or more sub-problems are iterated on a computer readable storage medium to provide at least one transmit precoder for each transmitter.

The present invention provides a blasting vibration predicting method based on a particle swarm algorithm optimization support vector machine. According to the method, firstly, blasting vibration influence factors are subjected to feature extraction. Secondly, the kernel function, the penalty factor, the slack variable and the kernel parameters of the support vector machine are subjected to combined optimization thorough the improved PSO algorithm, and then an optimal support vector regression machine and an optimal support vector classifier model are respectively obtained. In this way, the classified prediction of blasting vibration data is realized. Compared with the traditional blasting vibration velocity predicting method for support vector machines, optimized combined parameters are obtained, so that the performances of models can be better improved. The prediction accuracy of models is improved, and the prediction accuracy of the blasting vibration strength is greatly improved.

The invention relates to a power distribution method for a non-orthogonal multiple access system. The method comprises the following steps of S1, initializing an iteration coefficient and a group of power distribution schemes satisfying a constraint condition; S2, calculating a channel balance factor and a slack variable of each user based an initial power distribution scheme; S3, optimizing the power distribution of each user based on the calculated channel balance factor and the slack variable; S4, integrating the optimized power distribution of each user, thereby obtaining an optimized power distribution scheme, and calculating total utility of the system under the optimized power distribution scheme; and S5, repeatedly performing steps S2-S4 until the total utility of the system is converged through utilization of the optimized power distribution scheme, and outputting the corresponding optimized power distribution scheme as the optimized distribution scheme when the total utility of the system is converged.

Provided is an interval prediction control modeling and optimizing method based on soft constraints. The control method comprises the following steps: (1) a quadratic performance index including a constraint item, a control item and an economic item is established based on a process prediction model; (2) whether an overall optimization method is feasible is judged by solving a slack variable; (3) a method for solving a soft constraint slack variable when a control model output constraint is not feasible is provided, and adjustment of the range of a feasible region when an interval prediction control model output constraint is not feasible is realized; and (4) a boundary feasible sequence quadratic programming method is adopted to solve the problem that poor initial point selection causes calculation amount increase of the method or difficulty in finding an optimal solution, the problem that the positive definiteness of a Hessian matrix is destroyed due to the influence of round-off error in calculation, and the like, and to figure out the optimal control input. A complicated multivariable system control model can be established, the control law can be solved accurately and quickly based on soft constraint adjustment, and good control on a multivariable system can be achieved.

The present invention relates to a convex optimization-based high-precision positioning method in an underwater target positioning problem. The method of the present invention comprises a step of setting the to-be-calculated coordinate of a target as x=[x0,y0,z0]<T>, wherein the step comprises firstly measuring the distances ri between the target to several surrounding beacons and the coordinates ai=[xi, yi, zi]<T> corresponding to the beacons, setting the range finding errors to each beacon as Epsilon i which follow the Gaussian distribution of which the expected value is 0 and the variance is sigma i<2>, and obtaining a range finding equation of the target, etc. By carrying out the formal transformation and adding the limitation conditions on a least-square structure of an underwater target spherical intersecting positioning equation, the least-square structure is transformed into a DC structural form in a convex optimization theory, and further a convex-concave process (CCP) method can be utilized to solve, and aiming at the disadvantage that a direct CCP algorithm need to iterate an initial value in a feasible domain, a slack variable and a penalty function are added in an original optimization equation, thereby expanding the feasible domain, and broadening the limitation to the initial value. Compared with a linear least-square positioning calculation method, the convex optimization-based high-precision positioning method enables the positioning precision to be improved, and realizes the high-precision underwater target positioning.

The invention provides a robust pre-coding method based on user fairness in a multi-cell multi-user system. The method comprises the following steps that system parameters are set; MSEsk is defined to be the MSE of a k user in an m cell; the MSE of the k user in the m cell is defined when bounded errors exist in a channel; parameter defining is carried out; under the situation that bounded errors exist in channel information, a cost function is used for correcting the MSE; and a slack variable is introduced, and a subproblem with the existing bounded errors is converted into a GEVP problem to be solved. According to the method, influence from the bounded errors can be effectively handled, the fairness of users is guaranteed, feedback expenditure is lowered through a distributed algorithm, and good bit-error-rate performance is obtained.

Systems and methods for determining active constraints in a surface facility network, which include the use of slack variables and multipliers in system equations to eliminate the extraneous (inactive) constraints.

The invention provides a beam forming optimization method of an online NOMA multi-antenna system based on the Lyapunov theory. An online mode is considered, the data causality is solved via the Lyapunov theory, a long-term time average optimization problem is converted into an optimization problem of each moment, slack variables are imported by using a concave-convex optimization algorithm, multi-dimensional first-order Taylor expansion is performed to solve the non-convex problem of objective functions and constraints, and optimal beam forming and power allocation schemes at each moment are obtained through iteration.

The invention discloses a maize leaf disease and pest detection method based on image recognition. According to the method, the disease and pest type of a to-be-detected maize leaf is obtained; for anacquired learning sample set {(xi,yi)|i=1,2,...,N} of a diseased region of the maize leaf, an optimal hyperplane of a support vector machine (SVM) model is solved through an SMO algorithm, wherein xiis an input parameter vector of the i(th) sample, yi is an output result of the i(th) sample, and xi is composed of parameters including the area S, circumference P, circularity O, rectangularity R and shape complexity E of the diseased region of the maize leaf; and a to-be-detected specimen is obtained and substituted into the optimal hyperplane of the SVM model to obtain a value of yi, whereinyi=1 indicates the disease and pest type, and yi=-1 does not indicate the disease and pest type. An SVM introducing slack variables and classification error penalty factors is adopted to learn a disease and pest image of the maize leaf, so that a large quantity of disease and pest results of the maize leaf are acquired easily in batch through detection equipment.

The invention discloses an array antenna beam forming optimization method under non-convex multiple constraints. The method comprises the following steps: establishing a receiving signal far-field model for an array antenna beam forming problem; taking the side lobe level of the region of interest as a target function, considering main lobe interval shape control and output noise power requirements, adding main lobe interval level upper and lower bound limits and output noise power constraints, and establishing a non-convex optimization problem; therefore, the expected wave beam shape is obtained on the premise of not increasing the output noise power. According to the method, the non-convex constraint is approximated to be a convex upper bound function through the first-order iterative convex approximation algorithm, so that the non-convex multi-constraint optimization problem is approximated to be a convex optimization problem, and solving is easy; besides, the non-negative slack variable is introduced, so that the convergence speed of the algorithm is high, a self-adaptive change penalty factor is designed, and the parameter adjustment process of human experience is reduced; on the premise that it is guaranteed that the expected main lobe shape of a beam directional diagram is obtained, the obtained side lobe level of the region of interest is lower, and interference signals in the side lobe level direction are restrained.

An optimization technique for use in driving a process plant controller, such as a model predictive controller, uses an organized, systematic but computationally simple method of relaxing or redefining manipulated, control and/or auxiliary variable constraints when there is no feasible optimal solution within pre-established constraints, to thereby develop an achievable solution for use by the controller. The optimization routine uses penalized slack variables and/or redefines the constraint model in conjunction with the use of penalty variables to develop a new objective function, and then uses the new objective function to determine a control solution that bests meets the original constraint limits.

The invention provides a block diagonalization assisted robust transceiver design method in a multi-cell MIMO system. The method comprises the following steps: step 1: setting an operation mode of the multi-cell MIMO system; step 2: defining a signal to interference plus noise ratio (SINR) of a single user or receiver, and calculating the signal to interference plus noise ratio of the k user or receiver; step 3: performing block diagonalization precoding by each transmitter according to a local estimation channel; step 4: calculating a lower bound value of the signal to interference plus noise ratio when the channel has a bounded error; step 5: using the lower bound value of the signal to interference plus noise ratio as the signal to interference plus noise ratio of the worst case, importing a slack variable, and transforming to solve a beam forming vector problem of the transmitters or receivers by using a convex optimization method; and step 6: obtaining a robust beam forming vector by using an alternate optimization method. According to the block diagonalization assisted robust transceiver design method provided by the invention, the robustness for the bounded error is relatively high, and each transmitter only uses the local CSI to perform the block diagonalization precoding, thereby reducing the feedback cost and guaranteeing the minimum SINR performance.

The invention provides an energy collection method and device based on cognitive radio, and the method comprises the steps: obtaining a minimum transmitting power model through employing a transmitting wave beam with artificial noise, a confidentiality guarantee capability constraint and a received energy power constraint; carrying out joint design on the emission wave beam and the artificial noise to obtain a linear fractional programming constraint model; introducing a slack variable, and designing the linear fractional programming model by adopting an SCA method to obtain a convex optimization model; and proposing an SCA-based iterative algorithm, and solving an optimal solution by using a CVX toolbox to obtain the minimum transmitting power. The SCA-based iterative algorithm provided by the invention collects energy on the premise of ensuring minimization of information signal transmitting power, and compared with a traditional radio frequency energy collection scheme and an artificial noise energy collection scheme, the method provided by the invention combines an artificial noise technology and a cognitive radio technology, so that the signal transmitting power is remarkablyreduced.

A computer-implemented method for determining a boundary for binary classification includes providing a data set, initializing a value for noise in the data set, and determining a hyperplane dividing the data set and a slack variable given a current value for noise. The method further includes updating the value for noise and the slack variable given the hyperplane, and determining the hyperplane to be the boundary for binary classification of the data set upon determining a termination criterion to be met, wherein elements of the data set are classified according to the boundary.

The invention provides a convolutional neural network image restoration method for reflective metal visual detection, and the method comprises the steps: introducing a relaxation variable, and decoupling a maximum posteriori probability image restoration model; constructing a logarithmic likelihood item principal component based on Poisson distribution, introducing a nonlinear degradation model, and removing reflective metal saturated pixels; constructing a logarithm prior item principal component based on the convolutional neural network, and constraining an image restoration solution space;and alternately updating the likelihood item and the prior item, and optimizing the final restored image through multi-stage connection. According to the method, the advantages of the convolutional neural network are utilized, the tedious link of manual design of priori items is omitted, the image restoration quality is improved based on the nonlinear degradation model, and the application of theimage restoration technology in reflective metal visual detection is facilitated.