119 results about "Minimization problem" patented technology

A system for determining the sources of delay of a transportation mean includes a wayside sub-system, a locomotive sub-system, a railcar sub-system, a a yard sub-system, a schedule sub-system, a monitoring and diagnostic sub-system and a management sub-system. Real-time collected data from the sub-systems are compared to a standard data set to identify problem areas without human intervention. Utilizing a list of top problem areas, transportation means managers are able to identify corrective actions to eliminate or minimize the sources of problem areas. In addition, customers or users of specific transportation means are able to properly select the appropriate transportation means.

The invention discloses a mobile robot path planning method, which comprises the following steps of: A, determining a moving destination of a robot, and setting the number of sensors of the robot and the number of directions towards which the robot can move; B, detecting the environmental information of the surrounding by using the robot; C, defining a mapping relationship between the path planning method and an artificial immune network; and D, resolving the maximum concentration of the artificial immune network and determining an antibody corresponding to the maximum concentration as the moving direction of the robot. The mobile robot path planning method of the invention has the advantage of reaching a destination point under a complex obstacle environment with the local minimization problem, and is feasible and effective under the complex obstacle environment. Simulation results obtained under the U-shaped obstacle environment further show the high efficiency of planning results obtained by the method of the invention under the complex environment.

Based on maximum a posteriori (MAP) estimates, video denoising techniques for frames of noisy video are provided. With the assumptions that noise is similar to or satisfies Gaussian distribution and an a priori conditional density model measurable by bit rate, a MAP estimate of a denoised current frame can be expressed as a rate distortion optimization problem. A constraint minimization problem based on the rate distortion optimization problem is used to vary a lagrangian parameter to optimize the denoising process. The lagrangian parameter is determined as a function of distortion of the noise.

The invention provides a Split Bregman weight iteration image blind restoration method based on a non-convex higher-order total variation model, and belongs to the technical field of image processing. The method is characterized in that firstly, a non-convex higher-order total variation regularization blind restoration cost function is obtained by introducing image border sparse prior information meeting a hyper-Laplacian model and by combining a high-order filter bank capable of generating piecewise linear solutions; secondly, a weight iteration strategy is provided, a minimization problem of the non-convex higher-order total variation regularization blind restoration cost function is converted into a minimization problem of an approximate convexity cost function with the updated weight; thirdly, the minimization problem of the approximate convexity cost function with the updated weight is converted into a new constraint solving problem through an operator split technology, and the constraint solving problem is converted into a split cost function through the method of adding a penalty term; fourthly, the split cost function is solved through a Split Bregman iteration solving frame. According to the Split Bregman weight iteration image blind restoration method based on the non-convex higher-order total variation model, an image can be restored effectively and rapidly, the shortage that a staircase effect is generated in a traditional total variation regularization blind restoration method is overcome, and meanwhile a better restoration effect on manually degraded images and actually degraded images is achieved.

The present invention provides a path planning and wireless communication method for an unmanned aerial vehicle cluster in a ground sensor network. A plurality of ground sensor nodes are randomly distributed over a wide area. A path planning and wireless communication mechanism optimization model of unmanned aerial vehicle cluster information acquisition is established under the condition that each ground sensor node can successfully upload a certain amount of data with limited energy. A dichotomy-based algorithm is applied to solve the problem of minimizing the total flight time of the unmanned aerial vehicle in the path planning and wireless communication mechanism optimization model of the unmanned aerial vehicle cluster information acquisition, and the optimal time-of-flight interval number N of unmanned aerial vehicle is obtained. Specifically, the optimal flight trajectory of the unmanned aerial vehicle, unmanned aerial vehicle and ground sensor scheduling strategy and the corresponding ground sensor transmission time and transmitting power are solved by the path planning and wireless communication mechanism optimization model of unmanned aerial vehicle cluster information acquisition through the algorithm based on continuous convex approximation under the given N. The flight time of the unmanned aerial vehicle is minimized by jointly optimizing the flight trajectory of the unmanned aerial vehicle cluster, the scheduling strategy of the unmanned aerial vehicle and the ground sensor nodes and the corresponding ground sensor transmitting power and transmission time andthus the energy of the unmanned aerial vehicle can be saved.

In cognitive radio, frequency spectrum sensing is an important premise and a core link for realizing the frequency spectrum dynamic access. The invention aims to provide a method for selecting a multi-user sensing channel in a cognitive sensor network, which comprises the following steps of: on an energy detection method, sensing normalized throughput acquired by a primary user channel from a maximized secondary user; using a detection time optimal scheme to budget the maximum throughput of all the secondary users and the selected channels of the secondary users; constructing a maximum throughput matrix; using a bidiagraph to express the channel selection problem under a multi-user multi-channel environment; converting a maximization and minimization problem into a unified minimization solution problem; and under the limit of target detection probability, adopting an improved Hungarian algorithm for the optimal selection of the sensing channel. By the method disclosed by the invention, the minimized throughput of the whole secondary user network can be greatly improved.

The invention discloses a beam hardening correction method of CT projection data based on data dependency. The method is based on physics imaging model of medical X-ray, constructs a minimization solution problem between CT projection data, combines the minimization problem and obtains solution of intermediate parameter. For realizing more precise correction result, the method estimates reducing proportion of substance with high density in projection data according to primary correction construction result and CT reprojection. The invention is adaptable for realizing beam hardening correction function of various X-ray CT devices. Compared with the existing method only with water modeling correction, the method of the invention respectively considers differences of beam hardening effect of tissues with different density. Compared with the existing method of bone correction, the method of the invention can automatically adapted to different imaging subjects more flexibly and has better correction precision.

The invention relates to a Fisher discriminant dictionary learning-based warehouse goods identification method. The method comprises the following steps of: firstly dividing warehouse goods images acquired under different conditions into two parts: a training sample set and a test sample set; respectively preprocessing the two sample sets, rearranging pixel values and carrying out PCA dimensionality reduction; learning the training sample set through a Fisher criterion method to obtain a discriminant dictionary, and representing a test sample by using linear weighting of the discriminant dictionary; solving an L2 norm minimization problem by adoption of a least square method, so as to obtain a sparse representation matrix of the test sample under the discriminant dictionary; and finally realizing warehouse goods identification via ei formed by various types of reconstruction errors and sparse encoding coefficients. According to the method provided by the invention, the problems that the traditional identification method is greatly influenced by selected features, the identification process is relatively complicated and plenty of classification information is lost in the construction processes of common dictionaries are solved; and the correct and rapid identification of different goods can be realized, so that foundation is laid for the realization of intelligent warehouses.

The invention discloses a cloud computing data center based unified resource scheduling energy-saving method. The method comprises the following steps: 1, initializing a network node and a virtual machine queue; 2, storing a virtual machine request in the virtual machine queue; 3, arranging virtual machines in a descending order according to a resource request number of the virtual machines; 4, traversing all network nodes in sequence, and judging whether a network node meets a request requirement of a current virtual machine or not; if yes, taking a network node with lowest energy consumption, requested by the current virtual machine, as a target placement node, and otherwise, looking for a network node with most residual available resources, emigrating one virtual machine on the node, and placing the current virtual machine; 5, sequentially selecting a next virtual machine as a current virtual machine, and making a judgment again; and 6, optimizing system energy consumption again. The method has the advantages that a contradictory relation between a power consumption minimization problem and SLA requirement satisfaction is balanced; the method is an energy consumption optimization oriented resource scheduling algorithm; and the method has higher efficiency in energy consumption optimization.

The invention belongs to the field of computer vision, and relates to a moving target detection method based on low rank and sparse decomposition under a dynamic background. According to the method, firstly, a gamma norm is introduced to approximate a rank function in an approximately unbiased manner so as to solve the problem that the obtained minimization problem cannot obtain the optimal solution and further reduce the detection performance due to the fact that a nuclear norm excessively punishes a large singular value, and the L1 / 2 norm is utilized to extract a sparse foreground target so as to enhance the robustness to noise. Based on the sparse and spatial discontinuity characteristics of the false alarm pixels, the spatial continuity constraints are proposed to suppress dynamic background pixels, and then a target detection model is constructed, and the obtained optimization problem is solved by using an augmented Lagrange multiplier method based on alternating direction minimization strategy extension. According to the invention, the moving target detection precision under the dynamic background condition is obviously improved.

A recursive binary splitting and recombination procedure coupled with a specially tailored genetic algorithm provides a solution to the general binary minimization problem with much lower run times than other methods. A minor modification to the procedure solves a variant of the binary minimization problem using an alternate operator logic that is crucial to solving certain combinatorial geometry problems.

The invention relates to a method for controlling the water level of a reservoir of an urban drainage system. In the conventional control process, traditional control strategies are adopted mostly, and the intelligent control aspect is less related, therefore the sewage overflow problem and the energy consumption reduction problem cannot be solved well. In the invention, on the basis of analyzing operation experiences of actual operators of the urban drainage system, the whole urban pipe network system is divided into a plurality of layers by utilizing an approximate urban pipe network system established by an urban geographic information management system, each layer can be divided into a plurality of typical drainage system local parts, each local part systematically applies predication control to well solve the local sewage overflow minimization problem, and finally, the whole system region is ensured to achieve a effect of sewage overflow minimization. The control technology provided by the invention can effectively reduce the influence of uncertain factors on the water level, make up the defects of the traditional controller, ensure the stability of a closed-loop system and ensure that a water level value of the reservoir does not exceed an appointed value at the same time.

The invention discloses a direction-adaptive image deblurring method, comprising steps of: (1) defining a minimum cost function for deblurring an image by direction-adaptive total variation regularization; (2) converting the unconstrained minimization problem in step (1) to a constrained problem by auxiliary variables d1=Hu, d2=∇xu and d3=∇yu; (3) obtaining a new minimum cost function from the constrained problem in step (2) by introducing penalty terms; and (4) converting the minimization problem in step (3) to an alternating minimization problem about u, d1, d2 and d3, where a minimum of a variable is calculated as other variables are determined, and obtaining a deblurred image by solving the alternating minimization problem by an alternative and iterative minimization process. Compared with the prior art, the present invention obtains a new direction-adaptive cost function by introducing local direction information into a maximum a posteriori algorithm, solves a problem of edges of an image restored by traditional TV regularization terms being blurred, and can restore images of complex blurring types or images with abundant textures.

The invention relates to a method of decreasing sewage overflow in combined flow pipe, which is controlled by fuzzy control under the base of layered control principle. For decreasing the generation of sewage overflow in combined flow pipe, prior methods have many shortages. The invention makes use of the urban multi-pump pipe network system built by the urban GIS system to divide the multi-pump pipe network system into several layers; each layer can be divided into several water drainage cells; each water drainage cell solves the minimum problem of partial sewage overflow by using fuzzy control; finally the sewage overflow in the whole system area is controlled to improve the urban flood prevention and anti-waterlogging ability and establish the foundation of operation of combined water drainage system from hand to automation.

The invention relates to a very large scale integration (VLSI) standard unit overall arranging method based on an L1 form model and belongs to the technical field of VLSI physical design automation. The method includes indicating a circuit as a super graph, modeling a VLSI standard unit overall arranging problem which adopts semi-cycle long-line calculation with the density constrained to be non-smooth into an L1 form minimum problem, adopting an optimum selection clustering algorithm applicable to modification of an L1 form model in a clustering stage to conduct clustering on a unit and conducting declustering on clusters in a declustering stage by adopting a nonlinear planning overall arranging method. The VLSI standard unit overall arranging method is reasonable in arrangement, high in efficiency, practical and good in arranging effect.

The invention provides a passive filter parameter design method based on a multi-objective optimization algorithm. The method is characterized by: firstly, analyzing and establishing a topology structure model of an electric power system; regarding a reactive power capacity which can be compensated for the electric power system as a constraint condition, taking a size of each harmonic current of a filtering channel and a voltage distortion rate as an optimization object so as to obtain a constrained multi-objective optimization problem; finally, using a genetic algorithm to solve the constrained multi-objective optimization problem and optimally calculating so as to obtain a Pareto optimal solution set with various kinds of characteristics which can be selected by a designer. By using the method of the invention, during designing the passive filter of the electric power system, a minimization problem of the each harmonic current of the filtering channel and the voltage distortion rate can be solved and the passive filter can be guaranteed to acquire the best filtering effect.

The invention discloses an arrival time-based cooperative localization method applied to a wireless sensor network, which uses a second-order cone relaxation technique and a positive semi-definite relaxation technique to relax a minimization problem obtained by constructing a maximum likelihood function to obtain a description of the problem mixing positive half-definite and second-order cone programming; thus, a global optimal solution can be ensured to obtain without the influence of local convergence, and the localization accuracy is high; the influence of measurement noise error can be effectively suppressed; meanwhile, the prior art can be used for solving an estimate value of the coordinates of an unknown target source, thereby reducing the distribution density of the anchor nodes and reducing the cost.

The invention discloses a robust human face image principal component feature extraction method and identification apparatus. The method comprises: by considering low-rank and sparse characteristics of training sample data of a human face image at the same time, directly performing low-rank and L1-norm minimization on a principal component feature embedded through projection, performing encoding to obtain robust projection P with good descriptiveness, directly extracting a low-rank and sparse principal component union feature of the human face image, and finishing image error correction processing; and by utilizing the embedded principal component feature of a training sample of a robust projection model, obtaining a linear multi-class classifier W* for classifying human face test images through an additional classification error minimization problem. When test samples are processed, a union feature of the test samples is extracted by utilizing a linear matrix P and then the test samples are classified by utilizing the classifier W*; and by introducing a thought of low-rank recovery and sparse description, the principal component feature, with better descriptiveness, of the human face image can be obtained by encoding, the noise can be eliminated, and the effect of human face identification is effectively improved.

A transform-domain adaptive filter uses selective partial updating of adaptive filter parameters. This updating may be based on a constrained minimization problem. The adaptive filter parameters are separated into subsets, and a subset is selected to be updated at each iteration. A normalization process applied to the frequency bins prior to multiplication by the adaptive filter parameters is used to prevent adaptive filter lock-up that may be experienced in the event of high energy levels of signals in particular frequency bins. Convergence of the transform domain filter is ensured at a rate generally faster than a corresponding time-domain adaptive filter. The transform-domain adaptive filter may be used for various applications, including system identification, channel equalization, or echo cancellation.

A mobile robot visual servo trajectory tracking predictive control method based on a primal-dual neural network comprises the following steps: 1) building a mobile robot kinematical model; 2) fixing acamera on a ceiling, so that the camera can obtain global visual information, and building a visual servo mobile robot error model; 3) according to the error model, obtaining a prediction equation and defining a predictive control performance index; and 4) establishing a performance index minimization problem as a minimization problem based on the primal-dual neural network, and solving controller gain by combining a primal-dual neural network module in Matlab-Simulink, so that a trajectory tracking task is completed. The method provided by the invention transforms a problem into a multi-constrained linear quadratic programming problem and quickly finds optimal solution by utilizing a PDNN.

The invention belongs to the technical field of image processing and pattern recognition, and discloses a face identification method based on gradient sparse representation. In recent years, owing to excellent recognition effect and wide application prospect, the face identification algorithm based on sparse representation gains more and more attention. However, the face identification algorithm based on sparse representation requires a complete training set, which is hardly satisfied in practical application; and the face identification algorithm based on sparse representation needs to solve the 1<1> minimization problem, which consumes plenty of time. In consideration of insensitivity of image gradient to uniform illuminance, the method introduces image gradient under the framework of sparse representation, and meanwhile adopts X-direction gradient, Y-direction gradient and image pixel value of a gray level image to identify a face image. Therefore, the method relaxes the requirement on the completeness of the training sample set to a great extent, and a better identification effect can be obtained only by selecting a few training samples from each type. Besides, the method solves the sparse representation factor of a testing face image on a training face image set by minimization of the 1<2> norm, so the method is fast and has higher application value.

The invention discloses a reference power grid model used for power system evaluation and incremental planning, and a solving method. The solving method comprises the following steps: establishing an original model of a reference power grid; changing the original model of the reference power grid; adding an operating cost part of a system in a target function and a constraint condition after a circuit is disconnected so as to divide the variables of the whole model into two classes, and then, carrying out modeling and solving on two classes of variables; constructing a main problem model, wherein the model processes the problem of power generation dispatch and an optimal construction volume of the circuit under a normal operation state; constructing a sub problem model, wherein the model processes a problem of the operating cost minimization of the system under the state that the circuit is subjected to a disconnection accident; and constructing a connection between the main problem and the sub problem. A Benders decomposition method is applied to the solving of the reference power grid model, the integral scale of the model is dramatically lowered, the reference power grid model of a large-scale power system can be effectively solved, solving efficiency is improved, and the model can be applied to a progressive planning scheme of the system.

The invention discloses a robust prediction fault-tolerant control method for executor faults of a time-delay uncertain system. Considering the parameter uncertainty and executor failure faults of a linear discrete time-delay system, linear matrix inequality and robust prediction control are utilized to provide the robust prediction fault-tolerant control method. According to a system model, an augmentation state model with output errors is established, and the control efficiency is improved. Based on a prediction control theory, a robust prediction control algorithm is proposed, and proportion factors and time-delay control items of a fault model are added in state feedback control; it is conductive to that 'minimum-maximum' optimization problems are converted into minimization problems through the linear matrix inequality, an optimal control law is obtained, and the stability of the system is ensured. By adopting the method, the control precision and robustness are effectively and systematically controlled by establishing the new state model and the improved state feedback control law. The robust prediction fault-tolerant control method is used for passive fault-tolerant controlof a time-delay uncertain system with executor failure faults.

The invention discloses a fluorescence molecular tomography reconstruction method. The method comprises the following steps: S1, establishing a system equation for the measured data of a surface and the distribution of a fluorescent target inside an imaging object by taking the optical feature parameter and anatomical structure information of the imaging object as prior information based on an optical transmission model and a finite element theory; S2, preprocessing the system equation established in the step S1 in order that rows of a sparse matrix in the preprocessed system equation are orthogonal to each other; S3, fusing the sparse property of light source spatial distribution, selecting a plurality of rows from the system equation established in the step S1, and establishing a new coefficient matrix and a new system equation; S4, converting the new system equation established in the step S3 into a minimization problem with a constraint condition, and solving the new system equation by adopting a simultaneous algebraic reconstruction technique to obtain the three-dimensional distribution and concentration of the fluorescent target inside the imaging object. The method has the beneficial effects that the sparse property of the fluorescent target spatial distribution is fused, the prior information is increased, and the reconstruction accuracy can be increased; the system equation is preprocessed, and the rows of the coefficient matrix of the system equation are orthogonal to each other, so that the algorithm convergence can be accelerated, and the reconstruction speed is increased.

The present invention is drawn to inertial type transducers and a system for reducing the complexity and difficulty of installing the transducers internal to a structure. The transducer is equipped with a wireless receiver for receiving both sound content signals and control signals, and an amplifier, along with a power supply. Because the amplifier is activated by the wireless receiver, there are no wires necessary to connect the transducer to the source of the sound content or control signals thereby vastly simplifying installation. Heat dissipation and height minimization are also addressed.

The system assesses problem behaviors and identifies those behaviors to be targeted through the use of an interview process that generates a behavior rating report. Through a parenting style interview, the system provides parents with issues that may influence their parenting skills, and helps the parents discover the strengths and weaknesses of their particular parenting style. A unique plan for each child is then developed to minimize problem behavior by changing the parents' response to the behaviors. This teaches the child that good decisions lead to positive consequences and higher self-esteem. A behavior chart is created and used to track the child's problem behavior. After a period of time, one week, the parent returns to develop a consequence system to improve behavior of the child. The child's baseline behavior is calculated, which will be used to measure progress. Specific consequences are determined that will be used to motivate the child to change his or her behavior. The child's baseline behavior can then be recalculated.

The invention discloses a two-time scanning-based high high-resolution optical scanning holographic section imaging method, belongs to the field of optical scanning and mainly overcomes the defect that larger defocus noise exists in the prior art when any two-dimensional sliced image is reconstructed. The two-time scanning-based high high-resolution optical scanning holographic section imaging method comprises the following steps of carrying out two-dimensional scanning on an object on a two-dimensional scanning mirror for the first time, moving the object towards the direction of the two-dimensional scanning mirror by a distance deltaZ after a first matrix equation containing section information is obtained and carrying out scanning on the object for the second time to obtain a second matrix equation containing the section information; and then integrating the first matrix equation and the second matrix equation into a minimum linear equation, converting the solution of a linear problem into a minimum problem and realizing section imaging through introducing a conjugate gradient algorithm. Through the technical scheme, the two-time scanning-based high high-resolution optical scanning holographic section imaging method has the beneficial effects that the high-precision section imaging is realized, and the defocus noise is greatly reduced. The two-time scanning-based high high-resolution optical scanning holographic section imaging method is suitable for various fields.

The invention relates to a method for correcting cone beam CT system geometric distortion based on a symmetrically repetitive template and belongs to the computed tomography technical field in the image processing field. According to the method provided by the invention, relations between angle deviation of a detector and the rank of a texture image; the solving of the angle deviation of the detector is equivalent to the minimization of the rank of the texture image under a certain constraint; the detector is adjusted according to the obtained angle deviation; and after the angle deviation is eliminated, and the displacement deviation of the detector is solved through utilizing a specific geometrical relationship. The low-rank texture-containing template is utilized to solved and obtained the angle deviation of a CT system, and it only needs to acquire a single-angle projection image; and the solving of the displacement deviation also only needs moving a rotary table for once, so that two projection images before and after change can be obtained. The method has the advantages of easiness in template manufacture as well as being simple and effective.

The invention discloses a fluorescence molecular tomography reconstruction algorithm based on an alternative iterative operation, which is characterized in that a weighted algebraic reconstruction technique and a steepest descent method are used alternately for solving. The fluorescence molecular tomography reconstruction algorithm comprises the following steps that (1), measurement data is acquired; (2), a linear relationship between the measurement data and target distribution is established; (3), a 2 norm minimization problem with a constraint condition is constructed; and (4), the weighted algebraic reconstruction technique and the steepest descent method are used alternately for solving the minimization problem, and a target distribution diagram is obtained. According to the fluorescence molecular tomography reconstruction algorithm, based on a light transmission theory and a finite element method, prior information such as an optical characteristic parameter and an anatomical structure is used, multipoint excitation and multipoint measurement are adopted, and the measurement data is obtained as far as possible, so that the pathosis of the problem is reduced; the weighted algebraic reconstruction technique and the steepest descent method are used alternately for solving the problem, so that a reconstruction result of fluorescence molecular tomography is improved effectively; and the fluorescence molecular tomography reconstruction algorithm has an important application value in the fields of molecular imaging, reconstruction algorithms and the like.

A multivariable solver for proximity correction uses a Jacobian matrix to approximate effects of perturbations of segment locations in successive iterations of a design loop. The problem is formulated as a constrained minimization problem with box, linear equality, and linear inequality constraints. To improve computational efficiency, non-local interactions are ignored, which results in a sparse Jacobian matrix.