110results about How to "Solve optimization problems" patented technology

The invention discloses an improved particle swarm optimization based power distribution reconstruction optimization method. The improved particle swarm optimization based power distribution reconstruction optimization method comprises simplifying a power distribution network and encoding a particle swarm; decoding a particle swarm and calculating a fitness function which is corresponding to every particle; randomly initializing the particle swarm and giving a feasible solution for every particle; performing topology detection on the simplified network which is corresponding to every particle until all particles confirm to a power distribution network topology requirement; updating a position of every particle through algorithm iteration and performing simplified network topology detection; calculating a fitness value of every particle after position updating; assigning the position to Pi if the fitness value of the particle I is superior to the fitness value which is corresponding to an original individual extreme value position Pi; assigning the position to Pg if the fitness value of a current extreme value position Pi is superior to the fitness value of an original global extreme value Pg until a preset maximum number of iterations is achieved and enabling Pg to be an optimal solution. The improved particle swarm optimization based power distribution reconstruction optimization method has a rapid and efficient global optimization capability.

The embodiment of the invention discloses dispatching method, device and system of a wireless cellular network, which relate to the field of communication and can realize high-performance dispatching in a cognitive wireless cellular system. The dispatching method comprises the following steps of: determining resource allocation target information by a relay station according to obtained frequency spectrum perception information and channel quality information, and sending the resource allocation target information to a base station; allocating network resources to the relay station and notifying an allocation result to the relay station by the base station according to the obtained frequency spectrum perception information and channel quality information as well as the resource allocation target information sent by the relay station, and sending data to the relay station according to the allocation result; allocating resources to an accessed mobile terminal and notifying the allocation result to the mobile terminal by the relay station according to the data sent by the base station, the frequency spectrum perception information and the channel quality information, and sending data to the mobile terminal according to the allocation result.

The invention relates to a multi-objective decision engine parameter optimization method capable of enabling the minimum transmitting power, the minimum bit error rate and the maximum data rate to a cognitive radio system to be optimal at the same time. The method comprises steps of establishing a multi-objective decision engine model, calculating a multi-objective quantum ant colony algorithm path initial value, initializing a quantum information element of a multi-objective quantum ant colony algorithm, carrying out non dominated path sorting and the calculation of path congestion, sorting paths with the same non dominated path sorting rank, selecting a path with a non dominated path sorting rank of 1 and adding the path into an elite path set, calculating the path congestion, and selecting a path mapping to obtain the needed system parameter. According to the method, a discrete multiple-objective decision engine parameter optimization problem is solved, and the multi-objective quantum ant colony algorithm with non dominated path sorting is designed as a solution strategy, and the convergence precision is raised. The minimum transmitting power, the minimum bit error rate and the maximum data rate are considered at the same time, and the applicability is broadened.

The invention discloses a hybrid electric vehicle hydrogen consumption and load change energy management method. Through measures of model establishment, fuzzy rule introduction, energy management controller design and optimization and the like, a fuzzy energy management control method optimized through a genetic algorithm is established, and the operation efficiency of a fuel cell vehicle is improved. A subordinating degree function and a fuzzy rule base of the subordinating degree function are defined, and expert knowledge can be used for grasping a main output rule. The core of the method is optimization of parameters of the subordinating degree function, and inputs, outputs and fuzzy division of the fuzzy rule base of the inputs and the outputs are optimized. Due to the fact that the structure of a fuzzy rule cannot be directly expressed mathematically, an optimization problem is solved through the genetic algorithm.

The invention relates to a fan optimization arrangement method based on binary particle swarm optimization (BPSO), and aims to solve the problem of fan optimization arrangement of an irregularly-shaped wind field. The method comprises the following steps of: acquiring weather and geographical conditions of a wind field to be optimized and fan parameters to be selected; determining an optimized target and optimized constraint; dividing grid of a rectangular region; compiling an effective position array matrix corresponding to the shapes of the grid and the wind field, wherein in the effective position array matrix, each element is set as 1 or 0, 1 means a position at which a fan can be arranged, 0 means a position at which the fan cannot be arranged, the position at which the fan cannot bearranged is opposite to a place where the geographical condition is not suitable for arrangement of the fan in the wind field region and places outside the wind field region; constructing a solution space according to design variable to be optimized, looking for an optimal fan arrangement scheme in the solution space by adopting the BPSO according to a target function, optimizing and thus obtaining the optimal fan arrangement scheme.

The present invention relates to a finance modeling optimization method based on information self-circulation, characterized by: establishing a risk measurement initial model as the initial model, collecting customer information according to the data required by the model, measuring the risk value for each customer by using the model and the customer information, grouping the customers according to the measured risk value for each customer, making a statistics of default rate for each group of customers, determining whether the initial model is coincident with the measured result by contrasting the default rate for each group of customers with the defined default rate for each group of customers, correcting the risk measurement model if the initial model is not coincident with the measured result, and repeating the last step until the measured result of the model is coincident with the defined risk value. The adoption of the finance modeling optimization method combined with software in a credit management system can realize the risk management model optimization with the most rigorous thinking, the shortest time, the most convenient operation and the minimum cost.

The invention discloses a nuclear magnetic resonance imaging method and system. The method is provided based on a compact frame and some fixing sparsely variable limitations. The method comprises the steps of constructing an initial compact frame; establishing a reconstruction model of a target image based on a compact frame sparse expression item under the constraint condition of data fitting bound terms; conducting alternant iterative upgrade on the compact frame, a magnetic resonance image and k space data; obtaining a reconstructed target image according to the reconstruction model when iteration reaches the end condition. According to the nuclear magnetic resonance imaging method, sparsity of the magnetic resonance image is made full use of, firstly the initial compact frame is constructed through a two-dimensional filter theoretical compact frame, then in the image reconstructing process, an on-line study method is utilized to upgrade the compact frame, and a method of Bregman is utilized twice in the solving process to effectively solve the optimization problem of the model so as to achieve the fast and high-quality magnetic resonance image and to improve the imaging precision of the magnetic resonance image.

A method for calibrating a robot, wherein the robot comprises a movable part with a calibration marker. The method comprising: positioning the calibration marker in a plurality of positions along an optical line of a camera unit; imaging the calibration marker at the plurality of positions along the optical line to establish line positions P 1 ... P N of the calibration marker along the optical line within a tolerance k, while monitoring joint values j 1 ... j M of the robot; establishing an error function based on resulting calculated robot positions P' 1 ... P' N for the calibration marker for joint values j 1 ... j M at each line position P 1 ... P N for the calibration marker; identifying a set of robot kinematic parameters by solving an optimization problem based on the error function; and updating a kinematic model of the robot by means of the identified set of robot kinematic parameters. It is also referred to a robot system, a robot controller, a robot unit, use of a robot unit, a computer program P and a computer readable storage medium.

The invention discloses a multi-view learning method of joint low-rank representation (LRR) and sparse regression. The method comprises the following steps: respectively carrying out extraction of low-level features and high-level attribute features on an SUN data set with image memory degree score labels; placing three parts of low-rank representation, a sparse regression model and multi-view consistency losses under the same framework to form a whole, and constructing a multi-view-joint low-rank and sparse-regression (Mv-JLRSR) model; utilizing a multi-vision adaptive regression (MAR) algorithm to solve the problem of automatically predicting memoryability of images, and obtaining relationships of the low-layer image features, the image attribute features and image memory degrees under optimal parameters; and combining the low-level features and the high-level attribute features of the images, utilizing relationship results, which are obtained under the optimal parameters, to predictmemory degrees of the images of a database test set, and using relevant evaluation criteria to verify prediction results. According to the multi-view learning framework of joint low-rank representation and sparse regression of the invention, the memoryability of image areas is accurately predicted.

The invention discloses a robot trajectory tracking method, a robot trajectory tracking system and a storage medium which integrate the trajectory optimization and control, and better ensure the feasibility of trajectory tracking of a robot. When the robot passes through a dense obstacle, the weight in the target function is correspondingly adjusted according to the shortest distance between the current position of the differential robot and the obstacle by adopting the self-adaptive weight, so that the safety and the running speed can be considered when the robot performs trajectory tracking,and the trajectory tracking performance of an obstacle area is improved. According to the method, the obstacle avoidance constraint is put into the target function and is converted into the soft constraint, the optimal control input is found in the mode of sampling in the allowable control space, the non-convex optimization problem is solved, and meanwhile, the obstacle avoidance performance of the mobile robot is improved in the mode of dynamically adjusting the weight.

The invention discloses an SDN (Software Defined Network)-based space-ground integrated adaptive dynamic QoS (Quality of Service) routing method. The method comprises the following steps of: establishing an SDN-based hierarchical clustering network model; establishing network resource mapping; and establishing a multi-constraint QOS adaptive routing algorithm SDN-AD. According to the method, the control overhead can be effectively reduced by reducing long-distance transmission and shortest distance clustering of the control packet, and the transmission efficiency is improved. According to the method, the multi-constraint QoS problem is formulated into the optimization problem with the minimum transmission cost as the target, the transmission cost of links of different types can be effectively calculated, and the difference between satellites of different levels and a ground network is shielded. The method can better adapt to network changes, so that services meeting different service quality requirements are provided. According to the invention, the optimization problem is solved, and self-adaptive routing is realized. The method provided by the invention has better performance in the aspects of overhead control, network service quality and algorithm convergence speed.

The invention relates to the field of network communication, in particular to a method and equipment for sending a node list. The method comprises the following steps of: receiving a node list request message sent by a client, wherein the node list request message is used for requesting a node capable of providing a specific resource; acquiring the node list capable of providing the specific resource according to the node list request message; and sending nodes in the node list to the client in batches according to the priority level of each node in the node list and the client information ofthe client, wherein the priority level of each node is determined according to network topology information or an operator strategy. In the technical scheme provided by the embodiment of the invention, the nodes are sent to the client in batches according to the client information and the priority level of each node in the node list, so that P2P flow optimization efficiency is improved.

The present invention provides a terminal and terminal configuration parameters, involving wireless communications technology. Wherein the parameter configuration method comprising: at the terminal to obtain configuration information for this network side terminal set in advance; the terminal configuration parameters based on the configuration information. The terminal obtains under different scenarios of its own network side corresponding configuration information, and then its configuration parameters based on the configuration information to solve the optimization problem resides in the idle state and connection terminal when the parameters so that the terminal can reside in the correct cell and be in the correct network access, improve the performance of the terminal.

The invention discloses on-vehicle position monitoring equipment and a control method. The on-vehicle position monitoring equipment comprises a control unit, a display screen, an input device, a global positioning system (GPS) receiving unit which is shared by an on-vehicle map navigation system and an on-vehicle position monitoring system, and a data transmission unit which is in wireless communication with a monitoring center, wherein the GPS receiving unit, the data transmission unit, the display screen and the input device are respectively connected with the control unit; one channel of GPS position information of the equipment is transmitted to the monitoring center, and the other channel of GPS position information is transmitted to a local navigation map system; and when navigation track information which is sent by the monitoring center is received, navigation is performed according to the navigation track information of the monitoring center. A user only needs to purchase a set of equipment, so position monitoring and navigation functions can be realized; and meanwhile, operation of a driver is simplified, and the driver only needs to drive a vehicle along a navigation track, does not need to answer an instruction phone or receive a short message, and can concentrate on driving the vehicle, so driving safety is improved.

The invention discloses a GIS (Geographic Information System) and differential evolution algorithm-based railway line double-layer optimization method. The optimization method comprises the followingfour steps of: firstly, constructing a digital elevation model and an environmental constraint object between a starting point and an end point of a line by utilizing a GIS technology; determining a total investment objective function in combination with the engineering cost composition, the digital elevation model and the environmental constraint object; determining corresponding optimization strategy parameters according to the intersection point distribution, namely a design variable, a design variable value range and an optimization cycle termination condition; and finally, initializing ascheme population, carrying out differential evolution on the scheme population according to the double-layer optimization model of the nested vertical section optimization process in the planar optimization process, and selecting a line scheme with a minimum total investment objective function value as a final optimization line after a loop termination condition is met. The invention aims to provide an effective way for realizing intelligent optimization design of a railway line in a GIS environment, and points out the step of solving the optimization problem of the railway line by the swarmintelligence evolutionary algorithm.

The invention discloses an image classification method based on cross-type migration active learning. The method comprises the following steps of carrying out vectoring characteristic representation on images in auxiliary type data and target type data; constructing auxiliary type attribute representation and target type attribute representation; constructing a target function; optimizing and solving the target function to acquire a generation function; using the generation function and the target type attribute representation to acquire a classification model; using the classification model to calculate uncertainties of all the images without marks in the target type data; selecting the image without mark whose uncertainty is maximal and carrying out marking; updating a weight of an image in the target type data and reconstructing the target function. In the invention, an image sample who has the largest amount of information can be effectively selected in the target type data and is marked, which is good for training an accurate classification model under the condition that only a few of target types have marked data, and marking cost is reduced.

The invention discloses a smelting burdening method based on an optimized ecological niche genetic algorithm, which comprises the following steps: S1, establishing a multi-objective function, proposing indexes of various parameters as multi-constraint conditions, and establishing a burdening optimization mathematical model; S2, performing weighting processing on the multi-objective function basedon a difference particle swarm optimization method, and converting the multi-objective model into a problem of a single-objective function; S3, dividing the population into K clusters according to a K-means clustering algorithm, and determining a clustering center; S4, performing selection, self-adaptive crossover, self-adaptive variation and niche elimination operation; and S5, judging whether atermination condition is met or not to obtain the final addition amount of the ingredients. According to the invention, problems of difficult multi-objective solving and easy falling into a local optimal solution and the like existing in batching optimization are solved; according to the smelting batching method based on the optimized ecological niche genetic algorithm, the three processes of determining the weight of the multi-objective function, the ecological niche radius and crossover and mutation operators are improved, the accuracy of the batching ratio is effectively and remarkably improved, and the cost is saved.

The invention discloses a task unloading method and system in a vehicle edge computing network. The method comprises the steps: obtaining the initial unloading bit number and unit price of a vehicle,and building a utility function of an edge server; obtaining a data transmission rate between the vehicle and an edge server selected by the vehicle, a time delay generated by a calculation task between the vehicle and the edge server selected by the vehicle, and a cost required for calculating resources by using the edge server, and establishing a utility function of the vehicle; establishing anoptimization problem of maximizing the utility of the edge server according to the utility function of the edge server; establishing an optimization problem of maximizing the utility of the vehicle according to the utility function of the vehicle; solving the optimization problem based on the Steinberg game theory to obtain a vehicle selection variable and an unloading bit number variable corresponding to the equilibrium point and an optimal solution of the unit price collected by the edge server; and based on the vehicle selection variable and the unloading bit number variable corresponding to the equilibrium point and the optimal solution of the unit price collected by the edge server, realizing unloading of the vehicle task.

The present invention provides a data transmission method and device. According to the embodiment of the invention, a first node directly sends itself forward pass address to a second node in the condition that whether there is an X2 interface between the first node and the second node by using the first node or not. The problem is solved that how to perform optimization of the data in a dual-connection switching scene in the prior art so as to enhance the flexibility and the practicality of the data forward pass.

The invention relates to multi-modal multi-class Boosting frame construction method and device for cross-modal retrieval. The multi-modal multi-class Boosting frame construction method for the cross-modal retrieval comprises the steps: constructing a target risk function including intra-mode loss of each mode and inter-mode loss among all modes; according to gradient descent strategy, sequentially updating a predictor of each mode in the risk function, but fixing predictors of other modes; after the predictors of all modes are updated, defining primary cyclic iteration, performing T times of cyclic iteration, and then learning an optimal predictor of each mode, which enables a target function to be minimum; and transferring a fitted edge generated by the optimal predictor of each mode into a common semantic space by utilizing a Sigmoid function, thereby implementing the cross-modal retrieval. The multi-modal multi-class Boosting frame construction method for the cross-modal retrieval considers intra-mode semantic relevance, can reinforce intra-mode semantic information with poor quality to a certain extent, and has relatively greater performance in a cross-modal retrieval task.

According to a seed node selection method for unloading the cellular traffic through the opportunistic mobile network, the cellular traffic is transmitted to some selected initial seeds in the mobilenetwork through the cellular network, and then free propagation is carried out in an opportunistic communication mode, so that all nodes in the mobile network can access the cellular network; an optimal number of initial seeds are found out on the basis of considering the cost of direct transmission through the cellular network and the freshness of the propagated content, so as to improve the utility value of the overall content to the maximum extent; a content utility value gain is defined, and an optimal copy number k of the content transmitted through the cellular network is calculated; based on a seed selection method, the optimal number of initial seeds is found, so that the overall content utility value of nodes in the network is maximized. According to the method, the freshness of the content and the transmission cost from the cellular network to the initial seeds are considered, and in order to solve the utility optimization problem, two seed selection methods are provided to find the optimal number of the initial seeds, so that the overall content utility value of the nodes in the network is maximized.

The invention discloses a software optimizing method. The method comprises the following steps: determining symbol information for software debugging in software to be generated, wherein the symbol information comprises corresponding relationship between characteristic parameters of debugging symbols and debugging symbols; and removing the symbol information when the software is generated. The invention discloses a software debugging method. The method comprises the following steps: acquiring the removed symbol information for software debugging when the software is generated, wherein the symbol information comprises the corresponding relationship between the characteristic parameters of the debugging symbols and the debugging symbols; and debugging the software by utilizing the symbol information. The invention simultaneously discloses a software optimizing device and a software debugging device. Software optimization by adopting the invention can directly and efficiently solve the software optimization problem; software debugging by adopting the invention can reduce the volume of the software to achieve the aim of directly and efficiently optimizing the software, and ensure that the software developing staff can carry out software debugging smoothly by utilizing the symbol information.

The invention discloses a transcritical carbon dioxide system and an optimization coupling method of the transcritical carbon dioxide system. According to the system, a conventional transcritical carbon dioxide system cycle is taken as a main body, and a conventional subcritical R134a refrigeration cycle is taken as a precooler. By means of the optimization coupling method, thermodynamic operationparameters of the system can be accurately predicted according to hardware parameters configured in the system and set operation conditions, and optimization can be carried out according to the operation conditions of the system, wherein the optimization objects are the optimal exhaust pressure of a transcritical carbon dioxide circulation part and the optimal intermediate temperature of the system. Through the proper use of a full coupling mathematical model, the performance of the type of unit can be accurately predicted under specific working conditions, related systems can be theoretically studied, the experimental progress can be guided through the prediction of theoretical performance as well, and even the design process of the type of machine can be guided according to the performance conditions that the system can achieve, and good reference significance is provided for related industries.

The invention provides a microchip used for early diagnosis of the lung cancer and based on a semiconductor quantum dot, and a method of applying the microchip in preparation of a DNA probe used for detecting circulating miRNAs extracted from peripheral blood serum of a high risk group. The semiconductor quantum dot is used as a fluorescence detection signal of the microchip and is a multi-shell-structured Cd / Ge semiconductor quantum dot with a particle size of 3 to 6 nm; and the shell layer of the semiconductor quantum dot is coated by an inorganic layer selected from a group consisting of a CdS inorganic layer and a ZnS inorganic layer. According to the invention, directed at 7 to 38 circulating miRNAs in an early stage of the lung cancer, the method employs improved hybridization process and a DNA-quantum dot fluorescence signal to detect low-abundance miRNAs in serum, so a detection system applicable to clinical diagnosis and according with national in-vitro diagnostic reagent and quality control standards is formed; quantum dot nanometer technology and microchip technology are combined together, the miRNA detection system applicable to early serological diagnosis of the lung cancer is provided; and the detection system can distinguish benignancy and malignancy of lesion in the early stage of the lung cancer, enables patients with the lung cancer to be treated on time and prolongs the lifetime of the patients.

The invention discloses a parameter configuration method for a graphics processing unit (GPU) in a mobile terminal. The method comprises the following steps of acquiring a scene identification of themobile terminal at a current working scene; judging whether a corresponding relation between a preset scene identification and a GPU working parameter configuration file is matched with a GPU workingparameter configuration file corresponding to the acquired scene identification; and configuring GPU parameters of the mobile terminal in the current working scene according to the GPU working parameter configuration file corresponding to the scene identification under a condition that a judgment result is yes. The invention also discloses the mobile terminal and a computer readable storage medium, and solves a problem of how to optimize the GPU working parameters in a working process of an intelligent terminal in the related technology, and the responsive GPU working parameters are configuredfor different working scenes through the corresponding relation between the preset scene identification and the GPU working parameters, so that the working parameters are effectively optimized, and the user experience is improved.

The invention belongs to the technical field of multimodal transport service network optimization, and discloses a preference degree-based multimodal transport service network optimization method, which is an experimental method for discrete selection experiments (DCE) by taking transport price, transport time, transport frequency, transport reliability and transport safety as service attributes.Three problems of service type optimization, service path optimization and transfer process optimization are taken as main contents. The operation cost, the transportation cost, the transfer operationcost and the preference-degree-based service value cost of the service are used as constituent elements of the total cost of the service network. The multimodal transport service network collaborative optimization model with line capability constraints is constructed, and the service frequency representation method is processed in combination with the characteristics of the model, so that the solving difficulty of the model is reduced, and solving of the multimodal transport service network collaborative optimization model is realized.

The invention relates to the field of storage devices, in particular to a storage card device with an NFC function, which comprises an SD storage function module and an identity recognition function module, and the SD storage function module comprises a first interface unit, a control unit and a storage unit; the identity recognition function module comprises a second interface unit, a processor unit, a program storage unit, a data storage unit and a work storage unit; an interface part of the memory card device comprises a memory function interface and an identification function interface. The method is used for solving the problems of space structure waste and complexity caused by double NANO SIM card holders in the intelligent terminal; an SD card storage function can be used on one NANO SIM card seat at the same time; the SIM card identity recognition function and the NFC function can be used separately and independently and can also be used simultaneously, the size of the terminalcan be miniaturized, the optimization problem of the terminal space is solved, and the memory card has the mobile payment function while meeting the requirement of file storage.

The invention discloses a lithium ion battery state calculation method based on hybrid filtering. The method comprises the following steps: establishing a lithium battery second-order equivalent circuit model and obtaining a space state equation; performing online parameter identification on the second-order equivalent circuit model by adopting extended Kalman filtering to obtain an SOH estimatedvalue; estimating the SOC value of the lithium battery by adopting a sliding variable structure filtering algorithm; and correcting parameters of the hybrid filter by adopting a particle swarm optimization algorithm and obtaining an accurate SOH estimated value and an SOC estimated value of the lithium battery. The state of the battery can be estimated online in real time, and the method is high in reliability, good in stability and easy and convenient to implement.

The invention discloses an emotion recognition method in video learning based on eye movement measurement. The method comprises the steps of obtaining eye movement information such as pupil diameter,blink, gaze and eye jump of a learner by watching a learning video stimulation material and utilizing an eye movement tracking technology; and researching the relationship between the emotional stateof the learner and the eye movement physiological signal in the online learning process. The eye movement characteristics most relevant to the emotional state of the learner are obtained. After normalization processing is performed on the eye movement data, a support vector machine (SVM) machine learning model is adopted to classify the positive emotional state and the negative emotional state ofthe eye movement features, so that an eye movement information emotion recognition result can be obtained. The emotion recognition precision can be improved.