75results about How to "Computational time complexity is low" patented technology

The invention relates to a global voting map matching method based on low-sampling-rate floating vehicle data. On the basis of floating vehicle GPS (Global Positioning System) trajectory data, influence of a topological structure of a road network and the GPS trajectory data of the adjacent spatial positions in different distances on the map matching process is considered; new map matching function indexes are defined; influence of the geometrical property and the topological structure of the road network on matching is comprehensively considered, so that a static matching matrix (SMM) as an initial map matching result is obtained; on the basis, the SMM is corrected by a distance weighing function defining influence of distance among reflected nodes, so that a dynamic matching matrix (DMM) is obtained; and finally, an optimal trajectory as the map matching result is found out by performing global voting on the DMM. According to the invention, relatively good map matching effect can be realized with low computing time complexity at low sampling rate.

The invention claims a user incentive method in a mobile crowd-sensing network based on reverse auction. The method utilizes a reverse auction framework to motivate users to participate in location-related sensing activities, and considers random exit situations in the process of user sensing. The scheme aims to increase the enthusiasm of users to participate in the sensing activities with the goal of maximizing user utility under the premise of a feasible budget. First, a task-centric approach is used to select users as bid winners of sensing tasks to ensure high task coverage. Secondly, according to the completion of the tasks of the bid winners, the users are paid according to a time proportion sharing rule to ensure the authenticity of the incentive. The user incentive method in the mobile crowd-sensing network based on reverse auction has the characteristics of computational validity, individual rationality, budget balance, authenticity and the like. The user incentive method of the invention can enable the users to obtain maximum utility, motivate the users to participate in the tasks, and improve the enthusiasm of the users to participate.

The invention discloses a data-driven traffic network distributed region control method, which comprises steps of traffic data filtering, traffic network lane saturation modeling and signal light timing distributed region control. Rolling optimization is carried out based on a prediction model, a series of future control signals U(k) is set, under given constraints, the best solution U*(k) is solved and is used as input of a control object. A Nash optimization method is further used to solve the correlation problem among multiple subsystems, and the optimal signal period length U<n><*>(k) is acquired. The data driven method is used to do research on a lane saturation prediction model, the signal light region timing optimization based on distributed prediction control is put forward, and aset of high-efficiency control method is provided for avoiding or alleviating traffic congestion.

The invention provides an excitation method in a time-dependence mobile crowd-sensing system, and aims at the crowd-sensing system of a time window task to design a user excitation method. The method comprises one direction auction flow and two stages including a user selection stage and a payment decision stage, wherein a greedy algorithm is adopted to solve a problem of user selection with minimized social cost in the user selection stage, and a degree of approximation of the excitation method is 1n |W|+1, wherein W is the length of a platform-sensing time window; and in the payment decision stage, the key reward of each selected user is calculated. The method comprises that the total time complexity of the user selection stage and the payment decision stage is O(n3.max|R|), wherein the n is a user number, and the |R| is a time window number contained in a user biding document. The excitation method has the good properties of individual rationality and cheating prevention and can generate a good approximate solution.

The invention discloses a real task allocation method applied to a preference crowd-sourcing system. A preference crowd-sourcing system model is put forward, the model has multiple preference task requesters, and each task requester submits one task. Each task has different work load, and the value of each task is related to the difficulty of the task and the effort level of a crowd-sourcing worker executing the task. Regarding the model, the invention further provides a real task allocation method, having the target of maximizing the total value of the allocated task. The real task allocation method is mainly composed of two core algorithms: a task allocation algorithm based on value greed and a task allocation algorithm based on value density greed. The real task allocation method is a random algorithm established over the two core algorithms. The real task allocation method has the characteristics of calculation effectiveness, work load feasibility, preference reality and constant factor approximation ratio.

The invention provides a single time window task excitation method for mobile crowd-sourcing perception. A user excitation method is designed for a single-time-window-task crowd-sourcing perception system. The method comprises a directional auction process and two stages, namely a user selection stage and a payment decision stage, wherein at the user selection stage, the problem of social-cost-minimized user selecting is solved by adopting a dynamic planning method, and at the payment decision stage, the amount of remuneration of each selected user is calculated by adopting a VCG (Virtual Channel Group) auction mechanism. According to the method, the total time complexity of the user selection stage and the payment decision stage is O(n<2>logn). The excitation method has the good characteristics of personal rationality and cheating prevention, and an optimum solution can be generated, namely a minimum-social-cost user subset S covering a perception time window can be found in polynomial time.

The invention discloses a crowdsourcing task allocation method considering preferences. The objective of the invention is to solve a value maximized allocation problem. Two kinds of crowdsourcing models, namely, a same-value task crowdsourcing model and a different-value task crowdsourcing model, are provided, and the two models both consider competitive relationships between requesters and the preferences of the task requesters to crowdsourcing workers; and a matching-based task allocation mechanism is put forwards respectively for the same-value task crowdsourcing model and the different-value task crowdsourcing model. With the method adopted, a task total value maximized allocation problem can be solved. The task allocation method provided by the invention is advantageous in calculation effectiveness, work load feasibility, preference authenticity and constant factor approximation ratio.

The invention relates to an intelligent distribution network and new energy. The invention provides a method for forming islands of a distribution system containing distributed power supply in order to overcome the defects in the prior art. The invention adopts the following technical scheme: firstly adopting the building and merging processes of the islands containing a plurality of tree knapsack problems (TKP) to obtain the initial island division scheme; and then correspondingly analyzing and adjusting the initial islands to obtain the final island scheme, wherein obtaining the initial island division scheme refers to solving TKP by the branch and bound algorithm, which comprises the following processes: firstly solving the critical item of TKP and then finding out the upper bound of TKP in the polynomial time, namely computed time complexity O(n2), by adopting lagrangian relaxation, wherein n is the total number of nodes. The method is mainly applied to rapidly solving the problem of division of the optimal islands of the distribution system containing distributed generation.

The invention discloses an urban traffic control sub-area division method based on fast global K-means spectral clustering, and relates the field of the urban traffic sub-area division. The basic thought thereof is taking traffic correlation between adjacent signal intersections in a road network as a division evidence, the method comprises the following steps: 1) selecting a to-be-researched roadnetwork to acquire a road topology structure and traffic flow data of the road network, and establishing a road network undirected graph; 2) computing a road network density Laplace matrix L and feature vectors corresponding to front k minimum feature values to construct a clustering feature matrix; 3) implementing the global K-means clustering algorithm on the clustering feature matrix X, and dividing the intersections in the same cluster into the same sub-area; and 4) judging whether the amount (absolute value of Vi) of the intersection in the sub-area after the clustering satisfies a constraint condition, performing boundary adjusting on the sub-area dissatisfying the constraint condition , and performing traffic control sub-area division on the road network G by adopting the fast global K-means spectral clustering. The time required by the clustering is shortened, and the traffic sub-area division efficiency can be improved.

The invention discloses a bioluminescence tomography reconstruction method based on multi-light-source resolution. Optical property parameters and anatomical structure information are utilized as priori knowledge, and multispectral fluorescent data are combined, so that accuracy of BLT light source reconstruction and resolution of reconstructed images are improved; a self-adaptive feasible region iterative shrinkage strategy is adopted, so that morbidness of BLT reconstruction problem is reduced, and dependence, of reconstruction results, on reconstruction algorithms is lowered. A mixed clustering method combining AP clustering and K-means clustering is provided, so that limitation and defects caused when respective clustering algorithms are adopted solely are overcome. In the method, energy distribution information of grid nodes is used directly, and information such as energy, central coordinates and size of a tetrahedron to which reconstructed nodes belong is fully considered, so that stability of clustering algorithms is improved to greatest extent, and calculation time and complexity of the algorithms are reduced.

The invention discloses a color image tamper detection method based on local quaternion index moment through which algorithm time complexity can be greatly reduced and a replication tamper area can be accurately detected. Self-adaptive super-pixel partitioning is performed on an image to be detected after Gaussian smoothing filtering preprocessing by utilizing a super-pixel segmentation algorithm based on entropy rate with combination of non-subsample Shearlet transform; the quaternion index moment is utilized to express Sifer feature point local area features and similar block matching is performed on all the Sifer feature point local area features of each super-pixel block; a suspected tamper area is determined by utilizing the matching feature points in the similar blocks obtained in the step 2; and the replication tamper area is marked by utilizing morphological filtering operation.

The invention discloses an interference elimination method based on effective channel parameter semi-orthogonal, comprising the steps of: calculating an effective receiving channel parameter of a user side and regarding the parameter as the basis for the selection of a semi-orthogonal user by a base station according to effective channels and cross channel parameters fed back by a user. After selecting the semi-orthogonal user of the effective receiving channel parameter by the base station, double-layer pre-coding vector design is carried out by the base station so as to eliminate the interference among cells and in the cells. Through the interference elimination method based on effective channel parameter semi-orthogonal, the average user capacity can be improved and the computation time complexity for the selection of the semi-orthogonal user is greatly reduced.

The invention provides updating method and device of a community structure in a dynamic network. The updating method includes the following steps of firstly, computing an initial network composed of a plurality of nodes to generate an initial community structure of the network; secondly, judging types of newly added sides according to interaction among the nodes; and thirdly, selecting corresponding incremental update strategies according to the types of the newly added sides to update the initial community structure of the network so as to acquire an updated community structure of the network. The newly added sides include internal sides of the community, sides of a trans-community, semi-new sides and brand-new sides. The updating method has the advantages of quick updating speed and low time complexity of computing.

The invention provides an SURF-based compression tracing method and an SURF-based compression tracing system. The method comprises the following steps of respectively collecting a positive sample and a negative sample of a t-th frame image, and positive samples and negative samples of the t-N-th to t-th frame images of a video file, using SURF to extract image features and a compression perception technology to obtain a first classifier and a second classifier, and weighting the first classifier and the second classifier to obtain a third classifier; using the third classifier to trace target images in the t+1 frame images of the video file. According to the target tracing method and system provided by the invention, due to the fact that the SURF extracted image features are adopted, the feature extracting speed is high, the image features can be still correctly extracted when a target environment is subjected to size change, rotation change and the like; original image features are compressed through a sparse random measurement matrix, so that the number of processed data is small, the updating speed of the classifiers is increased, and the target tracing method can be used for accurately tracing a target in a complex environment, and has real-time property and robustness.

The invention discloses a rapid peak searching method for an online gas chromatographic peak, which comprises the following steps: calibrating a peak searching interval by using a standard gas, and taking out a chromatographic signal in the peak searching interval; smoothing the chromatographic signal by adopting a central sliding mean filtering method, and filtering burrs and noise; selecting thedata point with the maximum numerical value as a preset peak top point; determining a peak starting point and a peak ending point; calculating and processing data points on the left side and the right side of the preset peak point respectively; and setting a proper window width, sliding leftwards by taking a preset peak top point as a starting point, continuing to slide leftwards if the value inthe window meets a monotonous rising trend until the condition is not met, determining the first point of the first window which does not meet the condition as the peak starting point, similarly determining a peak end point, and finishing peak searching. The peak searching method disclosed by the invention can be used for rapidly and accurately searching peaks, and the accuracy and the real-time performance of the online gas chromatograph for measuring the components and the concentration are improved.

The invention discloses a high fidelity full face texture fusing method of a three-dimensional full face camera, comprising following steps: calibrating multiple external parameters of a three-dimensional texture camera, projecting a triangular patch on the surface of a three-dimensional model to texture images photographed from different view angles; shearing the triangular patches projected on the texture images, calculating the fusing weight of the triangular patch on every texture image; carrying out affine transformation to all triangular patches so as to form a shape the same to the triangular patch corresponding to the maximum fusing weight; carrying out texture triangular fusion; and adhering the fused texture triangles to the corresponding triangular patches of the three-dimensional model.

The invention discloses a supply-demand-relationship-based mobile crowd sensing excitation method with a budget and provides a scheme of interaction between a platform and a smart phone user in a crowd sensing system. Contribution made by ach user to the system is considered as resources that are consumed by the user for sensing; and each sensing task has a budge. According to a supply-demand-relationship-based mobile crowd sensing excitation method with a budget, contribution of resource prices and users is decided by the supply-demand relationship. And the method provided by the invention has characteristics of computer effectiveness, high individual rationality, high profitability, good cheating prevention performance, and good non-monopoly property and the like.

The invention relates to a no-reference image quality evaluation method based on phase consistency and frequency domain entropy, which is characterized by the steps of using frequency domain entropy histogram curve features and phase consistency histogram curve features of an image under different scales to act as features of the image quality through down-sampling processing, feature extraction processing and feature fusion processing; and mapping the acquired features into an image quality score by using a two-step framework for no-reference image quality evaluation. According to the invention, the histogram curve features of a phase consistency value of the image are used to replace the histogram curve features of the space entropy in an SSEQ algorithm, thereby being capable of carrying out quality evaluation on the image quickly and robustly under the condition that the image is distorted with the type being unknown, improving the consistency between an algorithm prediction score and subjective scoring, reducing the computation time complexity of the algorithm, and having higher prediction accuracy and lower computation time complexity.

The invention discloses an optimization method for data layout of multi-data centres based on calculating relevancy. The optimization method comprises the steps of: generating an access associated matrix of an aggregation of computing sets and data sets according to a situation of executing calculation and procession of the data sets; obtaining, by means of calculation, the calculating relevancy between any two data sets, generating a corresponding calculating relevancy matrix; calculating a basic capacity of each data centre, defining a layout associated matrix, and laying out the data sets according to the calculating relevancy. According to the optimization method for data layout of multi-data centres based on calculating relevancy disclosed by the invention, by means of establishing the access associated matrix and the layout matrix, specific mathematical expression of the calculating relevancy is provided; according to the established calculating relevancy matrix, data layout is realized by a method of a low complexity; moreover, new data and intermediate data are dynamically laid out to a proper data centre, so that data scheduling cross the data centres can be effectively reduced, and an access performance of a system is improved.

The invention provides an OSPF and ISIS routing traffic data fusion method and system. The system includes: an OSPF message collecting module for acquiring network routing information of an OSPF; an OSPF topological stitching module for stitching a current network topology and sending the current network topology to an OSPF routing analyzing module; the OSPF routing analyzing module for acquiring network topology information of the whole network, correlating with the network topology information of the whole network through streams in the network, and acquiring source router information, destination router information and middle router information, combined with a CD algorithm; an ISIS message collecting module for collecting the message and acquiring network routing information of an ISIS; an ISIS topological stitching module for stitching the current network topology and sending the current network topology to the OSPF routing analyzing module; and an ISIS routing analyzing module for acquiring the network topology information of the whole network, correlating with the network topology information of the whole network through the streams in the network, and acquiring the source router information, the destination router information and the middle router information, combined with the CD algorithm.

The invention discloses a social network-based mobile crowdsourcing online motivation method. The method comprises the following steps of 1, for a task T with a total budget of B, selecting gamma users from a registered user set J to form an agent set A by a crowdsourcing platform, and calculating a budget paid to the users in a social network by each agent Ak; 2, decomposing the task T into multiple sub-tasks, thereby forming a task set gamma; restricting starting time and ending time of each sub-task; releasing the task set gamma to each agent in the agent set A by the crowdsourcing platform; 3, after any agent in the agent set A is released, sending the unfinished sub-task set gamma k at current moment to the social network of each agent Ak by each agent Ak, competing the sub-tasks in abidding manner by the online users in the social network of each agent Ak, and calculating rewards for a bidding winner; and 4, executing the winning sub-tasks by the winner, sending an executive result to the crowdsourcing platform and obtaining the rewards. The method is suitable for large-scale crowdsourcing tasks.