61 results about "Hierarchical algorithm" patented technology

Systems and methods are described for adjusting the operation of implantable stimulation devices used to provide medical monitoring and treatment. Several hierarchical algorithms are described which operate according to conditionally obtaining a patient response to an alert signal. In one such strategy semi-automatic therapy adjustment occurs by automatically issuing patient alert messages when selected operations are to occur, and using a patient's response to the alert message that is provided within a selected time limit in order to contingently adjust therapy. Methods are also described for resolving conflicts which may occur when time information and sensed data information each indicate different patient states are occurring. Although treatment of neural and cardiac disorders is emphasized, the techniques can be applied to the monitoring and treatment of any medical disorder with an implanted device.

A system and technique for displaying a document's workflow history are disclosed. The system includes a graphical user interface for displaying one or more graphical representations of events generated by an application configured to edit a document. Each of the events generated by the application may be stored in a data structure that is associated with one or more portions of the document. The data structure may also be associated with a digital image that reflects the state of the document at the time the event was generated and one or more frames of digital video captured substantially simultaneously with the generation of the event. The system may display the stored events via graphical representations in the graphical user interface that represent a portion of the total document workflow history. A user may navigate through the graphical events based on a hierarchical algorithm for clustering events.

The invention provides a combined modulation recognition method based on clustering and a support vector machine in order to overcome the shortcoming of low modulation recognition rate of a clustering algorithm with a low signal to noise ratio. According to the method, a characteristic parameter of a modulation signal is extracted by using the clustering algorithm according to a phase shift keying/quadrature amplitude modulation (PSK/QAM) mode based on a constellation diagram; and a modulation mode for a signal is recognized through the support vector machine, so that the modulation recognition rate of a system is increased. The method comprises the following steps of: aiming at the PSK/QAM mode based on the constellation diagram, reconstructing the constellation diagram of a receiving signal by using the clustering algorithm; and obtaining an effective function value, which can reflect an outstanding difference of modulation types under different clustering central numbers, as the characteristic parameter input into the support vector machine by constructing an effectiveness evaluation function. In order to overcome the shortcoming that two common algorithms of one to multiple and one to one have high calculation complexity when the support vector machine recognizes multiple types, the support vector machine is trained by adopting a hierarchical algorithm.

Disclosed is a method for warning abnormal driving based on a reasonable driving range of intersection vehicles. Intersection vehicle trajectories are comprehensively analyzed based on historical surveillance video data by a hierarchical algorithm, an LCSS algorithm, or DTW algorithm to determine a reasonable vehicle trajectory range at an intersection in each flow direction, thereby realizing real-time warning of abnormal moving vehicles based on monitoring videos, efficiently judging illegal behaviors, knowing intersection anomalies in time, improving the efficiency of traffic management, strengthening the intensity of black spot control at the intersection, and providing a safe and smooth traffic environment for the intersection.

The invention relates to a method for predicting power load and provides the power load prediction method which can better solve the problem of low accuracy. The invention provides application thereof in load prediction based on CURE algorithm. Application of the CURE algorithm in the load prediction comprises the following steps: (1) extracting data sample from a historical database of load prediction; (2) adopting hierarchical algorithm to cluster each sub-region of the data sample; and (3) clustering all the data in the sample and inputting representative points only including clusters found when all sub-regions cluster independently.

The present disclosure relates to analysing input data, prior to encoding, using one or more hierarchical algorithms. According to a first aspect, there is provided a method for producing output data using one or more input data and one or more hierarchical algorithms, comprising the steps of applying the hierarchical algorithm to the one or more input data; and producing output data to be used by an encoder; wherein one of the one or more input data is uncompressed; and wherein the output data is used to modify a decision making process associated with the encoder.

Disclosed is a power consumption scheduling method capable of reducing averages and fluctuations of power costs. The method comprises (i) performing system modeling which includes power consumption modeling and modeling for generating renewable energy sources; (ii) treating reduction of power costs and fluctuation degrees of terminal users as an optimization objective according to system modeling, and establishing optimization problems; and (iii) using a fast hierarchical algorithm based on particle swarm optimization (PSO) for computing scheduling strategies of power consumption of electric apparatuses of optimal users. According to the power consumption scheduling method, averages and fluctuations of power costs of terminal users of smart power grids can be reduced effectively, and the fast hierarchical algorithm based on particle group optimization for computing scheduling strategies of power consumption of electric apparatuses of optimal users is provided with a low computation complexity.

The invention provides a hierarchical algorithm of selective laser sintering, which is used for establishing a precise product model. A method comprises the following steps: (1) reading and displaying an STL file; (2) locally encrypting an STL model; (3) selecting a layered slice plane; (4) sorting the layered slice plane; (5) compensating a positive and negative error; and (6) outputting a model. According to the hierarchical algorithm, through establishing a dynamic facet table for different tangent planes, the usage mount of a memory is lowered, time required by topological information is reduced, and the efficiency of hierarchical processing is improved; and meanwhile, through the method of locally encrypting the model and compensating the positive and negative error, the precision of the model is greatly increased, and a strong technical support is provided for the improvement of the precision of part manufacturing.

The invention provides a realizing method of network attack event quantitative hierarchical algorithm. According to the method, network attack events reported by a safety device are collected; a regular expression algorithm is used to obtain network attack qualitative hierarchical values (pv); network mirror image traffic is collected in real time; session information related to the attach events is obtained according to a traffic analyzing algorithm; network attack quantitative hierarchical values (cv) are obtained basing on the whole network influence ratio of the attack events; therefore, the quantitative hierarchy of the attack events is realized. According to the method, the quantitative hierarchical calculation is carried out to the safety events generated by the safety device; the problem that the current safety events are inaccurate and non-uniform in hierarchy is solved; and effective and reliable basis is provided for safety decision.

The invention discloses a self-adaptive hierarchical algorithm for 3D (three-dimensional) printing. The self-adaptive hierarchical algorithm comprises the following steps: (1) according to a triangle normal vector of a 3D model in a STL format, solving an included angle alpha 0 between the normal vector of each triangle and the horizontal plane; (2) solving the corresponding layer thickness t0 of each triangle; (3) taking the maximum value of t0 as a try-cut value, carrying out trial hierarchy on the 3D model in the STL format, and beginning from the bottom the model; (4) after completing the first trial hierarchy, determine a layer thickness value of a first layer; ( 5) after determining the layer thickness value of the first layer, on the basis of the first layer, carrying out secondary trial hierarchy with the try-cut value as a layer thickness; (6) repeating the fourth step to determine a layer thickness value of a second layer; and (7) frequently repeating the fifth and sixth steps until hierarchy of the entire 3D model in the STL format is completed. In the premise of ensuring the accuracy of a shaped product, the algorithm can shorten the shaping time and improve the shaping efficiency, guarantee the printing precision and improve the shaping efficiency.

The invention discloses a hybrid model decision-based remote sensing image berthing ship detection method, and aims at realizing the accurate detection of ships in ports by adoption of a hierarchical algorithm framework. In a candidate area screening stage, rapid water separation is carried out on input high-resolution large-size port images, and candidate areas are rapidly screened on the basis of an all-around bi-dimensional cross scanning method. In a candidate area discrimination state, a method for carrying reliable discrimination on the candidate areas on the basis of a hybrid decision template is proposed. The method disclosed by the invention comprises the following steps of: firstly carrying out training to obtain three decision sub-models according to key positions and overall features of ships and a context of the surrounding environment; and carrying out candidate area discrimination on judgement results of the sub-models on the basis of the hybrid model decision template. Compared with the traditional method, the method disclosed by the invention has the advantages of effectively overcoming the adverse effects caused by the factors such as wide ship varieties, different berthing postures and partial ship body sheltering, and obtaining detection results with relatively high accuracy through relatively short time.

The invention discloses a 3D printing pretreatment hierarchical algorithm based on the intensive feature. The method is implemented according to the following steps that firstly, a datum tangent planeis determined through minimum height triangular patch traverse searching and determining on a 3D model in the STL file format; secondly, upward and downward layering is conducted with the datum tangent plane as the datum, a minimum height triangular patch in an intersection triangular patch set is determined, the height of the triangular patch serves as the reference to determine the layer heightof the layer, self-adaption layering is conducted, and the appearance precision of the 3D printing model is guaranteed; and thirdly, by parity of reasoning, all layering work of the 3D model is finished. According to the algorithm, the position where the minimum height triangular patch of the 3D model is located serves as the layering datum, downward and upward layering treatment is conducted, layering is conducted according to the self-adaptation layering rule, the layering height is automatically set according to the patch height and size, the printing efficiency is improved, and the precision of the printed 3D model can be guaranteed as well.

The invention discloses an LMD laser manufacturing process based on a bionic function. The LMD laser manufacturing process concretely comprises the steps: step 1, conducting solid modeling for a formed part; step 2, transforming the solid model of a metal coating into an STL file format; step 3, formulating the hierarchical algorithm of the metal coating; step 4, planning the scanning path of the metal coating; step 5, conducting a forming test. The process realizes the control for the forming speed and precision of an LMD process, and ensures the production efficiency while obtaining the formed part with high quality. In addition, the obtained metal coating is a structure for simulating a tortoise shell and has the characteristics of high strength and light weight similar with the tortoise shell.

The invention discloses a non-intrusive load decomposition method based on a sparse classifier hierarchical algorithm. The method comprises the following steps: S1, acquiring voltage and current dataof an electric appliance; s2, calculating low-frequency active power, low-frequency reactive power and high-frequency current harmonics of the electric appliance; s3, performing clustering analysis; s4, constructing a feature dictionary; s5, constructing a sparse matrix; s6, performing power identification; s7, judging a result, and outputting a judgment result if the overlapping number in the judgment result is equal to 1; otherwise, executing the step S8; and S8, performing current harmonic identification and outputting an identification result. By adopting the non-intrusive load decomposition method based on the sparse classifier hierarchical algorithm, whether current harmonic identification is introduced or not can be determined by comprehensively considering active power identification and reactive power identification results of the electrical appliances when part of the electrical appliances with similar power exist in users, so it is ensured that when the power is similar, long-time and large-calculation-amount electric appliance identification does not need to be performed, and the electric appliance identification result can be guaranteed.

According to an energy efficiency sensitive index detection method and system for integrated energy system users, mathematical modeling is carried out on various influence factors through an analytic hierarchy algorithm fusing entropy weights, and quantitative conversion and unified dimensionless of the influence factors are achieved through an index fuzzification method of a multi-target evaluation system; and on the premise of the priority level of each evaluation index, based on energy efficiency index data of different users in a typical energy consumption scene, a hierarchical algorithm capable of realizing multi-target balance coordination is provided as a model solving strategy, and reasonable and effective guidance is provided for improving the energy efficiency of a user side. The comprehensive energy efficiency detection score model is established based on a sub-industry mode according to the difference of industries where energy users are located, so that the energy efficiency detection index system has representativeness, pertinence and comparability when representing the energy consumption characteristics of the industries, energy saving diagnosis and analysis can be carried out according to the energy efficiency detection result, a basis for formulating an energy saving scheme is provided for the users, and a user is promoted to realize energy-saving and consumption-reducing targets.

The invention relates to a thermal power plant control system information safety assessment system based on SA-PSO-AHP. The system comprises a data input module used for obtaining the scoring data ofa to-be-assessed control system; a matrix building module used for pre-storing a threat evaluation layered progressive model of the control system built by utilizing an analytic hierarchy process, andthe model is combined with the scoring data input by the data input module to build an evaluation matrix; a matrix correction module used for carrying out consistency judgment on the evaluation matrixes and carrying out consistency correction on the evaluation matrixes which do not meet the consistency standard by utilizing a simulated annealing optimization particle swarm algorithm; and a data output module used for obtaining a threat sequence of the power plant control system by utilizing a hierarchical algorithm and obtaining a comprehensive threat score in combination with the threat frequency. Compared with the prior art, the fuzziness of the evaluation elements and the process can be fully reflected. The influence caused by personal subjective factors is reduced. An objective and sufficient evaluation result is better obtained.