82 results about "Incremental algorithm" patented technology

Systems and methods for data processing using incremental algorithms. Embodiments of the invention decompose complex or monolithic data processing problems into one or more incremental computations called flows. These flows may be distributed across a networked cluster of commodity computers, facilitating the easy scaling of the system and robust recovery functionality. Once a request is submitted to the system, its solution may be maintained from that point in time forward, such that whenever changes are made to a problem's data the solution is efficiently recomputed.

This invention relates to dynamic polymer tree mode route method in network communication technique field. The invention uses the current router device as communication network joints and uses large scale of network polymer mode as router mode and introduces adding arithmetic adopting to network with time changing and designs one distributing router agreement function. The route steps comprises the following: establishing polymer tree model; aligning physical and interface mark; establishing and updating router list, high speed router buffer list; receiving router information; sending router list update information; sending router detecting package and determining linkage status.

The invention discloses a self-adaptive closed-loop measuring system of a resonant accelerometer, the frequency control adopts a self-adaptive control phase-locked loop structure, and the control modeof a loop filter can be automatically selected according to the change of the external acceleration, the problems that in the prior art, circuit parameters are fixed, and self-adaptive adjustment cannot be achieved are solved, and the contradiction between the response speed and the steady-state phase difference of the phase-locked loop is effectively overcome, so that the measuring range, the scale factor linearity and the zero bias stability of the accelerometer are effectively improved. Meanwhile, the structure is simple, a complex algorithm is not needed, and the system has the advantagesof being simple, efficient and high in adaptability. A PI control unit adopts an incremental algorithm, the improvement of a traditional position type PI algorithm is achieved, a better control effect can be obtained, and the stability of the system is improved; an amplitude control module adopts an alternating current automatic gain control method, a simple and effective amplitude demodulation method is adopted in the module, hardware resources are saved, and the amplitude of the driving signals can be adjusted within a large range.

The invention discloses a method for distinguishing a Thangka image from a non-Thangka image. The method is implemented on a computer by a training stage and an identification stage, wherein the training stage comprises preprocessing, characteristic extraction, similarity measurement and classifier design, and the identification stage comprises preprocessing, characteristic extraction, similarity measurement and classifier identification. The training stage comprises the following steps of: performing preprocessing operation such as normalization, blocking, graying and the like on an image of a training set; extracting characteristics such as an image information entropy, an image color change rate, image symmetry and the like to obtain identification characteristics for distinguishing the Thangka image from the non-Thangka image; performing template and threshold value training and the similarity measurement on the characteristics of the image information entropy, the image color change rate and the image symmetry; and designing a classifier by adopting an interval threshold incremental algorithm. The identification stage comprises the following steps of: preprocessing the image to be identified; and extracting the characteristics of the image, and classifying and judging whether the image is the Thangka image or the non-Thangka image by using the classifier. The method has high accuracy which reaches 95 percent.

The invention relates to a minimum-cost maximum-flow based large-scale resource scheduling system and a minimum-cost maximum-flow based large-scale resource scheduling method. The system comprises a task state table, a cluster state table, a scheduling target table, a minimum-cost maximum-flow constructor, a minimum-cost maximum-flow solver and a task executor. The task state table is used for receiving and storing task states submitted by users, and the task states include task CPU (central processing unit) utilization rate, memory utilization rate, network I/O, magnetic disk I/O and priority. The cluster state table is used for storing cluster state information including cluster CPU utilization rate, memory utilization rate and network and magnetic disk I/O and updating cluster states when the cluster state change. The scheduling target table is used for storing scheduling targets configured by the users, and the scheduling targets include priority, placement constraint and fairness currently. The minimum-cost maximum-flow constructor is used for selecting the scheduling targets from the scheduling target table according to information of the task state table and the cluster state table to construct a minimum-cost maximum-flow graph. The minimum-cost maximum-flow solver is used for solving the minimum-cost maximum-flow graph constructed by the minimum-cost maximum-flow constructor according to an incremental algorithm. The task executor is responsible for specific execution of tasks. The minimum-cost maximum-flow based large-scale resource scheduling system and the minimum-cost maximum-flow based large-scale resource scheduling method meet the requirement on flexibility of practical business scenarios.

The invention discloses a method for forecasting and producing narrow hardenability strip steel by hardenability, mainly aiming at steel grade which has a hardenability requirement and evaluates tail-end quenching hardness by a Jominy value. The strip steel comprises pinion steel, hardened and tempered steel, spring steel and bearing steel. The invention combines a BP (back propagation) manual neural network model with an incremental algorithm, takes the component increment of chemical components to a reference furnace as input, takes end quenching value increment of Jominy tail-end quenching hardness to the reference furnace as output, and builds a hardenability forecast model based on an incremental neural network; according to the difference of the end quenching precast value and the end quenching target value of the neural network model, a component rule base gives the adjustment quantity of each element component, and the process of charging is carried out according to the alloy addition calculated by an alloy charging model so as to realize on-line fine tuning and narrow hardenability strip steel control of the chemical components in the molten steel refining process. The method in the invention can be used to produce the end quenching steel of which the hardenability bandwidth is 4HRC.

The invention discloses a graph structure matching-based social network analysis method, and relates to the fields of a graph structure matching technique, an incremental calculation technique and the like. The graph structure matching-based social network analysis method mainly comprises the steps as follows: (a) a graphic interface helps a user manage graph data, and constructs a mode pattern and a visual query result; and (b) a query engine invokes a distributed algorithm to carry out structure matching calculation; meanwhile, the matching results are evaluated; and top-K results are selected and are visualized on the graphic interface; and (c) incremental calculation is carried out on the existing matching results by an incremental algorithm, so as to ensure the accuracy and the integrity of the results. Aiming at ''large data'' in a social network, efficient and convenient data analysis and maintenance are achieved through distributed, visual and incremental calculation techniques, so that a key technical support is provided for hotspot applications such as expert recommendation, social circle identification and social position analysis.

The invention discloses a pressure accuracy control method for isotemperature static pressing. A pressure control process of a pressure control system comprises segmentation control steps including pressurization segmentation control and pressure relief segmentation control. The pressure control process specifically comprises the following steps: (a) according to the pressurization segmentation control, first, a first-stage incremental algorithm is started for first-stage pressurization control, second, a second-stage PID follow control algorithm is started for second-stage pressurization control, and third, a third-stage PI proportional-integral algorithm is started for third-stage pressurization control; (b) according to the pressure relief segmentation control, a fuzzy algorithm is started, wherein first, a high pressure relief fuzzy control criterion table is selected for high pressure segment pressure relief control, and second, a low pressure relief fuzzy control criterion table is selected for low pressure segment pressure relief control. The pressure accuracy control method for isotemperature static pressing effectively realizes pressure accuracy control in the isotemperature static pressing near-net forming process, enables pressure control accuracy to be subjected to fine adjustment as a user required, is wide in application range, and lays a solid foundation for control over various pressure curves.

The invention provides a linear feature extraction method for the indoor environment. The method comprises the steps of reasonably selecting effective data information, effectively extracting the local feature information of the data, and integrating the extracted local feature information to obtain the feature information under the global environment. According to the method, by means of feature distribution and extraction and later-stage integration of the data, a robot can extract features of the indoor environment by using a sonar sensor; in comparison with other feature extraction schemes based on the increment algorithm, the calculation amount is reduced, and the performance and the accuracy are improved.

The invention discloses a pneumonia prediction method based on an incremental neural network model. The pneumonia prediction method comprises the following steps that a pneumonia daily data database is established; the neural network model is trained; daily life data is acquired and sent to a server and is saved in a daily data record sheet of a user; current-day data is extracted from the daily data record sheet of the user to form n-dimensional vectors, normalization processing is performed, and then the vectors are input into the neural network model of pneumonia pathology to perform pneumonia possibility prediction; an intelligent household pneumonia nursing device judges whether a pneumonia possibility value is greater than 0.5 or not; when the user determines pneumonia, the user goes to a hospital by himself/herself for examination and transmits an examination result to the server through the intelligent household pneumonia nursing device, and the server judges whether the examination result is correct or not; when the examination result is wrong, an incremental algorithm is executed, and dynamic correction is conducted on the neural network model. The pneumonia prediction method is accurate in prediction, and the neural network model is customized for each user.

The invention discloses a boiler combustion efficiency predicting method based on a support vector machine incremental algorithm. The boiler combustion efficiency predicting method based on the support vector machine incremental algorithm is characterized by including the following steps: (1) a kernel function is selected; (2) an initial data set is formed; (3) the initial data is pre-treated; (4) a training sample is taken out and tested; (5) a sensitivity coefficient Epsilon is 0.0001, a training precision is 0.00001 and the default values of a penalty coefficient C and a width coefficient sigmate Sigma are respectively 10 and 0.0001; (6) generalization is determined; (7) the optimum coefficient pair is selected; (8) an initial classifier Omega 0, a support vector set and a non-support vector set are obtained through training; (9) sample points which are not in line with a generalized karush-kuhn-tucker (KKT) condition, namely yif (xi)>1 are found out in a newly added sample set X1; (10) a new set is formed; (11) in terms of X, a classifier Omega and a support vector SV are determined; (12) a support vector machine predicting model on boiler combustion efficiency is established. Less input coefficients are input so as to facilitate measuring, a complicated calculation process is removed, training time of working conditions of boiler combustion is shortened, a requirement for online calculation of a distributed control system (DCS) is met and prediction precision is high.

The invention relates to a surge monitoring method based on incremental nonlinear manifold learning, high-dimensional characteristic information is constructed according to multipath dynamic characteristics expressed in the operating process of a compressor, a one-dimensional main manifold is extracted by utilizing a local tangent space algorithm, the one-dimensional main manifold is updated in real time by an incremental manifold learning method, and whether a surge occurs or not under the varying duty condition of the compressor is judged by monitoring the change of a geometrical structure of a main manifold time sequence. The monitoring method extracts a concealed nonlinear change rule from complete high-dimensional characteristic data in the self operation of compressor equipment, thereby avoiding a phenomenon of missing report; meanwhile, the main manifold has the characteristic of enlarging diversity, and on the basis of ensuring real-time performance, an incremental algorithm simplifies the setting of a warning line and achieves low false alarm rate, and lays a foundation for effectively realizing surge early warning.

The invention is suitable for the field of computers, and provides a supporting point set selection method, comprising receiving data sets and number of supporting points, determining a candidate set and an evaluation set from the data sets, obtaining a plurality of supporting point sets according to the candidate set and the number of the supporting points, grouping data in the evaluation set two by two to obtain data pairs, establishing an evaluation data pair set by using the grouped data pairs, judging whether each supporting point set can exclude each data pair in the evaluation data pair set according to preset exclusive rules, and obtaining excluded number of each supporting point set, selecting the maximum value in the excluded number, and using the supporting point set corresponding to the maximum value as a selected supporting point set. Under the condition of determining the candidate set and the evaluation set, the embodiment of the invention can select out the supporting point set with the optimal performance by only one time, and the problem in the prior art that combination of two supporting points, sequentially selected out by the optimal objective function value in the Incremental algorithm may not be the optimal supporting point combination is avoided.

A method for processing images acquired in real time through a medical device, said images being loaded into a buffer, comprising the steps of: stopping the loading of the images into the buffer, processing loaded images using an incremental algorithm, displaying successively intermediate results of the processing, resuming the loading and stopping the processing based on an evaluation of said intermediate results.

The invention discloses a hyperlipidemia prediction method based on an incremental neural network model. The hyperlipidemia prediction method comprises the following steps: establishing a database of daily data of hyperlipidemia; training a neural network model; acquiring daily living data and transmitting to a server; extracting data of a day from a daily data record table of a user, forming an n-dimensional vector, performing normalization processing, inputting into a hyperlipidemia pathology neural network model, and performing hyperlipidemia criticality probability prediction; determining whether a hyperlipidemia criticality value W is greater than or equal to 3 or not by using intelligent domestic hyperlipidemia nursing equipment; when the user receives an alert of an alarm, reminding the user to take inspection in a hospital, transmitting an inspection result to the server, and determining whether the inspection result is correct or not by the server; when the inspection result is wrong, implementing an incremental algorithm, and performing dynamic modification on the neural network model. The hyperlipidemia prediction method is accurate in prediction, and the neural network model can be customized for each user.

The invention discloses an incremental neural network model-based depression prediction method. The method comprises the following steps of establishing a depression daily data database; training a neural network model; acquiring daily life data, sending the daily life data to a server, and storing the daily life data in a user daily data record table; extracting day data in the user daily data record table to form an n-dimensional vector, performing normalization processing, and inputting the data to a depression pathologic neural network model to perform depression probability prediction; judging whether a depression probability value is greater than 0.5 or not by an intelligent household depression nursing device; if it is judged that a user suffers from depression, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household depression nursing device, and judging whether the examination result is correct or not by the server; and when the examination result is wrong, executing an incremental algorithm and performing dynamic correction on the neural network model. The method is accurate in prediction and the neural network model is customized for each user.

The invention discloses a method for predicting ureteral calculus based on an incremental neural network model, comprising the following steps: establishing a daily data database for ureteral calculus; training the neural network model; collecting daily life data and sending it to a server, and saving it to the user's daily data Record table; the data of the day is extracted from the user's daily data record table to form an n-dimensional vector. greater than 0.5; the user judges that he has ureteral calculi, and the user goes to the hospital for examination by himself, and sends the examination results back to the server through the smart home ureteral calculi care equipment, and the server judges whether the examination results are correct; The network model is dynamically corrected. The prediction of the present invention is accurate, and the neural network model is tailored for each user.

The invention discloses a binocular image-based visual map generation method for a mobile robot. The method includes: performing visual matching and loop detection on related images according to visual features of a binocular sequence image, and in order to better recover a scene scale, fully utilizing binocular baseline constraints to improve a parameterized expression mode of point cloud, and designing a mapping initialization algorithm, an incremental algorithm and a corresponding global smooth adjustment process designed for a binocular camera. Compared with an existing binocular mapping method, the method is higher in speed and lower in complexity; compared with an SLAM-based mapping algorithm, map drifting is smaller, scene robustness is better achieved, the adaptive capacity of a complex environment is enhanced, the scene composition requirement of an existing robot is met, meanwhile, high expandability is achieved, the whole mapping link step can be accelerated in combination with the output result of the SLAM, and meanwhile, the robustness can be further enhanced.

The invention discloses an incremental neural network model-based allergic dermatitis prediction method. The method comprises the following steps of establishing an allergic dermatitis daily data database; training a neural network model; acquiring daily life data, sending the daily life data to a server, and storing the daily life data in a user daily data record table; extracting day data in the user daily data record table to form an n-dimensional vector, performing normalization processing, and inputting the data to an allergic dermatitis pathologic neural network model to perform allergic dermatitis probability prediction; judging whether an allergic dermatitis probability value is greater than 0.5 or not by an intelligent household allergic dermatitis nursing device; if it is judged that a user suffers from allergic dermatitis, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household allergic dermatitis nursing device, and judging whether the examination result is correct or not by the server; and when the examination result is wrong, executing an incremental algorithm and performing dynamic correction on the neural network model. The method is accurate in prediction and the neural network model is customized for each user.

The invention discloses a face recognition tracker based on Haar-like features and an incremental learning algorithm, and mainly relates to the field of computer vision and image processing. Accordingto the method, Haar-like feature evaluation is accelerated by using an integral graph, strong classifiers for distinguishing human faces and non-human faces are trained by using an AdaBoost algorithm, and the strong classifiers are cascaded together by using screening type cascading, so that the accuracy is improved. And the face tracking part predicts the position of the central point of the current time frame according to the position of the central point of the previous frame of image tracking frame. And main features of the image in the frame are extracted by using a PCA algorithm, and acorresponding dimension-reduced graph is predicted according to the position of the center point of the frame at the moment. A forgetting factor is introduced, and image data is updated once every five frames. The incremental algorithm does not need to train a model, so that the efficiency is improved. Theoretics and practices show that the method can automatically recognize a human face, when thedirection of the human face changes greatly, for example, when the front face becomes a side face, recognition and tracking can be continued, continuous recognition is kept, and interruption is avoided.

The invention discloses a dynamic incremental energy consumption data processing method. The method comprises the following steps: (1) acquiring dynamic data through a meter; (2) calculating a metering difference value between the new data and the previous data; (3) calculating an energy consumption difference value through the metering difference value; and (4) adding the energy consumption dataof the last acquisition time point to the current energy consumption difference value to calculate the current energy consumption data. According to the invention, an end-to-end subtraction differencecalculation method commonly used in the current energy industry can be replaced; aiming at the continuously increased dynamic data, historical data are supported, and more real-time and accurate energy consumption data are obtained by means of an incremental algorithm of the energy consumption data; according to the method, more friendly energy consumption monitoring is provided for operation andmaintenance personnel and managers, the energy consumption use trend is mastered in real time, then industry indexes are referred, and the energy consumption use level of a building can be evaluatedmore reasonably.

The invention discloses an incremental neural network model-based allergic rhinitis prediction method. The method comprises the following steps of establishing an allergic rhinitis daily data database; training a neural network model; acquiring daily life data, sending the daily life data to a server, and storing the daily life data in a user daily data record table; extracting day data in the user daily data record table to form an n-dimensional vector, performing normalization processing, and inputting the data to an allergic rhinitis pathologic neural network model to perform allergic rhinitis probability prediction; judging whether an allergic rhinitis probability value is greater than 0.5 or not by an intelligent household allergic rhinitis nursing device; if it is judged that a user suffers from allergic rhinitis, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household allergic rhinitis nursing device, and judging whether the examination result is correct or not by the server; and when the examination result is wrong, executing an incremental algorithm and performing dynamic correction on the neural network model. The method is accurate in prediction and the neural network model is customized for each user.

The invention discloses a neurasthenia prediction method based on an incremental neural network model. The neurasthenia prediction method comprises following steps that a database of neurasthenia daily data is established; a neural network model is trained; daily life data is acquired and sent to a server, and is saved to a user daily data recording chart; intraday data is extracted from the user daily data recording chart to form an n-dimensional vector, after normalization processing, the n-dimensional vector is input into a neurasthenia pathology neural network model to carry out neurasthenia probability prediction; whether the neurasthenia probability value is larger than 0.5 or not is determined by an intelligent household neurasthenia nursing device; when that the user suffers from neurasthenia is determined, the user goes to the hospital for check-up himself, and sends the check-up result back to the server through the intelligent household neurasthenia nursing device, and the server determines whether the check-up result is correct or not; when the check-up result is wrong, an incremental algorithm is executed, and the neural network model is dynamically corrected. The neurasthenia prediction method based on the incremental neural network model is accurate in prediction, and the neural network model is customized for each user.

The invention discloses a hepatitis B prediction method based on an incremental neural network model. The hyperthyroidism prediction method comprises following steps that a database of hepatitis B daily data is established; a neural network model is trained; daily life data is acquired and sent to a server, and is saved to a user daily data recording chart; intraday data is extracted from the user daily data recording chart to form an n-dimensional vector, after normalization processing, the n-dimensional vector is input into a hepatitis B pathology neural network model to carry out hepatitis B probability prediction; whether the hepatitis B probability value is larger than 0.5 or not is determined by an intelligent household hepatitis B nursing device; when that the user suffers from the hepatitis B is determined, the user goes to the hospital for check-up himself, and sends the check-up result back to the server through the intelligent household hepatitis B nursing device, and the server determines whether the check-up result is correct or not; when the check-up result is wrong, an incremental algorithm is executed, and the neural network model is dynamically corrected. The hepatitis B prediction method based on the incremental neural network model is accurate in prediction, and the neural network model is customized for each user.

The invention discloses a data processing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: receiving a data request, and analyzing to obtain a time window of the request; calling a date management component, splitting the time window into one or more time sub-windows according to a preset partitioning model, andcalling index data corresponding to each time sub-window based on a current database; and calling a distributed incremental computation engine component, processing the index data through a preset generalized incremental algorithm to obtain a target index value, and returning the target index value and displaying the target index value. Therefore, the embodiment of the invention can solve the problem of low calculation efficiency of the user data request in the prior art.