209results about How to "Improve forecast" patented technology

A method for distributed tokenization of sensitive strings of characters, such as social security numbers, credit card numbers and the like, in a local server is disclosed. The method comprises the steps of receiving from a central server at least one, and preferably at least two, static token lookup tables, and receiving a sensitive string of characters. In a first tokenization step, a first substring of characters is substituted with a corresponding first token from the token lookup table(s) to form a first tokenized string of characters, wherein the first substring of characters is a substring of the sensitive string of characters. Thereafter, in a second step of tokenization, a second substring of characters is substituted with a corresponding second token from the token lookup table(s) to form a second tokenized string of characters, wherein the second substring of characters is a substring of the first tokenized string of characters. Optionally, one or more additional tokenization steps is / are used.

A method and system for secure handling of sensitive sets of characters in a distributed hierarchical system are disclosed, comprising at least one local server on a lower hierarchic level and at least one central server at a higher hierarchic level. The method comprises the steps: receiving a sensitive set of characters in said local server; replacing a part of said sensitive set of characters with a token to form a tokenized set of characters, said token belonging to a subset of possible tokens assigned to the local server by the central server; transferring at least one of said sensitive set of characters and said tokenized set of characters to the central server; and canceling said sensitive set of characters from said local server within a limited time from said transferring, while maintaining said tokenized set of characters in a local database connected to said local server.

A system, method and article of manufacture utilize word chunks to enhance word prediction. At least one of selectable words and word chunks are displayed in response to receipt of an input character. Thereafter, a selection of a displayed word or word chunk is received, and in response to receiving selection of a displayed word chunk, selectable words including the selected word chunk are displayed. A word chunk includes a word portion used in the formation of other words and includes a predetermined identifier, identifying it as a word chuck. Thus, the combination of chunking of parts of agglutinated words in combination with letter input activations reduces the overall number of input activations to complete a word, and further reduces the amount of choices to select from at any point during retrieval of the word. Accordingly, for such agglutinating languages such as German, for example, word chunks can be used to speed up word prediction and thereby eliminate unnecessary, time consuming, and sometimes difficult inputting of many individual characters.

The invention discloses an event atlas construction system and method based on multi-dimensional feature fusion and dependency syntax. The event graph construction method based on multi-dimensional feature fusion and dependency syntax is realized through joint learning of event extraction, event correction and alignment based on multi-dimensional feature fusion, relationship extraction based on enhanced structured events, causal relationship extraction based on dependency syntax and graph attention network and an event graph generation module. According to the event graph construction method and device, the event graph is constructed through the quintuple information of the enhanced structured events and the relations between the events in four dimensions, and the defects that in the priorart, event representation is simple and depends on an NLP tool, the event relation is single, and the influence of the relations between the events on event graph construction is not considered at the same time are overcome. According to the event atlas construction method provided by the invention, the relationships among the events in four dimensions can be randomly combined according to different downstream tasks, and the structural characteristics of the event atlas are learned to be associated with potential knowledge, so that downstream application is assisted.

Extremely hydrophobic nitric oxide (NO) releasing polymers are disclosed. The extremely hydrophobic NO-releasing polymers provided are extensively cross-linked polyamine-derivatized divinylbenzene diazeniumdiolates. These polymers can be loaded with extremely high NO levels and designed to release NO in manners than mimic natural biological systems. The NO-releasing extremely hydrophobic polymers provided can maintain a sustained NO release for periods exceeding nine months. Also provided are related medical devices made using these NO-releasing extremely hydrophobic polymers.

The invention provides a pollution source management system based on a geographic information system (GIS) technology and a one-dimensional water quality model. The pollution source management system comprises a basic geographic database, a water quality monitoring database, a pollution source monitoring database, a water quality model predicting module, a statistical analysis module and a geographic information system module. The invention also provides an operation method of the pollution source management system based on the GIS technology and the one-dimensional water quality model; and by means of the operation method, the docking between a geographic information system and a management information system is realized to complete the effective management of a large amount of environmental information, analyze the changes in the environmental pollutants, and realize the visualization of a regional environmental pollution status, and thus, the execution of the water environment planning and decision is facilitated.

The invention relates to a method and a device both based on annulus pressure measuring while drilling and for early monitoring gas invasion of a wellhole. The device is characterized in that the device comprises a drilling column arranged in the wellhole, the bottom of the drilling column is connected with an annulus pressure measuring while drilling device and a drilling bit, the annulus pressure measuring while drilling device comprises an annulus pressure sensor, an annulus temperature sensor, a signal monitoring circuit, a data storage circuit, a battery and a data connector, the top of the annulus pressure measuring while drilling device is connected with a measuring while drilling device which comprises a signal receiving module and a pulse generator, a drilling fluid returning flowmeter is arranged on the upper portion of the drilling column, one side of a wellhead is connected with a mud pump and a mud circulating pool through a pipeline, a mud pressure sensor is mounted on the mud pump, a mud level meter is arranged in the mud circulating pool, the drilling fluid returning flowmeter, the mud pressure sensor and the mud level meter are in wire connection with a well logging instrument respectively, a filter module and a signal measuring module are arranged in the well logging instrument, and the well logging instrument is connected with a data analyzing and alarming module through a wire. The method and the device can be applied to deepwater oil drilling operations.

The invention discloses a real-time monitoring and early warning system for foundation pit engineering. The real-time monitoring and early warning system for foundation pit engineering comprises a cloud terminal server, a field monitoring data acquisition subsystem, a digital foundation pit information analysis and management subsystem and an early warning and information publish subsystem. The field monitoring data acquisition subsystem is used for monitoring a plurality of monitoring points on the construction site in real time to obtain monitoring data, and uploading the monitoring data to the cloud terminal server. The digital foundation pit information analysis and management subsystem is used for predicting the monitoring data to generate predicted data, generating early warning information when the predicted data exceed preset warning values, and updating the early warning information to the cloud terminal server. The early warning and information publish subsystem is used for reminding staff of the situation according to the early warning information. The real-time monitoring and early warning system achieves automatic acquisition and transmission of the monitoring data, conducts prediction according to the monitoring data to generate the predicted data, generates the early warning information when the predicted data exceed the preset warning values, and reminds the staff of the situation according to the early warning information, and therefore the automatic and intelligent level of management, prediction and early warning of information of the construction site is greatly improved.

A system, method and article of manufacture utilize word chunks to enhance word prediction. At least one of selectable words and word chunks are displayed in response to receipt of an input character. Thereafter, a selection of a displayed word or word chunk is received, and in response to receiving selection of a displayed word chunk, selectable words including the selected word chunk are displayed. A word chunk includes a word portion used in the formation of other words and includes a predetermined identifier, identifying it as a word chuck. Thus, the combination of chunking of parts of agglutinated words in combination with letter input activations reduces the overall number of input activations to complete a word, and further reduces the amount of choices to select from at any point during retrieval of the word. Accordingly, for such agglutinating languages such as German, for example, word chunks can be used to speed up word prediction and thereby eliminate unnecessary, time consuming, and sometimes difficult inputting of many individual characters.

Embodiments of the invention disclose an information recommendation method, device and equipment. The method comprises the steps of obtaining entity information contained in to-be-recommended information and association relationship information between entities corresponding to the entity information; constructing a knowledge graph based on the entity information and the association relationship information, the knowledge graph including an association relationship between an entity node corresponding to the entity information and an entity node corresponding to the association relationship information; and performing feature sampling on a target entity node of which the node popularity is greater than a predetermined popularity threshold in the knowledge graph, and determining informationneeding to be recommended to a user in the to-be-recommended information based on features sampled by the target entity node.

The invention provides a kit suitable for rapidly detecting AMH and INHB by using a double-tagging time resolution fluorescence immunoassay method. The kit comprises (1) a solid phase carrier coated by an AMH coating antibody and an INHB coating antibody; (2) a mixed calibration product of AMH and INHB; (3) an AMH detection antibody marked by using a lanthanide element 1; (4) an INHB detection antibody marked by a lanthanide element 2; (5) an immunoreaction accelerant liquid; (6) a concentrated washing liquid; and (7) a reinforcing liquid. The invention further provides a use method of the kit for rapidly detecting AMH and INHB by using the double-tagging time resolution fluorescence immunoassay method. By adopting a double-tagging time resolution fluorescence immunoassay technique, the defect that conventional AMH and INHB respectively need a single kit, that is, one kit can be only used for detecting AMH or INHB can be overcome, the detection steps can be reduced, the sampling time can be shortened, and the use amount of a blood sample can be reduced.

The invention discloses a predicting method for short-term electric load of a Seq2seq network based on a multi-layer Bi-GRU, similar daily samples are extracted by using FCM method and the input variables are standardized by the Min max standardization method. The multi-level Seq2seq neural network structure is constructed with Bi GRU neurons as the basic unit. At the same time, the SELU activation function is selected as the output layer activation function of the whole neural network to reduce gradient vanishing and gradient explosion, to realize smooth operation of the whole model in the training process.

The invention relates to a method and an apparatus for the reduction of nitrogen oxide emissions in the exhaust of an internal combustion engine, wherein in a powered with the internal combustion engine vehicle data to a route in a navigation system and / or at least one driver assistance system can be provided. According to the invention, Ss is provided that even in transient operating conditions of the engine from the data of the expected route, an engine-load profile and thus a nitrogen oxide emission of the internal combustion engine, a nitrogen oxide conversion of the catalyst and / or a need of the metered for nitrogen oxide reduction are predicted and this data are considered correctively in the dosage of Reduktionsmittell or the timing and duration of loading and regeneration phase of the NOx storage catalytic converter can be influenced.

The invention provides a satellite clock error prediction method based on an empirical mode decomposition (EMD) model and a generalized autoregressive conditional heteroskedasticity (GARCH) model, relates to the field of clock error prediction of satellite clock, and solves the problem of difficulty in improving prediction precision because the traditional satellite clock error prediction method lacks prediction of an unstable random term. The method comprises the following steps of: 1, acquiring historical data of clock error, and correcting and pre-processing the data to obtain satellite clock error data; 2, decomposing an empirical mode of the satellite clock error data to obtain a random term part of the satellite clock error data; 3, predicting a trend term of the satellite clock error data, and building a Kalman prediction model to predict the trend term of the satellite clock error data; and 4, predicting a random term of the satellite clock error data, namely, removing the trend term to obtain the random term, predicting the random term by using an auto-regressive and moving average (ARMA) model and the GARCH model, and improving the precision of satellite clock error prediction. The method is used for high-precision time synchronization of a satellite navigation system.

The invention discloses a user driving behavior analysis method and system. The method comprises the steps of acquiring vehicle driving data of a plurality of users, and calculating a plurality of index items used for describing the driving behaviors of the users according to the vehicle driving data; Obtaining traffic accident information of a user; Taking the index items as characteristic variables, analyzing the correlation between each characteristic variable and the traffic accident information, and screening out N characteristic variables with the highest correlation with the traffic accident information to form an N-dimensional vector; Dimensionality reduction is carried out by using a nonlinear dimensionality reduction algorithm to obtain a variable set; Training a user driving behavior evaluation model by taking the variable set as an independent variable and the traffic accident information as a dependent variable; And evaluating the driving behavior of the to-be-analyzed user by using the user driving behavior evaluation model. The traffic accident information is used as a quantitative index for evaluating the driving behavior of the user, the user driving behavior evaluation model is trained, and the prediction and evaluation accuracy of the driving behavior of the user is improved.

A disease prediction method comprises the steps of acquiring the disease monitoring data, weather data and public sentiment data; preprocessing the disease monitoring data, the weather data and the public sentiment data; constructing a multi-layer GRU model; training and performing performance verification on the multi-layer GRU model to obtain the optimized multi-layer GRU model; and predicting the prediction time point by utilizing the optimized multi-layer GRU model to obtain the disease prediction result of the prediction time point. The invention further provides a disease prediction device, a computer device and a readable storage medium. According to the invention, rapid and high-accuracy disease prediction can be realized.

The invention provides a seismic property combination method based on a Dempster / Shafe (D-S) evidence theory. According to the seismic property combination method based on the D-S evidence theory, source property data undergoes feature extraction and evidences are formed to obtain basic probability values. Under instruction of the D-S evidence theory, the basic probability values are combined to obtain a combined probability value. The combined probability value is used as a weighting coefficient and furthermore, all partial small blocks of attribute data are combined with a weight number to achieve combination of multiple seismic properties. Therefore, when single seismic property information is used to explain a complicated geology problem, a problem that multiple solutions are produced by the single seismic property information is solved.

A computer-based system for configuring and displaying information on changes in, and present status of, a collection of events associated with a project. Classes of icons for decision events, configurations and feedback mechanisms, and time lines (sequential and / or simultaneous) for related events are displayed. Metadata for each icon in each class is displayed by choosing and activating the corresponding icon. Access control (viewing, reading, writing, editing, deleting, etc.) is optionally imposed for metadata and other displayed information.

The invention, which relates to the indoor scene classification field, provides a supergraph learning-based indoor scene classification method. The method comprises the following steps that: a target is extracted from an image by using nearly a hundred of target detectors and a super descriptor formed by the formed target descriptor is used as a feature descriptor of the image; a supergraph of the image descriptor is constructed by using a K neighbor method and a Laplacian matrix is calculated, thereby constructing a semi-supervised learning frame; a linear regression model is constructed and is added into the semi-supervised learning frame; according to the constructed semi-supervised learning frame, marking is carried out on the part of image descriptor by combining the extracted image feature descriptor, so that the semi-supervised learning frame can predetermine a label of an unmarked image automatically and iteratively and thus the image classification is completed; and meanwhile, the linear regression model is initialized during the automatic iteration process; and according to the linear regression model, image classification is carried out on data that are added newly directly by combining the extracted image feature descriptor, so that there is no need to construct a supergraph again.

The invention provides a real-time tracking and predicting control method for a maximum photovoltaic power point. The real-time tracking and predicting control method for the maximum photovoltaic power point comprises the following steps of: S1), establishing an environmental factor and a maximum photovoltaic power point function model in a photovoltaic controller, and establishing an output current and voltage mathematical model of the photovoltaic controller for real-time output current in the environmental factor and the maximum photovoltaic power point function model in the photovoltaic controller; S2), establishing photovoltaic controller mathematical modeling and a state space model, and establishing a constraint condition for the photovoltaic controller used for controlling output current; S3), establishing a real-time tracking and predicting target function, and establishing a performance index used for determining the target function; and S4), solving the target function under the constraint condition, and obtaining an optimal control sequence used for tracking the maximum photovoltaic power point. According to the real-time tracking and predicting control method for the maximum photovoltaic power point, which is disclosed by the invention, the real-time model prediction control of the photovoltaic system under the quickly-changed external environment condition can be realized so as to improve the predicting and tracking capability of the maximum photovoltaic power point.

The invention discloses an H.264 / advanced video coding (AVC)-standard-based intra-frame prediction mode rapid selection method, which comprises prediction mode type determination step and specifically comprises the following steps of: calculating a sum of different with mean (SDM) value of a current brightness macro block, comparing the SDM value with two preset flatness coefficient threshold values T1 and T2, performing a rate distortion optimization (RDO) operation on a current prediction block according to a preset 4*4 prediction mode algorithm to obtain a first optimal prediction mode if the SDM value is more than T2, and performing the RDO operation on the current prediction block according to a preset 16*16 prediction mode algorithm to obtain a second optimal prediction mode; and calculating the first and second optimal prediction modes and selecting a mode with the lowest RDO value as a third optimal prediction mode if the SDM is neither more than T2 nor less than T1. The invention also discloses a corresponding H.264 / AVC-standard-based intra-frame prediction mode rapid selection device. By the method and the device, a calculated amount can be decreased, operation time can be shortened, an intra-frame prediction process can be accelerated and coding speed can be increased under the condition of ensuring that a coding output signal to noise ratio and a code rate are invariable.

The invention discloses a visual depth estimation method based on a depth-deconvolution neural network. The method comprises steps: a depth-deconvolution neural network is constructed firstly, whereinthe hidden layer includes a convolution layer, a batch normalization layer, an activation layer, a maximum pool layer, a conv_block network block, a deep convolution network block, a concatanate fusion layer, an add fusion layer, a deconvolution layer and a separable convolution layer; then, the monocular images in the training set are used as the original input images and input to the depth-deconvolution neural network for training to obtain the estimated depth images corresponding to the monocular images; secondly, by calculating the loss function between the estimated depth image and the real depth image corresponding to the monocular image in the training set, the training model and the optimal weight vector of the depth-differentiable convolutional neural network are obtained; then the monocular image to be predicted is inputted into the depth-deconvolution neural network training model, and the corresponding predicted depth image is predicted by using the optimal weight vector.The advantage is that the prediction accuracy is high.

The present invention comprises methods for increasing the rank of the routing matrix of an IP network by systematically altering link weights in the IP network. A full rank routing matrix may be used with further methods in accordance with the present invention to estimate the mean traffic of the IP network based upon the full rank routing matrix and measured link utilization values. The mean traffic and the covariance of the traffic may be iteratively estimated until the estimates coverage. Example methods in accordance with the present invention for estimating mean traffic and covariance of traffic are described for both stationary and non-stationary link utilization data.

The invention provides a BP neural network WSN forest fire prevention system based on ant colony optimization, which comprises a wireless sensor network and a forest fire prediction model, wherein the wireless sensor network is used for acquiring environmental impact factor data in the forest and providing the acquired data for the forest fire prediction model; the forest fire prediction model judges the fire happening possibility according to the environmental impact factor data; the wireless sensor network is formed by a plurality of wireless sensors deployed in the forest in a ZigBee wireless networking mode; all wireless sensors transmit the acquired environmental impact factor data to a network coordination node, the network coordination node forwards the acquired data to a monitoring host in a monitoring center through wireless remote data transmission network GPRS, and the forest fire prediction model is arranged on the monitoring host. The fire scale can be predicted effectively and accurately.

The invention provides a method for predicting the tail gas pollution distribution in a city road network. The method comprises the following steps: acquiring multi-source heterogeneous data; carryingout stack-type self-encode features dimension reduction, and constructing a multi-layer sparse self-encoder network structure to extract the features of the multi-source heterogeneous data; generating sequential data based on spatio-temporal semi-supervised learning; pre-training a deep spatio-temporal network model replacing the corrected model data with the telemetry data of the real monitoringpoints, and re-training the corrected regional tail gas emission prediction model; determining the weighted parameters of the model to obtain a deep spatio-temporal network model, and inputting the multi-source heterogeneous data t to obtain a predicted regional tail gas pollution emission result. The invention is based on a stack-type self-encoder dimension reduction feature extraction method, which can learn essential feature mapping between road network information, meteorological information, traffic flow information, POIs information and regional tail gas emission, and can realize higherprecision regional tail gas prediction on real telemetry data.