636 results about "Data Noise" patented technology

A human assisted method of debugging training data used to train a machine learning classifier is provided. The method includes obtaining a classifier training data set. The training data set is then debugged using an integrated debugging tool configured to implement a debugging loop to obtain a debugged data set. The debugging tool can be configured to perform an estimation and simplification step to reduce data noise in the training data set prior to further analysis. The debugging tool also runs a panel of prediction-centric diagnostic metrics on the training data set, and provides the user prediction based listings of the results of the panel of prediction-centric diagnostic metrics.

Noise reduction in a one- or two-dimensional capacitive sensor array is achieved by rejecting and re-acquiring noisy signals. The sensor array is formed of crossed X and Y lines for drive and sense functions respectively, each of the X lines being driven in turn to acquire a full frame of data from the sensor array. A controller actuates the X lines in turn and, for each X line, charge is transferred to charge measurement capacitors connected to respective ones of the Y lines. The controller measures a signal value from a first one of the measurement capacitors, and then tests if that Y signal value has a magnitude lying within an acceptable range. If not, the measurement capacitors are all reset without their signal values being measured, and the controller re-drives the same X line to initiate another charge transfer into the measurement capacitors for that X line. It is then attempted once again to acquire Y signal values for that X line. Noisy signal values are thus rejected, and the sensor re-acquires substitute signal values. This is distinct from the approach of acquiring data and then reprocessing it to remove or suppress noisy data as in the prior art.

A method of image processing using kernel regression is provided. An image gradient is estimated from original data that is analyzed for local structures by computing a scaling parameter, a rotation parameter and an elongation parameter using singular value decomposition on local gradients of the estimated gradients locally to provide steering matrices. A steering kernel regression having steering matrices is applied to the original data to provide a reconstructed image and new image gradients. The new gradients are analyzed using singular value decomposition to provide new steering matrices. The steering kernel regression with the new steering matrices is applied to the noisy data to provide a new reconstructed image and further new gradients. The last two steps are repeated up to ten iterations to denoise the original noisy data and improve the local image structure.

The invention relates to a method for acquiring dynamic traffic information based on a middleware, which overcomes the shortcomings of data loss, data noise, and particularly the multi-source isomerism of data, reduces redundant data, ensures the accuracy of the data and improves the accuracy and the reliability of the data. The method comprises the following steps of: 1) transmitting the traffic information by adopting a serial interface communication mode and/or a network communication mode; 2) customizing an information acquisition port which can be matched with different inspection devices of the traffic information by using an interface definition language IDL in the CORBA middleware technique, identifying and normalizing the data from different inspection devices, thus acquiring the real-time dynamic traffic information, such as road traffic flow, vehicle velocity, road occupancy rate and the like; 3) performing preprocessing on the all data acquired by the method, and performing map matching on a floating automobile by using a road matching algorithm based on a network topology relationship; and 4) fusing and saving the multi-source isomerous real-time dynamic traffic data which is preprocessed by the method in a database by using an immune clustering neural network.

The invention discloses an electrocardiogram diagnosis method and system based on a deep convolution neural network. The diagnosis method comprises the steps that a collected original electrocardiogram of the human body is preprocessed to obtain a plurality of heartbeat fragments with a preset length; the heartbeat fragments are input into a trained deep convolution neural network model, and a prediction value of each category is obtained; the category of a single heartbeat corresponding to the maximum prediction value and the time interval of the heartbeat and a previous heartbeat are recorded, and a time series is formed; on the basis of a preset mapping relation between a clinical diagnosis criterion and the time series, the time series is matched with the diagnosis criterion, and a corresponding diagnosis result is obtained. Any feature in the electrocardiogram is not required to be extracted, so that valid information loss, noise data introduction and other problems caused by manual screening and processing are avoided to the largest extent, and the influence of manual characteristic extraction on the diagnosis accuracy is reduced.

The present invention provides a method for analyzing emotional polarity of heterogeneous migration images based on multimodal depth potential correlation. The method comprises the following steps: 1)constructing an initial emotional image dataset, and taking the emotional polarity corresponding to the emotional words as the emotional polarity tag; 2) removing the noise data in the initial emotion image data set, and removing the noise by using the method of emotional consistency and the probabilistic sampling model based on the multimodal deep convolution neural network; 3) constructing theheterogeneous migration model based on the multimodal depth potential correlation, and then training the source domain text and the target domain image; 4) constructing the multimodal embedded space,embedding semantic information of the source domain text into the target domain image; and 5) training the image emotional polarity classifier for the image emotional polarity analysis. According to the method provided by the present invention, the obtained data is large in scale, the labor cost is low, the data noise is small, the prediction accuracy is high, the model is strong in interpretability and has strong classification capability, and a better image emotional polarity analysis effect can be reached.

Methods and systems for recommending a service based on an electronic social media trust model. A user trust network and a service trust network can be constructed and the two separate trust networks can be combined to form a combined trust network. The combined trust network includes an explicit trust and an implicit trust in order to improve the recommendation coverage and consider a latent service rating without suffering noisy data. A trust-oriented random walk model can be conducted on a user node with respect to the combined trust network based on a user search intent and navigation behavior in order to select and recommend a service candidate. A service rating can then be predicted by considering the user ratings with respect to a target service, a propagated trust and an inferred service rating.

An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

Methods and apparatus for estimating and correcting carrier frequency offsets in a bust multi-tone receiver are described. Course and fine carrier frequency estimates are generated from the signal's preamble. Decision directed carrier frequency offset estimates are then generated from the signal field and data fields of the multi-tone signal. Frequency error estimates are generated for each tone of the signal and combined using a weighted average to generate the frequency error estimate used to perform the correction operation. Error estimates corresponding to noisy data tones are weighted less then estimates corresponding to less noisy data tones. In cases of low SNR frequency error estimates corresponding to pilots are weighted by an extra amount as compared to error estimates corresponding to tones used to transmit data symbols. During times of high SNR error estimates corresponding to pilot tones are weighted in the same manner as error estimates corresponding to data tones.

The invention provides an industrial soft gateway based on multiple access and edge computing and an implementation method. The industrial soft gateway comprises a configuration interaction module, adata collection module, a data edge computing module and a data sending control module, wherein the configuration interaction module comprises a connection configuration module and a data standardization module; the data collection module is used for a multiple access collection method; the data edge computing module is used for carrying out real-time computing on the data collected by the data collection module; and the data sending control module is used for carrying out caching and task scheduling and allocation of external forwarding on all data to be sent. According to the industrial softgateway based on multiple access and edge computing and the implementation method, a visual interface is arranged to provide connection configuration and standardized operation for various communication protocols and databases, and meanwhile, a graphical monitoring interface of a data collection state, an edge computing result and a forwarding state is provided. Data standardization can be realized, the data noise can be eliminated and the data features can be extracted through the edge computing module, the network transmission data volume of a data cloud platform is reduced, and the data transmission efficiency is improved.

A method and apparatus for ensuring a level of privacy for answering a convolution query on data stored in a database is provided. The method and apparatus includes the activities of determining (402) the level of privacy associated with at least a portion of the data stored in the database and receiving (404) query data, from a querier, for use in performing a convolution over the data stored in the database. The database is searched (406) for data related to the received query data and the data that corresponds to the received query data is retrieved (408) from the database. An amount of noise based on the determined privacy level is generated (410) and added (412) to the retrieved data to create noisy data which is then communicated (414) to the querier.

Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic multi-attribute graph matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

The invention provides a marketing analysis data market system. The marketing analysis data market system comprises a data access layer, a data extraction module, a data conversion module, a data cleaning module, a log and alarm sending module and a data downloading module, a data packet of the data access layer contains office data, external data and service data, and models of the system includes a data logic model and a data physics module. Firstly, necessity in designing the marketing data market is analyzed and ETL data processing including noise data processing, data uniformity and data quality and the like is analyzed by discussing a data integration method, and various data sources can be reorganized and processed by a data transition tool. In addition, the physics model in the data market is realized according to the physic list structure of the logic model. Finally, application prospect of the data market in the marketing analysis is expected.

The invention relates to the technical field of mobile internet, and discloses a method and a system for analyzing network behaviors of mobile terminals users. The method includes collecting access logs and signaling log data of the users to the mobile internet through a collection device, cleaning the collected data to form an effective data set, classifying and analyzing the data set through a matched mobile internet knowledge base, analyzing users' behaviors by loading a user behavior analysis algorithm to acquire analysis results of users' behaviors, and finally displaying the analysis results to the users. According to the collected network requests sent from mobile terminals and the signaling data, the users can be recognized accurately; by means of building the mobile internet knowledge base, users' behavior preferences can be found accurately; therefore, problems such as high data noises, small data sample and inaccurate prediction in the prior art are solved.

The invention discloses a method for predicting industrial sewage inflow based on an ARIMA model. The method comprises the following steps: analyzing initial time series data to meet a requirement onARIMA model establishment; preprocessing abnormal data by eliminating, filling and the like; removing data noise through moving average filtering; by a unit root testing method, performing ADF testingon the stability of a time series; analyzing and verifying the non-randomness through an autocorrelation coefficient; preliminarily determinating an autoregressive and moving average order of the ARIMA (p, d, q) model, and then performing order determination on the model through combination with an AIC information criterion; optimizing model parameters by a least squares method; finally, testinga residual and evaluating a simulation result to determine a final prediction model. Acquired sewage inflow data are determined, and the obtained prediction model is used for predicting test data, andan output of the model is a prediction result of the sewage inflow. By the method, the model is succinct, the fitting effect of the prediction model is very good, and the precision is high.

The invention discloses a method for microblog hot event online detection based on emotion analysis. According to the method for microblog hot event online detection based on emotion analysis, emotion extraction is conducted on online microblog texts according to emotion analysis, the bursting state of emotions and emotion texts is detected according to the improved Kleinberg algorithm, hot topics are extracted according to an affinity propagation clustering algorithm in the bursting period, so that only a small part of texts with a large amount of effective information participate in event extraction, the data volume is reduced, meanwhile a large amount of data noise is filtered out, and therefore the purpose that online real-time detection of the microblog hot events is achieved.