42results about How to "Reduce data noise" patented technology

This document describes a general system for noise reduction, as well as a specific system for Magnetic Resonance Imaging (MRI) and Nuclear Quadrupole Resonance (NQR). The general system, which is called Calculated Readout by Spectral Parallelism (CRISP), involves reconstruction and recombination of frequency-limited broadband data using separate narrowband data channels to create images or signal profiles. A multi-channel CRISP system can perform this separation using (1) frequency tuned hardware, (2) a frequency filter-bank (or equivalent), or (3) a combination of implementations (1) and (2). This system significantly reduces what we call cross-frequency noise, thereby increasing signal-to-noise-ratio (SNR). A multi-channel CRISP system applicable to MRI and NQR are described.

The invention discloses a two-stage data compression and decompression method on the basis of orthogonal wavelet packet transformation and a rotating door algorithm. Data compression comprises the following steps of: (1) carrying out orthogonal wavelet packet transformation on original data to obtain a wavelet packet coefficient; (2) carrying out threshold processing on the wavelet packet coefficient obtained in the step (1); and (3) carrying out secondary compression on the wavelet packet coefficient subjected to threshold processing by adopting the rotating door algorithm. Compressed data is stored into a historical database or a disk. Decompression on the compressed data comprises the following steps of: (4) carrying out linear interpolation on the compressed data and recovering to obtain primary compressed data; and (5) carrying out wavelet packet reconstitution on the primary compressed data to obtain the original data. The invention solves the problem of difficulty in compressing a nonstationary analog signal in a large-scale real-time database and provides the data compression and decompression method which is simple to implement, has a high data compression ratio and has an obvious compressing effect on the nonstationary analog signal.

The invention relates to the technical field of computer time series data analysis and prediction, in particular to a time series data filling and restoring method based on machine learning. The method includes: filling the missing value by using a domain-based median and mean value filling method; estimating a true value of an expected sampling moment through a linear rule; detecting wave crestsand wave troughs of the time sequence, and smoothing abnormal values; taking hundreds of thousands of collected real data as samples, designing and generating time sequence characteristics, taking real results as labels, and training a machine learning model based on an XGBoost (Extreme Gradient Boost) for predicting a large number of unknown data. According to the method, the problems of multiplemissing values, large volatility, error accumulation and the like of specific time sequence data are solved, and the accuracy of data filling and restoring is effectively improved; moreover, the complexity of a machine learning model is well controlled, the filling and restoration of hundreds of millions of data records can be completed within an hour level, and the method has a high practical value.

The invention provides a method and system for revising a user word bank. The method comprises checking whether current input contents are completely or partially same or similar with input contents on input codes and different on characters; and / or checking whether the current input contents are completely or partially same or similar with the input contents on the characters and different on the input codes; revising data in the user word bank based on the current input contents and error correction contents if conditions are met; and enabling the error correction contents to be a part of the input contents corresponding to the current input contents. The method and system can intelligently record user input information, avoids learn misinput words as much as possible and reduces data noise in the user word bank. The method and system does not need more limitation on user editing actions, greatly expand application range and depth of word bank revising, and can better remove the data noise which cannot be found in the prior art.

The invention discloses a deep learning-based picture sentiment polarity analysis method, and relates to the technical field of image content understanding and big data analysis. In a conventional picture sentiment analysis method, the final prediction precision is non-ideal due to simple models and features. At present, a deep learning method is used to perform training in a large-scale training set, but the noises of the training set are excessively high, so that the final performance is limited. In the deep learning-based picture sentiment polarity analysis method, a mode of obtaining data directly from a network is adopted, and slave data scale is large. Only sentiment polarity information of common words needed to be obtained during data preparation is possibly needed to be manually annotated. Later, the whole image obtaining and cleaning work can be automatically finished, so that the required labor cost is very low. In the data obtaining stage, two data cleaning processes are introduced, so that a large portion of noises due to inconsistence of pictures and tags can be eliminated. According to the method, priori knowledge is used for filtering the training set, so that the noises of the training set are reduced; and an improved network structure is used assistantly, so that the picture sentiment prediction accuracy is improved.

The invention discloses a pulse laser-based reflective digital holographic microscopy imaging system and method. The reflective digital holographic microscopy imaging system comprises an optical imaging sub system and a synchronous control sub system for controlling the operation of the optical imaging sub system, wherein the optical imaging sub system comprises a pulse laser, a laser attenuator, a steering apparatus, a light beam transfer apparatus and a holographic imaging apparatus which are arranged in sequence; the synchronous control sub system comprises an industrial control host and a synchronous controller; the reflective digital holographic microscopy imaging method comprises the following steps of 1, establishing the holographic microscopy imaging system; 2, performing synchronous control on the pulse laser and a digital camera; 3, obtaining hologram data; and 4, performing three-dimensional appearance holographic image display on the surface of a test sample. The system and the method are creative in design; by performing synchronous control on the pulse laser and the digital camera, the high-frequency micro-vibration test sample hologram is obtained; light path interference is completed through four beam splitters, so that a condition that reflective stray light enters the hologram caused by self parts in the light path can be avoided; and the hologram is high in quality.