414results about How to "Improve diagnostic capabilities" patented technology

The invention discloses a method for the fault diagnosis of wind turbines on the basis of a genetic neural network, in particular to a method for modeling the fault diagnosis of wind turbines on the basis of the genetic neural network capable of learning the operating data of the wind turbines in the history, more particularly a method for judging the probability that faults occur to a gearbox, a generator and a yaw system by reading the real-time operating data of the wind turbines online and calling the diagnosis model of the genetic neural network to carry out the analysis on the real-time data, thus judging the fault state of the wind turbines. Based on the method combining the genetic algorithm with the neural network, the invention can achieve the algorithm complementation, improve the model convergence and diagnostic capacity and ensure higher robustness; and the method capable of carrying out the online monitoring and fault diagnosis on the operating state of the wind turbines on a real-time basis to take maintenance measures as soon as possible can improve the reliability of the wind turbines and reduce the maintenance cost.

The invention discloses a computerized auxiliary diagnosing method for malignant tumor in early stage based on deep learning algorithm. According to the invention, a deep convolution neural network is used to extract the high-level characteristics of an image layer by layer so that the obtained characteristic dimensions become smaller and smaller. To realize the dimension matching between an outputted probability predicting image and a pre-segmented image, the method uses the reverse convolution network to expand the dimensions of the characteristic image so as to obtain a dimension-consistent probability predicting image. The network generated probability predicting image extracts the binarization of the predicting image through the training of a softmax classifier to finally obtain the segmentation result of the prostate tissue. To some extent, the method effectively increases the accuracy and efficiency for doctors to diagnose prostate diseases.

A method including receiving data corresponding to an original three-dimensional (3D) reconstructed image, smoothing homogenous areas and enhancing edges of the original reconstructed image using edge enhancing diffusion (EED) to create edge-enhanced image data, and calculating a structural importance map. The structural importance map includes a measure of structural importance for each voxel of data in the original reconstructed image. Voxel intensities to be used to create a filtered image are determined according to at least one rule applied to the measure of structural importance.

The invention discloses a quick prototype design method for gas path fault diagnosis for an aeroengine. The quick prototype design method comprises the following steps of: building an adaptive model of the engine; designing a gas path fault diagnosis logic in the full service life of the aeroengine, and implementing abnormal monitoring and gas path performance on-line estimation; and designing a quick prototype design platform for gas path fault diagnosis for the engine, and checking abnormal monitoring and gas path performance on-line estimation functions of the platform. The adaptive model of the engine is built by a compound interference method; an engine on-board adaptive model is used for estimating performance parameters; an engine adaptive base line model is used for monitoring abnormities and performs off-line period updating; and the quick prototype design method is used for checking the engine gas path fault diagnosis logic. The invention also discloses a corresponding quick prototype design platform. The method and the platform are relatively high in capacity of monitoring abnormities and estimating the performance in the overall service life of the engine and have great significance for shortening the development period and reducing the test risk and the test cost.

Quantification of a target analyte is performed using a single sample to which amounts of the target analyte are added. Calibration is performed as part of quantification on the same sample. The target analyte is detectable and quantifiable using label free reagents and requiring no sample preparation. Target analytes include biomarkers such as cancer biomarkers, pathogenic Escherichia coli, single stranded DNA, and staphylococcal enterotoxin. The quantification process includes determining a sensor response of a sensor exposed to the sample and configured to detect the target analyte. Sensor responses are determined after sequential additions of the target analyte to the sample. The amount of target analyte detected by the sensor when first exposed to the sample is determined in accordance with the multiple sensor responses.

A system and appertaining method isolates a hardware or user error in a software controlled apparatus e.g., an NMR-apparatus. A diagnostic function is added to an event log that uses a causality model to analyze the event log. A series of events in the event log is evaluated by comparing the events with at least one of a number of root cause related patterns, and a best match of these patterns is used to assign a root cause for the error. Various repair actions may be associated based on a probabilistic model. Additional information may be utilized to modify or improve respective probabilities associated with causes and/or repair actions. This additional information may utilize other automated diagnostic information or may query users for additional information.

The invention discloses an intelligent fault diagnosis system for an ICNI system, which can improve the maintenance efficiency, carry out intelligent and automatic diagnosis and is applicable to the ICNI system. According to the technical scheme of the invention, a knowledge base and a management module thereof carry out standardization research and mathematical modeling based on a fault tree, an SQL Server database software framework is adopted, a relational database is used for building a logic relation among a fault phenomenon, a fault mode, a detection method, a historical case and a fault tree internal event to form the knowledge base; and a diagnosis information acquisition module interacts with an automatic testing system via Ethernet to acquire diagnosis data from the ICNI system and a testing instrument, a reasoning machine module adopts CBR and RBR hybrid diagnostic reasoning, after comprehensive judgment is carried out on the fault phenomenon inputted by the user, the field knowledge stored by the knowledge base and the diagnosis data from the automatic testing system, a reasoning method is automatically selected to carry out reasoning diagnosis on the fault, a reasoning process and a reasoning result are outputted to an explanation machine module, and a diagnosis report is generated.

An X-ray apparatus in which a mean value of a weighted histogram obtained by multiplying a histogram of numbers of data corresponding to image intensities in a reference image which is an X-ray image picked up in an X-ray fluoroscopy mode and a weighting function whose variable depends on the image intensities is arithmetically determined for controlling an output power of an X-ray tube so that the mean value of the integrated histogram approaches a predetermined value.

The invention discloses a system and method for automatically detecting lesions in a medical image through multi-model fusion; the method comprises using single or fused detection models including deep learning technology to analyze and detect suspicious lesions in the medical image, such as a breast X-ray image. By using the system and method, lesion characteristics can be automatically extracted; the system and method are suitable for detecting or marking one or more types of lesions.

The invention discloses an ICA-PCA multi-working condition fault diagnosis method based on local neighborhood standardization and Bayesian inference. The method firstly carries out independent sampling of each normal working condition during an industrial course to obtain a training dataset, carries out the local neighborhood standardization of the training dataset to obtain a dataset which follows single distribution, and then uses an ICA-PCA method to respectively analyze and process Gaussian features and non-Gaussian features of the dataset so as to obtain an overall model. At an online monitoring stage, independent and repeated sampling is carried out to industrial course data, a plurality of statistical quantities are acquired by applying the model to carry out analysis and processing after the local neighborhood standardization processing, then the multiple statistical quantities are combined into one statistical quantity by the Bayesian inference, and a fault diagnosis result is acquired by comparing control limits. In comparison with traditional fault diagnosis methods, the ICA-PCA multi-working condition fault diagnosis method based on the local neighborhood standardization and the Bayesian inference disclosed by the invention can simplify processing courses, improve diagnosis effects and improve course monitoring performance, and can also make workers' monitoring and observation convenient, make for avoiding safety hidden dangers and guarantee normal running of the industrial course.

The invention discloses an intelligent heart sound diagnostic system and method based on in-depth learning and relates to the fields of bio-signal processing, pattern recognition, big data and in-depth learning. The method comprises the following steps: 1) acquiring heart sound audio data by a user through heart sound acquisition equipment or intelligent wearable equipment; 2) transmitting the data to a cloud server through a network, and storing and archiving the heart sound audio data; 3) segmenting the heart sound data on the cloud server by adopting a heart sound segmentation algorithm based on a logistic regression-hidden semi-Markov model, and performing automatic characteristic extraction and classification on the segmented heart sound data by using a one-dimensional convolutional neural network; 4) feeding diagnostic results to the user through a network and storing the results on a cloud so as to be provided for related institutions and designated hospitals as clinical historyreference of the user; and 5) expanding the heart sound data of the user confirmed by a professional doctor serving as training data into a heart sound database of a cloud server, so that the diagnostic capability of the heart sound diagnostic system is continuously improved.

The invention provides machine learning based human disease detection methods and a detection product. The method includes extracting the intrinsic data characteristics of electrocardial vector data,and quantitative index data thereof; constructing a machine learning classification model of the electrocardial vector data characteristics; and assigning corresponding weight values to different classification results identified by the model so that the comprehensive judgment results of human disease detection can be obtained. According to the provided detection method, the technical problems ofthe model processing of electrocardial continuous dynamic signals, the modeling analysis of quantitative data of data characteristics and the auxiliary diagnosis of human diseases can be solved. The detection methods and detection product can improve the accuracy and detection efficiency of human disease detection; and diagnosis effects can be enhanced with the increasing of the quantitative information of the electrocardial vector data characteristics expanded into a database.

The invention provides a network fault diagnosis system. The system comprises: a simple network management protocol (SNMP) communication module, a database management module, a core diagnosis module, a knowledge management module and a user interaction module. An expert system is used to carry out intelligent fault monitoring. And a hierarchy correlation knowledge representation model which is suitable for a network knowledge characteristic is designed and is used to carry out knowledge representation. A high-efficient inference machine rule matching algorithm is designed according to a network fault characteristic. The system is especially suitable for a small/medium-size local area network. The network fault monitoring and management can be intelligentized or half-intelligentized. An efficiency of network management can be improved and costs can be effectively reduced. The knowledge representation method can be conveniently used in the other engineering fields and has wide applicability.