1599results about How to "Improve diagnostic accuracy" patented technology

An image of an electro-anatomical map of a body structure having cyclical motion is overlaid on a 3D ultrasonic image of the structure. The electro-anatomical data and anatomic image data are synchronized by gating both electro-anatomical data acquisition and an anatomic image at a specific point in the motion cycle. Transfer of image data includes identification of a point in the motion cycle at which the 3-dimensional image was captured or is to be displayed.

To provide an endoscope apparatus in which the observation image can be varied continuously as the observation magnification is varied by a zoom magnification varying manipulation so that an observation image suitable for an endoscope diagnosis is obtained at each observation magnification, and to thereby prevent the operator from feeling uncomfortable and increase the accuracy of a diagnosis. An endoscope apparatus is equipped with illuminating unit having plural light sources which generate light beams having different spectra, for illuminating an observation subject; imaging unit for imaging the observation subject; observation magnification varying unit for varying observation magnification of the imaging of the imaging unit; and light quantity ratio varying unit for continuously varying an emission light quantity ratio between the plural light sources according to the observation magnification that is set by the observation magnification varying unit.

The invention relates to the field of elevators. In order to early discover and diagnose A elevator fault, the invention adopts the technical scheme that an elevator fault diagnosis and early-warning method based on data drive is achieved by means of a remote service center, a fault diagnosis and prediction terminal and an elevator controller, and the method comprises the steps as follows: firstly, elevator fault data are mined to obtain characteristic information in an elevator fault data stream, and the mined result is stored in an elevator fault case base of the fault diagnosis and prediction terminal; secondly, an elevator fault knowledge base on the fault diagnosis and prediction terminal is updated by the elevator fault case base; thirdly, the case retrieval is carried out on the characteristic of a new elevator fault problem, and the fault diagnosis is carried out on the elevator system by adopting the fault diagnosis method based on the case-base reasoning; and finally information with the characteristic that is most similar with that of the new elevator fault problem is acquired through retrieval of the knowledge or the case in the elevator fault knowledge base to solve the diagnosis problem. The method is mainly suitable for manufacturing and designing image sensors.

The invention relates to an extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform includes the following steps: (1), collected original vibration signals of mechanical and electrical equipment are decomposed according to the EEMD, white noise is added, and intrinsic mode function (IMF) components are obtained through decomposition; (2), the sensitive IMF components closely related to failure are chosen, and other irrelative IMF components are ignored; (3), the sensitive IMF components chosen through step (2) are decomposed in an orthogonal wavelet packet mode, and a wavelet coefficient of each node is obtained; and (4), envelopes are extracted from the obtained wavelet packet coefficients by adoption of the Hilbert transform and the Fourier transform, power spectrums are calculated, the power spectrum corresponding to each wavelet packet coefficient is obtained and serves as the early failure sensitive characteristic , and the sensitive characteristics are automatically obtained. Self-adapting signals can be decomposed, the sensitive characteristics can be convenient to obtain automatically, diagnosis precision and speed are improved, and a mechanical and electrical system can be diagnosed quickly, accurately and stably. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform can be applied to the field of mechanical and electrical equipment failure diagnosis.

The present disclosure relates to diagnostic apparatus and method for home appliance which make it easy to diagnose a breakdown of a home appliance, using a signal including product information and outputted from the home appliance, in which a signal is converted in accordance with signal distortion generated in the process of transmitting the output signal or in a terminal, so that the product information of the home appliance is easily extracted, accuracy of a diagnosis result is improved, an after-sales service for a breakdown of the home appliance is easily provided, and reliability of the product is improved by the improvement of accuracy of the diagnosis result.

The invention relates to a fault diagnosis device and method of a fuel cell system. The fault diagnosis device comprises a communication unit, a diagnosis core unit and a man-machine interaction unit and is characterized in that the communication unit is used for receiving the procedure parameters and working condition information of a main controller as well as the single cell voltage information of a monitor unit and sending diagnosis results and various parameters and data to a field remote control monitoring center and an upper computer; the diagnosis core unit is used for detecting and positioning the faults of the sensor, actuator, controller, monitor unit and galvanic pile of the fuel cell system through the formulated diagnosis strategy; and the main-machine interaction unit is used for displaying, setting, clearing and modifying the diagnosis results and providing maintenance suggestions and measures. By considering the electrochemical reaction mechanism of fuel cells, combining the knowledge and experience of experts and adopting a multisensor information fusion method, the device can be effectively applied to the long-range, short-range, off-line and online fault diagnosis of the fuel cell system, thus the fault diagnosis accuracy and maintainability are improved.

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.

An abnormality diagnosis device used for a machine apparatus having a part rotating and sliding relative to a stationary member is provided with a detection section (31) and a signal processing section (81). The detection section (31) is fixed to the part rotating or sliding or to the stationary member and has a vibration sensor (32) and a temperature sensor (33). The signal processing section (81) determines the condition of the part based on a signal detected and outputted by the detection section (31). The signal processing section (81) determines whether there is an abnormality in the part, or whether there is an abnormality in the part and the degree of damage of the part.

A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

The present invention relates to multimodal spectroscopy (MMS) as a clinical tool for the in vivo diagnosis of disease in humans. The MMS technology combines Raman and fluorescence spectroscopy. A preferred embodiment involves diagnosis cancer of the breast and of vulnerable atherosclerotic plaque, esophageal, colon, cervical and bladder cancer. MMS is used to provide a more comprehensive picture of the metabolic, biochemical and morphological state of a tissue than afforded by either Raman or fluorescence and reflectance spectroscopies alone.

The invention provides a rolling bearing fault diagnosis algorithm based on time-frequency domain multidimensional fault feature fusion. Aiming at the respective features of vibration signals of a rolling bearing in a normal state, a roller fault state, an inner ring fault state and an outer ring fault state in a time-frequency domain, through extraction of time domain and frequency domain features, redundancy removal and re-fusion, fault features are described in an optimal way to obtain an intelligent judgment result. First, wavelet de-noising is performed on extracted original rolling bearing vibration data; then, time domain feature vectors are extracted to form a time domain feature matrix, and coefficient energy moments after wavelet packet decomposition and reconstruction are extracted to form a frequency domain feature matrix; and the time and frequency domain matrixes are further fused to obtain a time-frequency domain multidimensional fault feature matrix. Redundancy of the multidimensional feature matrix is eliminated to obtain a new multidimensional feature matrix. Then, information of multidimensional features is fused with a weighted feature index distance, and a state judgment result of the rolling bearing is obtained through the feature index distance obtained through fusion.

A plurality of electrodes for measuring electrocardiogarphic waveforms are attached on body surface positions that constitute a subset of the standard 12-lead system. The measured electrocardiogarphic waveforms of said subset of said standard 12-lead system are used to calculate the electrocardiogarphic waveforms of remaining leads in the said standard 12-lead system. The measured and calculated electrocardiographic waveforms are synthesized to form a standard 12-lead electrocardiogram. The invention is capable of monitoring the 12-lead electrocardiogram with reduced number of electrodes, wherein a portion of waveforms are directly measured and used as primary information for diagnosing heart disease, and the other portion of waveforms are derived from the measured leads and are used as a secondary information for improving the accuracy of diagnosis. The invention is especially useful in monitoring ischemic heart disease and acute myocardial infarction in cases where mounting and maintaining ten electrodes to obtain 12-lead electrocardiogram is difficult, such as in ambulatory monitoring, long-term monitoring and home monitoring.

A method and a system evaluate an efficacy of a test suite in a model-based diagnostic testing system to determine a revision of the test suite. The evaluation comprises suggesting a test to be added to the test suite based on probabilities of a correct diagnosis and an incorrect diagnosis. The evaluation either alternatively or further comprises identifying a test to be deleted from the test suite based on probabilities of a correct diagnosis for the test suite and for a modified test suite that does not include a test. An efficacy value of each test in the test suite is computed. The system has a computer program comprising instructions that implement an evaluation of diagnostic efficacy of the test suite. The system either is a stand-alone system or is incorporated into the testing system.

The invention discloses an intelligent medical information remote processing system and an intelligent medical information remote processing method, and belongs to the technical field of medical diagnosis. The system comprises a remote processing end and remotely connected client ends, wherein the remote processing end comprises a communication unit, a first receiving unit, a first sending unit, a classifying unit, a storage unit and a sub-processing unit; the intelligent medical information remote processing method comprises the following steps that medical information sent by the client ends is acquired and sent to the corresponding sub-processing unit; disease condition information in the medical information and default medical information are matched by the sub-processing unit to obtain corresponding diagnostic information and / or treatment information; the remote processing end sends a processing result of the sub-processing unit to the client ends; the sub-processing unit updates the default medical information in the remote processing end according to the diagnostic information and / or the treatment information in the medical information. The technical scheme of the invention has the benefits of optimizing a whole treatment process to effectively improve the diagnostic accuracy and diagnostic efficiency of a doctor, and facilitating systemic storage of the medical information.

The invention provides methods of enhancing tissue sample images by filtering luminance values from an input image and transforming the filtered values to produce an enhanced image for use in diagnostic applications. Preferred methods of the invention provide image enhancement by filtering luminance values of an image that correspond to sample regions that are obstructed or are otherwise not of diagnostic interest, and applying a mathematical transformation based on luminance values from regions that are not filtered out. In this way, image correction is substantially based on portions of the image that are of diagnostic relevance.

The invention discloses a thyroid tumor pathological tissue section image classification method and a thyroid tumor pathological tissue section image classification device. The method comprises the steps of acquiring an original image set of classified thyroid tumor pathological tissue sections; automatically intercepting multiple area images including cells from each original image to serve as asub-image set; using the total or partial sub-image set as a training set; building a preliminary convolutional neural network model; training the preliminary convolutional neural network model by using the training set to acquire a mature convolutional neural network model; and classifying the classified thyroid tumor pathological tissue section images by using the mature convolutional neural network model. Cell nucleuses in the thyroid tumor pathological tissue sections are matched by using Gaussian laplace operator characteristics to find positions of cells, automatic image interception isimplemented in the area with many cells, so that the full-automatic cell image classification and cancer diagnosis are achieved, the workload of a doctor when in checking of the tissue section image can be greatly reduced, and the diagnosis accuracy is improved.

The invention provides an intelligent fault diagnosis method of a numerical control machine tool. The intelligent fault diagnosis method uses fault tree diagnosis as a base, faults are diagnosed by combining a Bayes probability and rule reasoning method, fault cause is found, and maintenance advices are proposed. The intelligent fault diagnosis method includes: firstly extracting fault information from historical maintenance records and machine tool operation instructions, and establishing a numerical control machine tool fault information knowledge base; searching fault trees belonging to a fault position in the fault information knowledge base according to fault characteristic information, calculating probability of occurrence of each fault tree under the current condition if a plurality of fault trees fit conditions, and determining sequence for diagnosing the fault trees according to size of the probability; and finally judging whether all sub-events of top events of the fault trees occur, returning to a fault treatment method corresponding to the events and serving as a solution if the sub-evens are bottom events of the fault trees, otherwise searching sub-events of the events continuously until finding all bottom events fit the fault characteristic information. The intelligent fault diagnosis method of the numerical control machine tool is rapid in diagnosis speed and accurate and reasonable in diagnosis result.

The invention relates to an interactive film interpretation method of a mammary gland CR image. At present, an artificial film interpretation method has low efficiency and accuracy. The method comprises: firstly, reading a mammary gland CR image to be processed; secondly, selecting a region of suspected lesion and computing a scanning line cross section histogram, a distance between two points, a gray scale histogram, a grey scale standard deviation, an area, a circumference, a compactness, an aspect ratio, a distance from edge to center and a fractal dimension; thirdly, performing visual enhancement processing of the region of the suspected lesion by using an image enhancement technique; and finally, searching a lesion having a similar appearance in a lesion database to provide a reference for tumor diagnosis. The method of the invention can conveniently and quickly perform local observation, detailed enhancement and parameter measurement of the mammary gland CR image, improve film interpretation efficiency, reduce working load and improve precision.

The invention discloses a gastrointestinal tumor microscopic hyper-spectral image processing method based on a convolutional neural network, comprising the following steps: reducing and de-noising the spectral dimension of an acquired gastrointestinal tissue hyper-spectral training image; constructing a convolutional neural network structure; and inputting obtained hyper-spectral data principal components (namely, a plurality of 2D gray images, which are equivalent to a plurality of feature maps of an input layer) as input images into the constructed convolutional neural network structure using a batch processing method, and by taking a cross entropy function as a loss function and using an error back propagation algorithm, training the parameters in the convolutional neural network and the parameters of a logistic regression layer according to the average loss function in a training batch until the network converges. According to the invention, the dimension of a hyper-spectral image is reduced using a principal component analysis method, enough spectral information and spatial texture information are retained, the complexity of the algorithm is reduced greatly, and the efficiency of the algorithm is improved.

The invention discloses an oil soaked transformer fault diagnosis method, including the following steps: a sample is firstly obtained, and the concentration data of five gases in the sample is in normalized treatment, so that a training sample set and a test sample set are formed; the number of elementary kernel functions and the parameters of each basis kernel are determined, and the optimal punitive parameters are determined by using the method of cross validation; according to the optimal punitive parameters, corresponding disaggregated model is obtained by using training sample and multi-classification multi-kernel learning method; and the disaggregated model after trained is used for carrying out fault diagnosis of the sample to be tested in verification centralization. The invention can guarantee very high accuracy rate of diagnosis, and has very good practicability and generalization.

The present invention discloses a cancer pathology auxiliary diagnosis method based on an artificial intelligence technology. The method comprises the following steps of: canning a plurality of digital pathology images of a system into a computer, performing marking of diseased areas by pathologists to form a digital pathology image database; performing preprocessing of the digital pathology images to form a data set for algorithm training, performing sample collection, and forming a data subset for training; allowing a full convolutional network to use the data subset for training to performiteration training to regulate parameters, and constructing an artificial intelligence analysis module; scanning diagnosis pathology images, and decoding the diagnosis pathology images to access the artificial intelligence analysis module; and performing diagnosis and marking of the diagnosis pathology images by employing the artificial intelligence analysis module, and performing feedback of themarked pathology information to doctors. The cancer pathology auxiliary diagnosis method based on the artificial intelligence technology is high in diagnosis accuracy and can effectively assist doctors in discrimination of cancer pathology information.

The present invention provides a method, device, equipment for the segmentation of a lesion in a biological image and a storage medium. The method comprises a step of acquiring a target biological image, a step of performing coarse segmentation processing on the target biological image and obtaining a coarse segmentation mask after the rough segmentation processing, wherein the coarse segmentationmask includes information of candidate lesions in the target biological image, a step of identifying a non-real lesion from the candidate lesions, correcting the rough segmentation mask based on a recognition result such that the information of an identified non-real lesion is not included in the coarse segmentation mask and a target segmentation mask obtained after the correction is used as a lesion segmentation mask corresponding to the target biological image. According to the method, the device, the equipment and the storage medium, the lesion can be automatically positioned from the target biological image, the mode is labor-saving, the time consumption of the positioning of the lesion is reduced, misdiagnosis and missed diagnosis caused by the manual positioning of the lesion are avoided, the positioned lesion can also assist the doctor to carry out fast and accurate analysis, and the diagnostic efficiency and diagnostic accuracy of doctors are improved.

The invention discloses a composite fault diagnosis method for a diesel engine and a diagnosis system. The fault of a fuel supply system of the diesel engine can be diagnosed based on comprehensive analysis of fuel flow monitoring, exhaust emissions, temperature detection and the like in the state parameter measuring method. In vibration analysis, vibrating signals are measured by a vibration acceleration sensor, noise is removed via empirical mode decomposition (EMD), and then characteristic parameters of the vibration signal acceleration are extracted in a manifold learning method. For failure of a piston cylinder, vibration analysis and fuel analysis are combined, and the degrees of friction and wear are judged according to the vibration amplitude and the content of Fe. The composite fault diagnosis of the diesel engine can be realized based on integration of the fault characteristic information and comparison with typical failure characteristics. The diesel engine fault diagnosis system is developed on the basis of integration of information of multiple characteristics, all characteristic information is fully utilized, the speed and accuracy of diagnosis can be improved, the advantages can be complemented, and the level of diesel engine fault diagnosis can be improved.

A method and system for Computer Aided Detection (CAD) of abnormalities in X-ray images in which the features and / or criteria used in the CAD analysis are displayed as coded descriptors on the digital image of the X-ray to provide an indication to a user of the basis underlying the identification of a particular abnormality therefore increasing the reliability of a diagnosis established by the user.

The invention discloses an image-based early diagnosis system for senile dementia. The early diagnosis system comprises a brain image collector (1) and a memorizer (2) and is characterized by comprising image preprocessing equipment (3), an image asymmetry characteristic extraction device (4), a consolidator (5), a characteristic vector selector (6), an optimization classifier (7) and an output device. The early diagnosis system has the remarkable effect of being capable of obviously improving the accuracy of early diagnosis of the senile dementia when being applied to the early diagnosis of the senile dementia. The early diagnosis system not only can be used for realizing the clinical application of sequential asymmetry characteristics of anatomical structures in the brain MR (Magnetic Resonance) images in the early diagnosis of the senile dementia, but also is high in diagnosis accuracy and real-time performance.

The invention discloses an ultrasound bone density measuring and analyzing system. The system comprises an ultrasound parameter measuring apparatus, a communication interface and a human machine interaction device, wherein the ultrasound parameter measuring apparatus comprises transmitting unit consisting of a pulse generator, a high-voltage pulse excitation module and a transmitting probe, an ultrasound receiving unit consisting of a receiving probe, a simulation pretreatment module, a gain adjustable amplifier, a phrase comparator, a high-speed ADC and an asynchronous FIFO, and a central processor, a power supply control module and a structural body. the human machine interaction device controls the ultrasound parameter measuring apparatus through the communication interface to measure the width of a calcaneus of a detected person, the transmission speed of an ultrasound wave in the calcaneus, broadband ultrasonic attenuation to calculate the bone intensity indexes and the bone density, so a medical report can be made according to diagnostic standards of osteoporosis and a special data base can be built for long term use. The system adopts wet or dry coupling and other technologies to improve the precision and accuracy of measure and has the advantages of easy carrying, low cost, no damage caused by radiation and can be use in long term monitoring of bone condition of the detected person.

The invention discloses a feature extraction method for a switch action current curve. The method comprises the following steps: obtaining current data of a switch action to generate the switch action current curve; converting the switch action current curve into a projection coordinate system; determining subsections of various switch action sections in the projection coordinate system; and outputting starting points and end points of current value ranges. The method is simple and efficient; the extracted features can provide bases for feature extraction of a diagnosis model; and the selected features can be taken as input parameters of the model. According to a switch fault diagnosis method employing the feature extraction method disclosed by the invention, maintenance information can be timely and accurately provided when a switch is broken down; and on-site maintenance personnel are guided to carry out a maintenance on the faulty switch in a targeted manner, so that the maintenance cost is reduced; the service efficiency of the switch is improved; various adverse effects caused by faults can be reduced; a fault time delay is compressed; a transport delay is further reduced; and the feature extraction method and the switch fault diagnosis method are of important and practical significance in improvement of the safety and the efficiency of a transportation system.

A novel analog circuit early fault diagnosis method includes the steps of (1) acquiring time domain response signals of an analog circuit and taking the time domain response signals as output voltage signals of the analog circuit; (2) performing wavelet transform to the acquired voltage signals; (3) performing fractal analysis to original signal patterns and wavelet sub patterns to generate wavelet fractal dimensions of different patterns; (4) performing kernel entropy component analysis to candidate feature vector data composed of the wavelet fractal dimensions to acquire low-dimension feature vector data; (5) creating a multi-class classifier of a least squares support vector machine, and optimally selecting penalty factor and width factor of the least squares support vector machine, which are used for distinguishing overlapped early fault categories, by a quantum-behaved particle swarm optimization algorithm; and (6) sending the low-dimension feature vector data into the multi-class classifier of the least squares support vector machine and then outputting early fault diagnosis results. The novel analog circuit early fault diagnosis method can effectively detect early faults of analog circuits.

The invention discloses a bearing fault diagnosis method based on transfer learning. The method comprises the steps of selection rules of a training data set and a test data set; auxiliary data normalization transferable degree quantification and a threshold selection method; and the algorithm flow of a TrAdaboost algorithm based on weight adjustment used for bearing fault classification. A thinking strategy that auxiliary data in transfer learning assist target data learning is introduced by aiming at problems of lacking of diagnosis generality caused by the conditions of variable working conditions, unavailable direct measurement and new fault types. According to the method, diagnosis precision of conventional machine learning in a small amount of target data can be enhanced, environmental adaptability of bearing fault diagnosis is enhanced, and the method has potential economic value in the field of bearing fault application under variable working condition loads, new faults and indirect measurement.